diff --git a/DurationPredictor_v2.mlmodelc/analytics/coremldata.bin b/DurationPredictor_v2.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..930ee5c8b507c2f2150fad684338896b99cfb5f7 --- /dev/null +++ b/DurationPredictor_v2.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8bfa7996e1b4c0e26c9c1edc3861f28726bbd30f74d4a7519e69ef78c1b2d1c +size 243 diff --git a/DurationPredictor_v2.mlmodelc/coremldata.bin b/DurationPredictor_v2.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..19561df6a77a35f7478a3d227637b7b14a91287b --- /dev/null +++ b/DurationPredictor_v2.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f4f1630670632dd149787fb6ea4f934ab915c390a88d87a4e825ca58f041b4b2 +size 426 diff --git a/DurationPredictor_v2.mlmodelc/model.mil b/DurationPredictor_v2.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..0b73a6a022dc31a198c17e2edca1538f8414885b --- /dev/null +++ b/DurationPredictor_v2.mlmodelc/model.mil @@ -0,0 +1,669 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor style_dp, tensor text_ids, tensor text_mask) { + int32 var_25 = const()[name = string("op_25"), val = int32(2)]; + int32 var_26 = const()[name = string("op_26"), val = int32(1)]; + int32 var_27 = const()[name = string("op_27"), val = int32(-1)]; + int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)]; + bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)]; + tensor m_char_embedder_weight_to_fp16 = const()[name = string("m_char_embedder_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string text_ids_to_int16_dtype_0 = const()[name = string("text_ids_to_int16_dtype_0"), val = string("int16")]; + string cast_19_dtype_0 = const()[name = string("cast_19_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor text_ids_to_int16 = cast(dtype = text_ids_to_int16_dtype_0, x = text_ids)[name = string("cast_25")]; + tensor cast_19 = cast(dtype = cast_19_dtype_0, x = text_ids_to_int16)[name = string("cast_24")]; + tensor greater_equal_0 = greater_equal(x = cast_19, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(8322)]; + tensor add_0 = add(x = cast_19, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_19, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + int32 x_1_cast_fp16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_axis_0"), val = int32(0)]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_23")]; + tensor x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_to_int16, validate_indices = x_1_validate_indices_0, x = m_char_embedder_weight_to_fp16)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor var_53_perm_0 = const()[name = string("op_53_perm_0"), val = tensor([0, 2, 1])]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_22")]; + tensor var_53_cast_fp16 = transpose(perm = var_53_perm_0, x = x_1_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_24")]; + tensor x_3_cast_fp16 = mul(x = var_53_cast_fp16, y = text_mask_to_fp16)[name = string("x_3_cast_fp16")]; + bool x_5_interleave_0 = const()[name = string("x_5_interleave_0"), val = bool(false)]; + tensor m_sentence_token_to_fp16 = const()[name = string("m_sentence_token_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065344)))]; + tensor x_5_cast_fp16 = concat(axis = var_25, interleave = x_5_interleave_0, values = (m_sentence_token_to_fp16, x_3_cast_fp16))[name = string("x_5_cast_fp16")]; + bool mask_interleave_0 = const()[name = string("mask_interleave_0"), val = bool(false)]; + tensor fill_0_to_fp16 = const()[name = string("fill_0_to_fp16"), val = tensor([[[0x1p+0]]])]; + tensor mask_cast_fp16 = concat(axis = var_25, interleave = mask_interleave_0, values = (fill_0_to_fp16, text_mask_to_fp16))[name = string("mask_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = x_5_cast_fp16, y = mask_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_1_begin_0 = const()[name = string("input_1_begin_0"), val = tensor([0, 0, 1])]; + tensor input_1_end_0 = const()[name = string("input_1_end_0"), val = tensor([1, 1, 129])]; + tensor input_1_end_mask_0 = const()[name = string("input_1_end_mask_0"), val = tensor([true, true, true])]; + tensor input_1_cast_fp16 = slice_by_index(begin = input_1_begin_0, end = input_1_end_0, end_mask = input_1_end_mask_0, x = mask_cast_fp16)[name = string("input_1_cast_fp16")]; + tensor var_71_pad_0 = const()[name = string("op_71_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_71_mode_0 = const()[name = string("op_71_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor var_71_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_71_mode_0, pad = var_71_pad_0, x = input_1_cast_fp16)[name = string("op_71_cast_fp16")]; + fp16 var_16_to_fp16 = const()[name = string("op_16_to_fp16"), val = fp16(0x1p+0)]; + tensor var_72_cast_fp16 = sub(x = var_16_to_fp16, y = var_71_cast_fp16)[name = string("op_72_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = mask_cast_fp16, y = var_72_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_74_cast_fp16 = mul(x = x_7_cast_fp16, y = last_1_cast_fp16)[name = string("op_74_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_74_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_77_cast_fp16 = mul(x = x_7_cast_fp16, y = mask_cast_fp16)[name = string("op_77_cast_fp16")]; + tensor var_78_cast_fp16 = sub(x = var_16_to_fp16, y = mask_cast_fp16)[name = string("op_78_cast_fp16")]; + tensor var_79_cast_fp16 = mul(x = var_78_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_79_cast_fp16")]; + tensor input_3_cast_fp16 = add(x = var_77_cast_fp16, y = var_79_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_5_mode_0 = const()[name = string("input_5_mode_0"), val = string("replicate")]; + fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)]; + tensor input_5_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + string h_1_pad_type_0 = const()[name = string("h_1_pad_type_0"), val = string("valid")]; + int32 h_1_groups_0 = const()[name = string("h_1_groups_0"), val = int32(64)]; + tensor h_1_strides_0 = const()[name = string("h_1_strides_0"), val = tensor([1])]; + tensor h_1_pad_0 = const()[name = string("h_1_pad_0"), val = tensor([0, 0])]; + tensor h_1_dilations_0 = const()[name = string("h_1_dilations_0"), val = tensor([1])]; + tensor m_convnext_0_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065536)))]; + tensor m_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066240)))]; + tensor h_1_cast_fp16 = conv(bias = m_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_1_dilations_0, groups = h_1_groups_0, pad = h_1_pad_0, pad_type = h_1_pad_type_0, strides = h_1_strides_0, weight = m_convnext_0_dwconv__conv_weight_to_fp16, x = input_5_cast_fp16)[name = string("h_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = h_1_cast_fp16, y = mask_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_7_perm_0 = const()[name = string("input_7_perm_0"), val = tensor([0, 2, 1])]; + tensor var_97_axes_0 = const()[name = string("op_97_axes_0"), val = tensor([-1])]; + tensor m_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066432)))]; + tensor m_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066624)))]; + fp16 var_20_to_fp16 = const()[name = string("op_20_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_7_cast_fp16 = transpose(perm = input_7_perm_0, x = x_9_cast_fp16)[name = string("transpose_23")]; + tensor var_97_cast_fp16 = layer_norm(axes = var_97_axes_0, beta = m_convnext_0_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_0_norm_norm_weight_to_fp16, x = input_7_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(1)]; + tensor m_convnext_0_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066816)))]; + tensor m_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1099648)))]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = var_97_cast_fp16)[name = string("transpose_22")]; + tensor h_3_cast_fp16 = conv(bias = m_convnext_0_pwconv1_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = m_convnext_0_pwconv1_weight_to_fp16, x = input_9_cast_fp16)[name = string("h_3_cast_fp16")]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("EXACT")]; + tensor input_11_cast_fp16 = gelu(mode = input_11_mode_0, x = h_3_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor var_114_weight_0_to_fp16 = const()[name = string("op_114_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1100224)))]; + tensor var_114_bias_0_to_fp16 = const()[name = string("op_114_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1133056)))]; + tensor var_114_cast_fp16 = conv(bias = var_114_bias_0_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = var_114_weight_0_to_fp16, x = input_11_cast_fp16)[name = string("op_114_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_7_cast_fp16, y = var_114_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = out_1_cast_fp16, y = mask_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = x_11_cast_fp16, y = mask_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_128_cast_fp16 = mul(x = x_13_cast_fp16, y = last_1_cast_fp16)[name = string("op_128_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_128_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_131_cast_fp16 = mul(x = x_13_cast_fp16, y = mask_cast_fp16)[name = string("op_131_cast_fp16")]; + tensor var_133_cast_fp16 = mul(x = var_78_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_133_cast_fp16")]; + tensor input_15_cast_fp16 = add(x = var_131_cast_fp16, y = var_133_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("replicate")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor input_17_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_17_mode_0, pad = input_17_pad_0, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(64)]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + tensor m_convnext_1_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_1_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1133248)))]; + tensor m_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1133952)))]; + tensor h_7_cast_fp16 = conv(bias = m_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = m_convnext_1_dwconv__conv_weight_to_fp16, x = input_17_cast_fp16)[name = string("h_7_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = h_7_cast_fp16, y = mask_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor input_19_perm_0 = const()[name = string("input_19_perm_0"), val = tensor([0, 2, 1])]; + tensor var_151_axes_0 = const()[name = string("op_151_axes_0"), val = tensor([-1])]; + tensor m_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134144)))]; + tensor m_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134336)))]; + tensor input_19_cast_fp16 = transpose(perm = input_19_perm_0, x = x_15_cast_fp16)[name = string("transpose_21")]; + tensor var_151_cast_fp16 = layer_norm(axes = var_151_axes_0, beta = m_convnext_1_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_1_norm_norm_weight_to_fp16, x = input_19_cast_fp16)[name = string("op_151_cast_fp16")]; + tensor input_21_perm_0 = const()[name = string("input_21_perm_0"), val = tensor([0, 2, 1])]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([1])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(1)]; + tensor m_convnext_1_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_1_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134528)))]; + tensor m_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1167360)))]; + tensor input_21_cast_fp16 = transpose(perm = input_21_perm_0, x = var_151_cast_fp16)[name = string("transpose_20")]; + tensor h_9_cast_fp16 = conv(bias = m_convnext_1_pwconv1_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = m_convnext_1_pwconv1_weight_to_fp16, x = input_21_cast_fp16)[name = string("h_9_cast_fp16")]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("EXACT")]; + tensor input_23_cast_fp16 = gelu(mode = input_23_mode_0, x = h_9_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor var_168_weight_0_to_fp16 = const()[name = string("op_168_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1167936)))]; + tensor var_168_bias_0_to_fp16 = const()[name = string("op_168_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1200768)))]; + tensor var_168_cast_fp16 = conv(bias = var_168_bias_0_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = var_168_weight_0_to_fp16, x = input_23_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_13_cast_fp16, y = var_168_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = out_3_cast_fp16, y = mask_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = x_17_cast_fp16, y = mask_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_182_cast_fp16 = mul(x = x_19_cast_fp16, y = last_1_cast_fp16)[name = string("op_182_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_182_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_185_cast_fp16 = mul(x = x_19_cast_fp16, y = mask_cast_fp16)[name = string("op_185_cast_fp16")]; + tensor var_187_cast_fp16 = mul(x = var_78_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_187_cast_fp16")]; + tensor input_27_cast_fp16 = add(x = var_185_cast_fp16, y = var_187_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("replicate")]; + fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)]; + tensor input_29_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_29_mode_0, pad = input_29_pad_0, x = input_27_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(64)]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + tensor m_convnext_2_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_2_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1200960)))]; + tensor m_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1201664)))]; + tensor h_13_cast_fp16 = conv(bias = m_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = m_convnext_2_dwconv__conv_weight_to_fp16, x = input_29_cast_fp16)[name = string("h_13_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = h_13_cast_fp16, y = mask_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor([0, 2, 1])]; + tensor var_205_axes_0 = const()[name = string("op_205_axes_0"), val = tensor([-1])]; + tensor m_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1201856)))]; + tensor m_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1202048)))]; + tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_19")]; + tensor var_205_cast_fp16 = layer_norm(axes = var_205_axes_0, beta = m_convnext_2_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_2_norm_norm_weight_to_fp16, x = input_31_cast_fp16)[name = string("op_205_cast_fp16")]; + tensor input_33_perm_0 = const()[name = string("input_33_perm_0"), val = tensor([0, 2, 1])]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([1])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(1)]; + tensor m_convnext_2_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_2_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1202240)))]; + tensor m_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1235072)))]; + tensor input_33_cast_fp16 = transpose(perm = input_33_perm_0, x = var_205_cast_fp16)[name = string("transpose_18")]; + tensor h_15_cast_fp16 = conv(bias = m_convnext_2_pwconv1_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = m_convnext_2_pwconv1_weight_to_fp16, x = input_33_cast_fp16)[name = string("h_15_cast_fp16")]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("EXACT")]; + tensor input_35_cast_fp16 = gelu(mode = input_35_mode_0, x = h_15_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor var_222_weight_0_to_fp16 = const()[name = string("op_222_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1235648)))]; + tensor var_222_bias_0_to_fp16 = const()[name = string("op_222_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1268480)))]; + tensor var_222_cast_fp16 = conv(bias = var_222_bias_0_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = var_222_weight_0_to_fp16, x = input_35_cast_fp16)[name = string("op_222_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_19_cast_fp16, y = var_222_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = out_5_cast_fp16, y = mask_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = x_23_cast_fp16, y = mask_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_236_cast_fp16 = mul(x = x_25_cast_fp16, y = last_1_cast_fp16)[name = string("op_236_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_236_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_239_cast_fp16 = mul(x = x_25_cast_fp16, y = mask_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor var_241_cast_fp16 = mul(x = var_78_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_241_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = var_239_cast_fp16, y = var_241_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("replicate")]; + fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)]; + tensor input_41_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = input_41_mode_0, pad = input_41_pad_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(64)]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + tensor m_convnext_3_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_3_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1268672)))]; + tensor m_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269376)))]; + tensor h_19_cast_fp16 = conv(bias = m_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = m_convnext_3_dwconv__conv_weight_to_fp16, x = input_41_cast_fp16)[name = string("h_19_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = h_19_cast_fp16, y = mask_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_43_perm_0 = const()[name = string("input_43_perm_0"), val = tensor([0, 2, 1])]; + tensor var_259_axes_0 = const()[name = string("op_259_axes_0"), val = tensor([-1])]; + tensor m_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269568)))]; + tensor m_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269760)))]; + tensor input_43_cast_fp16 = transpose(perm = input_43_perm_0, x = x_27_cast_fp16)[name = string("transpose_17")]; + tensor var_259_cast_fp16 = layer_norm(axes = var_259_axes_0, beta = m_convnext_3_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_3_norm_norm_weight_to_fp16, x = input_43_cast_fp16)[name = string("op_259_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([1])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(1)]; + tensor m_convnext_3_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_3_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269952)))]; + tensor m_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1302784)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = var_259_cast_fp16)[name = string("transpose_16")]; + tensor h_21_cast_fp16 = conv(bias = m_convnext_3_pwconv1_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = m_convnext_3_pwconv1_weight_to_fp16, x = input_45_cast_fp16)[name = string("h_21_cast_fp16")]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("EXACT")]; + tensor input_47_cast_fp16 = gelu(mode = input_47_mode_0, x = h_21_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor var_276_weight_0_to_fp16 = const()[name = string("op_276_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1303360)))]; + tensor var_276_bias_0_to_fp16 = const()[name = string("op_276_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1336192)))]; + tensor var_276_cast_fp16 = conv(bias = var_276_bias_0_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = var_276_weight_0_to_fp16, x = input_47_cast_fp16)[name = string("op_276_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_25_cast_fp16, y = var_276_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = out_7_cast_fp16, y = mask_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = mask_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_290_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_290_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_290_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_293_cast_fp16 = mul(x = x_31_cast_fp16, y = mask_cast_fp16)[name = string("op_293_cast_fp16")]; + tensor var_295_cast_fp16 = mul(x = var_78_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_295_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_293_cast_fp16, y = var_295_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("replicate")]; + fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)]; + tensor input_53_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_53_mode_0, pad = input_53_pad_0, x = input_51_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(64)]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + tensor m_convnext_4_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_4_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1336384)))]; + tensor m_convnext_4_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_4_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337088)))]; + tensor h_25_cast_fp16 = conv(bias = m_convnext_4_dwconv__conv_bias_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = m_convnext_4_dwconv__conv_weight_to_fp16, x = input_53_cast_fp16)[name = string("h_25_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_25_cast_fp16, y = mask_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_55_perm_0 = const()[name = string("input_55_perm_0"), val = tensor([0, 2, 1])]; + tensor var_313_axes_0 = const()[name = string("op_313_axes_0"), val = tensor([-1])]; + tensor m_convnext_4_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_4_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337280)))]; + tensor m_convnext_4_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_4_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337472)))]; + tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_33_cast_fp16)[name = string("transpose_15")]; + tensor var_313_cast_fp16 = layer_norm(axes = var_313_axes_0, beta = m_convnext_4_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_4_norm_norm_weight_to_fp16, x = input_55_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor input_57_perm_0 = const()[name = string("input_57_perm_0"), val = tensor([0, 2, 1])]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(1)]; + tensor m_convnext_4_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_4_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337664)))]; + tensor m_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1370496)))]; + tensor input_57_cast_fp16 = transpose(perm = input_57_perm_0, x = var_313_cast_fp16)[name = string("transpose_14")]; + tensor h_27_cast_fp16 = conv(bias = m_convnext_4_pwconv1_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = m_convnext_4_pwconv1_weight_to_fp16, x = input_57_cast_fp16)[name = string("h_27_cast_fp16")]; + string input_59_mode_0 = const()[name = string("input_59_mode_0"), val = string("EXACT")]; + tensor input_59_cast_fp16 = gelu(mode = input_59_mode_0, x = h_27_cast_fp16)[name = string("input_59_cast_fp16")]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor var_330_weight_0_to_fp16 = const()[name = string("op_330_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1371072)))]; + tensor var_330_bias_0_to_fp16 = const()[name = string("op_330_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1403904)))]; + tensor var_330_cast_fp16 = conv(bias = var_330_bias_0_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = var_330_weight_0_to_fp16, x = input_59_cast_fp16)[name = string("op_330_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_330_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = mask_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_37_cast_fp16 = mul(x = x_35_cast_fp16, y = mask_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_344_cast_fp16 = mul(x = x_37_cast_fp16, y = last_1_cast_fp16)[name = string("op_344_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_344_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_347_cast_fp16 = mul(x = x_37_cast_fp16, y = mask_cast_fp16)[name = string("op_347_cast_fp16")]; + tensor var_349_cast_fp16 = mul(x = var_78_cast_fp16, y = x_last_cast_fp16)[name = string("op_349_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = var_347_cast_fp16, y = var_349_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_65_mode_0 = const()[name = string("input_65_mode_0"), val = string("replicate")]; + fp16 const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = fp16(0x0p+0)]; + tensor input_65_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = input_65_mode_0, pad = input_65_pad_0, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(64)]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + tensor m_convnext_5_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_5_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1404096)))]; + tensor m_convnext_5_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_5_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1404800)))]; + tensor h_31_cast_fp16 = conv(bias = m_convnext_5_dwconv__conv_bias_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = m_convnext_5_dwconv__conv_weight_to_fp16, x = input_65_cast_fp16)[name = string("h_31_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = h_31_cast_fp16, y = mask_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor input_67_perm_0 = const()[name = string("input_67_perm_0"), val = tensor([0, 2, 1])]; + tensor var_367_axes_0 = const()[name = string("op_367_axes_0"), val = tensor([-1])]; + tensor m_convnext_5_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_5_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1404992)))]; + tensor m_convnext_5_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_5_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405184)))]; + tensor input_67_cast_fp16 = transpose(perm = input_67_perm_0, x = x_39_cast_fp16)[name = string("transpose_13")]; + tensor var_367_cast_fp16 = layer_norm(axes = var_367_axes_0, beta = m_convnext_5_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_5_norm_norm_weight_to_fp16, x = input_67_cast_fp16)[name = string("op_367_cast_fp16")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(1)]; + tensor m_convnext_5_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_5_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405376)))]; + tensor m_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1438208)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = var_367_cast_fp16)[name = string("transpose_12")]; + tensor h_33_cast_fp16 = conv(bias = m_convnext_5_pwconv1_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = m_convnext_5_pwconv1_weight_to_fp16, x = input_69_cast_fp16)[name = string("h_33_cast_fp16")]; + string input_71_mode_0 = const()[name = string("input_71_mode_0"), val = string("EXACT")]; + tensor input_71_cast_fp16 = gelu(mode = input_71_mode_0, x = h_33_cast_fp16)[name = string("input_71_cast_fp16")]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor var_384_weight_0_to_fp16 = const()[name = string("op_384_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1438784)))]; + tensor var_384_bias_0_to_fp16 = const()[name = string("op_384_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1471616)))]; + tensor var_384_cast_fp16 = conv(bias = var_384_bias_0_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = var_384_weight_0_to_fp16, x = input_71_cast_fp16)[name = string("op_384_cast_fp16")]; + tensor out_11_cast_fp16 = add(x = x_37_cast_fp16, y = var_384_cast_fp16)[name = string("out_11_cast_fp16")]; + tensor x_41_cast_fp16 = mul(x = out_11_cast_fp16, y = mask_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor x_43_cast_fp16 = mul(x = x_41_cast_fp16, y = mask_cast_fp16)[name = string("x_43_cast_fp16")]; + string var_407_pad_type_0 = const()[name = string("op_407_pad_type_0"), val = string("valid")]; + tensor var_407_strides_0 = const()[name = string("op_407_strides_0"), val = tensor([1])]; + tensor var_407_pad_0 = const()[name = string("op_407_pad_0"), val = tensor([0, 0])]; + tensor var_407_dilations_0 = const()[name = string("op_407_dilations_0"), val = tensor([1])]; + int32 var_407_groups_0 = const()[name = string("op_407_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1471808)))]; + tensor m_attn_layers_0_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1480064)))]; + tensor var_407_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_q_bias_to_fp16, dilations = var_407_dilations_0, groups = var_407_groups_0, pad = var_407_pad_0, pad_type = var_407_pad_type_0, strides = var_407_strides_0, weight = m_attn_layers_0_attn_conv_q_weight_to_fp16, x = x_43_cast_fp16)[name = string("op_407_cast_fp16")]; + tensor var_408 = const()[name = string("op_408"), val = tensor([1, 2, 32, 129])]; + tensor var_409_cast_fp16 = reshape(shape = var_408, x = var_407_cast_fp16)[name = string("op_409_cast_fp16")]; + tensor q_1_perm_0 = const()[name = string("q_1_perm_0"), val = tensor([0, 1, 3, 2])]; + string var_417_pad_type_0 = const()[name = string("op_417_pad_type_0"), val = string("valid")]; + tensor var_417_strides_0 = const()[name = string("op_417_strides_0"), val = tensor([1])]; + tensor var_417_pad_0 = const()[name = string("op_417_pad_0"), val = tensor([0, 0])]; + tensor var_417_dilations_0 = const()[name = string("op_417_dilations_0"), val = tensor([1])]; + int32 var_417_groups_0 = const()[name = string("op_417_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1480256)))]; + tensor m_attn_layers_0_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1488512)))]; + tensor var_417_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_k_bias_to_fp16, dilations = var_417_dilations_0, groups = var_417_groups_0, pad = var_417_pad_0, pad_type = var_417_pad_type_0, strides = var_417_strides_0, weight = m_attn_layers_0_attn_conv_k_weight_to_fp16, x = x_43_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor var_418 = const()[name = string("op_418"), val = tensor([1, 2, 32, 129])]; + tensor var_419_cast_fp16 = reshape(shape = var_418, x = var_417_cast_fp16)[name = string("op_419_cast_fp16")]; + string var_427_pad_type_0 = const()[name = string("op_427_pad_type_0"), val = string("valid")]; + tensor var_427_strides_0 = const()[name = string("op_427_strides_0"), val = tensor([1])]; + tensor var_427_pad_0 = const()[name = string("op_427_pad_0"), val = tensor([0, 0])]; + tensor var_427_dilations_0 = const()[name = string("op_427_dilations_0"), val = tensor([1])]; + int32 var_427_groups_0 = const()[name = string("op_427_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1488704)))]; + tensor m_attn_layers_0_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1496960)))]; + tensor var_427_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_v_bias_to_fp16, dilations = var_427_dilations_0, groups = var_427_groups_0, pad = var_427_pad_0, pad_type = var_427_pad_type_0, strides = var_427_strides_0, weight = m_attn_layers_0_attn_conv_v_weight_to_fp16, x = x_43_cast_fp16)[name = string("op_427_cast_fp16")]; + tensor var_428 = const()[name = string("op_428"), val = tensor([1, 2, 32, 129])]; + tensor var_429_cast_fp16 = reshape(shape = var_428, x = var_427_cast_fp16)[name = string("op_429_cast_fp16")]; + fp16 var_431_to_fp16 = const()[name = string("op_431_to_fp16"), val = fp16(0x1.6ap-3)]; + tensor q_1_cast_fp16 = transpose(perm = q_1_perm_0, x = var_409_cast_fp16)[name = string("transpose_11")]; + tensor var_432_cast_fp16 = mul(x = q_1_cast_fp16, y = var_431_to_fp16)[name = string("op_432_cast_fp16")]; + bool scores_1_transpose_x_0 = const()[name = string("scores_1_transpose_x_0"), val = bool(false)]; + bool scores_1_transpose_y_0 = const()[name = string("scores_1_transpose_y_0"), val = bool(false)]; + tensor scores_1_cast_fp16 = matmul(transpose_x = scores_1_transpose_x_0, transpose_y = scores_1_transpose_y_0, x = var_432_cast_fp16, y = var_419_cast_fp16)[name = string("scores_1_cast_fp16")]; + bool x_45_transpose_x_0 = const()[name = string("x_45_transpose_x_0"), val = bool(false)]; + bool x_45_transpose_y_0 = const()[name = string("x_45_transpose_y_0"), val = bool(false)]; + tensor var_456_to_fp16 = const()[name = string("op_456_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1497152)))]; + tensor x_45_cast_fp16 = matmul(transpose_x = x_45_transpose_x_0, transpose_y = x_45_transpose_y_0, x = var_432_cast_fp16, y = var_456_to_fp16)[name = string("x_45_cast_fp16")]; + tensor x_47_pad_0 = const()[name = string("x_47_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 1])]; + string x_47_mode_0 = const()[name = string("x_47_mode_0"), val = string("constant")]; + fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)]; + tensor x_47_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = x_47_mode_0, pad = x_47_pad_0, x = x_45_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor var_468 = const()[name = string("op_468"), val = tensor([1, 2, 33282])]; + tensor input_75_cast_fp16 = reshape(shape = var_468, x = x_47_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor x_49_pad_0 = const()[name = string("x_49_pad_0"), val = tensor([0, 0, 0, 0, 0, 128])]; + string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("constant")]; + fp16 const_20_to_fp16 = const()[name = string("const_20_to_fp16"), val = fp16(0x0p+0)]; + tensor x_49_cast_fp16 = pad(constant_val = const_20_to_fp16, mode = x_49_mode_0, pad = x_49_pad_0, x = input_75_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_483 = const()[name = string("op_483"), val = tensor([1, 2, 130, 257])]; + tensor x_51_cast_fp16 = reshape(shape = var_483, x = x_49_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_490_begin_0 = const()[name = string("op_490_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_490_end_0 = const()[name = string("op_490_end_0"), val = tensor([1, 2, 129, 257])]; + tensor var_490_end_mask_0 = const()[name = string("op_490_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_490_cast_fp16 = slice_by_index(begin = var_490_begin_0, end = var_490_end_0, end_mask = var_490_end_mask_0, x = x_51_cast_fp16)[name = string("op_490_cast_fp16")]; + tensor var_491_begin_0 = const()[name = string("op_491_begin_0"), val = tensor([0, 0, 0, 128])]; + tensor var_491_end_0 = const()[name = string("op_491_end_0"), val = tensor([1, 2, 129, 257])]; + tensor var_491_end_mask_0 = const()[name = string("op_491_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_491_cast_fp16 = slice_by_index(begin = var_491_begin_0, end = var_491_end_0, end_mask = var_491_end_mask_0, x = var_490_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor scores_3_cast_fp16 = add(x = scores_1_cast_fp16, y = var_491_cast_fp16)[name = string("scores_3_cast_fp16")]; + tensor var_493_axes_0 = const()[name = string("op_493_axes_0"), val = tensor([2])]; + tensor var_493_cast_fp16 = expand_dims(axes = var_493_axes_0, x = mask_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor var_494_cast_fp16 = sub(x = var_16_to_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")]; + fp16 var_495_to_fp16 = const()[name = string("op_495_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_496_cast_fp16 = mul(x = var_494_cast_fp16, y = var_495_to_fp16)[name = string("op_496_cast_fp16")]; + tensor input_77_cast_fp16 = sub(x = scores_3_cast_fp16, y = var_496_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor p_attn_1_cast_fp16 = softmax(axis = var_27, x = input_77_cast_fp16)[name = string("p_attn_1_cast_fp16")]; + bool out_13_transpose_x_1 = const()[name = string("out_13_transpose_x_1"), val = bool(false)]; + bool out_13_transpose_y_1 = const()[name = string("out_13_transpose_y_1"), val = bool(true)]; + tensor out_13_cast_fp16 = matmul(transpose_x = out_13_transpose_x_1, transpose_y = out_13_transpose_y_1, x = p_attn_1_cast_fp16, y = var_429_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 128])]; + string x_53_mode_0 = const()[name = string("x_53_mode_0"), val = string("constant")]; + fp16 const_25_to_fp16 = const()[name = string("const_25_to_fp16"), val = fp16(0x0p+0)]; + tensor x_53_cast_fp16 = pad(constant_val = const_25_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = p_attn_1_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor var_533 = const()[name = string("op_533"), val = tensor([1, 2, 33153])]; + tensor input_81_cast_fp16 = reshape(shape = var_533, x = x_53_cast_fp16)[name = string("input_81_cast_fp16")]; + tensor x_55_pad_0 = const()[name = string("x_55_pad_0"), val = tensor([0, 0, 0, 0, 129, 0])]; + string x_55_mode_0 = const()[name = string("x_55_mode_0"), val = string("constant")]; + fp16 const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = fp16(0x0p+0)]; + tensor x_55_cast_fp16 = pad(constant_val = const_26_to_fp16, mode = x_55_mode_0, pad = x_55_pad_0, x = input_81_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_540 = const()[name = string("op_540"), val = tensor([1, 2, 129, 258])]; + tensor x_57_cast_fp16 = reshape(shape = var_540, x = x_55_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor rel_weights_1_begin_0 = const()[name = string("rel_weights_1_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor rel_weights_1_end_0 = const()[name = string("rel_weights_1_end_0"), val = tensor([1, 2, 129, 258])]; + tensor rel_weights_1_end_mask_0 = const()[name = string("rel_weights_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor rel_weights_1_cast_fp16 = slice_by_index(begin = rel_weights_1_begin_0, end = rel_weights_1_end_0, end_mask = rel_weights_1_end_mask_0, x = x_57_cast_fp16)[name = string("rel_weights_1_cast_fp16")]; + bool var_547_transpose_x_0 = const()[name = string("op_547_transpose_x_0"), val = bool(false)]; + bool var_547_transpose_y_0 = const()[name = string("op_547_transpose_y_0"), val = bool(false)]; + tensor var_546_to_fp16 = const()[name = string("op_546_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1513664)))]; + tensor var_547_cast_fp16 = matmul(transpose_x = var_547_transpose_x_0, transpose_y = var_547_transpose_y_0, x = rel_weights_1_cast_fp16, y = var_546_to_fp16)[name = string("op_547_cast_fp16")]; + tensor out_15_cast_fp16 = add(x = out_13_cast_fp16, y = var_547_cast_fp16)[name = string("out_15_cast_fp16")]; + tensor var_549_perm_0 = const()[name = string("op_549_perm_0"), val = tensor([0, 1, 3, 2])]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, 64, 129])]; + tensor var_549_cast_fp16 = transpose(perm = var_549_perm_0, x = out_15_cast_fp16)[name = string("transpose_10")]; + tensor input_83_cast_fp16 = reshape(shape = var_550, x = var_549_cast_fp16)[name = string("input_83_cast_fp16")]; + string y_1_pad_type_0 = const()[name = string("y_1_pad_type_0"), val = string("valid")]; + tensor y_1_strides_0 = const()[name = string("y_1_strides_0"), val = tensor([1])]; + tensor y_1_pad_0 = const()[name = string("y_1_pad_0"), val = tensor([0, 0])]; + tensor y_1_dilations_0 = const()[name = string("y_1_dilations_0"), val = tensor([1])]; + int32 y_1_groups_0 = const()[name = string("y_1_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_o_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530176)))]; + tensor m_attn_layers_0_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_o_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1538432)))]; + tensor y_1_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_o_bias_to_fp16, dilations = y_1_dilations_0, groups = y_1_groups_0, pad = y_1_pad_0, pad_type = y_1_pad_type_0, strides = y_1_strides_0, weight = m_attn_layers_0_attn_conv_o_weight_to_fp16, x = input_83_cast_fp16)[name = string("y_1_cast_fp16")]; + tensor x_59_cast_fp16 = add(x = x_43_cast_fp16, y = y_1_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor input_85_perm_0 = const()[name = string("input_85_perm_0"), val = tensor([0, 2, 1])]; + tensor var_565_axes_0 = const()[name = string("op_565_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_0_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_0_norm_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1538624)))]; + tensor m_attn_layers_0_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_0_norm_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1538816)))]; + tensor input_85_cast_fp16 = transpose(perm = input_85_perm_0, x = x_59_cast_fp16)[name = string("transpose_9")]; + tensor var_565_cast_fp16 = layer_norm(axes = var_565_axes_0, beta = m_attn_layers_0_norm_1_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_0_norm_1_norm_weight_to_fp16, x = input_85_cast_fp16)[name = string("op_565_cast_fp16")]; + tensor x_61_perm_0 = const()[name = string("x_61_perm_0"), val = tensor([0, 2, 1])]; + tensor x_61_cast_fp16 = transpose(perm = x_61_perm_0, x = var_565_cast_fp16)[name = string("transpose_8")]; + tensor input_87_cast_fp16 = mul(x = x_61_cast_fp16, y = mask_cast_fp16)[name = string("input_87_cast_fp16")]; + string input_89_pad_type_0 = const()[name = string("input_89_pad_type_0"), val = string("valid")]; + tensor input_89_strides_0 = const()[name = string("input_89_strides_0"), val = tensor([1])]; + tensor input_89_pad_0 = const()[name = string("input_89_pad_0"), val = tensor([0, 0])]; + tensor input_89_dilations_0 = const()[name = string("input_89_dilations_0"), val = tensor([1])]; + int32 input_89_groups_0 = const()[name = string("input_89_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1539008)))]; + tensor m_attn_layers_0_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1571840)))]; + tensor input_89_cast_fp16 = conv(bias = m_attn_layers_0_ffn_conv_1_bias_to_fp16, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = m_attn_layers_0_ffn_conv_1_weight_to_fp16, x = input_87_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor h_37_cast_fp16 = relu(x = input_89_cast_fp16)[name = string("h_37_cast_fp16")]; + tensor input_91_cast_fp16 = mul(x = h_37_cast_fp16, y = mask_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1572416)))]; + tensor m_attn_layers_0_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605248)))]; + tensor h_39_cast_fp16 = conv(bias = m_attn_layers_0_ffn_conv_2_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = m_attn_layers_0_ffn_conv_2_weight_to_fp16, x = input_91_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor y_3_cast_fp16 = mul(x = h_39_cast_fp16, y = mask_cast_fp16)[name = string("y_3_cast_fp16")]; + tensor x_63_cast_fp16 = add(x = x_61_cast_fp16, y = y_3_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_0_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_0_norm_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605440)))]; + tensor m_attn_layers_0_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_0_norm_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605632)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_63_cast_fp16)[name = string("transpose_7")]; + tensor var_593_cast_fp16 = layer_norm(axes = var_593_axes_0, beta = m_attn_layers_0_norm_2_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_0_norm_2_norm_weight_to_fp16, x = input_93_cast_fp16)[name = string("op_593_cast_fp16")]; + tensor x_65_perm_0 = const()[name = string("x_65_perm_0"), val = tensor([0, 2, 1])]; + tensor x_65_cast_fp16 = transpose(perm = x_65_perm_0, x = var_593_cast_fp16)[name = string("transpose_6")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = mask_cast_fp16)[name = string("x_67_cast_fp16")]; + string var_615_pad_type_0 = const()[name = string("op_615_pad_type_0"), val = string("valid")]; + tensor var_615_strides_0 = const()[name = string("op_615_strides_0"), val = tensor([1])]; + tensor var_615_pad_0 = const()[name = string("op_615_pad_0"), val = tensor([0, 0])]; + tensor var_615_dilations_0 = const()[name = string("op_615_dilations_0"), val = tensor([1])]; + int32 var_615_groups_0 = const()[name = string("op_615_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605824)))]; + tensor m_attn_layers_1_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1614080)))]; + tensor var_615_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_q_bias_to_fp16, dilations = var_615_dilations_0, groups = var_615_groups_0, pad = var_615_pad_0, pad_type = var_615_pad_type_0, strides = var_615_strides_0, weight = m_attn_layers_1_attn_conv_q_weight_to_fp16, x = x_67_cast_fp16)[name = string("op_615_cast_fp16")]; + tensor var_616 = const()[name = string("op_616"), val = tensor([1, 2, 32, 129])]; + tensor var_617_cast_fp16 = reshape(shape = var_616, x = var_615_cast_fp16)[name = string("op_617_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 1, 3, 2])]; + string var_625_pad_type_0 = const()[name = string("op_625_pad_type_0"), val = string("valid")]; + tensor var_625_strides_0 = const()[name = string("op_625_strides_0"), val = tensor([1])]; + tensor var_625_pad_0 = const()[name = string("op_625_pad_0"), val = tensor([0, 0])]; + tensor var_625_dilations_0 = const()[name = string("op_625_dilations_0"), val = tensor([1])]; + int32 var_625_groups_0 = const()[name = string("op_625_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1614272)))]; + tensor m_attn_layers_1_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1622528)))]; + tensor var_625_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_k_bias_to_fp16, dilations = var_625_dilations_0, groups = var_625_groups_0, pad = var_625_pad_0, pad_type = var_625_pad_type_0, strides = var_625_strides_0, weight = m_attn_layers_1_attn_conv_k_weight_to_fp16, x = x_67_cast_fp16)[name = string("op_625_cast_fp16")]; + tensor var_626 = const()[name = string("op_626"), val = tensor([1, 2, 32, 129])]; + tensor var_627_cast_fp16 = reshape(shape = var_626, x = var_625_cast_fp16)[name = string("op_627_cast_fp16")]; + string var_635_pad_type_0 = const()[name = string("op_635_pad_type_0"), val = string("valid")]; + tensor var_635_strides_0 = const()[name = string("op_635_strides_0"), val = tensor([1])]; + tensor var_635_pad_0 = const()[name = string("op_635_pad_0"), val = tensor([0, 0])]; + tensor var_635_dilations_0 = const()[name = string("op_635_dilations_0"), val = tensor([1])]; + int32 var_635_groups_0 = const()[name = string("op_635_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1622720)))]; + tensor m_attn_layers_1_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1630976)))]; + tensor var_635_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_v_bias_to_fp16, dilations = var_635_dilations_0, groups = var_635_groups_0, pad = var_635_pad_0, pad_type = var_635_pad_type_0, strides = var_635_strides_0, weight = m_attn_layers_1_attn_conv_v_weight_to_fp16, x = x_67_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636 = const()[name = string("op_636"), val = tensor([1, 2, 32, 129])]; + tensor var_637_cast_fp16 = reshape(shape = var_636, x = var_635_cast_fp16)[name = string("op_637_cast_fp16")]; + fp16 var_639_to_fp16 = const()[name = string("op_639_to_fp16"), val = fp16(0x1.6ap-3)]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_617_cast_fp16)[name = string("transpose_5")]; + tensor var_640_cast_fp16 = mul(x = q_cast_fp16, y = var_639_to_fp16)[name = string("op_640_cast_fp16")]; + bool scores_5_transpose_x_0 = const()[name = string("scores_5_transpose_x_0"), val = bool(false)]; + bool scores_5_transpose_y_0 = const()[name = string("scores_5_transpose_y_0"), val = bool(false)]; + tensor scores_5_cast_fp16 = matmul(transpose_x = scores_5_transpose_x_0, transpose_y = scores_5_transpose_y_0, x = var_640_cast_fp16, y = var_627_cast_fp16)[name = string("scores_5_cast_fp16")]; + bool x_69_transpose_x_0 = const()[name = string("x_69_transpose_x_0"), val = bool(false)]; + bool x_69_transpose_y_0 = const()[name = string("x_69_transpose_y_0"), val = bool(false)]; + tensor var_664_to_fp16 = const()[name = string("op_664_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1631168)))]; + tensor x_69_cast_fp16 = matmul(transpose_x = x_69_transpose_x_0, transpose_y = x_69_transpose_y_0, x = var_640_cast_fp16, y = var_664_to_fp16)[name = string("x_69_cast_fp16")]; + tensor x_71_pad_0 = const()[name = string("x_71_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 1])]; + string x_71_mode_0 = const()[name = string("x_71_mode_0"), val = string("constant")]; + fp16 const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = fp16(0x0p+0)]; + tensor x_71_cast_fp16 = pad(constant_val = const_33_to_fp16, mode = x_71_mode_0, pad = x_71_pad_0, x = x_69_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor var_676 = const()[name = string("op_676"), val = tensor([1, 2, 33282])]; + tensor input_97_cast_fp16 = reshape(shape = var_676, x = x_71_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor x_73_pad_0 = const()[name = string("x_73_pad_0"), val = tensor([0, 0, 0, 0, 0, 128])]; + string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("constant")]; + fp16 const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = fp16(0x0p+0)]; + tensor x_73_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = x_73_mode_0, pad = x_73_pad_0, x = input_97_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_691 = const()[name = string("op_691"), val = tensor([1, 2, 130, 257])]; + tensor x_75_cast_fp16 = reshape(shape = var_691, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor var_698_begin_0 = const()[name = string("op_698_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_698_end_0 = const()[name = string("op_698_end_0"), val = tensor([1, 2, 129, 257])]; + tensor var_698_end_mask_0 = const()[name = string("op_698_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_698_cast_fp16 = slice_by_index(begin = var_698_begin_0, end = var_698_end_0, end_mask = var_698_end_mask_0, x = x_75_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_699_begin_0 = const()[name = string("op_699_begin_0"), val = tensor([0, 0, 0, 128])]; + tensor var_699_end_0 = const()[name = string("op_699_end_0"), val = tensor([1, 2, 129, 257])]; + tensor var_699_end_mask_0 = const()[name = string("op_699_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_699_cast_fp16 = slice_by_index(begin = var_699_begin_0, end = var_699_end_0, end_mask = var_699_end_mask_0, x = var_698_cast_fp16)[name = string("op_699_cast_fp16")]; + tensor scores_cast_fp16 = add(x = scores_5_cast_fp16, y = var_699_cast_fp16)[name = string("scores_cast_fp16")]; + tensor input_99_cast_fp16 = sub(x = scores_cast_fp16, y = var_496_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor p_attn_cast_fp16 = softmax(axis = var_27, x = input_99_cast_fp16)[name = string("p_attn_cast_fp16")]; + bool out_17_transpose_x_1 = const()[name = string("out_17_transpose_x_1"), val = bool(false)]; + bool out_17_transpose_y_1 = const()[name = string("out_17_transpose_y_1"), val = bool(true)]; + tensor out_17_cast_fp16 = matmul(transpose_x = out_17_transpose_x_1, transpose_y = out_17_transpose_y_1, x = p_attn_cast_fp16, y = var_637_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 128])]; + string x_77_mode_0 = const()[name = string("x_77_mode_0"), val = string("constant")]; + fp16 const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = fp16(0x0p+0)]; + tensor x_77_cast_fp16 = pad(constant_val = const_39_to_fp16, mode = x_77_mode_0, pad = x_77_pad_0, x = p_attn_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor var_741 = const()[name = string("op_741"), val = tensor([1, 2, 33153])]; + tensor input_103_cast_fp16 = reshape(shape = var_741, x = x_77_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor x_79_pad_0 = const()[name = string("x_79_pad_0"), val = tensor([0, 0, 0, 0, 129, 0])]; + string x_79_mode_0 = const()[name = string("x_79_mode_0"), val = string("constant")]; + fp16 const_40_to_fp16 = const()[name = string("const_40_to_fp16"), val = fp16(0x0p+0)]; + tensor x_79_cast_fp16 = pad(constant_val = const_40_to_fp16, mode = x_79_mode_0, pad = x_79_pad_0, x = input_103_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor var_748 = const()[name = string("op_748"), val = tensor([1, 2, 129, 258])]; + tensor x_81_cast_fp16 = reshape(shape = var_748, x = x_79_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor rel_weights_begin_0 = const()[name = string("rel_weights_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor rel_weights_end_0 = const()[name = string("rel_weights_end_0"), val = tensor([1, 2, 129, 258])]; + tensor rel_weights_end_mask_0 = const()[name = string("rel_weights_end_mask_0"), val = tensor([true, true, true, true])]; + tensor rel_weights_cast_fp16 = slice_by_index(begin = rel_weights_begin_0, end = rel_weights_end_0, end_mask = rel_weights_end_mask_0, x = x_81_cast_fp16)[name = string("rel_weights_cast_fp16")]; + bool var_755_transpose_x_0 = const()[name = string("op_755_transpose_x_0"), val = bool(false)]; + bool var_755_transpose_y_0 = const()[name = string("op_755_transpose_y_0"), val = bool(false)]; + tensor var_754_to_fp16 = const()[name = string("op_754_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1647680)))]; + tensor var_755_cast_fp16 = matmul(transpose_x = var_755_transpose_x_0, transpose_y = var_755_transpose_y_0, x = rel_weights_cast_fp16, y = var_754_to_fp16)[name = string("op_755_cast_fp16")]; + tensor out_cast_fp16 = add(x = out_17_cast_fp16, y = var_755_cast_fp16)[name = string("out_cast_fp16")]; + tensor var_757_perm_0 = const()[name = string("op_757_perm_0"), val = tensor([0, 1, 3, 2])]; + tensor var_758 = const()[name = string("op_758"), val = tensor([1, 64, 129])]; + tensor var_757_cast_fp16 = transpose(perm = var_757_perm_0, x = out_cast_fp16)[name = string("transpose_4")]; + tensor input_105_cast_fp16 = reshape(shape = var_758, x = var_757_cast_fp16)[name = string("input_105_cast_fp16")]; + string y_5_pad_type_0 = const()[name = string("y_5_pad_type_0"), val = string("valid")]; + tensor y_5_strides_0 = const()[name = string("y_5_strides_0"), val = tensor([1])]; + tensor y_5_pad_0 = const()[name = string("y_5_pad_0"), val = tensor([0, 0])]; + tensor y_5_dilations_0 = const()[name = string("y_5_dilations_0"), val = tensor([1])]; + int32 y_5_groups_0 = const()[name = string("y_5_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_o_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1664192)))]; + tensor m_attn_layers_1_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_o_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672448)))]; + tensor y_5_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_o_bias_to_fp16, dilations = y_5_dilations_0, groups = y_5_groups_0, pad = y_5_pad_0, pad_type = y_5_pad_type_0, strides = y_5_strides_0, weight = m_attn_layers_1_attn_conv_o_weight_to_fp16, x = input_105_cast_fp16)[name = string("y_5_cast_fp16")]; + tensor x_83_cast_fp16 = add(x = x_67_cast_fp16, y = y_5_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_107_perm_0 = const()[name = string("input_107_perm_0"), val = tensor([0, 2, 1])]; + tensor var_773_axes_0 = const()[name = string("op_773_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_1_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_1_norm_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672640)))]; + tensor m_attn_layers_1_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_1_norm_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832)))]; + tensor input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_83_cast_fp16)[name = string("transpose_3")]; + tensor var_773_cast_fp16 = layer_norm(axes = var_773_axes_0, beta = m_attn_layers_1_norm_1_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_1_norm_1_norm_weight_to_fp16, x = input_107_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor x_85_perm_0 = const()[name = string("x_85_perm_0"), val = tensor([0, 2, 1])]; + tensor x_85_cast_fp16 = transpose(perm = x_85_perm_0, x = var_773_cast_fp16)[name = string("transpose_2")]; + tensor input_109_cast_fp16 = mul(x = x_85_cast_fp16, y = mask_cast_fp16)[name = string("input_109_cast_fp16")]; + string input_111_pad_type_0 = const()[name = string("input_111_pad_type_0"), val = string("valid")]; + tensor input_111_strides_0 = const()[name = string("input_111_strides_0"), val = tensor([1])]; + tensor input_111_pad_0 = const()[name = string("input_111_pad_0"), val = tensor([0, 0])]; + tensor input_111_dilations_0 = const()[name = string("input_111_dilations_0"), val = tensor([1])]; + int32 input_111_groups_0 = const()[name = string("input_111_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673024)))]; + tensor m_attn_layers_1_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1705856)))]; + tensor input_111_cast_fp16 = conv(bias = m_attn_layers_1_ffn_conv_1_bias_to_fp16, dilations = input_111_dilations_0, groups = input_111_groups_0, pad = input_111_pad_0, pad_type = input_111_pad_type_0, strides = input_111_strides_0, weight = m_attn_layers_1_ffn_conv_1_weight_to_fp16, x = input_109_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor h_41_cast_fp16 = relu(x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + tensor input_113_cast_fp16 = mul(x = h_41_cast_fp16, y = mask_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1706432)))]; + tensor m_attn_layers_1_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739264)))]; + tensor h_cast_fp16 = conv(bias = m_attn_layers_1_ffn_conv_2_bias_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = m_attn_layers_1_ffn_conv_2_weight_to_fp16, x = input_113_cast_fp16)[name = string("h_cast_fp16")]; + tensor y_cast_fp16 = mul(x = h_cast_fp16, y = mask_cast_fp16)[name = string("y_cast_fp16")]; + tensor x_87_cast_fp16 = add(x = x_85_cast_fp16, y = y_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor([0, 2, 1])]; + tensor var_801_axes_0 = const()[name = string("op_801_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_1_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_1_norm_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739456)))]; + tensor m_attn_layers_1_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_1_norm_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739648)))]; + tensor input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_87_cast_fp16)[name = string("transpose_1")]; + tensor var_801_cast_fp16 = layer_norm(axes = var_801_axes_0, beta = m_attn_layers_1_norm_2_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_1_norm_2_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_801_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_801_cast_fp16)[name = string("transpose_0")]; + tensor x_91_cast_fp16 = mul(x = x_89_cast_fp16, y = mask_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor x_93_cast_fp16 = add(x = x_91_cast_fp16, y = x_41_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor input_117_begin_0 = const()[name = string("input_117_begin_0"), val = tensor([0, 0, 0])]; + tensor input_117_end_0 = const()[name = string("input_117_end_0"), val = tensor([1, 64, 1])]; + tensor input_117_end_mask_0 = const()[name = string("input_117_end_mask_0"), val = tensor([true, true, false])]; + tensor input_117_cast_fp16 = slice_by_index(begin = input_117_begin_0, end = input_117_end_0, end_mask = input_117_end_mask_0, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor sentence_mask_begin_0 = const()[name = string("sentence_mask_begin_0"), val = tensor([0, 0, 0])]; + tensor sentence_mask_end_0 = const()[name = string("sentence_mask_end_0"), val = tensor([1, 1, 1])]; + tensor sentence_mask_end_mask_0 = const()[name = string("sentence_mask_end_mask_0"), val = tensor([true, true, false])]; + tensor sentence_mask_cast_fp16 = slice_by_index(begin = sentence_mask_begin_0, end = sentence_mask_end_0, end_mask = sentence_mask_end_mask_0, x = mask_cast_fp16)[name = string("sentence_mask_cast_fp16")]; + string var_816_pad_type_0 = const()[name = string("op_816_pad_type_0"), val = string("valid")]; + tensor var_816_strides_0 = const()[name = string("op_816_strides_0"), val = tensor([1])]; + tensor var_816_pad_0 = const()[name = string("op_816_pad_0"), val = tensor([0, 0])]; + tensor var_816_dilations_0 = const()[name = string("op_816_dilations_0"), val = tensor([1])]; + int32 var_816_groups_0 = const()[name = string("op_816_groups_0"), val = int32(1)]; + tensor m_proj_out_conv_weight_to_fp16 = const()[name = string("m_proj_out_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739840)))]; + tensor var_816_cast_fp16 = conv(dilations = var_816_dilations_0, groups = var_816_groups_0, pad = var_816_pad_0, pad_type = var_816_pad_type_0, strides = var_816_strides_0, weight = m_proj_out_conv_weight_to_fp16, x = input_117_cast_fp16)[name = string("op_816_cast_fp16")]; + tensor sentence_emb_cast_fp16 = mul(x = var_816_cast_fp16, y = sentence_mask_cast_fp16)[name = string("sentence_emb_cast_fp16")]; + tensor var_824 = const()[name = string("op_824"), val = tensor([1, -1])]; + tensor s_cast_fp16 = reshape(shape = var_824, x = sentence_emb_cast_fp16)[name = string("s_cast_fp16")]; + tensor var_826 = const()[name = string("op_826"), val = tensor([1, -1])]; + string style_dp_to_fp16_dtype_0 = const()[name = string("style_dp_to_fp16_dtype_0"), val = string("fp16")]; + tensor style_dp_to_fp16 = cast(dtype = style_dp_to_fp16_dtype_0, x = style_dp)[name = string("cast_21")]; + tensor v_cast_fp16 = reshape(shape = var_826, x = style_dp_to_fp16)[name = string("v_cast_fp16")]; + bool input_119_interleave_0 = const()[name = string("input_119_interleave_0"), val = bool(false)]; + tensor input_119_cast_fp16 = concat(axis = var_26, interleave = input_119_interleave_0, values = (s_cast_fp16, v_cast_fp16))[name = string("input_119_cast_fp16")]; + tensor m_predictor_layers_0_weight_to_fp16 = const()[name = string("m_predictor_layers_0_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1748096)))]; + tensor m_predictor_layers_0_bias_to_fp16 = const()[name = string("m_predictor_layers_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797312)))]; + tensor linear_0_cast_fp16 = linear(bias = m_predictor_layers_0_bias_to_fp16, weight = m_predictor_layers_0_weight_to_fp16, x = input_119_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor([2])]; + tensor expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = linear_0_cast_fp16)[name = string("expand_dims_0_cast_fp16")]; + fp32 prelu_0_alpha_1 = const()[name = string("prelu_0_alpha_1"), val = fp32(0x1.b19f5ap-3)]; + tensor prelu_0_cast_fp16 = leaky_relu(alpha = prelu_0_alpha_1, x = expand_dims_0_cast_fp16)[name = string("prelu_0_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([2])]; + tensor input_cast_fp16 = squeeze(axes = input_axes_0, x = prelu_0_cast_fp16)[name = string("input_cast_fp16")]; + tensor m_predictor_layers_1_weight_to_fp16 = const()[name = string("m_predictor_layers_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797632)))]; + tensor m_predictor_layers_1_bias_to_fp16 = const()[name = string("m_predictor_layers_1_bias_to_fp16"), val = tensor([0x1.60cp-6])]; + tensor linear_1_cast_fp16 = linear(bias = m_predictor_layers_1_bias_to_fp16, weight = m_predictor_layers_1_weight_to_fp16, x = input_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor x_cast_fp16 = exp(x = linear_1_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_839_axes_0 = const()[name = string("op_839_axes_0"), val = tensor([-1])]; + tensor var_839_cast_fp16 = squeeze(axes = var_839_axes_0, x = x_cast_fp16)[name = string("op_839_cast_fp16")]; + string var_839_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_839_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor duration = cast(dtype = var_839_cast_fp16_to_fp32_dtype_0, x = var_839_cast_fp16)[name = string("cast_20")]; + } -> (duration); +} \ No newline at end of file diff --git a/DurationPredictor_v2.mlmodelc/weights/weight.bin b/DurationPredictor_v2.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..5bf280b2a55701c68a753cc6e66a5d6c13f16577 --- /dev/null +++ b/DurationPredictor_v2.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f709c544087966d6b2538ac50b7a4014d6e1049e21d713cf3bc71c4c5b9307c +size 1797952 diff --git a/DurationPredictor_v2.mlpackage/Data/com.apple.CoreML/model.mlmodel b/DurationPredictor_v2.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..606bec67576eba6eac62919bbdba067a97f4886f --- /dev/null +++ b/DurationPredictor_v2.mlpackage/Data/com.apple.CoreML/model.mlmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cc85f1e1bb85e8bddf155e9a9ec4727562b934f7b2a2299084e48c4cf8f0cdc2 +size 101155 diff --git a/DurationPredictor_v2.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/DurationPredictor_v2.mlpackage/Data/com.apple.CoreML/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..5bf280b2a55701c68a753cc6e66a5d6c13f16577 --- /dev/null +++ b/DurationPredictor_v2.mlpackage/Data/com.apple.CoreML/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f709c544087966d6b2538ac50b7a4014d6e1049e21d713cf3bc71c4c5b9307c +size 1797952 diff --git a/DurationPredictor_v2.mlpackage/Manifest.json b/DurationPredictor_v2.mlpackage/Manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..af753e9746f6839fc136f751d4b5553282fe7a12 --- /dev/null +++ b/DurationPredictor_v2.mlpackage/Manifest.json @@ -0,0 +1,18 @@ +{ + "fileFormatVersion": "1.0.0", + "itemInfoEntries": { + "6354641A-2464-4C56-AE5B-0C17C350235B": { + "author": "com.apple.CoreML", + "description": "CoreML Model Specification", + "name": "model.mlmodel", + "path": "com.apple.CoreML/model.mlmodel" + }, + "9E8B9E89-99CA-4AC7-9BCB-ED5E7F73E57D": { + "author": "com.apple.CoreML", + "description": "CoreML Model Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + } + }, + "rootModelIdentifier": "6354641A-2464-4C56-AE5B-0C17C350235B" +} diff --git a/TextEncoder_v2.mlmodelc/analytics/coremldata.bin b/TextEncoder_v2.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..73ef25384c2696ab8222d86e9094f8a45a034ee7 --- /dev/null +++ b/TextEncoder_v2.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:973b90afda5d837c037cef2dcbbda275f005eeeb55f2712ca6fbcfa68220f5b5 +size 243 diff --git a/TextEncoder_v2.mlmodelc/coremldata.bin b/TextEncoder_v2.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..40bac4c3fc02554cd8a82f39e168b69faba54783 --- /dev/null +++ b/TextEncoder_v2.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1177e3e03cc4130e5dca18eb007f20573bf95d3938abf62ab2d3470b04cbe7f +size 432 diff --git a/TextEncoder_v2.mlmodelc/model.mil b/TextEncoder_v2.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..26a3afd70795027bf7d7df32acddba93e33953f5 --- /dev/null +++ b/TextEncoder_v2.mlmodelc/model.mil @@ -0,0 +1,984 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor style_ttl, tensor text_ids, tensor text_mask) { + int32 var_29 = const()[name = string("op_29"), val = int32(-1)]; + int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)]; + bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)]; + tensor m_char_embedder_weight_to_fp16 = const()[name = string("m_char_embedder_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + string text_ids_to_int16_dtype_0 = const()[name = string("text_ids_to_int16_dtype_0"), val = string("int16")]; + string cast_45_dtype_0 = const()[name = string("cast_45_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor text_ids_to_int16 = cast(dtype = text_ids_to_int16_dtype_0, x = text_ids)[name = string("cast_51")]; + tensor cast_45 = cast(dtype = cast_45_dtype_0, x = text_ids_to_int16)[name = string("cast_50")]; + tensor greater_equal_0 = greater_equal(x = cast_45, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(8322)]; + tensor add_0 = add(x = cast_45, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_45, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + int32 x_1_cast_fp16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_axis_0"), val = int32(0)]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_49")]; + tensor x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_to_int16, validate_indices = x_1_validate_indices_0, x = m_char_embedder_weight_to_fp16)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor var_55_perm_0 = const()[name = string("op_55_perm_0"), val = tensor([0, 2, 1])]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_48")]; + tensor var_55_cast_fp16 = transpose(perm = var_55_perm_0, x = x_1_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_45")]; + tensor x_3_cast_fp16 = mul(x = var_55_cast_fp16, y = text_mask_to_fp16)[name = string("x_3_cast_fp16")]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = text_mask_to_fp16)[name = string("x_5_cast_fp16")]; + tensor input_3_begin_0 = const()[name = string("input_3_begin_0"), val = tensor([0, 0, 1])]; + tensor input_3_end_0 = const()[name = string("input_3_end_0"), val = tensor([1, 1, 128])]; + tensor input_3_end_mask_0 = const()[name = string("input_3_end_mask_0"), val = tensor([true, true, true])]; + tensor input_3_cast_fp16 = slice_by_index(begin = input_3_begin_0, end = input_3_end_0, end_mask = input_3_end_mask_0, x = text_mask_to_fp16)[name = string("input_3_cast_fp16")]; + tensor var_65_pad_0 = const()[name = string("op_65_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_65_mode_0 = const()[name = string("op_65_mode_0"), val = string("constant")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)]; + tensor var_65_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = var_65_mode_0, pad = var_65_pad_0, x = input_3_cast_fp16)[name = string("op_65_cast_fp16")]; + fp16 var_21_to_fp16 = const()[name = string("op_21_to_fp16"), val = fp16(0x1p+0)]; + tensor var_66_cast_fp16 = sub(x = var_21_to_fp16, y = var_65_cast_fp16)[name = string("op_66_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = text_mask_to_fp16, y = var_66_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_68_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_68_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_68_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_71_cast_fp16 = mul(x = x_5_cast_fp16, y = text_mask_to_fp16)[name = string("op_71_cast_fp16")]; + tensor var_72_cast_fp16 = sub(x = var_21_to_fp16, y = text_mask_to_fp16)[name = string("op_72_cast_fp16")]; + tensor var_73_cast_fp16 = mul(x = var_72_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_73_cast_fp16")]; + tensor input_5_cast_fp16 = add(x = var_71_cast_fp16, y = var_73_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_7_mode_0 = const()[name = string("input_7_mode_0"), val = string("replicate")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor input_7_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_7_mode_0, pad = input_7_pad_0, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")]; + string h_1_pad_type_0 = const()[name = string("h_1_pad_type_0"), val = string("valid")]; + int32 h_1_groups_0 = const()[name = string("h_1_groups_0"), val = int32(256)]; + tensor h_1_strides_0 = const()[name = string("h_1_strides_0"), val = tensor([1])]; + tensor h_1_pad_0 = const()[name = string("h_1_pad_0"), val = tensor([0, 0])]; + tensor h_1_dilations_0 = const()[name = string("h_1_dilations_0"), val = tensor([1])]; + tensor m_convnext_0_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4260992)))]; + tensor m_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4263616)))]; + tensor h_1_cast_fp16 = conv(bias = m_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_1_dilations_0, groups = h_1_groups_0, pad = h_1_pad_0, pad_type = h_1_pad_type_0, strides = h_1_strides_0, weight = m_convnext_0_dwconv__conv_weight_to_fp16, x = input_7_cast_fp16)[name = string("h_1_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_1_cast_fp16, y = text_mask_to_fp16)[name = string("x_7_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor var_91_axes_0 = const()[name = string("op_91_axes_0"), val = tensor([-1])]; + tensor m_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4264192)))]; + tensor m_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4264768)))]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_7_cast_fp16)[name = string("transpose_44")]; + tensor var_91_cast_fp16 = layer_norm(axes = var_91_axes_0, beta = m_convnext_0_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_convnext_0_norm_norm_weight_to_fp16, x = input_9_cast_fp16)[name = string("op_91_cast_fp16")]; + tensor input_11_perm_0 = const()[name = string("input_11_perm_0"), val = tensor([0, 2, 1])]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(1)]; + tensor m_convnext_0_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4265344)))]; + tensor m_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4789696)))]; + tensor input_11_cast_fp16 = transpose(perm = input_11_perm_0, x = var_91_cast_fp16)[name = string("transpose_43")]; + tensor h_3_cast_fp16 = conv(bias = m_convnext_0_pwconv1_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = m_convnext_0_pwconv1_weight_to_fp16, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = h_3_cast_fp16)[name = string("input_13_cast_fp16")]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor var_108_weight_0_to_fp16 = const()[name = string("op_108_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4791808)))]; + tensor var_108_bias_0_to_fp16 = const()[name = string("op_108_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5316160)))]; + tensor var_108_cast_fp16 = conv(bias = var_108_bias_0_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = var_108_weight_0_to_fp16, x = input_13_cast_fp16)[name = string("op_108_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_108_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = text_mask_to_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = text_mask_to_fp16)[name = string("x_11_cast_fp16")]; + tensor var_122_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_122_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_122_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_125_cast_fp16 = mul(x = x_11_cast_fp16, y = text_mask_to_fp16)[name = string("op_125_cast_fp16")]; + tensor var_127_cast_fp16 = mul(x = var_72_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_127_cast_fp16")]; + tensor input_17_cast_fp16 = add(x = var_125_cast_fp16, y = var_127_cast_fp16)[name = string("input_17_cast_fp16")]; + tensor input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_19_mode_0 = const()[name = string("input_19_mode_0"), val = string("replicate")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor input_19_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_19_mode_0, pad = input_19_pad_0, x = input_17_cast_fp16)[name = string("input_19_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(256)]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + tensor m_convnext_1_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_1_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5316736)))]; + tensor m_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319360)))]; + tensor h_7_cast_fp16 = conv(bias = m_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = m_convnext_1_dwconv__conv_weight_to_fp16, x = input_19_cast_fp16)[name = string("h_7_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_7_cast_fp16, y = text_mask_to_fp16)[name = string("x_13_cast_fp16")]; + tensor input_21_perm_0 = const()[name = string("input_21_perm_0"), val = tensor([0, 2, 1])]; + tensor var_145_axes_0 = const()[name = string("op_145_axes_0"), val = tensor([-1])]; + tensor m_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319936)))]; + tensor m_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5320512)))]; + tensor input_21_cast_fp16 = transpose(perm = input_21_perm_0, x = x_13_cast_fp16)[name = string("transpose_42")]; + tensor var_145_cast_fp16 = layer_norm(axes = var_145_axes_0, beta = m_convnext_1_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_convnext_1_norm_norm_weight_to_fp16, x = input_21_cast_fp16)[name = string("op_145_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([1])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(1)]; + tensor m_convnext_1_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_1_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5321088)))]; + tensor m_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5845440)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = var_145_cast_fp16)[name = string("transpose_41")]; + tensor h_9_cast_fp16 = conv(bias = m_convnext_1_pwconv1_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = m_convnext_1_pwconv1_weight_to_fp16, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = h_9_cast_fp16)[name = string("input_25_cast_fp16")]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor var_162_weight_0_to_fp16 = const()[name = string("op_162_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5847552)))]; + tensor var_162_bias_0_to_fp16 = const()[name = string("op_162_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6371904)))]; + tensor var_162_cast_fp16 = conv(bias = var_162_bias_0_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = var_162_weight_0_to_fp16, x = input_25_cast_fp16)[name = string("op_162_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_162_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = text_mask_to_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = text_mask_to_fp16)[name = string("x_17_cast_fp16")]; + tensor var_176_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_176_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_176_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_179_cast_fp16 = mul(x = x_17_cast_fp16, y = text_mask_to_fp16)[name = string("op_179_cast_fp16")]; + tensor var_181_cast_fp16 = mul(x = var_72_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_181_cast_fp16")]; + tensor input_29_cast_fp16 = add(x = var_179_cast_fp16, y = var_181_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_31_mode_0 = const()[name = string("input_31_mode_0"), val = string("replicate")]; + fp16 const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = fp16(0x0p+0)]; + tensor input_31_cast_fp16 = pad(constant_val = const_5_to_fp16, mode = input_31_mode_0, pad = input_31_pad_0, x = input_29_cast_fp16)[name = string("input_31_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([2])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(256)]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor m_convnext_2_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_2_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6372480)))]; + tensor m_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6375104)))]; + tensor h_13_cast_fp16 = conv(bias = m_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = m_convnext_2_dwconv__conv_weight_to_fp16, x = input_31_cast_fp16)[name = string("h_13_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_13_cast_fp16, y = text_mask_to_fp16)[name = string("x_19_cast_fp16")]; + tensor input_33_perm_0 = const()[name = string("input_33_perm_0"), val = tensor([0, 2, 1])]; + tensor var_199_axes_0 = const()[name = string("op_199_axes_0"), val = tensor([-1])]; + tensor m_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6375680)))]; + tensor m_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6376256)))]; + tensor input_33_cast_fp16 = transpose(perm = input_33_perm_0, x = x_19_cast_fp16)[name = string("transpose_40")]; + tensor var_199_cast_fp16 = layer_norm(axes = var_199_axes_0, beta = m_convnext_2_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_convnext_2_norm_norm_weight_to_fp16, x = input_33_cast_fp16)[name = string("op_199_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([1])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(1)]; + tensor m_convnext_2_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_2_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6376832)))]; + tensor m_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6901184)))]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = var_199_cast_fp16)[name = string("transpose_39")]; + tensor h_15_cast_fp16 = conv(bias = m_convnext_2_pwconv1_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = m_convnext_2_pwconv1_weight_to_fp16, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + string input_37_mode_0 = const()[name = string("input_37_mode_0"), val = string("EXACT")]; + tensor input_37_cast_fp16 = gelu(mode = input_37_mode_0, x = h_15_cast_fp16)[name = string("input_37_cast_fp16")]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor var_216_weight_0_to_fp16 = const()[name = string("op_216_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6903296)))]; + tensor var_216_bias_0_to_fp16 = const()[name = string("op_216_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7427648)))]; + tensor var_216_cast_fp16 = conv(bias = var_216_bias_0_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = var_216_weight_0_to_fp16, x = input_37_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_216_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = text_mask_to_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = text_mask_to_fp16)[name = string("x_23_cast_fp16")]; + tensor var_230_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_230_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_230_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = x_23_cast_fp16, y = text_mask_to_fp16)[name = string("op_233_cast_fp16")]; + tensor var_235_cast_fp16 = mul(x = var_72_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_235_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = var_233_cast_fp16, y = var_235_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_43_mode_0 = const()[name = string("input_43_mode_0"), val = string("replicate")]; + fp16 const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = fp16(0x0p+0)]; + tensor input_43_cast_fp16 = pad(constant_val = const_7_to_fp16, mode = input_43_mode_0, pad = input_43_pad_0, x = input_41_cast_fp16)[name = string("input_43_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([2])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(256)]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor m_convnext_3_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_3_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7428224)))]; + tensor m_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7430848)))]; + tensor h_19_cast_fp16 = conv(bias = m_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = m_convnext_3_dwconv__conv_weight_to_fp16, x = input_43_cast_fp16)[name = string("h_19_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_19_cast_fp16, y = text_mask_to_fp16)[name = string("x_25_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor var_253_axes_0 = const()[name = string("op_253_axes_0"), val = tensor([-1])]; + tensor m_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7431424)))]; + tensor m_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7432000)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_25_cast_fp16)[name = string("transpose_38")]; + tensor var_253_cast_fp16 = layer_norm(axes = var_253_axes_0, beta = m_convnext_3_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_convnext_3_norm_norm_weight_to_fp16, x = input_45_cast_fp16)[name = string("op_253_cast_fp16")]; + tensor input_47_perm_0 = const()[name = string("input_47_perm_0"), val = tensor([0, 2, 1])]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([1])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(1)]; + tensor m_convnext_3_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_3_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7432576)))]; + tensor m_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7956928)))]; + tensor input_47_cast_fp16 = transpose(perm = input_47_perm_0, x = var_253_cast_fp16)[name = string("transpose_37")]; + tensor h_21_cast_fp16 = conv(bias = m_convnext_3_pwconv1_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = m_convnext_3_pwconv1_weight_to_fp16, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = h_21_cast_fp16)[name = string("input_49_cast_fp16")]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor var_270_weight_0_to_fp16 = const()[name = string("op_270_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7959040)))]; + tensor var_270_bias_0_to_fp16 = const()[name = string("op_270_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8483392)))]; + tensor var_270_cast_fp16 = conv(bias = var_270_bias_0_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = var_270_weight_0_to_fp16, x = input_49_cast_fp16)[name = string("op_270_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_270_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = text_mask_to_fp16)[name = string("x_27_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = x_27_cast_fp16, y = text_mask_to_fp16)[name = string("x_29_cast_fp16")]; + tensor var_284_cast_fp16 = mul(x = x_29_cast_fp16, y = last_1_cast_fp16)[name = string("op_284_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_284_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_287_cast_fp16 = mul(x = x_29_cast_fp16, y = text_mask_to_fp16)[name = string("op_287_cast_fp16")]; + tensor var_289_cast_fp16 = mul(x = var_72_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_289_cast_fp16")]; + tensor input_53_cast_fp16 = add(x = var_287_cast_fp16, y = var_289_cast_fp16)[name = string("input_53_cast_fp16")]; + tensor input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("replicate")]; + fp16 const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = fp16(0x0p+0)]; + tensor input_55_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_55_mode_0, pad = input_55_pad_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([4])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(256)]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor m_convnext_4_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_4_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8483968)))]; + tensor m_convnext_4_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_4_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8486592)))]; + tensor h_25_cast_fp16 = conv(bias = m_convnext_4_dwconv__conv_bias_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = m_convnext_4_dwconv__conv_weight_to_fp16, x = input_55_cast_fp16)[name = string("h_25_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = h_25_cast_fp16, y = text_mask_to_fp16)[name = string("x_31_cast_fp16")]; + tensor input_57_perm_0 = const()[name = string("input_57_perm_0"), val = tensor([0, 2, 1])]; + tensor var_307_axes_0 = const()[name = string("op_307_axes_0"), val = tensor([-1])]; + tensor m_convnext_4_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_4_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8487168)))]; + tensor m_convnext_4_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_4_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8487744)))]; + tensor input_57_cast_fp16 = transpose(perm = input_57_perm_0, x = x_31_cast_fp16)[name = string("transpose_36")]; + tensor var_307_cast_fp16 = layer_norm(axes = var_307_axes_0, beta = m_convnext_4_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_convnext_4_norm_norm_weight_to_fp16, x = input_57_cast_fp16)[name = string("op_307_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(1)]; + tensor m_convnext_4_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_4_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8488320)))]; + tensor m_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9012672)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = var_307_cast_fp16)[name = string("transpose_35")]; + tensor h_27_cast_fp16 = conv(bias = m_convnext_4_pwconv1_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = m_convnext_4_pwconv1_weight_to_fp16, x = input_59_cast_fp16)[name = string("h_27_cast_fp16")]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("EXACT")]; + tensor input_61_cast_fp16 = gelu(mode = input_61_mode_0, x = h_27_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor var_324_weight_0_to_fp16 = const()[name = string("op_324_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9014784)))]; + tensor var_324_bias_0_to_fp16 = const()[name = string("op_324_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9539136)))]; + tensor var_324_cast_fp16 = conv(bias = var_324_bias_0_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = var_324_weight_0_to_fp16, x = input_61_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_29_cast_fp16, y = var_324_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = out_9_cast_fp16, y = text_mask_to_fp16)[name = string("x_33_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = x_33_cast_fp16, y = text_mask_to_fp16)[name = string("x_35_cast_fp16")]; + tensor var_338_cast_fp16 = mul(x = x_35_cast_fp16, y = last_1_cast_fp16)[name = string("op_338_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_338_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_35_cast_fp16, y = text_mask_to_fp16)[name = string("op_341_cast_fp16")]; + tensor var_343_cast_fp16 = mul(x = var_72_cast_fp16, y = x_last_cast_fp16)[name = string("op_343_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_341_cast_fp16, y = var_343_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor input_67_pad_0 = const()[name = string("input_67_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("replicate")]; + fp16 const_11_to_fp16 = const()[name = string("const_11_to_fp16"), val = fp16(0x0p+0)]; + tensor input_67_cast_fp16 = pad(constant_val = const_11_to_fp16, mode = input_67_mode_0, pad = input_67_pad_0, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([4])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(256)]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor m_convnext_5_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_5_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9539712)))]; + tensor m_convnext_5_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_5_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9542336)))]; + tensor h_31_cast_fp16 = conv(bias = m_convnext_5_dwconv__conv_bias_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = m_convnext_5_dwconv__conv_weight_to_fp16, x = input_67_cast_fp16)[name = string("h_31_cast_fp16")]; + tensor x_37_cast_fp16 = mul(x = h_31_cast_fp16, y = text_mask_to_fp16)[name = string("x_37_cast_fp16")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor var_361_axes_0 = const()[name = string("op_361_axes_0"), val = tensor([-1])]; + tensor m_convnext_5_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_5_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9542912)))]; + tensor m_convnext_5_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_5_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9543488)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_37_cast_fp16)[name = string("transpose_34")]; + tensor var_361_cast_fp16 = layer_norm(axes = var_361_axes_0, beta = m_convnext_5_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_convnext_5_norm_norm_weight_to_fp16, x = input_69_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(1)]; + tensor m_convnext_5_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_5_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9544064)))]; + tensor m_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10068416)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = var_361_cast_fp16)[name = string("transpose_33")]; + tensor h_33_cast_fp16 = conv(bias = m_convnext_5_pwconv1_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = m_convnext_5_pwconv1_weight_to_fp16, x = input_71_cast_fp16)[name = string("h_33_cast_fp16")]; + string input_73_mode_0 = const()[name = string("input_73_mode_0"), val = string("EXACT")]; + tensor input_73_cast_fp16 = gelu(mode = input_73_mode_0, x = h_33_cast_fp16)[name = string("input_73_cast_fp16")]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor var_378_weight_0_to_fp16 = const()[name = string("op_378_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10070528)))]; + tensor var_378_bias_0_to_fp16 = const()[name = string("op_378_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10594880)))]; + tensor var_378_cast_fp16 = conv(bias = var_378_bias_0_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = var_378_weight_0_to_fp16, x = input_73_cast_fp16)[name = string("op_378_cast_fp16")]; + tensor out_11_cast_fp16 = add(x = x_35_cast_fp16, y = var_378_cast_fp16)[name = string("out_11_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = out_11_cast_fp16, y = text_mask_to_fp16)[name = string("x_39_cast_fp16")]; + tensor x_41_cast_fp16 = mul(x = x_39_cast_fp16, y = text_mask_to_fp16)[name = string("x_41_cast_fp16")]; + string var_401_pad_type_0 = const()[name = string("op_401_pad_type_0"), val = string("valid")]; + tensor var_401_strides_0 = const()[name = string("op_401_strides_0"), val = tensor([1])]; + tensor var_401_pad_0 = const()[name = string("op_401_pad_0"), val = tensor([0, 0])]; + tensor var_401_dilations_0 = const()[name = string("op_401_dilations_0"), val = tensor([1])]; + int32 var_401_groups_0 = const()[name = string("op_401_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10595456)))]; + tensor m_attn_layers_0_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10726592)))]; + tensor var_401_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_q_bias_to_fp16, dilations = var_401_dilations_0, groups = var_401_groups_0, pad = var_401_pad_0, pad_type = var_401_pad_type_0, strides = var_401_strides_0, weight = m_attn_layers_0_attn_conv_q_weight_to_fp16, x = x_41_cast_fp16)[name = string("op_401_cast_fp16")]; + tensor var_402 = const()[name = string("op_402"), val = tensor([1, 4, 64, 128])]; + tensor var_403_cast_fp16 = reshape(shape = var_402, x = var_401_cast_fp16)[name = string("op_403_cast_fp16")]; + tensor q_1_perm_0 = const()[name = string("q_1_perm_0"), val = tensor([0, 1, 3, 2])]; + string var_411_pad_type_0 = const()[name = string("op_411_pad_type_0"), val = string("valid")]; + tensor var_411_strides_0 = const()[name = string("op_411_strides_0"), val = tensor([1])]; + tensor var_411_pad_0 = const()[name = string("op_411_pad_0"), val = tensor([0, 0])]; + tensor var_411_dilations_0 = const()[name = string("op_411_dilations_0"), val = tensor([1])]; + int32 var_411_groups_0 = const()[name = string("op_411_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10727168)))]; + tensor m_attn_layers_0_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10858304)))]; + tensor var_411_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_k_bias_to_fp16, dilations = var_411_dilations_0, groups = var_411_groups_0, pad = var_411_pad_0, pad_type = var_411_pad_type_0, strides = var_411_strides_0, weight = m_attn_layers_0_attn_conv_k_weight_to_fp16, x = x_41_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor var_412 = const()[name = string("op_412"), val = tensor([1, 4, 64, 128])]; + tensor var_413_cast_fp16 = reshape(shape = var_412, x = var_411_cast_fp16)[name = string("op_413_cast_fp16")]; + string var_421_pad_type_0 = const()[name = string("op_421_pad_type_0"), val = string("valid")]; + tensor var_421_strides_0 = const()[name = string("op_421_strides_0"), val = tensor([1])]; + tensor var_421_pad_0 = const()[name = string("op_421_pad_0"), val = tensor([0, 0])]; + tensor var_421_dilations_0 = const()[name = string("op_421_dilations_0"), val = tensor([1])]; + int32 var_421_groups_0 = const()[name = string("op_421_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10858880)))]; + tensor m_attn_layers_0_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10990016)))]; + tensor var_421_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_v_bias_to_fp16, dilations = var_421_dilations_0, groups = var_421_groups_0, pad = var_421_pad_0, pad_type = var_421_pad_type_0, strides = var_421_strides_0, weight = m_attn_layers_0_attn_conv_v_weight_to_fp16, x = x_41_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422 = const()[name = string("op_422"), val = tensor([1, 4, 64, 128])]; + tensor var_423_cast_fp16 = reshape(shape = var_422, x = var_421_cast_fp16)[name = string("op_423_cast_fp16")]; + fp16 var_425_to_fp16 = const()[name = string("op_425_to_fp16"), val = fp16(0x1p-3)]; + tensor q_1_cast_fp16 = transpose(perm = q_1_perm_0, x = var_403_cast_fp16)[name = string("transpose_32")]; + tensor var_426_cast_fp16 = mul(x = q_1_cast_fp16, y = var_425_to_fp16)[name = string("op_426_cast_fp16")]; + bool scores_1_transpose_x_0 = const()[name = string("scores_1_transpose_x_0"), val = bool(false)]; + bool scores_1_transpose_y_0 = const()[name = string("scores_1_transpose_y_0"), val = bool(false)]; + tensor scores_1_cast_fp16 = matmul(transpose_x = scores_1_transpose_x_0, transpose_y = scores_1_transpose_y_0, x = var_426_cast_fp16, y = var_413_cast_fp16)[name = string("scores_1_cast_fp16")]; + bool x_43_transpose_x_0 = const()[name = string("x_43_transpose_x_0"), val = bool(false)]; + bool x_43_transpose_y_0 = const()[name = string("x_43_transpose_y_0"), val = bool(false)]; + tensor var_450_to_fp16 = const()[name = string("op_450_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10990592)))]; + tensor x_43_cast_fp16 = matmul(transpose_x = x_43_transpose_x_0, transpose_y = x_43_transpose_y_0, x = var_426_cast_fp16, y = var_450_to_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 1])]; + string x_45_mode_0 = const()[name = string("x_45_mode_0"), val = string("constant")]; + fp16 const_18_to_fp16 = const()[name = string("const_18_to_fp16"), val = fp16(0x0p+0)]; + tensor x_45_cast_fp16 = pad(constant_val = const_18_to_fp16, mode = x_45_mode_0, pad = x_45_pad_0, x = x_43_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_462 = const()[name = string("op_462"), val = tensor([1, 4, 32768])]; + tensor input_77_cast_fp16 = reshape(shape = var_462, x = x_45_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor x_47_pad_0 = const()[name = string("x_47_pad_0"), val = tensor([0, 0, 0, 0, 0, 127])]; + string x_47_mode_0 = const()[name = string("x_47_mode_0"), val = string("constant")]; + fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)]; + tensor x_47_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = x_47_mode_0, pad = x_47_pad_0, x = input_77_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor var_477 = const()[name = string("op_477"), val = tensor([1, 4, 129, 255])]; + tensor x_49_cast_fp16 = reshape(shape = var_477, x = x_47_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_484_begin_0 = const()[name = string("op_484_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_484_end_0 = const()[name = string("op_484_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_484_end_mask_0 = const()[name = string("op_484_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_484_cast_fp16 = slice_by_index(begin = var_484_begin_0, end = var_484_end_0, end_mask = var_484_end_mask_0, x = x_49_cast_fp16)[name = string("op_484_cast_fp16")]; + tensor var_485_begin_0 = const()[name = string("op_485_begin_0"), val = tensor([0, 0, 0, 127])]; + tensor var_485_end_0 = const()[name = string("op_485_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_485_end_mask_0 = const()[name = string("op_485_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_485_cast_fp16 = slice_by_index(begin = var_485_begin_0, end = var_485_end_0, end_mask = var_485_end_mask_0, x = var_484_cast_fp16)[name = string("op_485_cast_fp16")]; + tensor scores_3_cast_fp16 = add(x = scores_1_cast_fp16, y = var_485_cast_fp16)[name = string("scores_3_cast_fp16")]; + tensor var_487_axes_0 = const()[name = string("op_487_axes_0"), val = tensor([2])]; + tensor var_487_cast_fp16 = expand_dims(axes = var_487_axes_0, x = text_mask_to_fp16)[name = string("op_487_cast_fp16")]; + tensor var_488_cast_fp16 = sub(x = var_21_to_fp16, y = var_487_cast_fp16)[name = string("op_488_cast_fp16")]; + fp16 var_489_to_fp16 = const()[name = string("op_489_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_490_cast_fp16 = mul(x = var_488_cast_fp16, y = var_489_to_fp16)[name = string("op_490_cast_fp16")]; + tensor input_79_cast_fp16 = sub(x = scores_3_cast_fp16, y = var_490_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor p_attn_1_cast_fp16 = softmax(axis = var_29, x = input_79_cast_fp16)[name = string("p_attn_1_cast_fp16")]; + bool out_13_transpose_x_1 = const()[name = string("out_13_transpose_x_1"), val = bool(false)]; + bool out_13_transpose_y_1 = const()[name = string("out_13_transpose_y_1"), val = bool(true)]; + tensor out_13_cast_fp16 = matmul(transpose_x = out_13_transpose_x_1, transpose_y = out_13_transpose_y_1, x = p_attn_1_cast_fp16, y = var_423_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_51_pad_0 = const()[name = string("x_51_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 127])]; + string x_51_mode_0 = const()[name = string("x_51_mode_0"), val = string("constant")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor x_51_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = x_51_mode_0, pad = x_51_pad_0, x = p_attn_1_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_527 = const()[name = string("op_527"), val = tensor([1, 4, 32640])]; + tensor input_83_cast_fp16 = reshape(shape = var_527, x = x_51_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([0, 0, 0, 0, 128, 0])]; + string x_53_mode_0 = const()[name = string("x_53_mode_0"), val = string("constant")]; + fp16 const_25_to_fp16 = const()[name = string("const_25_to_fp16"), val = fp16(0x0p+0)]; + tensor x_53_cast_fp16 = pad(constant_val = const_25_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = input_83_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor var_534 = const()[name = string("op_534"), val = tensor([1, 4, 128, 256])]; + tensor x_55_cast_fp16 = reshape(shape = var_534, x = x_53_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor rel_weights_1_begin_0 = const()[name = string("rel_weights_1_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor rel_weights_1_end_0 = const()[name = string("rel_weights_1_end_0"), val = tensor([1, 4, 128, 256])]; + tensor rel_weights_1_end_mask_0 = const()[name = string("rel_weights_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor rel_weights_1_cast_fp16 = slice_by_index(begin = rel_weights_1_begin_0, end = rel_weights_1_end_0, end_mask = rel_weights_1_end_mask_0, x = x_55_cast_fp16)[name = string("rel_weights_1_cast_fp16")]; + bool var_541_transpose_x_0 = const()[name = string("op_541_transpose_x_0"), val = bool(false)]; + bool var_541_transpose_y_0 = const()[name = string("op_541_transpose_y_0"), val = bool(false)]; + tensor var_540_to_fp16 = const()[name = string("op_540_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11023296)))]; + tensor var_541_cast_fp16 = matmul(transpose_x = var_541_transpose_x_0, transpose_y = var_541_transpose_y_0, x = rel_weights_1_cast_fp16, y = var_540_to_fp16)[name = string("op_541_cast_fp16")]; + tensor out_15_cast_fp16 = add(x = out_13_cast_fp16, y = var_541_cast_fp16)[name = string("out_15_cast_fp16")]; + tensor var_543_perm_0 = const()[name = string("op_543_perm_0"), val = tensor([0, 1, 3, 2])]; + tensor var_544 = const()[name = string("op_544"), val = tensor([1, 256, 128])]; + tensor var_543_cast_fp16 = transpose(perm = var_543_perm_0, x = out_15_cast_fp16)[name = string("transpose_31")]; + tensor input_85_cast_fp16 = reshape(shape = var_544, x = var_543_cast_fp16)[name = string("input_85_cast_fp16")]; + string y_1_pad_type_0 = const()[name = string("y_1_pad_type_0"), val = string("valid")]; + tensor y_1_strides_0 = const()[name = string("y_1_strides_0"), val = tensor([1])]; + tensor y_1_pad_0 = const()[name = string("y_1_pad_0"), val = tensor([0, 0])]; + tensor y_1_dilations_0 = const()[name = string("y_1_dilations_0"), val = tensor([1])]; + int32 y_1_groups_0 = const()[name = string("y_1_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_o_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11056000)))]; + tensor m_attn_layers_0_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_o_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11187136)))]; + tensor y_1_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_o_bias_to_fp16, dilations = y_1_dilations_0, groups = y_1_groups_0, pad = y_1_pad_0, pad_type = y_1_pad_type_0, strides = y_1_strides_0, weight = m_attn_layers_0_attn_conv_o_weight_to_fp16, x = input_85_cast_fp16)[name = string("y_1_cast_fp16")]; + tensor x_57_cast_fp16 = add(x = x_41_cast_fp16, y = y_1_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_0_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_0_norm_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11187712)))]; + tensor m_attn_layers_0_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_0_norm_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11188288)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_57_cast_fp16)[name = string("transpose_30")]; + tensor var_559_cast_fp16 = layer_norm(axes = var_559_axes_0, beta = m_attn_layers_0_norm_1_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_0_norm_1_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_559_cast_fp16")]; + tensor x_59_perm_0 = const()[name = string("x_59_perm_0"), val = tensor([0, 2, 1])]; + tensor x_59_cast_fp16 = transpose(perm = x_59_perm_0, x = var_559_cast_fp16)[name = string("transpose_29")]; + tensor input_89_cast_fp16 = mul(x = x_59_cast_fp16, y = text_mask_to_fp16)[name = string("input_89_cast_fp16")]; + string input_91_pad_type_0 = const()[name = string("input_91_pad_type_0"), val = string("valid")]; + tensor input_91_strides_0 = const()[name = string("input_91_strides_0"), val = tensor([1])]; + tensor input_91_pad_0 = const()[name = string("input_91_pad_0"), val = tensor([0, 0])]; + tensor input_91_dilations_0 = const()[name = string("input_91_dilations_0"), val = tensor([1])]; + int32 input_91_groups_0 = const()[name = string("input_91_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11188864)))]; + tensor m_attn_layers_0_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11713216)))]; + tensor input_91_cast_fp16 = conv(bias = m_attn_layers_0_ffn_conv_1_bias_to_fp16, dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = m_attn_layers_0_ffn_conv_1_weight_to_fp16, x = input_89_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor h_37_cast_fp16 = relu(x = input_91_cast_fp16)[name = string("h_37_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = h_37_cast_fp16, y = text_mask_to_fp16)[name = string("input_93_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(1)]; + tensor m_attn_layers_0_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11715328)))]; + tensor m_attn_layers_0_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12239680)))]; + tensor h_39_cast_fp16 = conv(bias = m_attn_layers_0_ffn_conv_2_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = m_attn_layers_0_ffn_conv_2_weight_to_fp16, x = input_93_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor y_3_cast_fp16 = mul(x = h_39_cast_fp16, y = text_mask_to_fp16)[name = string("y_3_cast_fp16")]; + tensor x_61_cast_fp16 = add(x = x_59_cast_fp16, y = y_3_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor var_587_axes_0 = const()[name = string("op_587_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_0_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_0_norm_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12240256)))]; + tensor m_attn_layers_0_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_0_norm_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12240832)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_61_cast_fp16)[name = string("transpose_28")]; + tensor var_587_cast_fp16 = layer_norm(axes = var_587_axes_0, beta = m_attn_layers_0_norm_2_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_0_norm_2_norm_weight_to_fp16, x = input_95_cast_fp16)[name = string("op_587_cast_fp16")]; + tensor x_63_perm_0 = const()[name = string("x_63_perm_0"), val = tensor([0, 2, 1])]; + tensor x_63_cast_fp16 = transpose(perm = x_63_perm_0, x = var_587_cast_fp16)[name = string("transpose_27")]; + tensor x_65_cast_fp16 = mul(x = x_63_cast_fp16, y = text_mask_to_fp16)[name = string("x_65_cast_fp16")]; + string var_609_pad_type_0 = const()[name = string("op_609_pad_type_0"), val = string("valid")]; + tensor var_609_strides_0 = const()[name = string("op_609_strides_0"), val = tensor([1])]; + tensor var_609_pad_0 = const()[name = string("op_609_pad_0"), val = tensor([0, 0])]; + tensor var_609_dilations_0 = const()[name = string("op_609_dilations_0"), val = tensor([1])]; + int32 var_609_groups_0 = const()[name = string("op_609_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12241408)))]; + tensor m_attn_layers_1_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12372544)))]; + tensor var_609_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_q_bias_to_fp16, dilations = var_609_dilations_0, groups = var_609_groups_0, pad = var_609_pad_0, pad_type = var_609_pad_type_0, strides = var_609_strides_0, weight = m_attn_layers_1_attn_conv_q_weight_to_fp16, x = x_65_cast_fp16)[name = string("op_609_cast_fp16")]; + tensor var_610 = const()[name = string("op_610"), val = tensor([1, 4, 64, 128])]; + tensor var_611_cast_fp16 = reshape(shape = var_610, x = var_609_cast_fp16)[name = string("op_611_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 1, 3, 2])]; + string var_619_pad_type_0 = const()[name = string("op_619_pad_type_0"), val = string("valid")]; + tensor var_619_strides_0 = const()[name = string("op_619_strides_0"), val = tensor([1])]; + tensor var_619_pad_0 = const()[name = string("op_619_pad_0"), val = tensor([0, 0])]; + tensor var_619_dilations_0 = const()[name = string("op_619_dilations_0"), val = tensor([1])]; + int32 var_619_groups_0 = const()[name = string("op_619_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12373120)))]; + tensor m_attn_layers_1_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12504256)))]; + tensor var_619_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_k_bias_to_fp16, dilations = var_619_dilations_0, groups = var_619_groups_0, pad = var_619_pad_0, pad_type = var_619_pad_type_0, strides = var_619_strides_0, weight = m_attn_layers_1_attn_conv_k_weight_to_fp16, x = x_65_cast_fp16)[name = string("op_619_cast_fp16")]; + tensor var_620 = const()[name = string("op_620"), val = tensor([1, 4, 64, 128])]; + tensor var_621_cast_fp16 = reshape(shape = var_620, x = var_619_cast_fp16)[name = string("op_621_cast_fp16")]; + string var_629_pad_type_0 = const()[name = string("op_629_pad_type_0"), val = string("valid")]; + tensor var_629_strides_0 = const()[name = string("op_629_strides_0"), val = tensor([1])]; + tensor var_629_pad_0 = const()[name = string("op_629_pad_0"), val = tensor([0, 0])]; + tensor var_629_dilations_0 = const()[name = string("op_629_dilations_0"), val = tensor([1])]; + int32 var_629_groups_0 = const()[name = string("op_629_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12504832)))]; + tensor m_attn_layers_1_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12635968)))]; + tensor var_629_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_v_bias_to_fp16, dilations = var_629_dilations_0, groups = var_629_groups_0, pad = var_629_pad_0, pad_type = var_629_pad_type_0, strides = var_629_strides_0, weight = m_attn_layers_1_attn_conv_v_weight_to_fp16, x = x_65_cast_fp16)[name = string("op_629_cast_fp16")]; + tensor var_630 = const()[name = string("op_630"), val = tensor([1, 4, 64, 128])]; + tensor var_631_cast_fp16 = reshape(shape = var_630, x = var_629_cast_fp16)[name = string("op_631_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p-3)]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_611_cast_fp16)[name = string("transpose_26")]; + tensor var_634_cast_fp16 = mul(x = q_3_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + bool scores_5_transpose_x_0 = const()[name = string("scores_5_transpose_x_0"), val = bool(false)]; + bool scores_5_transpose_y_0 = const()[name = string("scores_5_transpose_y_0"), val = bool(false)]; + tensor scores_5_cast_fp16 = matmul(transpose_x = scores_5_transpose_x_0, transpose_y = scores_5_transpose_y_0, x = var_634_cast_fp16, y = var_621_cast_fp16)[name = string("scores_5_cast_fp16")]; + bool x_67_transpose_x_0 = const()[name = string("x_67_transpose_x_0"), val = bool(false)]; + bool x_67_transpose_y_0 = const()[name = string("x_67_transpose_y_0"), val = bool(false)]; + tensor var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12636544)))]; + tensor x_67_cast_fp16 = matmul(transpose_x = x_67_transpose_x_0, transpose_y = x_67_transpose_y_0, x = var_634_cast_fp16, y = var_658_to_fp16)[name = string("x_67_cast_fp16")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 1])]; + string x_69_mode_0 = const()[name = string("x_69_mode_0"), val = string("constant")]; + fp16 const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = fp16(0x0p+0)]; + tensor x_69_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = x_69_mode_0, pad = x_69_pad_0, x = x_67_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_670 = const()[name = string("op_670"), val = tensor([1, 4, 32768])]; + tensor input_99_cast_fp16 = reshape(shape = var_670, x = x_69_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor x_71_pad_0 = const()[name = string("x_71_pad_0"), val = tensor([0, 0, 0, 0, 0, 127])]; + string x_71_mode_0 = const()[name = string("x_71_mode_0"), val = string("constant")]; + fp16 const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = fp16(0x0p+0)]; + tensor x_71_cast_fp16 = pad(constant_val = const_33_to_fp16, mode = x_71_mode_0, pad = x_71_pad_0, x = input_99_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor var_685 = const()[name = string("op_685"), val = tensor([1, 4, 129, 255])]; + tensor x_73_cast_fp16 = reshape(shape = var_685, x = x_71_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_692_begin_0 = const()[name = string("op_692_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_692_end_0 = const()[name = string("op_692_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_692_end_mask_0 = const()[name = string("op_692_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_692_cast_fp16 = slice_by_index(begin = var_692_begin_0, end = var_692_end_0, end_mask = var_692_end_mask_0, x = x_73_cast_fp16)[name = string("op_692_cast_fp16")]; + tensor var_693_begin_0 = const()[name = string("op_693_begin_0"), val = tensor([0, 0, 0, 127])]; + tensor var_693_end_0 = const()[name = string("op_693_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_693_end_mask_0 = const()[name = string("op_693_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_693_cast_fp16 = slice_by_index(begin = var_693_begin_0, end = var_693_end_0, end_mask = var_693_end_mask_0, x = var_692_cast_fp16)[name = string("op_693_cast_fp16")]; + tensor scores_7_cast_fp16 = add(x = scores_5_cast_fp16, y = var_693_cast_fp16)[name = string("scores_7_cast_fp16")]; + tensor input_101_cast_fp16 = sub(x = scores_7_cast_fp16, y = var_490_cast_fp16)[name = string("input_101_cast_fp16")]; + tensor p_attn_3_cast_fp16 = softmax(axis = var_29, x = input_101_cast_fp16)[name = string("p_attn_3_cast_fp16")]; + bool out_17_transpose_x_1 = const()[name = string("out_17_transpose_x_1"), val = bool(false)]; + bool out_17_transpose_y_1 = const()[name = string("out_17_transpose_y_1"), val = bool(true)]; + tensor out_17_cast_fp16 = matmul(transpose_x = out_17_transpose_x_1, transpose_y = out_17_transpose_y_1, x = p_attn_3_cast_fp16, y = var_631_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_75_pad_0 = const()[name = string("x_75_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 127])]; + string x_75_mode_0 = const()[name = string("x_75_mode_0"), val = string("constant")]; + fp16 const_38_to_fp16 = const()[name = string("const_38_to_fp16"), val = fp16(0x0p+0)]; + tensor x_75_cast_fp16 = pad(constant_val = const_38_to_fp16, mode = x_75_mode_0, pad = x_75_pad_0, x = p_attn_3_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor var_735 = const()[name = string("op_735"), val = tensor([1, 4, 32640])]; + tensor input_105_cast_fp16 = reshape(shape = var_735, x = x_75_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([0, 0, 0, 0, 128, 0])]; + string x_77_mode_0 = const()[name = string("x_77_mode_0"), val = string("constant")]; + fp16 const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = fp16(0x0p+0)]; + tensor x_77_cast_fp16 = pad(constant_val = const_39_to_fp16, mode = x_77_mode_0, pad = x_77_pad_0, x = input_105_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor var_742 = const()[name = string("op_742"), val = tensor([1, 4, 128, 256])]; + tensor x_79_cast_fp16 = reshape(shape = var_742, x = x_77_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor rel_weights_3_begin_0 = const()[name = string("rel_weights_3_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor rel_weights_3_end_0 = const()[name = string("rel_weights_3_end_0"), val = tensor([1, 4, 128, 256])]; + tensor rel_weights_3_end_mask_0 = const()[name = string("rel_weights_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor rel_weights_3_cast_fp16 = slice_by_index(begin = rel_weights_3_begin_0, end = rel_weights_3_end_0, end_mask = rel_weights_3_end_mask_0, x = x_79_cast_fp16)[name = string("rel_weights_3_cast_fp16")]; + bool var_749_transpose_x_0 = const()[name = string("op_749_transpose_x_0"), val = bool(false)]; + bool var_749_transpose_y_0 = const()[name = string("op_749_transpose_y_0"), val = bool(false)]; + tensor var_748_to_fp16 = const()[name = string("op_748_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12669248)))]; + tensor var_749_cast_fp16 = matmul(transpose_x = var_749_transpose_x_0, transpose_y = var_749_transpose_y_0, x = rel_weights_3_cast_fp16, y = var_748_to_fp16)[name = string("op_749_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = out_17_cast_fp16, y = var_749_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor var_751_perm_0 = const()[name = string("op_751_perm_0"), val = tensor([0, 1, 3, 2])]; + tensor var_752 = const()[name = string("op_752"), val = tensor([1, 256, 128])]; + tensor var_751_cast_fp16 = transpose(perm = var_751_perm_0, x = out_19_cast_fp16)[name = string("transpose_25")]; + tensor input_107_cast_fp16 = reshape(shape = var_752, x = var_751_cast_fp16)[name = string("input_107_cast_fp16")]; + string y_5_pad_type_0 = const()[name = string("y_5_pad_type_0"), val = string("valid")]; + tensor y_5_strides_0 = const()[name = string("y_5_strides_0"), val = tensor([1])]; + tensor y_5_pad_0 = const()[name = string("y_5_pad_0"), val = tensor([0, 0])]; + tensor y_5_dilations_0 = const()[name = string("y_5_dilations_0"), val = tensor([1])]; + int32 y_5_groups_0 = const()[name = string("y_5_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_o_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12701952)))]; + tensor m_attn_layers_1_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_o_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12833088)))]; + tensor y_5_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_o_bias_to_fp16, dilations = y_5_dilations_0, groups = y_5_groups_0, pad = y_5_pad_0, pad_type = y_5_pad_type_0, strides = y_5_strides_0, weight = m_attn_layers_1_attn_conv_o_weight_to_fp16, x = input_107_cast_fp16)[name = string("y_5_cast_fp16")]; + tensor x_81_cast_fp16 = add(x = x_65_cast_fp16, y = y_5_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_767_axes_0 = const()[name = string("op_767_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_1_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_1_norm_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12833664)))]; + tensor m_attn_layers_1_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_1_norm_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12834240)))]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_81_cast_fp16)[name = string("transpose_24")]; + tensor var_767_cast_fp16 = layer_norm(axes = var_767_axes_0, beta = m_attn_layers_1_norm_1_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_1_norm_1_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor x_83_perm_0 = const()[name = string("x_83_perm_0"), val = tensor([0, 2, 1])]; + tensor x_83_cast_fp16 = transpose(perm = x_83_perm_0, x = var_767_cast_fp16)[name = string("transpose_23")]; + tensor input_111_cast_fp16 = mul(x = x_83_cast_fp16, y = text_mask_to_fp16)[name = string("input_111_cast_fp16")]; + string input_113_pad_type_0 = const()[name = string("input_113_pad_type_0"), val = string("valid")]; + tensor input_113_strides_0 = const()[name = string("input_113_strides_0"), val = tensor([1])]; + tensor input_113_pad_0 = const()[name = string("input_113_pad_0"), val = tensor([0, 0])]; + tensor input_113_dilations_0 = const()[name = string("input_113_dilations_0"), val = tensor([1])]; + int32 input_113_groups_0 = const()[name = string("input_113_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12834816)))]; + tensor m_attn_layers_1_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13359168)))]; + tensor input_113_cast_fp16 = conv(bias = m_attn_layers_1_ffn_conv_1_bias_to_fp16, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = m_attn_layers_1_ffn_conv_1_weight_to_fp16, x = input_111_cast_fp16)[name = string("input_113_cast_fp16")]; + tensor h_41_cast_fp16 = relu(x = input_113_cast_fp16)[name = string("h_41_cast_fp16")]; + tensor input_115_cast_fp16 = mul(x = h_41_cast_fp16, y = text_mask_to_fp16)[name = string("input_115_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor m_attn_layers_1_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13361280)))]; + tensor m_attn_layers_1_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13885632)))]; + tensor h_43_cast_fp16 = conv(bias = m_attn_layers_1_ffn_conv_2_bias_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = m_attn_layers_1_ffn_conv_2_weight_to_fp16, x = input_115_cast_fp16)[name = string("h_43_cast_fp16")]; + tensor y_7_cast_fp16 = mul(x = h_43_cast_fp16, y = text_mask_to_fp16)[name = string("y_7_cast_fp16")]; + tensor x_85_cast_fp16 = add(x = x_83_cast_fp16, y = y_7_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_117_perm_0 = const()[name = string("input_117_perm_0"), val = tensor([0, 2, 1])]; + tensor var_795_axes_0 = const()[name = string("op_795_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_1_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_1_norm_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13886208)))]; + tensor m_attn_layers_1_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_1_norm_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13886784)))]; + tensor input_117_cast_fp16 = transpose(perm = input_117_perm_0, x = x_85_cast_fp16)[name = string("transpose_22")]; + tensor var_795_cast_fp16 = layer_norm(axes = var_795_axes_0, beta = m_attn_layers_1_norm_2_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_1_norm_2_norm_weight_to_fp16, x = input_117_cast_fp16)[name = string("op_795_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_795_cast_fp16)[name = string("transpose_21")]; + tensor x_89_cast_fp16 = mul(x = x_87_cast_fp16, y = text_mask_to_fp16)[name = string("x_89_cast_fp16")]; + string var_817_pad_type_0 = const()[name = string("op_817_pad_type_0"), val = string("valid")]; + tensor var_817_strides_0 = const()[name = string("op_817_strides_0"), val = tensor([1])]; + tensor var_817_pad_0 = const()[name = string("op_817_pad_0"), val = tensor([0, 0])]; + tensor var_817_dilations_0 = const()[name = string("op_817_dilations_0"), val = tensor([1])]; + int32 var_817_groups_0 = const()[name = string("op_817_groups_0"), val = int32(1)]; + tensor m_attn_layers_2_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13887360)))]; + tensor m_attn_layers_2_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14018496)))]; + tensor var_817_cast_fp16 = conv(bias = m_attn_layers_2_attn_conv_q_bias_to_fp16, dilations = var_817_dilations_0, groups = var_817_groups_0, pad = var_817_pad_0, pad_type = var_817_pad_type_0, strides = var_817_strides_0, weight = m_attn_layers_2_attn_conv_q_weight_to_fp16, x = x_89_cast_fp16)[name = string("op_817_cast_fp16")]; + tensor var_818 = const()[name = string("op_818"), val = tensor([1, 4, 64, 128])]; + tensor var_819_cast_fp16 = reshape(shape = var_818, x = var_817_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor q_5_perm_0 = const()[name = string("q_5_perm_0"), val = tensor([0, 1, 3, 2])]; + string var_827_pad_type_0 = const()[name = string("op_827_pad_type_0"), val = string("valid")]; + tensor var_827_strides_0 = const()[name = string("op_827_strides_0"), val = tensor([1])]; + tensor var_827_pad_0 = const()[name = string("op_827_pad_0"), val = tensor([0, 0])]; + tensor var_827_dilations_0 = const()[name = string("op_827_dilations_0"), val = tensor([1])]; + int32 var_827_groups_0 = const()[name = string("op_827_groups_0"), val = int32(1)]; + tensor m_attn_layers_2_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14019072)))]; + tensor m_attn_layers_2_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14150208)))]; + tensor var_827_cast_fp16 = conv(bias = m_attn_layers_2_attn_conv_k_bias_to_fp16, dilations = var_827_dilations_0, groups = var_827_groups_0, pad = var_827_pad_0, pad_type = var_827_pad_type_0, strides = var_827_strides_0, weight = m_attn_layers_2_attn_conv_k_weight_to_fp16, x = x_89_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_828 = const()[name = string("op_828"), val = tensor([1, 4, 64, 128])]; + tensor var_829_cast_fp16 = reshape(shape = var_828, x = var_827_cast_fp16)[name = string("op_829_cast_fp16")]; + string var_837_pad_type_0 = const()[name = string("op_837_pad_type_0"), val = string("valid")]; + tensor var_837_strides_0 = const()[name = string("op_837_strides_0"), val = tensor([1])]; + tensor var_837_pad_0 = const()[name = string("op_837_pad_0"), val = tensor([0, 0])]; + tensor var_837_dilations_0 = const()[name = string("op_837_dilations_0"), val = tensor([1])]; + int32 var_837_groups_0 = const()[name = string("op_837_groups_0"), val = int32(1)]; + tensor m_attn_layers_2_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14150784)))]; + tensor m_attn_layers_2_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14281920)))]; + tensor var_837_cast_fp16 = conv(bias = m_attn_layers_2_attn_conv_v_bias_to_fp16, dilations = var_837_dilations_0, groups = var_837_groups_0, pad = var_837_pad_0, pad_type = var_837_pad_type_0, strides = var_837_strides_0, weight = m_attn_layers_2_attn_conv_v_weight_to_fp16, x = x_89_cast_fp16)[name = string("op_837_cast_fp16")]; + tensor var_838 = const()[name = string("op_838"), val = tensor([1, 4, 64, 128])]; + tensor var_839_cast_fp16 = reshape(shape = var_838, x = var_837_cast_fp16)[name = string("op_839_cast_fp16")]; + fp16 var_841_to_fp16 = const()[name = string("op_841_to_fp16"), val = fp16(0x1p-3)]; + tensor q_5_cast_fp16 = transpose(perm = q_5_perm_0, x = var_819_cast_fp16)[name = string("transpose_20")]; + tensor var_842_cast_fp16 = mul(x = q_5_cast_fp16, y = var_841_to_fp16)[name = string("op_842_cast_fp16")]; + bool scores_9_transpose_x_0 = const()[name = string("scores_9_transpose_x_0"), val = bool(false)]; + bool scores_9_transpose_y_0 = const()[name = string("scores_9_transpose_y_0"), val = bool(false)]; + tensor scores_9_cast_fp16 = matmul(transpose_x = scores_9_transpose_x_0, transpose_y = scores_9_transpose_y_0, x = var_842_cast_fp16, y = var_829_cast_fp16)[name = string("scores_9_cast_fp16")]; + bool x_91_transpose_x_0 = const()[name = string("x_91_transpose_x_0"), val = bool(false)]; + bool x_91_transpose_y_0 = const()[name = string("x_91_transpose_y_0"), val = bool(false)]; + tensor var_866_to_fp16 = const()[name = string("op_866_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14282496)))]; + tensor x_91_cast_fp16 = matmul(transpose_x = x_91_transpose_x_0, transpose_y = x_91_transpose_y_0, x = var_842_cast_fp16, y = var_866_to_fp16)[name = string("x_91_cast_fp16")]; + tensor x_93_pad_0 = const()[name = string("x_93_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 1])]; + string x_93_mode_0 = const()[name = string("x_93_mode_0"), val = string("constant")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor x_93_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = x_93_mode_0, pad = x_93_pad_0, x = x_91_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor var_878 = const()[name = string("op_878"), val = tensor([1, 4, 32768])]; + tensor input_121_cast_fp16 = reshape(shape = var_878, x = x_93_cast_fp16)[name = string("input_121_cast_fp16")]; + tensor x_95_pad_0 = const()[name = string("x_95_pad_0"), val = tensor([0, 0, 0, 0, 0, 127])]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("constant")]; + fp16 const_47_to_fp16 = const()[name = string("const_47_to_fp16"), val = fp16(0x0p+0)]; + tensor x_95_cast_fp16 = pad(constant_val = const_47_to_fp16, mode = x_95_mode_0, pad = x_95_pad_0, x = input_121_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_893 = const()[name = string("op_893"), val = tensor([1, 4, 129, 255])]; + tensor x_97_cast_fp16 = reshape(shape = var_893, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor var_900_begin_0 = const()[name = string("op_900_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_900_end_0 = const()[name = string("op_900_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_900_end_mask_0 = const()[name = string("op_900_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_900_cast_fp16 = slice_by_index(begin = var_900_begin_0, end = var_900_end_0, end_mask = var_900_end_mask_0, x = x_97_cast_fp16)[name = string("op_900_cast_fp16")]; + tensor var_901_begin_0 = const()[name = string("op_901_begin_0"), val = tensor([0, 0, 0, 127])]; + tensor var_901_end_0 = const()[name = string("op_901_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_901_end_mask_0 = const()[name = string("op_901_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_901_cast_fp16 = slice_by_index(begin = var_901_begin_0, end = var_901_end_0, end_mask = var_901_end_mask_0, x = var_900_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor scores_11_cast_fp16 = add(x = scores_9_cast_fp16, y = var_901_cast_fp16)[name = string("scores_11_cast_fp16")]; + tensor input_123_cast_fp16 = sub(x = scores_11_cast_fp16, y = var_490_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor p_attn_5_cast_fp16 = softmax(axis = var_29, x = input_123_cast_fp16)[name = string("p_attn_5_cast_fp16")]; + bool out_21_transpose_x_1 = const()[name = string("out_21_transpose_x_1"), val = bool(false)]; + bool out_21_transpose_y_1 = const()[name = string("out_21_transpose_y_1"), val = bool(true)]; + tensor out_21_cast_fp16 = matmul(transpose_x = out_21_transpose_x_1, transpose_y = out_21_transpose_y_1, x = p_attn_5_cast_fp16, y = var_839_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_99_pad_0 = const()[name = string("x_99_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 127])]; + string x_99_mode_0 = const()[name = string("x_99_mode_0"), val = string("constant")]; + fp16 const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = fp16(0x0p+0)]; + tensor x_99_cast_fp16 = pad(constant_val = const_52_to_fp16, mode = x_99_mode_0, pad = x_99_pad_0, x = p_attn_5_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor var_943 = const()[name = string("op_943"), val = tensor([1, 4, 32640])]; + tensor input_127_cast_fp16 = reshape(shape = var_943, x = x_99_cast_fp16)[name = string("input_127_cast_fp16")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([0, 0, 0, 0, 128, 0])]; + string x_101_mode_0 = const()[name = string("x_101_mode_0"), val = string("constant")]; + fp16 const_53_to_fp16 = const()[name = string("const_53_to_fp16"), val = fp16(0x0p+0)]; + tensor x_101_cast_fp16 = pad(constant_val = const_53_to_fp16, mode = x_101_mode_0, pad = x_101_pad_0, x = input_127_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor var_950 = const()[name = string("op_950"), val = tensor([1, 4, 128, 256])]; + tensor x_103_cast_fp16 = reshape(shape = var_950, x = x_101_cast_fp16)[name = string("x_103_cast_fp16")]; + tensor rel_weights_5_begin_0 = const()[name = string("rel_weights_5_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor rel_weights_5_end_0 = const()[name = string("rel_weights_5_end_0"), val = tensor([1, 4, 128, 256])]; + tensor rel_weights_5_end_mask_0 = const()[name = string("rel_weights_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor rel_weights_5_cast_fp16 = slice_by_index(begin = rel_weights_5_begin_0, end = rel_weights_5_end_0, end_mask = rel_weights_5_end_mask_0, x = x_103_cast_fp16)[name = string("rel_weights_5_cast_fp16")]; + bool var_957_transpose_x_0 = const()[name = string("op_957_transpose_x_0"), val = bool(false)]; + bool var_957_transpose_y_0 = const()[name = string("op_957_transpose_y_0"), val = bool(false)]; + tensor var_956_to_fp16 = const()[name = string("op_956_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14315200)))]; + tensor var_957_cast_fp16 = matmul(transpose_x = var_957_transpose_x_0, transpose_y = var_957_transpose_y_0, x = rel_weights_5_cast_fp16, y = var_956_to_fp16)[name = string("op_957_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = out_21_cast_fp16, y = var_957_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor var_959_perm_0 = const()[name = string("op_959_perm_0"), val = tensor([0, 1, 3, 2])]; + tensor var_960 = const()[name = string("op_960"), val = tensor([1, 256, 128])]; + tensor var_959_cast_fp16 = transpose(perm = var_959_perm_0, x = out_23_cast_fp16)[name = string("transpose_19")]; + tensor input_129_cast_fp16 = reshape(shape = var_960, x = var_959_cast_fp16)[name = string("input_129_cast_fp16")]; + string y_9_pad_type_0 = const()[name = string("y_9_pad_type_0"), val = string("valid")]; + tensor y_9_strides_0 = const()[name = string("y_9_strides_0"), val = tensor([1])]; + tensor y_9_pad_0 = const()[name = string("y_9_pad_0"), val = tensor([0, 0])]; + tensor y_9_dilations_0 = const()[name = string("y_9_dilations_0"), val = tensor([1])]; + int32 y_9_groups_0 = const()[name = string("y_9_groups_0"), val = int32(1)]; + tensor m_attn_layers_2_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_o_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14347904)))]; + tensor m_attn_layers_2_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_2_attn_conv_o_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14479040)))]; + tensor y_9_cast_fp16 = conv(bias = m_attn_layers_2_attn_conv_o_bias_to_fp16, dilations = y_9_dilations_0, groups = y_9_groups_0, pad = y_9_pad_0, pad_type = y_9_pad_type_0, strides = y_9_strides_0, weight = m_attn_layers_2_attn_conv_o_weight_to_fp16, x = input_129_cast_fp16)[name = string("y_9_cast_fp16")]; + tensor x_105_cast_fp16 = add(x = x_89_cast_fp16, y = y_9_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor input_131_perm_0 = const()[name = string("input_131_perm_0"), val = tensor([0, 2, 1])]; + tensor var_975_axes_0 = const()[name = string("op_975_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_2_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_2_norm_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14479616)))]; + tensor m_attn_layers_2_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_2_norm_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14480192)))]; + tensor input_131_cast_fp16 = transpose(perm = input_131_perm_0, x = x_105_cast_fp16)[name = string("transpose_18")]; + tensor var_975_cast_fp16 = layer_norm(axes = var_975_axes_0, beta = m_attn_layers_2_norm_1_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_2_norm_1_norm_weight_to_fp16, x = input_131_cast_fp16)[name = string("op_975_cast_fp16")]; + tensor x_107_perm_0 = const()[name = string("x_107_perm_0"), val = tensor([0, 2, 1])]; + tensor x_107_cast_fp16 = transpose(perm = x_107_perm_0, x = var_975_cast_fp16)[name = string("transpose_17")]; + tensor input_133_cast_fp16 = mul(x = x_107_cast_fp16, y = text_mask_to_fp16)[name = string("input_133_cast_fp16")]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor m_attn_layers_2_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_2_ffn_conv_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14480768)))]; + tensor m_attn_layers_2_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_2_ffn_conv_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15005120)))]; + tensor input_135_cast_fp16 = conv(bias = m_attn_layers_2_ffn_conv_1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = m_attn_layers_2_ffn_conv_1_weight_to_fp16, x = input_133_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor h_45_cast_fp16 = relu(x = input_135_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor input_137_cast_fp16 = mul(x = h_45_cast_fp16, y = text_mask_to_fp16)[name = string("input_137_cast_fp16")]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor m_attn_layers_2_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_2_ffn_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15007232)))]; + tensor m_attn_layers_2_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_2_ffn_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15531584)))]; + tensor h_47_cast_fp16 = conv(bias = m_attn_layers_2_ffn_conv_2_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = m_attn_layers_2_ffn_conv_2_weight_to_fp16, x = input_137_cast_fp16)[name = string("h_47_cast_fp16")]; + tensor y_11_cast_fp16 = mul(x = h_47_cast_fp16, y = text_mask_to_fp16)[name = string("y_11_cast_fp16")]; + tensor x_109_cast_fp16 = add(x = x_107_cast_fp16, y = y_11_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1003_axes_0 = const()[name = string("op_1003_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_2_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_2_norm_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15532160)))]; + tensor m_attn_layers_2_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_2_norm_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15532736)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_109_cast_fp16)[name = string("transpose_16")]; + tensor var_1003_cast_fp16 = layer_norm(axes = var_1003_axes_0, beta = m_attn_layers_2_norm_2_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_2_norm_2_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_1003_cast_fp16")]; + tensor x_111_perm_0 = const()[name = string("x_111_perm_0"), val = tensor([0, 2, 1])]; + tensor x_111_cast_fp16 = transpose(perm = x_111_perm_0, x = var_1003_cast_fp16)[name = string("transpose_15")]; + tensor x_113_cast_fp16 = mul(x = x_111_cast_fp16, y = text_mask_to_fp16)[name = string("x_113_cast_fp16")]; + string var_1025_pad_type_0 = const()[name = string("op_1025_pad_type_0"), val = string("valid")]; + tensor var_1025_strides_0 = const()[name = string("op_1025_strides_0"), val = tensor([1])]; + tensor var_1025_pad_0 = const()[name = string("op_1025_pad_0"), val = tensor([0, 0])]; + tensor var_1025_dilations_0 = const()[name = string("op_1025_dilations_0"), val = tensor([1])]; + int32 var_1025_groups_0 = const()[name = string("op_1025_groups_0"), val = int32(1)]; + tensor m_attn_layers_3_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_q_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15533312)))]; + tensor m_attn_layers_3_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_q_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15664448)))]; + tensor var_1025_cast_fp16 = conv(bias = m_attn_layers_3_attn_conv_q_bias_to_fp16, dilations = var_1025_dilations_0, groups = var_1025_groups_0, pad = var_1025_pad_0, pad_type = var_1025_pad_type_0, strides = var_1025_strides_0, weight = m_attn_layers_3_attn_conv_q_weight_to_fp16, x = x_113_cast_fp16)[name = string("op_1025_cast_fp16")]; + tensor var_1026 = const()[name = string("op_1026"), val = tensor([1, 4, 64, 128])]; + tensor var_1027_cast_fp16 = reshape(shape = var_1026, x = var_1025_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 1, 3, 2])]; + string var_1035_pad_type_0 = const()[name = string("op_1035_pad_type_0"), val = string("valid")]; + tensor var_1035_strides_0 = const()[name = string("op_1035_strides_0"), val = tensor([1])]; + tensor var_1035_pad_0 = const()[name = string("op_1035_pad_0"), val = tensor([0, 0])]; + tensor var_1035_dilations_0 = const()[name = string("op_1035_dilations_0"), val = tensor([1])]; + int32 var_1035_groups_0 = const()[name = string("op_1035_groups_0"), val = int32(1)]; + tensor m_attn_layers_3_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_k_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15665024)))]; + tensor m_attn_layers_3_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_k_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796160)))]; + tensor var_1035_cast_fp16 = conv(bias = m_attn_layers_3_attn_conv_k_bias_to_fp16, dilations = var_1035_dilations_0, groups = var_1035_groups_0, pad = var_1035_pad_0, pad_type = var_1035_pad_type_0, strides = var_1035_strides_0, weight = m_attn_layers_3_attn_conv_k_weight_to_fp16, x = x_113_cast_fp16)[name = string("op_1035_cast_fp16")]; + tensor var_1036 = const()[name = string("op_1036"), val = tensor([1, 4, 64, 128])]; + tensor var_1037_cast_fp16 = reshape(shape = var_1036, x = var_1035_cast_fp16)[name = string("op_1037_cast_fp16")]; + string var_1045_pad_type_0 = const()[name = string("op_1045_pad_type_0"), val = string("valid")]; + tensor var_1045_strides_0 = const()[name = string("op_1045_strides_0"), val = tensor([1])]; + tensor var_1045_pad_0 = const()[name = string("op_1045_pad_0"), val = tensor([0, 0])]; + tensor var_1045_dilations_0 = const()[name = string("op_1045_dilations_0"), val = tensor([1])]; + int32 var_1045_groups_0 = const()[name = string("op_1045_groups_0"), val = int32(1)]; + tensor m_attn_layers_3_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_v_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796736)))]; + tensor m_attn_layers_3_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_v_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15927872)))]; + tensor var_1045_cast_fp16 = conv(bias = m_attn_layers_3_attn_conv_v_bias_to_fp16, dilations = var_1045_dilations_0, groups = var_1045_groups_0, pad = var_1045_pad_0, pad_type = var_1045_pad_type_0, strides = var_1045_strides_0, weight = m_attn_layers_3_attn_conv_v_weight_to_fp16, x = x_113_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor var_1046 = const()[name = string("op_1046"), val = tensor([1, 4, 64, 128])]; + tensor var_1047_cast_fp16 = reshape(shape = var_1046, x = var_1045_cast_fp16)[name = string("op_1047_cast_fp16")]; + fp16 var_1049_to_fp16 = const()[name = string("op_1049_to_fp16"), val = fp16(0x1p-3)]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1027_cast_fp16)[name = string("transpose_14")]; + tensor var_1050_cast_fp16 = mul(x = q_7_cast_fp16, y = var_1049_to_fp16)[name = string("op_1050_cast_fp16")]; + bool scores_13_transpose_x_0 = const()[name = string("scores_13_transpose_x_0"), val = bool(false)]; + bool scores_13_transpose_y_0 = const()[name = string("scores_13_transpose_y_0"), val = bool(false)]; + tensor scores_13_cast_fp16 = matmul(transpose_x = scores_13_transpose_x_0, transpose_y = scores_13_transpose_y_0, x = var_1050_cast_fp16, y = var_1037_cast_fp16)[name = string("scores_13_cast_fp16")]; + bool x_115_transpose_x_0 = const()[name = string("x_115_transpose_x_0"), val = bool(false)]; + bool x_115_transpose_y_0 = const()[name = string("x_115_transpose_y_0"), val = bool(false)]; + tensor var_1074_to_fp16 = const()[name = string("op_1074_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15928448)))]; + tensor x_115_cast_fp16 = matmul(transpose_x = x_115_transpose_x_0, transpose_y = x_115_transpose_y_0, x = var_1050_cast_fp16, y = var_1074_to_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 1])]; + string x_117_mode_0 = const()[name = string("x_117_mode_0"), val = string("constant")]; + fp16 const_60_to_fp16 = const()[name = string("const_60_to_fp16"), val = fp16(0x0p+0)]; + tensor x_117_cast_fp16 = pad(constant_val = const_60_to_fp16, mode = x_117_mode_0, pad = x_117_pad_0, x = x_115_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1086 = const()[name = string("op_1086"), val = tensor([1, 4, 32768])]; + tensor input_143_cast_fp16 = reshape(shape = var_1086, x = x_117_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor x_119_pad_0 = const()[name = string("x_119_pad_0"), val = tensor([0, 0, 0, 0, 0, 127])]; + string x_119_mode_0 = const()[name = string("x_119_mode_0"), val = string("constant")]; + fp16 const_61_to_fp16 = const()[name = string("const_61_to_fp16"), val = fp16(0x0p+0)]; + tensor x_119_cast_fp16 = pad(constant_val = const_61_to_fp16, mode = x_119_mode_0, pad = x_119_pad_0, x = input_143_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor var_1101 = const()[name = string("op_1101"), val = tensor([1, 4, 129, 255])]; + tensor x_121_cast_fp16 = reshape(shape = var_1101, x = x_119_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor var_1108_begin_0 = const()[name = string("op_1108_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1108_end_0 = const()[name = string("op_1108_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_1108_end_mask_0 = const()[name = string("op_1108_end_mask_0"), val = tensor([true, true, false, true])]; + tensor var_1108_cast_fp16 = slice_by_index(begin = var_1108_begin_0, end = var_1108_end_0, end_mask = var_1108_end_mask_0, x = x_121_cast_fp16)[name = string("op_1108_cast_fp16")]; + tensor var_1109_begin_0 = const()[name = string("op_1109_begin_0"), val = tensor([0, 0, 0, 127])]; + tensor var_1109_end_0 = const()[name = string("op_1109_end_0"), val = tensor([1, 4, 128, 255])]; + tensor var_1109_end_mask_0 = const()[name = string("op_1109_end_mask_0"), val = tensor([true, true, true, true])]; + tensor var_1109_cast_fp16 = slice_by_index(begin = var_1109_begin_0, end = var_1109_end_0, end_mask = var_1109_end_mask_0, x = var_1108_cast_fp16)[name = string("op_1109_cast_fp16")]; + tensor scores_cast_fp16 = add(x = scores_13_cast_fp16, y = var_1109_cast_fp16)[name = string("scores_cast_fp16")]; + tensor input_145_cast_fp16 = sub(x = scores_cast_fp16, y = var_490_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor p_attn_cast_fp16 = softmax(axis = var_29, x = input_145_cast_fp16)[name = string("p_attn_cast_fp16")]; + bool out_25_transpose_x_1 = const()[name = string("out_25_transpose_x_1"), val = bool(false)]; + bool out_25_transpose_y_1 = const()[name = string("out_25_transpose_y_1"), val = bool(true)]; + tensor out_25_cast_fp16 = matmul(transpose_x = out_25_transpose_x_1, transpose_y = out_25_transpose_y_1, x = p_attn_cast_fp16, y = var_1047_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_123_pad_0 = const()[name = string("x_123_pad_0"), val = tensor([0, 0, 0, 0, 0, 0, 0, 127])]; + string x_123_mode_0 = const()[name = string("x_123_mode_0"), val = string("constant")]; + fp16 const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = fp16(0x0p+0)]; + tensor x_123_cast_fp16 = pad(constant_val = const_66_to_fp16, mode = x_123_mode_0, pad = x_123_pad_0, x = p_attn_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1151 = const()[name = string("op_1151"), val = tensor([1, 4, 32640])]; + tensor input_149_cast_fp16 = reshape(shape = var_1151, x = x_123_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor x_125_pad_0 = const()[name = string("x_125_pad_0"), val = tensor([0, 0, 0, 0, 128, 0])]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("constant")]; + fp16 const_67_to_fp16 = const()[name = string("const_67_to_fp16"), val = fp16(0x0p+0)]; + tensor x_125_cast_fp16 = pad(constant_val = const_67_to_fp16, mode = x_125_mode_0, pad = x_125_pad_0, x = input_149_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor var_1158 = const()[name = string("op_1158"), val = tensor([1, 4, 128, 256])]; + tensor x_127_cast_fp16 = reshape(shape = var_1158, x = x_125_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor rel_weights_begin_0 = const()[name = string("rel_weights_begin_0"), val = tensor([0, 0, 0, 1])]; + tensor rel_weights_end_0 = const()[name = string("rel_weights_end_0"), val = tensor([1, 4, 128, 256])]; + tensor rel_weights_end_mask_0 = const()[name = string("rel_weights_end_mask_0"), val = tensor([true, true, true, true])]; + tensor rel_weights_cast_fp16 = slice_by_index(begin = rel_weights_begin_0, end = rel_weights_end_0, end_mask = rel_weights_end_mask_0, x = x_127_cast_fp16)[name = string("rel_weights_cast_fp16")]; + bool var_1165_transpose_x_0 = const()[name = string("op_1165_transpose_x_0"), val = bool(false)]; + bool var_1165_transpose_y_0 = const()[name = string("op_1165_transpose_y_0"), val = bool(false)]; + tensor var_1164_to_fp16 = const()[name = string("op_1164_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15961152)))]; + tensor var_1165_cast_fp16 = matmul(transpose_x = var_1165_transpose_x_0, transpose_y = var_1165_transpose_y_0, x = rel_weights_cast_fp16, y = var_1164_to_fp16)[name = string("op_1165_cast_fp16")]; + tensor out_27_cast_fp16 = add(x = out_25_cast_fp16, y = var_1165_cast_fp16)[name = string("out_27_cast_fp16")]; + tensor var_1167_perm_0 = const()[name = string("op_1167_perm_0"), val = tensor([0, 1, 3, 2])]; + tensor var_1168 = const()[name = string("op_1168"), val = tensor([1, 256, 128])]; + tensor var_1167_cast_fp16 = transpose(perm = var_1167_perm_0, x = out_27_cast_fp16)[name = string("transpose_13")]; + tensor input_151_cast_fp16 = reshape(shape = var_1168, x = var_1167_cast_fp16)[name = string("input_151_cast_fp16")]; + string y_13_pad_type_0 = const()[name = string("y_13_pad_type_0"), val = string("valid")]; + tensor y_13_strides_0 = const()[name = string("y_13_strides_0"), val = tensor([1])]; + tensor y_13_pad_0 = const()[name = string("y_13_pad_0"), val = tensor([0, 0])]; + tensor y_13_dilations_0 = const()[name = string("y_13_dilations_0"), val = tensor([1])]; + int32 y_13_groups_0 = const()[name = string("y_13_groups_0"), val = int32(1)]; + tensor m_attn_layers_3_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_o_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15993856)))]; + tensor m_attn_layers_3_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_3_attn_conv_o_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16124992)))]; + tensor y_13_cast_fp16 = conv(bias = m_attn_layers_3_attn_conv_o_bias_to_fp16, dilations = y_13_dilations_0, groups = y_13_groups_0, pad = y_13_pad_0, pad_type = y_13_pad_type_0, strides = y_13_strides_0, weight = m_attn_layers_3_attn_conv_o_weight_to_fp16, x = input_151_cast_fp16)[name = string("y_13_cast_fp16")]; + tensor x_129_cast_fp16 = add(x = x_113_cast_fp16, y = y_13_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_153_perm_0 = const()[name = string("input_153_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1183_axes_0 = const()[name = string("op_1183_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_3_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_3_norm_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16125568)))]; + tensor m_attn_layers_3_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_3_norm_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16126144)))]; + tensor input_153_cast_fp16 = transpose(perm = input_153_perm_0, x = x_129_cast_fp16)[name = string("transpose_12")]; + tensor var_1183_cast_fp16 = layer_norm(axes = var_1183_axes_0, beta = m_attn_layers_3_norm_1_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_3_norm_1_norm_weight_to_fp16, x = input_153_cast_fp16)[name = string("op_1183_cast_fp16")]; + tensor x_131_perm_0 = const()[name = string("x_131_perm_0"), val = tensor([0, 2, 1])]; + tensor x_131_cast_fp16 = transpose(perm = x_131_perm_0, x = var_1183_cast_fp16)[name = string("transpose_11")]; + tensor input_155_cast_fp16 = mul(x = x_131_cast_fp16, y = text_mask_to_fp16)[name = string("input_155_cast_fp16")]; + string input_157_pad_type_0 = const()[name = string("input_157_pad_type_0"), val = string("valid")]; + tensor input_157_strides_0 = const()[name = string("input_157_strides_0"), val = tensor([1])]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0])]; + tensor input_157_dilations_0 = const()[name = string("input_157_dilations_0"), val = tensor([1])]; + int32 input_157_groups_0 = const()[name = string("input_157_groups_0"), val = int32(1)]; + tensor m_attn_layers_3_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_3_ffn_conv_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16126720)))]; + tensor m_attn_layers_3_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_3_ffn_conv_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16651072)))]; + tensor input_157_cast_fp16 = conv(bias = m_attn_layers_3_ffn_conv_1_bias_to_fp16, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = m_attn_layers_3_ffn_conv_1_weight_to_fp16, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor h_49_cast_fp16 = relu(x = input_157_cast_fp16)[name = string("h_49_cast_fp16")]; + tensor input_159_cast_fp16 = mul(x = h_49_cast_fp16, y = text_mask_to_fp16)[name = string("input_159_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor m_attn_layers_3_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_3_ffn_conv_2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16653184)))]; + tensor m_attn_layers_3_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_3_ffn_conv_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17177536)))]; + tensor h_cast_fp16 = conv(bias = m_attn_layers_3_ffn_conv_2_bias_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = m_attn_layers_3_ffn_conv_2_weight_to_fp16, x = input_159_cast_fp16)[name = string("h_cast_fp16")]; + tensor y_cast_fp16 = mul(x = h_cast_fp16, y = text_mask_to_fp16)[name = string("y_cast_fp16")]; + tensor x_133_cast_fp16 = add(x = x_131_cast_fp16, y = y_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1211_axes_0 = const()[name = string("op_1211_axes_0"), val = tensor([-1])]; + tensor m_attn_layers_3_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_3_norm_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17178112)))]; + tensor m_attn_layers_3_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_3_norm_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17178688)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_133_cast_fp16)[name = string("transpose_10")]; + tensor var_1211_cast_fp16 = layer_norm(axes = var_1211_axes_0, beta = m_attn_layers_3_norm_2_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_attn_layers_3_norm_2_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor x_135_perm_0 = const()[name = string("x_135_perm_0"), val = tensor([0, 2, 1])]; + tensor x_135_cast_fp16 = transpose(perm = x_135_perm_0, x = var_1211_cast_fp16)[name = string("transpose_9")]; + tensor x_137_cast_fp16 = mul(x = x_135_cast_fp16, y = text_mask_to_fp16)[name = string("x_137_cast_fp16")]; + tensor var_1214_cast_fp16 = add(x = x_137_cast_fp16, y = x_39_cast_fp16)[name = string("op_1214_cast_fp16")]; + tensor text_1_cast_fp16 = mul(x = var_1214_cast_fp16, y = text_mask_to_fp16)[name = string("text_1_cast_fp16")]; + tensor text_3_perm_0 = const()[name = string("text_3_perm_0"), val = tensor([0, 2, 1])]; + tensor m_speech_prompted_attention1_W_query_linear_weight_to_fp16 = const()[name = string("m_speech_prompted_attention1_W_query_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17179264)))]; + tensor m_speech_prompted_attention1_W_query_linear_bias_to_fp16 = const()[name = string("m_speech_prompted_attention1_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17310400)))]; + tensor text_3_cast_fp16 = transpose(perm = text_3_perm_0, x = text_1_cast_fp16)[name = string("transpose_8")]; + tensor linear_0_cast_fp16 = linear(bias = m_speech_prompted_attention1_W_query_linear_bias_to_fp16, weight = m_speech_prompted_attention1_W_query_linear_weight_to_fp16, x = text_3_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_1235 = const()[name = string("op_1235"), val = tensor([1, 128, 2, 128])]; + tensor var_1236_cast_fp16 = reshape(shape = var_1235, x = linear_0_cast_fp16)[name = string("op_1236_cast_fp16")]; + tensor q_9_perm_0 = const()[name = string("q_9_perm_0"), val = tensor([0, 2, 1, 3])]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor m_speech_prompted_attention1_W_value_linear_weight_to_fp16 = const()[name = string("m_speech_prompted_attention1_W_value_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17310976)))]; + tensor m_speech_prompted_attention1_W_value_linear_bias_to_fp16 = const()[name = string("m_speech_prompted_attention1_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17442112)))]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_47")]; + tensor linear_2_cast_fp16 = linear(bias = m_speech_prompted_attention1_W_value_linear_bias_to_fp16, weight = m_speech_prompted_attention1_W_value_linear_weight_to_fp16, x = style_ttl_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_1249 = const()[name = string("op_1249"), val = tensor([1, 50, 2, 128])]; + tensor var_1250_cast_fp16 = reshape(shape = var_1249, x = linear_2_cast_fp16)[name = string("op_1250_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, 1, 3])]; + bool var_1254_transpose_x_0 = const()[name = string("op_1254_transpose_x_0"), val = bool(false)]; + bool var_1254_transpose_y_0 = const()[name = string("op_1254_transpose_y_0"), val = bool(false)]; + tensor var_1253_to_fp16 = const()[name = string("op_1253_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17442688)))]; + tensor q_9_cast_fp16 = transpose(perm = q_9_perm_0, x = var_1236_cast_fp16)[name = string("transpose_7")]; + tensor var_1254_cast_fp16 = matmul(transpose_x = var_1254_transpose_x_0, transpose_y = var_1254_transpose_y_0, x = q_9_cast_fp16, y = var_1253_to_fp16)[name = string("op_1254_cast_fp16")]; + fp16 _inversed_input_163_y_0_to_fp16 = const()[name = string("_inversed_input_163_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_163_cast_fp16 = mul(x = var_1254_cast_fp16, y = _inversed_input_163_y_0_to_fp16)[name = string("_inversed_input_163_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_29, x = _inversed_input_163_cast_fp16)[name = string("attn_1_cast_fp16")]; + tensor var_1258_perm_0 = const()[name = string("op_1258_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_q_1_axes_0 = const()[name = string("mask_q_1_axes_0"), val = tensor([1])]; + tensor var_1258_cast_fp16 = transpose(perm = var_1258_perm_0, x = text_mask_to_fp16)[name = string("transpose_5")]; + tensor mask_q_1_cast_fp16 = expand_dims(axes = mask_q_1_axes_0, x = var_1258_cast_fp16)[name = string("mask_q_1_cast_fp16")]; + tensor attn_3_cast_fp16 = mul(x = attn_1_cast_fp16, y = mask_q_1_cast_fp16)[name = string("attn_3_cast_fp16")]; + bool out_29_transpose_x_0 = const()[name = string("out_29_transpose_x_0"), val = bool(false)]; + bool out_29_transpose_y_0 = const()[name = string("out_29_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1250_cast_fp16)[name = string("transpose_6")]; + tensor out_29_cast_fp16 = matmul(transpose_x = out_29_transpose_x_0, transpose_y = out_29_transpose_y_0, x = attn_3_cast_fp16, y = v_9_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor var_1262_perm_0 = const()[name = string("op_1262_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1263 = const()[name = string("op_1263"), val = tensor([1, 128, 256])]; + tensor var_1262_cast_fp16 = transpose(perm = var_1262_perm_0, x = out_29_cast_fp16)[name = string("transpose_4")]; + tensor input_165_cast_fp16 = reshape(shape = var_1263, x = var_1262_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor m_speech_prompted_attention1_out_fc_linear_weight_to_fp16 = const()[name = string("m_speech_prompted_attention1_out_fc_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17468352)))]; + tensor m_speech_prompted_attention1_out_fc_linear_bias_to_fp16 = const()[name = string("m_speech_prompted_attention1_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17599488)))]; + tensor linear_3_cast_fp16 = linear(bias = m_speech_prompted_attention1_out_fc_linear_bias_to_fp16, weight = m_speech_prompted_attention1_out_fc_linear_weight_to_fp16, x = input_165_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor attn1_out_cast_fp16 = mul(x = linear_3_cast_fp16, y = var_1258_cast_fp16)[name = string("attn1_out_cast_fp16")]; + tensor text_cast_fp16 = add(x = text_3_cast_fp16, y = attn1_out_cast_fp16)[name = string("text_cast_fp16")]; + tensor m_speech_prompted_attention2_W_query_linear_weight_to_fp16 = const()[name = string("m_speech_prompted_attention2_W_query_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17600064)))]; + tensor m_speech_prompted_attention2_W_query_linear_bias_to_fp16 = const()[name = string("m_speech_prompted_attention2_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17731200)))]; + tensor linear_4_cast_fp16 = linear(bias = m_speech_prompted_attention2_W_query_linear_bias_to_fp16, weight = m_speech_prompted_attention2_W_query_linear_weight_to_fp16, x = text_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_1283 = const()[name = string("op_1283"), val = tensor([1, 128, 2, 128])]; + tensor var_1284_cast_fp16 = reshape(shape = var_1283, x = linear_4_cast_fp16)[name = string("op_1284_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor m_speech_prompted_attention2_W_value_linear_weight_to_fp16 = const()[name = string("m_speech_prompted_attention2_W_value_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17731776)))]; + tensor m_speech_prompted_attention2_W_value_linear_bias_to_fp16 = const()[name = string("m_speech_prompted_attention2_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17862912)))]; + tensor linear_6_cast_fp16 = linear(bias = m_speech_prompted_attention2_W_value_linear_bias_to_fp16, weight = m_speech_prompted_attention2_W_value_linear_weight_to_fp16, x = style_ttl_to_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_1297 = const()[name = string("op_1297"), val = tensor([1, 50, 2, 128])]; + tensor var_1298_cast_fp16 = reshape(shape = var_1297, x = linear_6_cast_fp16)[name = string("op_1298_cast_fp16")]; + tensor v_perm_0 = const()[name = string("v_perm_0"), val = tensor([0, 2, 1, 3])]; + bool var_1302_transpose_x_0 = const()[name = string("op_1302_transpose_x_0"), val = bool(false)]; + bool var_1302_transpose_y_0 = const()[name = string("op_1302_transpose_y_0"), val = bool(false)]; + tensor var_1301_to_fp16 = const()[name = string("op_1301_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17863488)))]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_1284_cast_fp16)[name = string("transpose_3")]; + tensor var_1302_cast_fp16 = matmul(transpose_x = var_1302_transpose_x_0, transpose_y = var_1302_transpose_y_0, x = q_cast_fp16, y = var_1301_to_fp16)[name = string("op_1302_cast_fp16")]; + fp16 _inversed_input_167_y_0_to_fp16 = const()[name = string("_inversed_input_167_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_167_cast_fp16 = mul(x = var_1302_cast_fp16, y = _inversed_input_167_y_0_to_fp16)[name = string("_inversed_input_167_cast_fp16")]; + tensor attn_5_cast_fp16 = softmax(axis = var_29, x = _inversed_input_167_cast_fp16)[name = string("attn_5_cast_fp16")]; + tensor attn_7_cast_fp16 = mul(x = attn_5_cast_fp16, y = mask_q_1_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool out_33_transpose_x_0 = const()[name = string("out_33_transpose_x_0"), val = bool(false)]; + bool out_33_transpose_y_0 = const()[name = string("out_33_transpose_y_0"), val = bool(false)]; + tensor v_cast_fp16 = transpose(perm = v_perm_0, x = var_1298_cast_fp16)[name = string("transpose_2")]; + tensor out_33_cast_fp16 = matmul(transpose_x = out_33_transpose_x_0, transpose_y = out_33_transpose_y_0, x = attn_7_cast_fp16, y = v_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor var_1310_perm_0 = const()[name = string("op_1310_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1311 = const()[name = string("op_1311"), val = tensor([1, 128, 256])]; + tensor var_1310_cast_fp16 = transpose(perm = var_1310_perm_0, x = out_33_cast_fp16)[name = string("transpose_1")]; + tensor input_169_cast_fp16 = reshape(shape = var_1311, x = var_1310_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor m_speech_prompted_attention2_out_fc_linear_weight_to_fp16 = const()[name = string("m_speech_prompted_attention2_out_fc_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17889152)))]; + tensor m_speech_prompted_attention2_out_fc_linear_bias_to_fp16 = const()[name = string("m_speech_prompted_attention2_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18020288)))]; + tensor linear_7_cast_fp16 = linear(bias = m_speech_prompted_attention2_out_fc_linear_bias_to_fp16, weight = m_speech_prompted_attention2_out_fc_linear_weight_to_fp16, x = input_169_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor attn2_out_cast_fp16 = mul(x = linear_7_cast_fp16, y = var_1258_cast_fp16)[name = string("attn2_out_cast_fp16")]; + tensor y_final_cast_fp16 = add(x = text_3_cast_fp16, y = attn2_out_cast_fp16)[name = string("y_final_cast_fp16")]; + tensor var_1326_axes_0 = const()[name = string("op_1326_axes_0"), val = tensor([-1])]; + tensor m_speech_prompted_norm_norm_weight_to_fp16 = const()[name = string("m_speech_prompted_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18020864)))]; + tensor m_speech_prompted_norm_norm_bias_to_fp16 = const()[name = string("m_speech_prompted_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18021440)))]; + tensor var_1326_cast_fp16 = layer_norm(axes = var_1326_axes_0, beta = m_speech_prompted_norm_norm_bias_to_fp16, epsilon = var_25_to_fp16, gamma = m_speech_prompted_norm_norm_weight_to_fp16, x = y_final_cast_fp16)[name = string("op_1326_cast_fp16")]; + tensor x_perm_0 = const()[name = string("x_perm_0"), val = tensor([0, 2, 1])]; + tensor x_cast_fp16 = transpose(perm = x_perm_0, x = var_1326_cast_fp16)[name = string("transpose_0")]; + tensor var_1328_cast_fp16 = mul(x = x_cast_fp16, y = text_mask_to_fp16)[name = string("op_1328_cast_fp16")]; + string var_1328_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1328_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor text_emb = cast(dtype = var_1328_cast_fp16_to_fp32_dtype_0, x = var_1328_cast_fp16)[name = string("cast_46")]; + } -> (text_emb); +} \ No newline at end of file diff --git a/TextEncoder_v2.mlmodelc/weights/weight.bin b/TextEncoder_v2.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..475509ae228817602aad84dcffb0f29c4a085503 --- /dev/null +++ b/TextEncoder_v2.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7c7afa5d02426a8a363c9cef0e27ce64a5c57a67d1a59f60aac0ac58ffb9877f +size 18022016 diff --git 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b/VectorEstimatorVariants/VectorEstimator_v2_L128_int4.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..af791ab8b5e7906bfbbbd619379424048d62b069 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L128_int4.mlmodelc/model.mil @@ -0,0 +1,2101 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor current_step, tensor latent_mask, tensor noisy_latent, tensor style_ttl, tensor text_emb, tensor text_mask, tensor total_step) { + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + bool text_emb_interleave_0 = const()[name = string("text_emb_interleave_0"), val = bool(false)]; + string text_emb_to_fp16_dtype_0 = const()[name = string("text_emb_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_uncond_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16512))))[name = string("text_uncond_to_fp16_palettized")]; + tensor text_emb_to_fp16 = cast(dtype = text_emb_to_fp16_dtype_0, x = text_emb)[name = string("cast_7")]; + tensor text_emb_cast_fp16 = concat(axis = var_39, interleave = text_emb_interleave_0, values = (text_emb_to_fp16, text_uncond_to_fp16_palettized))[name = string("text_emb_cast_fp16")]; + bool input_95_interleave_0 = const()[name = string("input_95_interleave_0"), val = bool(false)]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor uncond_masker_style_value_special_token_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23104))))[name = string("uncond_masker_style_value_special_token_to_fp16_palettized")]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_6")]; + tensor input_95_cast_fp16 = concat(axis = var_39, interleave = input_95_interleave_0, values = (style_ttl_to_fp16, uncond_masker_style_value_special_token_to_fp16_palettized))[name = string("input_95_cast_fp16")]; + int32 var_64 = const()[name = string("op_64"), val = int32(0)]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + string noisy_latent_to_fp16_dtype_0 = const()[name = string("noisy_latent_to_fp16_dtype_0"), val = string("fp16")]; + tensor noisy_latent_to_fp16 = cast(dtype = noisy_latent_to_fp16_dtype_0, x = noisy_latent)[name = string("cast_5")]; + tensor input_5_cast_fp16 = concat(axis = var_64, interleave = input_5_interleave_0, values = (noisy_latent_to_fp16, noisy_latent_to_fp16))[name = string("input_5_cast_fp16")]; + int32 var_67 = const()[name = string("op_67"), val = int32(0)]; + bool latent_mask_b_interleave_0 = const()[name = string("latent_mask_b_interleave_0"), val = bool(false)]; + string latent_mask_to_fp16_dtype_0 = const()[name = string("latent_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor latent_mask_to_fp16 = cast(dtype = latent_mask_to_fp16_dtype_0, x = latent_mask)[name = string("cast_4")]; + tensor latent_mask_b_cast_fp16 = concat(axis = var_67, interleave = latent_mask_b_interleave_0, values = (latent_mask_to_fp16, latent_mask_to_fp16))[name = string("latent_mask_b_cast_fp16")]; + int32 var_70 = const()[name = string("op_70"), val = int32(0)]; + bool text_mask_interleave_0 = const()[name = string("text_mask_interleave_0"), val = bool(false)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_3")]; + tensor text_mask_cast_fp16 = concat(axis = var_70, interleave = text_mask_interleave_0, values = (text_mask_to_fp16, text_mask_to_fp16))[name = string("text_mask_cast_fp16")]; + string current_step_to_fp16_dtype_0 = const()[name = string("current_step_to_fp16_dtype_0"), val = string("fp16")]; + string total_step_to_fp16_dtype_0 = const()[name = string("total_step_to_fp16_dtype_0"), val = string("fp16")]; + tensor current_step_to_fp16 = cast(dtype = current_step_to_fp16_dtype_0, x = current_step)[name = string("cast_1")]; + tensor total_step_to_fp16 = cast(dtype = total_step_to_fp16_dtype_0, x = total_step)[name = string("cast_2")]; + tensor t_1_cast_fp16 = real_div(x = current_step_to_fp16, y = total_step_to_fp16)[name = string("t_1_cast_fp16")]; + int32 var_74 = const()[name = string("op_74"), val = int32(0)]; + bool t_3_interleave_0 = const()[name = string("t_3_interleave_0"), val = bool(false)]; + tensor t_3_cast_fp16 = concat(axis = var_74, interleave = t_3_interleave_0, values = (t_1_cast_fp16, t_1_cast_fp16))[name = string("t_3_cast_fp16")]; + int32 var_78 = const()[name = string("op_78"), val = int32(-1)]; + tensor var_85 = const()[name = string("op_85"), val = tensor([-1, 1])]; + tensor var_86_cast_fp16 = reshape(shape = var_85, x = t_3_cast_fp16)[name = string("op_86_cast_fp16")]; + fp16 time_encoder_time_scale_to_fp16 = const()[name = string("time_encoder_time_scale_to_fp16"), val = fp16(0x1.f4p+9)]; + tensor t_scaled_cast_fp16 = mul(x = var_86_cast_fp16, y = time_encoder_time_scale_to_fp16)[name = string("t_scaled_cast_fp16")]; + tensor time_encoder_omegas_to_fp16 = const()[name = string("time_encoder_omegas_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23232)))]; + tensor phase_cast_fp16 = mul(x = t_scaled_cast_fp16, y = time_encoder_omegas_to_fp16)[name = string("phase_cast_fp16")]; + tensor var_89_cast_fp16 = sin(x = phase_cast_fp16)[name = string("op_89_cast_fp16")]; + tensor var_90_cast_fp16 = cos(x = phase_cast_fp16)[name = string("op_90_cast_fp16")]; + bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)]; + tensor input_1_cast_fp16 = concat(axis = var_78, interleave = input_1_interleave_0, values = (var_89_cast_fp16, var_90_cast_fp16))[name = string("input_1_cast_fp16")]; + tensor time_encoder_mlp_0_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31616))))[name = string("time_encoder_mlp_0_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_0_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31744)))]; + tensor linear_0_cast_fp16 = linear(bias = time_encoder_mlp_0_linear_bias_to_fp16, weight = time_encoder_mlp_0_linear_weight_to_fp16_palettized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_97_cast_fp16 = softplus(x = linear_0_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = tanh(x = var_97_cast_fp16)[name = string("op_98_cast_fp16")]; + tensor input_3_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_98_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor time_encoder_mlp_2_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40576))))[name = string("time_encoder_mlp_2_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_2_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40704)))]; + tensor linear_1_cast_fp16 = linear(bias = time_encoder_mlp_2_linear_bias_to_fp16, weight = time_encoder_mlp_2_linear_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor time_emb_axes_0 = const()[name = string("time_emb_axes_0"), val = tensor([-1])]; + tensor time_emb_cast_fp16 = expand_dims(axes = time_emb_axes_0, x = linear_1_cast_fp16)[name = string("time_emb_cast_fp16")]; + string var_115_pad_type_0 = const()[name = string("op_115_pad_type_0"), val = string("valid")]; + tensor var_115_strides_0 = const()[name = string("op_115_strides_0"), val = tensor([1])]; + tensor var_115_pad_0 = const()[name = string("op_115_pad_0"), val = tensor([0, 0])]; + tensor var_115_dilations_0 = const()[name = string("op_115_dilations_0"), val = tensor([1])]; + int32 var_115_groups_0 = const()[name = string("op_115_groups_0"), val = int32(1)]; + tensor proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77824))))[name = string("proj_in_weight_to_fp16_palettized")]; + tensor var_115_cast_fp16 = conv(dilations = var_115_dilations_0, groups = var_115_groups_0, pad = var_115_pad_0, pad_type = var_115_pad_type_0, strides = var_115_strides_0, weight = proj_in_weight_to_fp16_palettized, x = input_5_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = var_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_3_cast_fp16")]; + int32 var_134 = const()[name = string("op_134"), val = int32(-1)]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([0, 0, 1])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 128])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([true, true, true])]; + tensor input_7_cast_fp16 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, x = latent_mask_b_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_166_pad_0 = const()[name = string("op_166_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_166_mode_0 = const()[name = string("op_166_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_166_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_166_mode_0, pad = var_166_pad_0, x = input_7_cast_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_133_to_fp16 = const()[name = string("op_133_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = sub(x = var_133_to_fp16, y = var_166_cast_fp16)[name = string("op_167_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = latent_mask_b_cast_fp16, y = var_167_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_169_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_172_cast_fp16 = mul(x = x_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor var_173_cast_fp16 = sub(x = var_133_to_fp16, y = latent_mask_b_cast_fp16)[name = string("op_173_cast_fp16")]; + tensor var_174_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_174_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_174_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("replicate")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor input_11_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_11_mode_0, pad = input_11_pad_0, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(512)]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79296))))[name = string("main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79424)))]; + tensor h_3_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, 2, 1])]; + tensor var_192_axes_0 = const()[name = string("op_192_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80512)))]; + tensor main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81600)))]; + fp16 var_139_to_fp16 = const()[name = string("op_139_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_7_cast_fp16)[name = string("transpose_117")]; + tensor var_192_cast_fp16 = layer_norm(axes = var_192_axes_0, beta = main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16, x = input_13_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, 2, 1])]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(607040))))[name = string("main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(607168)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = var_192_cast_fp16)[name = string("transpose_116")]; + tensor h_5_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = string("h_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = h_5_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)]; + tensor op_209_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(611328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135680))))[name = string("op_209_weight_0_to_fp16_palettized")]; + tensor var_209_bias_0_to_fp16 = const()[name = string("op_209_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135808)))]; + tensor var_209_cast_fp16 = conv(bias = var_209_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = op_209_weight_0_to_fp16_palettized, x = input_17_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_209_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_223_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_223_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_226_cast_fp16 = mul(x = x_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_226_cast_fp16")]; + tensor var_228_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_228_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_226_cast_fp16, y = var_228_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")]; + fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)]; + tensor input_23_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([2])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(512)]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1138240))))[name = string("main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1138368)))]; + tensor h_9_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor var_246_axes_0 = const()[name = string("op_246_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1139456)))]; + tensor main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1140544)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_13_cast_fp16)[name = string("transpose_115")]; + tensor var_246_cast_fp16 = layer_norm(axes = var_246_axes_0, beta = main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_246_cast_fp16")]; + tensor input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor([0, 2, 1])]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1141632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1665984))))[name = string("main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1666112)))]; + tensor input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_246_cast_fp16)[name = string("transpose_114")]; + tensor h_11_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_27_cast_fp16)[name = string("h_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_11_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)]; + tensor op_263_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1670272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2194624))))[name = string("op_263_weight_0_to_fp16_palettized")]; + tensor var_263_bias_0_to_fp16 = const()[name = string("op_263_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2194752)))]; + tensor var_263_cast_fp16 = conv(bias = var_263_bias_0_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = op_263_weight_0_to_fp16_palettized, x = input_29_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_263_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_277_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_277_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_277_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_280_cast_fp16 = mul(x = x_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_280_cast_fp16")]; + tensor var_282_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_280_cast_fp16, y = var_282_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("replicate")]; + fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)]; + tensor input_35_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = input_35_mode_0, pad = input_35_pad_0, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([4])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(512)]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2195840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2197184))))[name = string("main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2197312)))]; + tensor h_15_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor var_300_axes_0 = const()[name = string("op_300_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2198400)))]; + tensor main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2199488)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_19_cast_fp16)[name = string("transpose_113")]; + tensor var_300_cast_fp16 = layer_norm(axes = var_300_axes_0, beta = main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16, x = input_37_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor([0, 2, 1])]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2200576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2724928))))[name = string("main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2725056)))]; + tensor input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_300_cast_fp16)[name = string("transpose_112")]; + tensor h_17_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = string("h_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = h_17_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)]; + tensor op_317_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2729216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3253568))))[name = string("op_317_weight_0_to_fp16_palettized")]; + tensor var_317_bias_0_to_fp16 = const()[name = string("op_317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3253696)))]; + tensor var_317_cast_fp16 = conv(bias = var_317_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = op_317_weight_0_to_fp16_palettized, x = input_41_cast_fp16)[name = string("op_317_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_317_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor var_331_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_331_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_331_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = x_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor var_336_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_336_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_334_cast_fp16, y = var_336_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")]; + fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([8])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(512)]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3254784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3256128))))[name = string("main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3256256)))]; + tensor h_21_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3257344)))]; + tensor main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3258432)))]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_25_cast_fp16)[name = string("transpose_111")]; + tensor var_354_cast_fp16 = layer_norm(axes = var_354_axes_0, beta = main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_354_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3259520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3783872))))[name = string("main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3784000)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_354_cast_fp16)[name = string("transpose_110")]; + tensor h_23_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("h_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_23_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)]; + tensor op_371_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3788160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4312512))))[name = string("op_371_weight_0_to_fp16_palettized")]; + tensor var_371_bias_0_to_fp16 = const()[name = string("op_371_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4312640)))]; + tensor var_371_cast_fp16 = conv(bias = var_371_bias_0_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = op_371_weight_0_to_fp16_palettized, x = input_53_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_371_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor input_55_cast_fp16 = squeeze(axes = input_55_axes_0, x = time_emb_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4313728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4330176))))[name = string("main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4330304)))]; + tensor linear_2_cast_fp16 = linear(bias = main_blocks_0_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor t_5_axes_0 = const()[name = string("t_5_axes_0"), val = tensor([-1])]; + tensor t_5_cast_fp16 = expand_dims(axes = t_5_axes_0, x = linear_2_cast_fp16)[name = string("t_5_cast_fp16")]; + tensor var_381_cast_fp16 = add(x = x_27_cast_fp16, y = t_5_cast_fp16)[name = string("op_381_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_381_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_394_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_394_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_394_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_397_cast_fp16 = mul(x = x_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = var_397_cast_fp16, y = var_399_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("replicate")]; + fp16 const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = fp16(0x0p+0)]; + tensor input_61_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = input_61_mode_0, pad = input_61_pad_0, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(512)]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4331392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4332736))))[name = string("main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4332864)))]; + tensor h_27_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417_axes_0 = const()[name = string("op_417_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4333952)))]; + tensor main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4335040)))]; + tensor input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_33_cast_fp16)[name = string("transpose_109")]; + tensor var_417_cast_fp16 = layer_norm(axes = var_417_axes_0, beta = main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16, x = input_63_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4336128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4860480))))[name = string("main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4860608)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = var_417_cast_fp16)[name = string("transpose_108")]; + tensor h_29_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_65_cast_fp16)[name = string("h_29_cast_fp16")]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("EXACT")]; + tensor input_67_cast_fp16 = gelu(mode = input_67_mode_0, x = h_29_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)]; + tensor op_434_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4864768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5389120))))[name = string("op_434_weight_0_to_fp16_palettized")]; + tensor var_434_bias_0_to_fp16 = const()[name = string("op_434_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5389248)))]; + tensor var_434_cast_fp16 = conv(bias = var_434_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = op_434_weight_0_to_fp16_palettized, x = input_67_cast_fp16)[name = string("op_434_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_434_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_m_1_cast_fp16 = mul(x = x_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_1_cast_fp16")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5390336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5521472))))[name = string("main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5521600)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_35_cast_fp16)[name = string("transpose_107")]; + tensor linear_3_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([2, 128, 8, 64])]; + tensor var_453_cast_fp16 = reshape(shape = var_452, x = linear_3_cast_fp16)[name = string("op_453_cast_fp16")]; + tensor x_37_perm_0 = const()[name = string("x_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5522688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5588288))))[name = string("main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5588416)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = text_emb_cast_fp16)[name = string("transpose_106")]; + tensor linear_4_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([2, 128, 8, 64])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = linear_4_cast_fp16)[name = string("op_461_cast_fp16")]; + tensor x_39_perm_0 = const()[name = string("x_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5589504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5655104))))[name = string("main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5655232)))]; + tensor linear_5_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_468 = const()[name = string("op_468"), val = tensor([2, 128, 8, 64])]; + tensor var_469_cast_fp16 = reshape(shape = var_468, x = linear_5_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor divisor_1_axes_0 = const()[name = string("divisor_1_axes_0"), val = tensor([1, 2])]; + bool divisor_1_keep_dims_0 = const()[name = string("divisor_1_keep_dims_0"), val = bool(false)]; + tensor divisor_1_cast_fp16 = reduce_sum(axes = divisor_1_axes_0, keep_dims = divisor_1_keep_dims_0, x = latent_mask_b_cast_fp16)[name = string("divisor_1_cast_fp16")]; + tensor divisor_3_axes_0 = const()[name = string("divisor_3_axes_0"), val = tensor([1, 2])]; + bool divisor_3_keep_dims_0 = const()[name = string("divisor_3_keep_dims_0"), val = bool(false)]; + tensor divisor_3_cast_fp16 = reduce_sum(axes = divisor_3_axes_0, keep_dims = divisor_3_keep_dims_0, x = text_mask_cast_fp16)[name = string("divisor_3_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([-1, 1, 1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = divisor_1_cast_fp16)[name = string("op_488_cast_fp16")]; + tensor const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5656320)))]; + tensor scaled_1_cast_fp16 = real_div(x = const_16_to_fp16, y = var_488_cast_fp16)[name = string("scaled_1_cast_fp16")]; + tensor main_blocks_0_text_attn_theta_to_fp16 = const()[name = string("main_blocks_0_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5656896)))]; + tensor angles_1_cast_fp16 = mul(x = scaled_1_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_1_cast_fp16")]; + tensor var_491_cast_fp16 = cos(x = angles_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = var_491_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor var_493_cast_fp16 = sin(x = angles_1_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = var_493_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor x_a_1_begin_0 = const()[name = string("x_a_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_1_end_0 = const()[name = string("x_a_1_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_1_end_mask_0 = const()[name = string("x_a_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_37_cast_fp16 = transpose(perm = x_37_perm_0, x = var_453_cast_fp16)[name = string("transpose_105")]; + tensor x_a_1_cast_fp16 = slice_by_index(begin = x_a_1_begin_0, end = x_a_1_end_0, end_mask = x_a_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_a_1_cast_fp16")]; + tensor x_b_1_begin_0 = const()[name = string("x_b_1_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_1_end_0 = const()[name = string("x_b_1_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_1_end_mask_0 = const()[name = string("x_b_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_1_cast_fp16 = slice_by_index(begin = x_b_1_begin_0, end = x_b_1_end_0, end_mask = x_b_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_b_1_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = x_a_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor var_498_cast_fp16 = mul(x = x_b_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_498_cast_fp16")]; + tensor rot_a_1_cast_fp16 = sub(x = var_497_cast_fp16, y = var_498_cast_fp16)[name = string("rot_a_1_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = x_a_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = x_b_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor rot_b_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("rot_b_1_cast_fp16")]; + bool q_1_interleave_0 = const()[name = string("q_1_interleave_0"), val = bool(false)]; + tensor q_1_cast_fp16 = concat(axis = var_134, interleave = q_1_interleave_0, values = (rot_a_1_cast_fp16, rot_b_1_cast_fp16))[name = string("q_1_cast_fp16")]; + tensor var_505 = const()[name = string("op_505"), val = tensor([-1, 1, 1])]; + tensor var_506_cast_fp16 = reshape(shape = var_505, x = divisor_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor scaled_3_cast_fp16 = real_div(x = const_16_to_fp16, y = var_506_cast_fp16)[name = string("scaled_3_cast_fp16")]; + tensor angles_3_cast_fp16 = mul(x = scaled_3_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_3_cast_fp16")]; + tensor var_509_cast_fp16 = cos(x = angles_3_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor cos_3_axes_0 = const()[name = string("cos_3_axes_0"), val = tensor([1])]; + tensor cos_3_cast_fp16 = expand_dims(axes = cos_3_axes_0, x = var_509_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor var_511_cast_fp16 = sin(x = angles_3_cast_fp16)[name = string("op_511_cast_fp16")]; + tensor sin_3_axes_0 = const()[name = string("sin_3_axes_0"), val = tensor([1])]; + tensor sin_3_cast_fp16 = expand_dims(axes = sin_3_axes_0, x = var_511_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor x_a_3_begin_0 = const()[name = string("x_a_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_3_end_0 = const()[name = string("x_a_3_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_3_end_mask_0 = const()[name = string("x_a_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_39_cast_fp16 = transpose(perm = x_39_perm_0, x = var_461_cast_fp16)[name = string("transpose_104")]; + tensor x_a_3_cast_fp16 = slice_by_index(begin = x_a_3_begin_0, end = x_a_3_end_0, end_mask = x_a_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_a_3_cast_fp16")]; + tensor x_b_3_begin_0 = const()[name = string("x_b_3_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_3_end_0 = const()[name = string("x_b_3_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_3_end_mask_0 = const()[name = string("x_b_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_3_cast_fp16 = slice_by_index(begin = x_b_3_begin_0, end = x_b_3_end_0, end_mask = x_b_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_b_3_cast_fp16")]; + tensor var_515_cast_fp16 = mul(x = x_a_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_515_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = x_b_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor rot_a_3_cast_fp16 = sub(x = var_515_cast_fp16, y = var_516_cast_fp16)[name = string("rot_a_3_cast_fp16")]; + tensor var_518_cast_fp16 = mul(x = x_a_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = mul(x = x_b_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor rot_b_3_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = string("rot_b_3_cast_fp16")]; + bool k_1_interleave_0 = const()[name = string("k_1_interleave_0"), val = bool(false)]; + tensor k_1_cast_fp16 = concat(axis = var_134, interleave = k_1_interleave_0, values = (rot_a_3_cast_fp16, rot_b_3_cast_fp16))[name = string("k_1_cast_fp16")]; + bool var_524_transpose_x_1 = const()[name = string("op_524_transpose_x_1"), val = bool(false)]; + bool var_524_transpose_y_1 = const()[name = string("op_524_transpose_y_1"), val = bool(true)]; + tensor var_524_cast_fp16 = matmul(transpose_x = var_524_transpose_x_1, transpose_y = var_524_transpose_y_1, x = q_1_cast_fp16, y = k_1_cast_fp16)[name = string("op_524_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_524_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([2])]; + tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = text_mask_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528_cast_fp16 = sub(x = var_133_to_fp16, y = var_527_cast_fp16)[name = string("op_528_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_530_cast_fp16 = mul(x = var_528_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_75_cast_fp16 = sub(x = _inversed_scores_1_cast_fp16, y = var_530_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_134, x = input_75_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool var_533_transpose_x_0 = const()[name = string("op_533_transpose_x_0"), val = bool(false)]; + bool var_533_transpose_y_0 = const()[name = string("op_533_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_469_cast_fp16)[name = string("transpose_103")]; + tensor var_533_cast_fp16 = matmul(transpose_x = var_533_transpose_x_0, transpose_y = var_533_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("op_533_cast_fp16")]; + tensor var_534_perm_0 = const()[name = string("op_534_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_535 = const()[name = string("op_535"), val = tensor([2, 128, 512])]; + tensor var_534_cast_fp16 = transpose(perm = var_534_perm_0, x = var_533_cast_fp16)[name = string("transpose_102")]; + tensor input_77_cast_fp16 = reshape(shape = var_535, x = var_534_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5657024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5788160))))[name = string("main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5788288)))]; + tensor linear_6_cast_fp16 = linear(bias = main_blocks_0_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor out_11_perm_0 = const()[name = string("out_11_perm_0"), val = tensor([0, 2, 1])]; + tensor out_11_cast_fp16 = transpose(perm = out_11_perm_0, x = linear_6_cast_fp16)[name = string("transpose_101")]; + tensor var_542_cast_fp16 = mul(x = out_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_542_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = x_m_1_cast_fp16, y = var_542_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor var_549_axes_0 = const()[name = string("op_549_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5789376)))]; + tensor main_blocks_0_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5790464)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_41_cast_fp16)[name = string("transpose_100")]; + tensor var_549_cast_fp16 = layer_norm(axes = var_549_axes_0, beta = main_blocks_0_text_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_text_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor var_550_perm_0 = const()[name = string("op_550_perm_0"), val = tensor([0, 2, 1])]; + tensor var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = var_549_cast_fp16)[name = string("transpose_99")]; + tensor x_43_cast_fp16 = mul(x = var_550_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = x_45_cast_fp16, y = last_1_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor x_last_11_axes_0 = const()[name = string("x_last_11_axes_0"), val = tensor([-1])]; + bool x_last_11_keep_dims_0 = const()[name = string("x_last_11_keep_dims_0"), val = bool(true)]; + tensor x_last_11_cast_fp16 = reduce_sum(axes = x_last_11_axes_0, keep_dims = x_last_11_keep_dims_0, x = var_563_cast_fp16)[name = string("x_last_11_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = x_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor var_568_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_11_cast_fp16)[name = string("op_568_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = var_566_cast_fp16, y = var_568_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_85_mode_0 = const()[name = string("input_85_mode_0"), val = string("replicate")]; + fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)]; + tensor input_85_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(512)]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5791552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5792896))))[name = string("main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5793024)))]; + tensor h_33_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("h_33_cast_fp16")]; + tensor x_47_cast_fp16 = mul(x = h_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5794112)))]; + tensor main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5795200)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_47_cast_fp16)[name = string("transpose_98")]; + tensor var_586_cast_fp16 = layer_norm(axes = var_586_axes_0, beta = main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_586_cast_fp16")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5796288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6320640))))[name = string("main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6320768)))]; + tensor input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = var_586_cast_fp16)[name = string("transpose_97")]; + tensor h_35_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_89_cast_fp16)[name = string("h_35_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = h_35_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")]; + tensor h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor([1])]; + tensor h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor([0, 0])]; + tensor h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor([1])]; + int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)]; + tensor op_603_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6324928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6849280))))[name = string("op_603_weight_0_to_fp16_palettized")]; + tensor var_603_bias_0_to_fp16 = const()[name = string("op_603_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6849408)))]; + tensor var_603_cast_fp16 = conv(bias = var_603_bias_0_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = op_603_weight_0_to_fp16_palettized, x = input_91_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor out_13_cast_fp16 = add(x = x_45_cast_fp16, y = var_603_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_49_cast_fp16 = mul(x = out_13_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_m_3_cast_fp16 = mul(x = x_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_3_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6850496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6916096))))[name = string("main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6916224)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_49_cast_fp16)[name = string("transpose_96")]; + tensor linear_7_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([2, 128, 2, 128])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = linear_7_cast_fp16)[name = string("op_620_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6916800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6949632))))[name = string("main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6949760)))]; + tensor linear_9_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_633 = const()[name = string("op_633"), val = tensor([2, 50, 2, 128])]; + tensor var_634_cast_fp16 = reshape(shape = var_633, x = linear_9_cast_fp16)[name = string("op_634_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_638_transpose_x_0 = const()[name = string("op_638_transpose_x_0"), val = bool(false)]; + bool var_638_transpose_y_0 = const()[name = string("op_638_transpose_y_0"), val = bool(false)]; + tensor op_637_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6950336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6963200))))[name = string("op_637_to_fp16_palettized")]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_620_cast_fp16)[name = string("transpose_95")]; + tensor var_638_cast_fp16 = matmul(transpose_x = var_638_transpose_x_0, transpose_y = var_638_transpose_y_0, x = q_3_cast_fp16, y = op_637_to_fp16_palettized)[name = string("op_638_cast_fp16")]; + fp16 _inversed_input_97_y_0_to_fp16 = const()[name = string("_inversed_input_97_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_97_cast_fp16 = mul(x = var_638_cast_fp16, y = _inversed_input_97_y_0_to_fp16)[name = string("_inversed_input_97_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_134, x = _inversed_input_97_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_642_perm_0 = const()[name = string("op_642_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_1_axes_0 = const()[name = string("mask_1_axes_0"), val = tensor([1])]; + tensor var_642_cast_fp16 = transpose(perm = var_642_perm_0, x = latent_mask_b_cast_fp16)[name = string("transpose_94")]; + tensor mask_1_cast_fp16 = expand_dims(axes = mask_1_axes_0, x = var_642_cast_fp16)[name = string("mask_1_cast_fp16")]; + tensor attn_5_cast_fp16 = mul(x = attn_3_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool var_645_transpose_x_0 = const()[name = string("op_645_transpose_x_0"), val = bool(false)]; + bool var_645_transpose_y_0 = const()[name = string("op_645_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_634_cast_fp16)[name = string("transpose_93")]; + tensor var_645_cast_fp16 = matmul(transpose_x = var_645_transpose_x_0, transpose_y = var_645_transpose_y_0, x = attn_5_cast_fp16, y = v_3_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_646_perm_0 = const()[name = string("op_646_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_647 = const()[name = string("op_647"), val = tensor([2, 128, 256])]; + tensor var_646_cast_fp16 = transpose(perm = var_646_perm_0, x = var_645_cast_fp16)[name = string("transpose_92")]; + tensor input_99_cast_fp16 = reshape(shape = var_647, x = var_646_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6963328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7028928))))[name = string("main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7029056)))]; + tensor linear_10_cast_fp16 = linear(bias = main_blocks_0_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor out_15_perm_0 = const()[name = string("out_15_perm_0"), val = tensor([0, 2, 1])]; + tensor out_15_cast_fp16 = transpose(perm = out_15_perm_0, x = linear_10_cast_fp16)[name = string("transpose_91")]; + tensor var_654_cast_fp16 = mul(x = out_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor x_51_cast_fp16 = add(x = x_m_3_cast_fp16, y = var_654_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7030144)))]; + tensor main_blocks_0_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7031232)))]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_51_cast_fp16)[name = string("transpose_90")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = main_blocks_0_style_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_style_norm_norm_weight_to_fp16, x = input_101_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_662_perm_0 = const()[name = string("op_662_perm_0"), val = tensor([0, 2, 1])]; + tensor var_662_cast_fp16 = transpose(perm = var_662_perm_0, x = var_661_cast_fp16)[name = string("transpose_89")]; + tensor x_53_cast_fp16 = mul(x = var_662_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_53_cast_fp16")]; + int32 var_681 = const()[name = string("op_681"), val = int32(-1)]; + tensor x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = x_55_cast_fp16, y = last_1_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor x_last_13_axes_0 = const()[name = string("x_last_13_axes_0"), val = tensor([-1])]; + bool x_last_13_keep_dims_0 = const()[name = string("x_last_13_keep_dims_0"), val = bool(true)]; + tensor x_last_13_cast_fp16 = reduce_sum(axes = x_last_13_axes_0, keep_dims = x_last_13_keep_dims_0, x = var_716_cast_fp16)[name = string("x_last_13_cast_fp16")]; + tensor var_719_cast_fp16 = mul(x = x_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_721_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_13_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_719_cast_fp16, y = var_721_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("replicate")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(512)]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7032320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7033664))))[name = string("main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7033792)))]; + tensor h_39_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor x_57_cast_fp16 = mul(x = h_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_739_axes_0 = const()[name = string("op_739_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7034880)))]; + tensor main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7035968)))]; + fp16 var_686_to_fp16 = const()[name = string("op_686_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_57_cast_fp16)[name = string("transpose_88")]; + tensor var_739_cast_fp16 = layer_norm(axes = var_739_axes_0, beta = main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_739_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + string h_41_pad_type_0 = const()[name = string("h_41_pad_type_0"), val = string("valid")]; + tensor h_41_strides_0 = const()[name = string("h_41_strides_0"), val = tensor([1])]; + tensor h_41_pad_0 = const()[name = string("h_41_pad_0"), val = tensor([0, 0])]; + tensor h_41_dilations_0 = const()[name = string("h_41_dilations_0"), val = tensor([1])]; + int32 h_41_groups_0 = const()[name = string("h_41_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7037056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7561408))))[name = string("main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7561536)))]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = var_739_cast_fp16)[name = string("transpose_87")]; + tensor h_41_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_41_dilations_0, groups = h_41_groups_0, pad = h_41_pad_0, pad_type = h_41_pad_type_0, strides = h_41_strides_0, weight = main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = h_41_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor op_756_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7565696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8090048))))[name = string("op_756_weight_0_to_fp16_palettized")]; + tensor var_756_bias_0_to_fp16 = const()[name = string("op_756_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8090176)))]; + tensor var_756_cast_fp16 = conv(bias = var_756_bias_0_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = op_756_weight_0_to_fp16_palettized, x = input_113_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor out_17_cast_fp16 = add(x = x_55_cast_fp16, y = var_756_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_59_cast_fp16 = mul(x = out_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor x_61_cast_fp16 = mul(x = x_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_770_cast_fp16 = mul(x = x_61_cast_fp16, y = last_1_cast_fp16)[name = string("op_770_cast_fp16")]; + tensor x_last_15_axes_0 = const()[name = string("x_last_15_axes_0"), val = tensor([-1])]; + bool x_last_15_keep_dims_0 = const()[name = string("x_last_15_keep_dims_0"), val = bool(true)]; + tensor x_last_15_cast_fp16 = reduce_sum(axes = x_last_15_axes_0, keep_dims = x_last_15_keep_dims_0, x = var_770_cast_fp16)[name = string("x_last_15_cast_fp16")]; + tensor var_773_cast_fp16 = mul(x = x_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor var_775_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_15_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = var_773_cast_fp16, y = var_775_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("replicate")]; + fp16 const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = fp16(0x0p+0)]; + tensor input_119_cast_fp16 = pad(constant_val = const_26_to_fp16, mode = input_119_mode_0, pad = input_119_pad_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")]; + string h_45_pad_type_0 = const()[name = string("h_45_pad_type_0"), val = string("valid")]; + tensor h_45_dilations_0 = const()[name = string("h_45_dilations_0"), val = tensor([2])]; + int32 h_45_groups_0 = const()[name = string("h_45_groups_0"), val = int32(512)]; + tensor h_45_strides_0 = const()[name = string("h_45_strides_0"), val = tensor([1])]; + tensor h_45_pad_0 = const()[name = string("h_45_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8091264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8092608))))[name = string("main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8092736)))]; + tensor h_45_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_45_dilations_0, groups = h_45_groups_0, pad = h_45_pad_0, pad_type = h_45_pad_type_0, strides = h_45_strides_0, weight = main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = h_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8093824)))]; + tensor main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8094912)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_63_cast_fp16)[name = string("transpose_86")]; + tensor var_793_cast_fp16 = layer_norm(axes = var_793_axes_0, beta = main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor input_123_perm_0 = const()[name = string("input_123_perm_0"), val = tensor([0, 2, 1])]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8096000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8620352))))[name = string("main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8620480)))]; + tensor input_123_cast_fp16 = transpose(perm = input_123_perm_0, x = var_793_cast_fp16)[name = string("transpose_85")]; + tensor h_47_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = string("h_47_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = h_47_cast_fp16)[name = string("input_125_cast_fp16")]; + string h_49_pad_type_0 = const()[name = string("h_49_pad_type_0"), val = string("valid")]; + tensor h_49_strides_0 = const()[name = string("h_49_strides_0"), val = tensor([1])]; + tensor h_49_pad_0 = const()[name = string("h_49_pad_0"), val = tensor([0, 0])]; + tensor h_49_dilations_0 = const()[name = string("h_49_dilations_0"), val = tensor([1])]; + int32 h_49_groups_0 = const()[name = string("h_49_groups_0"), val = int32(1)]; + tensor op_810_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8624640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9148992))))[name = string("op_810_weight_0_to_fp16_palettized")]; + tensor var_810_bias_0_to_fp16 = const()[name = string("op_810_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9149120)))]; + tensor var_810_cast_fp16 = conv(bias = var_810_bias_0_to_fp16, dilations = h_49_dilations_0, groups = h_49_groups_0, pad = h_49_pad_0, pad_type = h_49_pad_type_0, strides = h_49_strides_0, weight = op_810_weight_0_to_fp16_palettized, x = input_125_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = x_61_cast_fp16, y = var_810_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor x_65_cast_fp16 = mul(x = out_19_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = x_67_cast_fp16, y = last_1_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_last_17_axes_0 = const()[name = string("x_last_17_axes_0"), val = tensor([-1])]; + bool x_last_17_keep_dims_0 = const()[name = string("x_last_17_keep_dims_0"), val = bool(true)]; + tensor x_last_17_cast_fp16 = reduce_sum(axes = x_last_17_axes_0, keep_dims = x_last_17_keep_dims_0, x = var_824_cast_fp16)[name = string("x_last_17_cast_fp16")]; + tensor var_827_cast_fp16 = mul(x = x_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_829_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_17_cast_fp16)[name = string("op_829_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = var_827_cast_fp16, y = var_829_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("replicate")]; + fp16 const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = fp16(0x0p+0)]; + tensor input_131_cast_fp16 = pad(constant_val = const_28_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + string h_51_pad_type_0 = const()[name = string("h_51_pad_type_0"), val = string("valid")]; + tensor h_51_dilations_0 = const()[name = string("h_51_dilations_0"), val = tensor([4])]; + int32 h_51_groups_0 = const()[name = string("h_51_groups_0"), val = int32(512)]; + tensor h_51_strides_0 = const()[name = string("h_51_strides_0"), val = tensor([1])]; + tensor h_51_pad_0 = const()[name = string("h_51_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9150208))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9151552))))[name = string("main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9151680)))]; + tensor h_51_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_51_dilations_0, groups = h_51_groups_0, pad = h_51_pad_0, pad_type = h_51_pad_type_0, strides = h_51_strides_0, weight = main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor x_69_cast_fp16 = mul(x = h_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_847_axes_0 = const()[name = string("op_847_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9152768)))]; + tensor main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9153856)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_69_cast_fp16)[name = string("transpose_84")]; + tensor var_847_cast_fp16 = layer_norm(axes = var_847_axes_0, beta = main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_847_cast_fp16")]; + tensor input_135_perm_0 = const()[name = string("input_135_perm_0"), val = tensor([0, 2, 1])]; + string h_53_pad_type_0 = const()[name = string("h_53_pad_type_0"), val = string("valid")]; + tensor h_53_strides_0 = const()[name = string("h_53_strides_0"), val = tensor([1])]; + tensor h_53_pad_0 = const()[name = string("h_53_pad_0"), val = tensor([0, 0])]; + tensor h_53_dilations_0 = const()[name = string("h_53_dilations_0"), val = tensor([1])]; + int32 h_53_groups_0 = const()[name = string("h_53_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9154944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9679296))))[name = string("main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9679424)))]; + tensor input_135_cast_fp16 = transpose(perm = input_135_perm_0, x = var_847_cast_fp16)[name = string("transpose_83")]; + tensor h_53_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_53_dilations_0, groups = h_53_groups_0, pad = h_53_pad_0, pad_type = h_53_pad_type_0, strides = h_53_strides_0, weight = main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = string("h_53_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = h_53_cast_fp16)[name = string("input_137_cast_fp16")]; + string h_55_pad_type_0 = const()[name = string("h_55_pad_type_0"), val = string("valid")]; + tensor h_55_strides_0 = const()[name = string("h_55_strides_0"), val = tensor([1])]; + tensor h_55_pad_0 = const()[name = string("h_55_pad_0"), val = tensor([0, 0])]; + tensor h_55_dilations_0 = const()[name = string("h_55_dilations_0"), val = tensor([1])]; + int32 h_55_groups_0 = const()[name = string("h_55_groups_0"), val = int32(1)]; + tensor op_864_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10207936))))[name = string("op_864_weight_0_to_fp16_palettized")]; + tensor var_864_bias_0_to_fp16 = const()[name = string("op_864_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10208064)))]; + tensor var_864_cast_fp16 = conv(bias = var_864_bias_0_to_fp16, dilations = h_55_dilations_0, groups = h_55_groups_0, pad = h_55_pad_0, pad_type = h_55_pad_type_0, strides = h_55_strides_0, weight = op_864_weight_0_to_fp16_palettized, x = input_137_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor out_21_cast_fp16 = add(x = x_67_cast_fp16, y = var_864_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = out_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor x_73_cast_fp16 = mul(x = x_71_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_878_cast_fp16 = mul(x = x_73_cast_fp16, y = last_1_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor x_last_19_axes_0 = const()[name = string("x_last_19_axes_0"), val = tensor([-1])]; + bool x_last_19_keep_dims_0 = const()[name = string("x_last_19_keep_dims_0"), val = bool(true)]; + tensor x_last_19_cast_fp16 = reduce_sum(axes = x_last_19_axes_0, keep_dims = x_last_19_keep_dims_0, x = var_878_cast_fp16)[name = string("x_last_19_cast_fp16")]; + tensor var_881_cast_fp16 = mul(x = x_73_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_881_cast_fp16")]; + tensor var_883_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_19_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = var_881_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("replicate")]; + fp16 const_30_to_fp16 = const()[name = string("const_30_to_fp16"), val = fp16(0x0p+0)]; + tensor input_143_cast_fp16 = pad(constant_val = const_30_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + string h_57_pad_type_0 = const()[name = string("h_57_pad_type_0"), val = string("valid")]; + tensor h_57_dilations_0 = const()[name = string("h_57_dilations_0"), val = tensor([8])]; + int32 h_57_groups_0 = const()[name = string("h_57_groups_0"), val = int32(512)]; + tensor h_57_strides_0 = const()[name = string("h_57_strides_0"), val = tensor([1])]; + tensor h_57_pad_0 = const()[name = string("h_57_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10209152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10210496))))[name = string("main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10210624)))]; + tensor h_57_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_57_dilations_0, groups = h_57_groups_0, pad = h_57_pad_0, pad_type = h_57_pad_type_0, strides = h_57_strides_0, weight = main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = string("h_57_cast_fp16")]; + tensor x_75_cast_fp16 = mul(x = h_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_901_axes_0 = const()[name = string("op_901_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10211712)))]; + tensor main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10212800)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_75_cast_fp16)[name = string("transpose_82")]; + tensor var_901_cast_fp16 = layer_norm(axes = var_901_axes_0, beta = main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor input_147_perm_0 = const()[name = string("input_147_perm_0"), val = tensor([0, 2, 1])]; + string h_59_pad_type_0 = const()[name = string("h_59_pad_type_0"), val = string("valid")]; + tensor h_59_strides_0 = const()[name = string("h_59_strides_0"), val = tensor([1])]; + tensor h_59_pad_0 = const()[name = string("h_59_pad_0"), val = tensor([0, 0])]; + tensor h_59_dilations_0 = const()[name = string("h_59_dilations_0"), val = tensor([1])]; + int32 h_59_groups_0 = const()[name = string("h_59_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10213888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10738240))))[name = string("main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10738368)))]; + tensor input_147_cast_fp16 = transpose(perm = input_147_perm_0, x = var_901_cast_fp16)[name = string("transpose_81")]; + tensor h_59_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_59_dilations_0, groups = h_59_groups_0, pad = h_59_pad_0, pad_type = h_59_pad_type_0, strides = h_59_strides_0, weight = main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("h_59_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = h_59_cast_fp16)[name = string("input_149_cast_fp16")]; + string h_61_pad_type_0 = const()[name = string("h_61_pad_type_0"), val = string("valid")]; + tensor h_61_strides_0 = const()[name = string("h_61_strides_0"), val = tensor([1])]; + tensor h_61_pad_0 = const()[name = string("h_61_pad_0"), val = tensor([0, 0])]; + tensor h_61_dilations_0 = const()[name = string("h_61_dilations_0"), val = tensor([1])]; + int32 h_61_groups_0 = const()[name = string("h_61_groups_0"), val = int32(1)]; + tensor op_918_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10742528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11266880))))[name = string("op_918_weight_0_to_fp16_palettized")]; + tensor var_918_bias_0_to_fp16 = const()[name = string("op_918_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11267008)))]; + tensor var_918_cast_fp16 = conv(bias = var_918_bias_0_to_fp16, dilations = h_61_dilations_0, groups = h_61_groups_0, pad = h_61_pad_0, pad_type = h_61_pad_type_0, strides = h_61_strides_0, weight = op_918_weight_0_to_fp16_palettized, x = input_149_cast_fp16)[name = string("op_918_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = x_73_cast_fp16, y = var_918_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor x_77_cast_fp16 = mul(x = out_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11268096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11284544))))[name = string("main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11284672)))]; + tensor linear_11_cast_fp16 = linear(bias = main_blocks_1_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor t_7_axes_0 = const()[name = string("t_7_axes_0"), val = tensor([-1])]; + tensor t_7_cast_fp16 = expand_dims(axes = t_7_axes_0, x = linear_11_cast_fp16)[name = string("t_7_cast_fp16")]; + tensor var_928_cast_fp16 = add(x = x_77_cast_fp16, y = t_7_cast_fp16)[name = string("op_928_cast_fp16")]; + tensor x_79_cast_fp16 = mul(x = var_928_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor x_81_cast_fp16 = mul(x = x_79_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = x_81_cast_fp16, y = last_1_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor x_last_21_axes_0 = const()[name = string("x_last_21_axes_0"), val = tensor([-1])]; + bool x_last_21_keep_dims_0 = const()[name = string("x_last_21_keep_dims_0"), val = bool(true)]; + tensor x_last_21_cast_fp16 = reduce_sum(axes = x_last_21_axes_0, keep_dims = x_last_21_keep_dims_0, x = var_941_cast_fp16)[name = string("x_last_21_cast_fp16")]; + tensor var_944_cast_fp16 = mul(x = x_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor var_946_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_21_cast_fp16)[name = string("op_946_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = var_944_cast_fp16, y = var_946_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("replicate")]; + fp16 const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = fp16(0x0p+0)]; + tensor input_157_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string h_63_pad_type_0 = const()[name = string("h_63_pad_type_0"), val = string("valid")]; + int32 h_63_groups_0 = const()[name = string("h_63_groups_0"), val = int32(512)]; + tensor h_63_strides_0 = const()[name = string("h_63_strides_0"), val = tensor([1])]; + tensor h_63_pad_0 = const()[name = string("h_63_pad_0"), val = tensor([0, 0])]; + tensor h_63_dilations_0 = const()[name = string("h_63_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11285760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11287104))))[name = string("main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11287232)))]; + tensor h_63_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_63_dilations_0, groups = h_63_groups_0, pad = h_63_pad_0, pad_type = h_63_pad_type_0, strides = h_63_strides_0, weight = main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor x_83_cast_fp16 = mul(x = h_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor var_964_axes_0 = const()[name = string("op_964_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11288320)))]; + tensor main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11289408)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_83_cast_fp16)[name = string("transpose_80")]; + tensor var_964_cast_fp16 = layer_norm(axes = var_964_axes_0, beta = main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + string h_65_pad_type_0 = const()[name = string("h_65_pad_type_0"), val = string("valid")]; + tensor h_65_strides_0 = const()[name = string("h_65_strides_0"), val = tensor([1])]; + tensor h_65_pad_0 = const()[name = string("h_65_pad_0"), val = tensor([0, 0])]; + tensor h_65_dilations_0 = const()[name = string("h_65_dilations_0"), val = tensor([1])]; + int32 h_65_groups_0 = const()[name = string("h_65_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11290496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11814848))))[name = string("main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11814976)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = var_964_cast_fp16)[name = string("transpose_79")]; + tensor h_65_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_65_dilations_0, groups = h_65_groups_0, pad = h_65_pad_0, pad_type = h_65_pad_type_0, strides = h_65_strides_0, weight = main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = string("h_65_cast_fp16")]; + string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("EXACT")]; + tensor input_163_cast_fp16 = gelu(mode = input_163_mode_0, x = h_65_cast_fp16)[name = string("input_163_cast_fp16")]; + string h_67_pad_type_0 = const()[name = string("h_67_pad_type_0"), val = string("valid")]; + tensor h_67_strides_0 = const()[name = string("h_67_strides_0"), val = tensor([1])]; + tensor h_67_pad_0 = const()[name = string("h_67_pad_0"), val = tensor([0, 0])]; + tensor h_67_dilations_0 = const()[name = string("h_67_dilations_0"), val = tensor([1])]; + int32 h_67_groups_0 = const()[name = string("h_67_groups_0"), val = int32(1)]; + tensor op_981_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11819136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12343488))))[name = string("op_981_weight_0_to_fp16_palettized")]; + tensor var_981_bias_0_to_fp16 = const()[name = string("op_981_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12343616)))]; + tensor var_981_cast_fp16 = conv(bias = var_981_bias_0_to_fp16, dilations = h_67_dilations_0, groups = h_67_groups_0, pad = h_67_pad_0, pad_type = h_67_pad_type_0, strides = h_67_strides_0, weight = op_981_weight_0_to_fp16_palettized, x = input_163_cast_fp16)[name = string("op_981_cast_fp16")]; + tensor out_25_cast_fp16 = add(x = x_81_cast_fp16, y = var_981_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = out_25_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor x_m_5_cast_fp16 = mul(x = x_85_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_5_cast_fp16")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12344704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12475840))))[name = string("main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12475968)))]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_85_cast_fp16)[name = string("transpose_78")]; + tensor linear_12_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_999 = const()[name = string("op_999"), val = tensor([2, 128, 8, 64])]; + tensor var_1000_cast_fp16 = reshape(shape = var_999, x = linear_12_cast_fp16)[name = string("op_1000_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12477056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12542656))))[name = string("main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12542784)))]; + tensor linear_13_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_1007 = const()[name = string("op_1007"), val = tensor([2, 128, 8, 64])]; + tensor var_1008_cast_fp16 = reshape(shape = var_1007, x = linear_13_cast_fp16)[name = string("op_1008_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12543872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12609472))))[name = string("main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12609600)))]; + tensor linear_14_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_1015 = const()[name = string("op_1015"), val = tensor([2, 128, 8, 64])]; + tensor var_1016_cast_fp16 = reshape(shape = var_1015, x = linear_14_cast_fp16)[name = string("op_1016_cast_fp16")]; + tensor v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_5_begin_0 = const()[name = string("x_a_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_5_end_0 = const()[name = string("x_a_5_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_5_end_mask_0 = const()[name = string("x_a_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_1000_cast_fp16)[name = string("transpose_77")]; + tensor x_a_5_cast_fp16 = slice_by_index(begin = x_a_5_begin_0, end = x_a_5_end_0, end_mask = x_a_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_a_5_cast_fp16")]; + tensor x_b_5_begin_0 = const()[name = string("x_b_5_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_5_end_0 = const()[name = string("x_b_5_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_5_end_mask_0 = const()[name = string("x_b_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_5_cast_fp16 = slice_by_index(begin = x_b_5_begin_0, end = x_b_5_end_0, end_mask = x_b_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_b_5_cast_fp16")]; + tensor var_1044_cast_fp16 = mul(x = x_a_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1044_cast_fp16")]; + tensor var_1045_cast_fp16 = mul(x = x_b_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor rot_a_5_cast_fp16 = sub(x = var_1044_cast_fp16, y = var_1045_cast_fp16)[name = string("rot_a_5_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_a_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_cast_fp16 = mul(x = x_b_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1048_cast_fp16")]; + tensor rot_b_5_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("rot_b_5_cast_fp16")]; + bool q_5_interleave_0 = const()[name = string("q_5_interleave_0"), val = bool(false)]; + tensor q_5_cast_fp16 = concat(axis = var_681, interleave = q_5_interleave_0, values = (rot_a_5_cast_fp16, rot_b_5_cast_fp16))[name = string("q_5_cast_fp16")]; + tensor x_a_7_begin_0 = const()[name = string("x_a_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_7_end_0 = const()[name = string("x_a_7_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_7_end_mask_0 = const()[name = string("x_a_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_1008_cast_fp16)[name = string("transpose_76")]; + tensor x_a_7_cast_fp16 = slice_by_index(begin = x_a_7_begin_0, end = x_a_7_end_0, end_mask = x_a_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_a_7_cast_fp16")]; + tensor x_b_7_begin_0 = const()[name = string("x_b_7_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_7_end_0 = const()[name = string("x_b_7_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_7_end_mask_0 = const()[name = string("x_b_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_7_cast_fp16 = slice_by_index(begin = x_b_7_begin_0, end = x_b_7_end_0, end_mask = x_b_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_b_7_cast_fp16")]; + tensor var_1062_cast_fp16 = mul(x = x_a_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = x_b_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor rot_a_7_cast_fp16 = sub(x = var_1062_cast_fp16, y = var_1063_cast_fp16)[name = string("rot_a_7_cast_fp16")]; + tensor var_1065_cast_fp16 = mul(x = x_a_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1065_cast_fp16")]; + tensor var_1066_cast_fp16 = mul(x = x_b_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1066_cast_fp16")]; + tensor rot_b_7_cast_fp16 = add(x = var_1065_cast_fp16, y = var_1066_cast_fp16)[name = string("rot_b_7_cast_fp16")]; + bool k_5_interleave_0 = const()[name = string("k_5_interleave_0"), val = bool(false)]; + tensor k_5_cast_fp16 = concat(axis = var_681, interleave = k_5_interleave_0, values = (rot_a_7_cast_fp16, rot_b_7_cast_fp16))[name = string("k_5_cast_fp16")]; + bool var_1071_transpose_x_1 = const()[name = string("op_1071_transpose_x_1"), val = bool(false)]; + bool var_1071_transpose_y_1 = const()[name = string("op_1071_transpose_y_1"), val = bool(true)]; + tensor var_1071_cast_fp16 = matmul(transpose_x = var_1071_transpose_x_1, transpose_y = var_1071_transpose_y_1, x = q_5_cast_fp16, y = k_5_cast_fp16)[name = string("op_1071_cast_fp16")]; + fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_3_cast_fp16 = mul(x = var_1071_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")]; + tensor input_171_cast_fp16 = sub(x = _inversed_scores_3_cast_fp16, y = var_530_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_681, x = input_171_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool var_1080_transpose_x_0 = const()[name = string("op_1080_transpose_x_0"), val = bool(false)]; + bool var_1080_transpose_y_0 = const()[name = string("op_1080_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1016_cast_fp16)[name = string("transpose_75")]; + tensor var_1080_cast_fp16 = matmul(transpose_x = var_1080_transpose_x_0, transpose_y = var_1080_transpose_y_0, x = attn_7_cast_fp16, y = v_5_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_perm_0 = const()[name = string("op_1081_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1082 = const()[name = string("op_1082"), val = tensor([2, 128, 512])]; + tensor var_1081_cast_fp16 = transpose(perm = var_1081_perm_0, x = var_1080_cast_fp16)[name = string("transpose_74")]; + tensor input_173_cast_fp16 = reshape(shape = var_1082, x = var_1081_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12610688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12741824))))[name = string("main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12741952)))]; + tensor linear_15_cast_fp16 = linear(bias = main_blocks_1_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor out_27_perm_0 = const()[name = string("out_27_perm_0"), val = tensor([0, 2, 1])]; + tensor out_27_cast_fp16 = transpose(perm = out_27_perm_0, x = linear_15_cast_fp16)[name = string("transpose_73")]; + tensor var_1089_cast_fp16 = mul(x = out_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor x_91_cast_fp16 = add(x = x_m_5_cast_fp16, y = var_1089_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_175_perm_0 = const()[name = string("input_175_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1096_axes_0 = const()[name = string("op_1096_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12743040)))]; + tensor main_blocks_1_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12744128)))]; + tensor input_175_cast_fp16 = transpose(perm = input_175_perm_0, x = x_91_cast_fp16)[name = string("transpose_72")]; + tensor var_1096_cast_fp16 = layer_norm(axes = var_1096_axes_0, beta = main_blocks_1_text_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_text_norm_norm_weight_to_fp16, x = input_175_cast_fp16)[name = string("op_1096_cast_fp16")]; + tensor var_1097_perm_0 = const()[name = string("op_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1097_cast_fp16 = transpose(perm = var_1097_perm_0, x = var_1096_cast_fp16)[name = string("transpose_71")]; + tensor x_93_cast_fp16 = mul(x = var_1097_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor x_95_cast_fp16 = mul(x = x_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_1110_cast_fp16 = mul(x = x_95_cast_fp16, y = last_1_cast_fp16)[name = string("op_1110_cast_fp16")]; + tensor x_last_23_axes_0 = const()[name = string("x_last_23_axes_0"), val = tensor([-1])]; + bool x_last_23_keep_dims_0 = const()[name = string("x_last_23_keep_dims_0"), val = bool(true)]; + tensor x_last_23_cast_fp16 = reduce_sum(axes = x_last_23_axes_0, keep_dims = x_last_23_keep_dims_0, x = var_1110_cast_fp16)[name = string("x_last_23_cast_fp16")]; + tensor var_1113_cast_fp16 = mul(x = x_95_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1115_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_23_cast_fp16)[name = string("op_1115_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = var_1113_cast_fp16, y = var_1115_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("replicate")]; + fp16 const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = fp16(0x0p+0)]; + tensor input_181_cast_fp16 = pad(constant_val = const_39_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + string h_69_pad_type_0 = const()[name = string("h_69_pad_type_0"), val = string("valid")]; + int32 h_69_groups_0 = const()[name = string("h_69_groups_0"), val = int32(512)]; + tensor h_69_strides_0 = const()[name = string("h_69_strides_0"), val = tensor([1])]; + tensor h_69_pad_0 = const()[name = string("h_69_pad_0"), val = tensor([0, 0])]; + tensor h_69_dilations_0 = const()[name = string("h_69_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12745216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12746560))))[name = string("main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12746688)))]; + tensor h_69_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_69_dilations_0, groups = h_69_groups_0, pad = h_69_pad_0, pad_type = h_69_pad_type_0, strides = h_69_strides_0, weight = main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = string("h_69_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = h_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_183_perm_0 = const()[name = string("input_183_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1133_axes_0 = const()[name = string("op_1133_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12747776)))]; + tensor main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12748864)))]; + tensor input_183_cast_fp16 = transpose(perm = input_183_perm_0, x = x_97_cast_fp16)[name = string("transpose_70")]; + tensor var_1133_cast_fp16 = layer_norm(axes = var_1133_axes_0, beta = main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16, x = input_183_cast_fp16)[name = string("op_1133_cast_fp16")]; + tensor input_185_perm_0 = const()[name = string("input_185_perm_0"), val = tensor([0, 2, 1])]; + string h_71_pad_type_0 = const()[name = string("h_71_pad_type_0"), val = string("valid")]; + tensor h_71_strides_0 = const()[name = string("h_71_strides_0"), val = tensor([1])]; + tensor h_71_pad_0 = const()[name = string("h_71_pad_0"), val = tensor([0, 0])]; + tensor h_71_dilations_0 = const()[name = string("h_71_dilations_0"), val = tensor([1])]; + int32 h_71_groups_0 = const()[name = string("h_71_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12749952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13274304))))[name = string("main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13274432)))]; + tensor input_185_cast_fp16 = transpose(perm = input_185_perm_0, x = var_1133_cast_fp16)[name = string("transpose_69")]; + tensor h_71_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_71_dilations_0, groups = h_71_groups_0, pad = h_71_pad_0, pad_type = h_71_pad_type_0, strides = h_71_strides_0, weight = main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = string("h_71_cast_fp16")]; + string input_187_mode_0 = const()[name = string("input_187_mode_0"), val = string("EXACT")]; + tensor input_187_cast_fp16 = gelu(mode = input_187_mode_0, x = h_71_cast_fp16)[name = string("input_187_cast_fp16")]; + string h_73_pad_type_0 = const()[name = string("h_73_pad_type_0"), val = string("valid")]; + tensor h_73_strides_0 = const()[name = string("h_73_strides_0"), val = tensor([1])]; + tensor h_73_pad_0 = const()[name = string("h_73_pad_0"), val = tensor([0, 0])]; + tensor h_73_dilations_0 = const()[name = string("h_73_dilations_0"), val = tensor([1])]; + int32 h_73_groups_0 = const()[name = string("h_73_groups_0"), val = int32(1)]; + tensor op_1150_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13278592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13802944))))[name = string("op_1150_weight_0_to_fp16_palettized")]; + tensor var_1150_bias_0_to_fp16 = const()[name = string("op_1150_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13803072)))]; + tensor var_1150_cast_fp16 = conv(bias = var_1150_bias_0_to_fp16, dilations = h_73_dilations_0, groups = h_73_groups_0, pad = h_73_pad_0, pad_type = h_73_pad_type_0, strides = h_73_strides_0, weight = op_1150_weight_0_to_fp16_palettized, x = input_187_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor out_29_cast_fp16 = add(x = x_95_cast_fp16, y = var_1150_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor x_99_cast_fp16 = mul(x = out_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor x_m_7_cast_fp16 = mul(x = x_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_7_cast_fp16")]; + tensor input_189_perm_0 = const()[name = string("input_189_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13804160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13869760))))[name = string("main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13869888)))]; + tensor input_189_cast_fp16 = transpose(perm = input_189_perm_0, x = x_99_cast_fp16)[name = string("transpose_68")]; + tensor linear_16_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_1166 = const()[name = string("op_1166"), val = tensor([2, 128, 2, 128])]; + tensor var_1167_cast_fp16 = reshape(shape = var_1166, x = linear_16_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13870464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13903296))))[name = string("main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13903424)))]; + tensor linear_18_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_1180 = const()[name = string("op_1180"), val = tensor([2, 50, 2, 128])]; + tensor var_1181_cast_fp16 = reshape(shape = var_1180, x = linear_18_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1185_transpose_x_0 = const()[name = string("op_1185_transpose_x_0"), val = bool(false)]; + bool var_1185_transpose_y_0 = const()[name = string("op_1185_transpose_y_0"), val = bool(false)]; + tensor op_1184_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13904000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13916864))))[name = string("op_1184_to_fp16_palettized")]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1167_cast_fp16)[name = string("transpose_67")]; + tensor var_1185_cast_fp16 = matmul(transpose_x = var_1185_transpose_x_0, transpose_y = var_1185_transpose_y_0, x = q_7_cast_fp16, y = op_1184_to_fp16_palettized)[name = string("op_1185_cast_fp16")]; + fp16 _inversed_input_191_y_0_to_fp16 = const()[name = string("_inversed_input_191_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_191_cast_fp16 = mul(x = var_1185_cast_fp16, y = _inversed_input_191_y_0_to_fp16)[name = string("_inversed_input_191_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_681, x = _inversed_input_191_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor attn_11_cast_fp16 = mul(x = attn_9_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool var_1192_transpose_x_0 = const()[name = string("op_1192_transpose_x_0"), val = bool(false)]; + bool var_1192_transpose_y_0 = const()[name = string("op_1192_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1181_cast_fp16)[name = string("transpose_66")]; + tensor var_1192_cast_fp16 = matmul(transpose_x = var_1192_transpose_x_0, transpose_y = var_1192_transpose_y_0, x = attn_11_cast_fp16, y = v_7_cast_fp16)[name = string("op_1192_cast_fp16")]; + tensor var_1193_perm_0 = const()[name = string("op_1193_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1194 = const()[name = string("op_1194"), val = tensor([2, 128, 256])]; + tensor var_1193_cast_fp16 = transpose(perm = var_1193_perm_0, x = var_1192_cast_fp16)[name = string("transpose_65")]; + tensor input_193_cast_fp16 = reshape(shape = var_1194, x = var_1193_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13916992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13982592))))[name = string("main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13982720)))]; + tensor linear_19_cast_fp16 = linear(bias = main_blocks_1_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_193_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor out_31_perm_0 = const()[name = string("out_31_perm_0"), val = tensor([0, 2, 1])]; + tensor out_31_cast_fp16 = transpose(perm = out_31_perm_0, x = linear_19_cast_fp16)[name = string("transpose_64")]; + tensor var_1201_cast_fp16 = mul(x = out_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1201_cast_fp16")]; + tensor x_101_cast_fp16 = add(x = x_m_7_cast_fp16, y = var_1201_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_195_perm_0 = const()[name = string("input_195_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1208_axes_0 = const()[name = string("op_1208_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13983808)))]; + tensor main_blocks_1_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13984896)))]; + tensor input_195_cast_fp16 = transpose(perm = input_195_perm_0, x = x_101_cast_fp16)[name = string("transpose_63")]; + tensor var_1208_cast_fp16 = layer_norm(axes = var_1208_axes_0, beta = main_blocks_1_style_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_style_norm_norm_weight_to_fp16, x = input_195_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_perm_0 = const()[name = string("op_1209_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1209_cast_fp16 = transpose(perm = var_1209_perm_0, x = var_1208_cast_fp16)[name = string("transpose_62")]; + tensor x_103_cast_fp16 = mul(x = var_1209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_103_cast_fp16")]; + int32 var_1228 = const()[name = string("op_1228"), val = int32(-1)]; + tensor x_105_cast_fp16 = mul(x = x_103_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_1263_cast_fp16 = mul(x = x_105_cast_fp16, y = last_1_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor x_last_25_axes_0 = const()[name = string("x_last_25_axes_0"), val = tensor([-1])]; + bool x_last_25_keep_dims_0 = const()[name = string("x_last_25_keep_dims_0"), val = bool(true)]; + tensor x_last_25_cast_fp16 = reduce_sum(axes = x_last_25_axes_0, keep_dims = x_last_25_keep_dims_0, x = var_1263_cast_fp16)[name = string("x_last_25_cast_fp16")]; + tensor var_1266_cast_fp16 = mul(x = x_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1266_cast_fp16")]; + tensor var_1268_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_25_cast_fp16)[name = string("op_1268_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1266_cast_fp16, y = var_1268_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_201_mode_0 = const()[name = string("input_201_mode_0"), val = string("replicate")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_201_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")]; + string h_75_pad_type_0 = const()[name = string("h_75_pad_type_0"), val = string("valid")]; + int32 h_75_groups_0 = const()[name = string("h_75_groups_0"), val = int32(512)]; + tensor h_75_strides_0 = const()[name = string("h_75_strides_0"), val = tensor([1])]; + tensor h_75_pad_0 = const()[name = string("h_75_pad_0"), val = tensor([0, 0])]; + tensor h_75_dilations_0 = const()[name = string("h_75_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13985984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13987328))))[name = string("main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13987456)))]; + tensor h_75_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_75_dilations_0, groups = h_75_groups_0, pad = h_75_pad_0, pad_type = h_75_pad_type_0, strides = h_75_strides_0, weight = main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_201_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = h_75_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_203_perm_0 = const()[name = string("input_203_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1286_axes_0 = const()[name = string("op_1286_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13988544)))]; + tensor main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13989632)))]; + fp16 var_1233_to_fp16 = const()[name = string("op_1233_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_203_cast_fp16 = transpose(perm = input_203_perm_0, x = x_107_cast_fp16)[name = string("transpose_61")]; + tensor var_1286_cast_fp16 = layer_norm(axes = var_1286_axes_0, beta = main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16, x = input_203_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string h_77_pad_type_0 = const()[name = string("h_77_pad_type_0"), val = string("valid")]; + tensor h_77_strides_0 = const()[name = string("h_77_strides_0"), val = tensor([1])]; + tensor h_77_pad_0 = const()[name = string("h_77_pad_0"), val = tensor([0, 0])]; + tensor h_77_dilations_0 = const()[name = string("h_77_dilations_0"), val = tensor([1])]; + int32 h_77_groups_0 = const()[name = string("h_77_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13990720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14515072))))[name = string("main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14515200)))]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = var_1286_cast_fp16)[name = string("transpose_60")]; + tensor h_77_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_77_dilations_0, groups = h_77_groups_0, pad = h_77_pad_0, pad_type = h_77_pad_type_0, strides = h_77_strides_0, weight = main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_205_cast_fp16)[name = string("h_77_cast_fp16")]; + string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")]; + tensor input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = h_77_cast_fp16)[name = string("input_207_cast_fp16")]; + string h_79_pad_type_0 = const()[name = string("h_79_pad_type_0"), val = string("valid")]; + tensor h_79_strides_0 = const()[name = string("h_79_strides_0"), val = tensor([1])]; + tensor h_79_pad_0 = const()[name = string("h_79_pad_0"), val = tensor([0, 0])]; + tensor h_79_dilations_0 = const()[name = string("h_79_dilations_0"), val = tensor([1])]; + int32 h_79_groups_0 = const()[name = string("h_79_groups_0"), val = int32(1)]; + tensor op_1303_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14519360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15043712))))[name = string("op_1303_weight_0_to_fp16_palettized")]; + tensor var_1303_bias_0_to_fp16 = const()[name = string("op_1303_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15043840)))]; + tensor var_1303_cast_fp16 = conv(bias = var_1303_bias_0_to_fp16, dilations = h_79_dilations_0, groups = h_79_groups_0, pad = h_79_pad_0, pad_type = h_79_pad_type_0, strides = h_79_strides_0, weight = op_1303_weight_0_to_fp16_palettized, x = input_207_cast_fp16)[name = string("op_1303_cast_fp16")]; + tensor out_33_cast_fp16 = add(x = x_105_cast_fp16, y = var_1303_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor x_109_cast_fp16 = mul(x = out_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor x_111_cast_fp16 = mul(x = x_109_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_1317_cast_fp16 = mul(x = x_111_cast_fp16, y = last_1_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor x_last_27_axes_0 = const()[name = string("x_last_27_axes_0"), val = tensor([-1])]; + bool x_last_27_keep_dims_0 = const()[name = string("x_last_27_keep_dims_0"), val = bool(true)]; + tensor x_last_27_cast_fp16 = reduce_sum(axes = x_last_27_axes_0, keep_dims = x_last_27_keep_dims_0, x = var_1317_cast_fp16)[name = string("x_last_27_cast_fp16")]; + tensor var_1320_cast_fp16 = mul(x = x_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1322_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_27_cast_fp16)[name = string("op_1322_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1320_cast_fp16, y = var_1322_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("replicate")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor input_213_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = input_213_mode_0, pad = input_213_pad_0, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + string h_81_pad_type_0 = const()[name = string("h_81_pad_type_0"), val = string("valid")]; + tensor h_81_dilations_0 = const()[name = string("h_81_dilations_0"), val = tensor([2])]; + int32 h_81_groups_0 = const()[name = string("h_81_groups_0"), val = int32(512)]; + tensor h_81_strides_0 = const()[name = string("h_81_strides_0"), val = tensor([1])]; + tensor h_81_pad_0 = const()[name = string("h_81_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15044928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15046272))))[name = string("main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15046400)))]; + tensor h_81_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_81_dilations_0, groups = h_81_groups_0, pad = h_81_pad_0, pad_type = h_81_pad_type_0, strides = h_81_strides_0, weight = main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_213_cast_fp16)[name = string("h_81_cast_fp16")]; + tensor x_113_cast_fp16 = mul(x = h_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor input_215_perm_0 = const()[name = string("input_215_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1340_axes_0 = const()[name = string("op_1340_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15047488)))]; + tensor main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15048576)))]; + tensor input_215_cast_fp16 = transpose(perm = input_215_perm_0, x = x_113_cast_fp16)[name = string("transpose_59")]; + tensor var_1340_cast_fp16 = layer_norm(axes = var_1340_axes_0, beta = main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16, x = input_215_cast_fp16)[name = string("op_1340_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + string h_83_pad_type_0 = const()[name = string("h_83_pad_type_0"), val = string("valid")]; + tensor h_83_strides_0 = const()[name = string("h_83_strides_0"), val = tensor([1])]; + tensor h_83_pad_0 = const()[name = string("h_83_pad_0"), val = tensor([0, 0])]; + tensor h_83_dilations_0 = const()[name = string("h_83_dilations_0"), val = tensor([1])]; + int32 h_83_groups_0 = const()[name = string("h_83_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15049664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15574016))))[name = string("main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15574144)))]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = var_1340_cast_fp16)[name = string("transpose_58")]; + tensor h_83_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_83_dilations_0, groups = h_83_groups_0, pad = h_83_pad_0, pad_type = h_83_pad_type_0, strides = h_83_strides_0, weight = main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = string("h_83_cast_fp16")]; + string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")]; + tensor input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = h_83_cast_fp16)[name = string("input_219_cast_fp16")]; + string h_85_pad_type_0 = const()[name = string("h_85_pad_type_0"), val = string("valid")]; + tensor h_85_strides_0 = const()[name = string("h_85_strides_0"), val = tensor([1])]; + tensor h_85_pad_0 = const()[name = string("h_85_pad_0"), val = tensor([0, 0])]; + tensor h_85_dilations_0 = const()[name = string("h_85_dilations_0"), val = tensor([1])]; + int32 h_85_groups_0 = const()[name = string("h_85_groups_0"), val = int32(1)]; + tensor op_1357_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15578304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16102656))))[name = string("op_1357_weight_0_to_fp16_palettized")]; + tensor var_1357_bias_0_to_fp16 = const()[name = string("op_1357_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16102784)))]; + tensor var_1357_cast_fp16 = conv(bias = var_1357_bias_0_to_fp16, dilations = h_85_dilations_0, groups = h_85_groups_0, pad = h_85_pad_0, pad_type = h_85_pad_type_0, strides = h_85_strides_0, weight = op_1357_weight_0_to_fp16_palettized, x = input_219_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor out_35_cast_fp16 = add(x = x_111_cast_fp16, y = var_1357_cast_fp16)[name = string("out_35_cast_fp16")]; + tensor x_115_cast_fp16 = mul(x = out_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_cast_fp16 = mul(x = x_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1371_cast_fp16 = mul(x = x_117_cast_fp16, y = last_1_cast_fp16)[name = string("op_1371_cast_fp16")]; + tensor x_last_29_axes_0 = const()[name = string("x_last_29_axes_0"), val = tensor([-1])]; + bool x_last_29_keep_dims_0 = const()[name = string("x_last_29_keep_dims_0"), val = bool(true)]; + tensor x_last_29_cast_fp16 = reduce_sum(axes = x_last_29_axes_0, keep_dims = x_last_29_keep_dims_0, x = var_1371_cast_fp16)[name = string("x_last_29_cast_fp16")]; + tensor var_1374_cast_fp16 = mul(x = x_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_29_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1376_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_225_mode_0 = const()[name = string("input_225_mode_0"), val = string("replicate")]; + fp16 const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = fp16(0x0p+0)]; + tensor input_225_cast_fp16 = pad(constant_val = const_48_to_fp16, mode = input_225_mode_0, pad = input_225_pad_0, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")]; + string h_87_pad_type_0 = const()[name = string("h_87_pad_type_0"), val = string("valid")]; + tensor h_87_dilations_0 = const()[name = string("h_87_dilations_0"), val = tensor([4])]; + int32 h_87_groups_0 = const()[name = string("h_87_groups_0"), val = int32(512)]; + tensor h_87_strides_0 = const()[name = string("h_87_strides_0"), val = tensor([1])]; + tensor h_87_pad_0 = const()[name = string("h_87_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16103872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16105216))))[name = string("main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16105344)))]; + tensor h_87_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_87_dilations_0, groups = h_87_groups_0, pad = h_87_pad_0, pad_type = h_87_pad_type_0, strides = h_87_strides_0, weight = main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor x_119_cast_fp16 = mul(x = h_87_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_227_perm_0 = const()[name = string("input_227_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1394_axes_0 = const()[name = string("op_1394_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16106432)))]; + tensor main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16107520)))]; + tensor input_227_cast_fp16 = transpose(perm = input_227_perm_0, x = x_119_cast_fp16)[name = string("transpose_57")]; + tensor var_1394_cast_fp16 = layer_norm(axes = var_1394_axes_0, beta = main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16, x = input_227_cast_fp16)[name = string("op_1394_cast_fp16")]; + tensor input_229_perm_0 = const()[name = string("input_229_perm_0"), val = tensor([0, 2, 1])]; + string h_89_pad_type_0 = const()[name = string("h_89_pad_type_0"), val = string("valid")]; + tensor h_89_strides_0 = const()[name = string("h_89_strides_0"), val = tensor([1])]; + tensor h_89_pad_0 = const()[name = string("h_89_pad_0"), val = tensor([0, 0])]; + tensor h_89_dilations_0 = const()[name = string("h_89_dilations_0"), val = tensor([1])]; + int32 h_89_groups_0 = const()[name = string("h_89_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16108608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16632960))))[name = string("main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16633088)))]; + tensor input_229_cast_fp16 = transpose(perm = input_229_perm_0, x = var_1394_cast_fp16)[name = string("transpose_56")]; + tensor h_89_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_89_dilations_0, groups = h_89_groups_0, pad = h_89_pad_0, pad_type = h_89_pad_type_0, strides = h_89_strides_0, weight = main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = string("h_89_cast_fp16")]; + string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")]; + tensor input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = h_89_cast_fp16)[name = string("input_231_cast_fp16")]; + string h_91_pad_type_0 = const()[name = string("h_91_pad_type_0"), val = string("valid")]; + tensor h_91_strides_0 = const()[name = string("h_91_strides_0"), val = tensor([1])]; + tensor h_91_pad_0 = const()[name = string("h_91_pad_0"), val = tensor([0, 0])]; + tensor h_91_dilations_0 = const()[name = string("h_91_dilations_0"), val = tensor([1])]; + int32 h_91_groups_0 = const()[name = string("h_91_groups_0"), val = int32(1)]; + tensor op_1411_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16637248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17161600))))[name = string("op_1411_weight_0_to_fp16_palettized")]; + tensor var_1411_bias_0_to_fp16 = const()[name = string("op_1411_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17161728)))]; + tensor var_1411_cast_fp16 = conv(bias = var_1411_bias_0_to_fp16, dilations = h_91_dilations_0, groups = h_91_groups_0, pad = h_91_pad_0, pad_type = h_91_pad_type_0, strides = h_91_strides_0, weight = op_1411_weight_0_to_fp16_palettized, x = input_231_cast_fp16)[name = string("op_1411_cast_fp16")]; + tensor out_37_cast_fp16 = add(x = x_117_cast_fp16, y = var_1411_cast_fp16)[name = string("out_37_cast_fp16")]; + tensor x_121_cast_fp16 = mul(x = out_37_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_121_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1425_cast_fp16 = mul(x = x_123_cast_fp16, y = last_1_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor x_last_31_axes_0 = const()[name = string("x_last_31_axes_0"), val = tensor([-1])]; + bool x_last_31_keep_dims_0 = const()[name = string("x_last_31_keep_dims_0"), val = bool(true)]; + tensor x_last_31_cast_fp16 = reduce_sum(axes = x_last_31_axes_0, keep_dims = x_last_31_keep_dims_0, x = var_1425_cast_fp16)[name = string("x_last_31_cast_fp16")]; + tensor var_1428_cast_fp16 = mul(x = x_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_31_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = var_1428_cast_fp16, y = var_1430_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("replicate")]; + fp16 const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = fp16(0x0p+0)]; + tensor input_237_cast_fp16 = pad(constant_val = const_50_to_fp16, mode = input_237_mode_0, pad = input_237_pad_0, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + string h_93_pad_type_0 = const()[name = string("h_93_pad_type_0"), val = string("valid")]; + tensor h_93_dilations_0 = const()[name = string("h_93_dilations_0"), val = tensor([8])]; + int32 h_93_groups_0 = const()[name = string("h_93_groups_0"), val = int32(512)]; + tensor h_93_strides_0 = const()[name = string("h_93_strides_0"), val = tensor([1])]; + tensor h_93_pad_0 = const()[name = string("h_93_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17162816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17164160))))[name = string("main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17164288)))]; + tensor h_93_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_93_dilations_0, groups = h_93_groups_0, pad = h_93_pad_0, pad_type = h_93_pad_type_0, strides = h_93_strides_0, weight = main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = string("h_93_cast_fp16")]; + tensor x_125_cast_fp16 = mul(x = h_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_239_perm_0 = const()[name = string("input_239_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1448_axes_0 = const()[name = string("op_1448_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17165376)))]; + tensor main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17166464)))]; + tensor input_239_cast_fp16 = transpose(perm = input_239_perm_0, x = x_125_cast_fp16)[name = string("transpose_55")]; + tensor var_1448_cast_fp16 = layer_norm(axes = var_1448_axes_0, beta = main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16, x = input_239_cast_fp16)[name = string("op_1448_cast_fp16")]; + tensor input_241_perm_0 = const()[name = string("input_241_perm_0"), val = tensor([0, 2, 1])]; + string h_95_pad_type_0 = const()[name = string("h_95_pad_type_0"), val = string("valid")]; + tensor h_95_strides_0 = const()[name = string("h_95_strides_0"), val = tensor([1])]; + tensor h_95_pad_0 = const()[name = string("h_95_pad_0"), val = tensor([0, 0])]; + tensor h_95_dilations_0 = const()[name = string("h_95_dilations_0"), val = tensor([1])]; + int32 h_95_groups_0 = const()[name = string("h_95_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17167552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17691904))))[name = string("main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17692032)))]; + tensor input_241_cast_fp16 = transpose(perm = input_241_perm_0, x = var_1448_cast_fp16)[name = string("transpose_54")]; + tensor h_95_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_95_dilations_0, groups = h_95_groups_0, pad = h_95_pad_0, pad_type = h_95_pad_type_0, strides = h_95_strides_0, weight = main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_241_cast_fp16)[name = string("h_95_cast_fp16")]; + string input_243_mode_0 = const()[name = string("input_243_mode_0"), val = string("EXACT")]; + tensor input_243_cast_fp16 = gelu(mode = input_243_mode_0, x = h_95_cast_fp16)[name = string("input_243_cast_fp16")]; + string h_97_pad_type_0 = const()[name = string("h_97_pad_type_0"), val = string("valid")]; + tensor h_97_strides_0 = const()[name = string("h_97_strides_0"), val = tensor([1])]; + tensor h_97_pad_0 = const()[name = string("h_97_pad_0"), val = tensor([0, 0])]; + tensor h_97_dilations_0 = const()[name = string("h_97_dilations_0"), val = tensor([1])]; + int32 h_97_groups_0 = const()[name = string("h_97_groups_0"), val = int32(1)]; + tensor op_1465_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17696192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18220544))))[name = string("op_1465_weight_0_to_fp16_palettized")]; + tensor var_1465_bias_0_to_fp16 = const()[name = string("op_1465_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18220672)))]; + tensor var_1465_cast_fp16 = conv(bias = var_1465_bias_0_to_fp16, dilations = h_97_dilations_0, groups = h_97_groups_0, pad = h_97_pad_0, pad_type = h_97_pad_type_0, strides = h_97_strides_0, weight = op_1465_weight_0_to_fp16_palettized, x = input_243_cast_fp16)[name = string("op_1465_cast_fp16")]; + tensor out_39_cast_fp16 = add(x = x_123_cast_fp16, y = var_1465_cast_fp16)[name = string("out_39_cast_fp16")]; + tensor x_127_cast_fp16 = mul(x = out_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18221760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18238208))))[name = string("main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18238336)))]; + tensor linear_20_cast_fp16 = linear(bias = main_blocks_2_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor t_9_axes_0 = const()[name = string("t_9_axes_0"), val = tensor([-1])]; + tensor t_9_cast_fp16 = expand_dims(axes = t_9_axes_0, x = linear_20_cast_fp16)[name = string("t_9_cast_fp16")]; + tensor var_1475_cast_fp16 = add(x = x_127_cast_fp16, y = t_9_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = var_1475_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor x_131_cast_fp16 = mul(x = x_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor var_1488_cast_fp16 = mul(x = x_131_cast_fp16, y = last_1_cast_fp16)[name = string("op_1488_cast_fp16")]; + tensor x_last_33_axes_0 = const()[name = string("x_last_33_axes_0"), val = tensor([-1])]; + bool x_last_33_keep_dims_0 = const()[name = string("x_last_33_keep_dims_0"), val = bool(true)]; + tensor x_last_33_cast_fp16 = reduce_sum(axes = x_last_33_axes_0, keep_dims = x_last_33_keep_dims_0, x = var_1488_cast_fp16)[name = string("x_last_33_cast_fp16")]; + tensor var_1491_cast_fp16 = mul(x = x_131_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1491_cast_fp16")]; + tensor var_1493_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_33_cast_fp16)[name = string("op_1493_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = var_1491_cast_fp16, y = var_1493_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_251_mode_0 = const()[name = string("input_251_mode_0"), val = string("replicate")]; + fp16 const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = fp16(0x0p+0)]; + tensor input_251_cast_fp16 = pad(constant_val = const_52_to_fp16, mode = input_251_mode_0, pad = input_251_pad_0, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + string h_99_pad_type_0 = const()[name = string("h_99_pad_type_0"), val = string("valid")]; + int32 h_99_groups_0 = const()[name = string("h_99_groups_0"), val = int32(512)]; + tensor h_99_strides_0 = const()[name = string("h_99_strides_0"), val = tensor([1])]; + tensor h_99_pad_0 = const()[name = string("h_99_pad_0"), val = tensor([0, 0])]; + tensor h_99_dilations_0 = const()[name = string("h_99_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18239424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18240768))))[name = string("main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18240896)))]; + tensor h_99_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_99_dilations_0, groups = h_99_groups_0, pad = h_99_pad_0, pad_type = h_99_pad_type_0, strides = h_99_strides_0, weight = main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor x_133_cast_fp16 = mul(x = h_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_253_perm_0 = const()[name = string("input_253_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1511_axes_0 = const()[name = string("op_1511_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18241984)))]; + tensor main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18243072)))]; + tensor input_253_cast_fp16 = transpose(perm = input_253_perm_0, x = x_133_cast_fp16)[name = string("transpose_53")]; + tensor var_1511_cast_fp16 = layer_norm(axes = var_1511_axes_0, beta = main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16, x = input_253_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor input_255_perm_0 = const()[name = string("input_255_perm_0"), val = tensor([0, 2, 1])]; + string h_101_pad_type_0 = const()[name = string("h_101_pad_type_0"), val = string("valid")]; + tensor h_101_strides_0 = const()[name = string("h_101_strides_0"), val = tensor([1])]; + tensor h_101_pad_0 = const()[name = string("h_101_pad_0"), val = tensor([0, 0])]; + tensor h_101_dilations_0 = const()[name = string("h_101_dilations_0"), val = tensor([1])]; + int32 h_101_groups_0 = const()[name = string("h_101_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18244160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18768512))))[name = string("main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18768640)))]; + tensor input_255_cast_fp16 = transpose(perm = input_255_perm_0, x = var_1511_cast_fp16)[name = string("transpose_52")]; + tensor h_101_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_101_dilations_0, groups = h_101_groups_0, pad = h_101_pad_0, pad_type = h_101_pad_type_0, strides = h_101_strides_0, weight = main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_255_cast_fp16)[name = string("h_101_cast_fp16")]; + string input_257_mode_0 = const()[name = string("input_257_mode_0"), val = string("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = h_101_cast_fp16)[name = string("input_257_cast_fp16")]; + string h_103_pad_type_0 = const()[name = string("h_103_pad_type_0"), val = string("valid")]; + tensor h_103_strides_0 = const()[name = string("h_103_strides_0"), val = tensor([1])]; + tensor h_103_pad_0 = const()[name = string("h_103_pad_0"), val = tensor([0, 0])]; + tensor h_103_dilations_0 = const()[name = string("h_103_dilations_0"), val = tensor([1])]; + int32 h_103_groups_0 = const()[name = string("h_103_groups_0"), val = int32(1)]; + tensor op_1528_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18772800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19297152))))[name = string("op_1528_weight_0_to_fp16_palettized")]; + tensor var_1528_bias_0_to_fp16 = const()[name = string("op_1528_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19297280)))]; + tensor var_1528_cast_fp16 = conv(bias = var_1528_bias_0_to_fp16, dilations = h_103_dilations_0, groups = h_103_groups_0, pad = h_103_pad_0, pad_type = h_103_pad_type_0, strides = h_103_strides_0, weight = op_1528_weight_0_to_fp16_palettized, x = input_257_cast_fp16)[name = string("op_1528_cast_fp16")]; + tensor out_41_cast_fp16 = add(x = x_131_cast_fp16, y = var_1528_cast_fp16)[name = string("out_41_cast_fp16")]; + tensor x_135_cast_fp16 = mul(x = out_41_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor x_m_9_cast_fp16 = mul(x = x_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_9_cast_fp16")]; + tensor input_259_perm_0 = const()[name = string("input_259_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19298368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19429504))))[name = string("main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19429632)))]; + tensor input_259_cast_fp16 = transpose(perm = input_259_perm_0, x = x_135_cast_fp16)[name = string("transpose_51")]; + tensor linear_21_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_259_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_1546 = const()[name = string("op_1546"), val = tensor([2, 128, 8, 64])]; + tensor var_1547_cast_fp16 = reshape(shape = var_1546, x = linear_21_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor x_137_perm_0 = const()[name = string("x_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19430720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19496320))))[name = string("main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19496448)))]; + tensor linear_22_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_1554 = const()[name = string("op_1554"), val = tensor([2, 128, 8, 64])]; + tensor var_1555_cast_fp16 = reshape(shape = var_1554, x = linear_22_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_139_perm_0 = const()[name = string("x_139_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19497536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19563136))))[name = string("main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19563264)))]; + tensor linear_23_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_1562 = const()[name = string("op_1562"), val = tensor([2, 128, 8, 64])]; + tensor var_1563_cast_fp16 = reshape(shape = var_1562, x = linear_23_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_9_begin_0 = const()[name = string("x_a_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_9_end_0 = const()[name = string("x_a_9_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_9_end_mask_0 = const()[name = string("x_a_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_137_cast_fp16 = transpose(perm = x_137_perm_0, x = var_1547_cast_fp16)[name = string("transpose_50")]; + tensor x_a_9_cast_fp16 = slice_by_index(begin = x_a_9_begin_0, end = x_a_9_end_0, end_mask = x_a_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_a_9_cast_fp16")]; + tensor x_b_9_begin_0 = const()[name = string("x_b_9_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_9_end_0 = const()[name = string("x_b_9_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_9_end_mask_0 = const()[name = string("x_b_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_9_cast_fp16 = slice_by_index(begin = x_b_9_begin_0, end = x_b_9_end_0, end_mask = x_b_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_b_9_cast_fp16")]; + tensor var_1591_cast_fp16 = mul(x = x_a_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1591_cast_fp16")]; + tensor var_1592_cast_fp16 = mul(x = x_b_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor rot_a_9_cast_fp16 = sub(x = var_1591_cast_fp16, y = var_1592_cast_fp16)[name = string("rot_a_9_cast_fp16")]; + tensor var_1594_cast_fp16 = mul(x = x_a_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595_cast_fp16 = mul(x = x_b_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1595_cast_fp16")]; + tensor rot_b_9_cast_fp16 = add(x = var_1594_cast_fp16, y = var_1595_cast_fp16)[name = string("rot_b_9_cast_fp16")]; + bool q_9_interleave_0 = const()[name = string("q_9_interleave_0"), val = bool(false)]; + tensor q_9_cast_fp16 = concat(axis = var_1228, interleave = q_9_interleave_0, values = (rot_a_9_cast_fp16, rot_b_9_cast_fp16))[name = string("q_9_cast_fp16")]; + tensor x_a_11_begin_0 = const()[name = string("x_a_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_11_end_0 = const()[name = string("x_a_11_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_11_end_mask_0 = const()[name = string("x_a_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_139_cast_fp16 = transpose(perm = x_139_perm_0, x = var_1555_cast_fp16)[name = string("transpose_49")]; + tensor x_a_11_cast_fp16 = slice_by_index(begin = x_a_11_begin_0, end = x_a_11_end_0, end_mask = x_a_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_a_11_cast_fp16")]; + tensor x_b_11_begin_0 = const()[name = string("x_b_11_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_11_end_0 = const()[name = string("x_b_11_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_11_end_mask_0 = const()[name = string("x_b_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_11_cast_fp16 = slice_by_index(begin = x_b_11_begin_0, end = x_b_11_end_0, end_mask = x_b_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_b_11_cast_fp16")]; + tensor var_1609_cast_fp16 = mul(x = x_a_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor var_1610_cast_fp16 = mul(x = x_b_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor rot_a_11_cast_fp16 = sub(x = var_1609_cast_fp16, y = var_1610_cast_fp16)[name = string("rot_a_11_cast_fp16")]; + tensor var_1612_cast_fp16 = mul(x = x_a_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1612_cast_fp16")]; + tensor var_1613_cast_fp16 = mul(x = x_b_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1613_cast_fp16")]; + tensor rot_b_11_cast_fp16 = add(x = var_1612_cast_fp16, y = var_1613_cast_fp16)[name = string("rot_b_11_cast_fp16")]; + bool k_9_interleave_0 = const()[name = string("k_9_interleave_0"), val = bool(false)]; + tensor k_9_cast_fp16 = concat(axis = var_1228, interleave = k_9_interleave_0, values = (rot_a_11_cast_fp16, rot_b_11_cast_fp16))[name = string("k_9_cast_fp16")]; + bool var_1618_transpose_x_1 = const()[name = string("op_1618_transpose_x_1"), val = bool(false)]; + bool var_1618_transpose_y_1 = const()[name = string("op_1618_transpose_y_1"), val = bool(true)]; + tensor var_1618_cast_fp16 = matmul(transpose_x = var_1618_transpose_x_1, transpose_y = var_1618_transpose_y_1, x = q_9_cast_fp16, y = k_9_cast_fp16)[name = string("op_1618_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_1618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor input_265_cast_fp16 = sub(x = _inversed_scores_5_cast_fp16, y = var_530_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1228, x = input_265_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool var_1627_transpose_x_0 = const()[name = string("op_1627_transpose_x_0"), val = bool(false)]; + bool var_1627_transpose_y_0 = const()[name = string("op_1627_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1563_cast_fp16)[name = string("transpose_48")]; + tensor var_1627_cast_fp16 = matmul(transpose_x = var_1627_transpose_x_0, transpose_y = var_1627_transpose_y_0, x = attn_13_cast_fp16, y = v_9_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor var_1628_perm_0 = const()[name = string("op_1628_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1629 = const()[name = string("op_1629"), val = tensor([2, 128, 512])]; + tensor var_1628_cast_fp16 = transpose(perm = var_1628_perm_0, x = var_1627_cast_fp16)[name = string("transpose_47")]; + tensor input_267_cast_fp16 = reshape(shape = var_1629, x = var_1628_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19564352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19695488))))[name = string("main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19695616)))]; + tensor linear_24_cast_fp16 = linear(bias = main_blocks_2_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor out_43_perm_0 = const()[name = string("out_43_perm_0"), val = tensor([0, 2, 1])]; + tensor out_43_cast_fp16 = transpose(perm = out_43_perm_0, x = linear_24_cast_fp16)[name = string("transpose_46")]; + tensor var_1636_cast_fp16 = mul(x = out_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1636_cast_fp16")]; + tensor x_141_cast_fp16 = add(x = x_m_9_cast_fp16, y = var_1636_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1643_axes_0 = const()[name = string("op_1643_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19696704)))]; + tensor main_blocks_2_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19697792)))]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_141_cast_fp16)[name = string("transpose_45")]; + tensor var_1643_cast_fp16 = layer_norm(axes = var_1643_axes_0, beta = main_blocks_2_text_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_text_norm_norm_weight_to_fp16, x = input_269_cast_fp16)[name = string("op_1643_cast_fp16")]; + tensor var_1644_perm_0 = const()[name = string("op_1644_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1644_cast_fp16 = transpose(perm = var_1644_perm_0, x = var_1643_cast_fp16)[name = string("transpose_44")]; + tensor x_143_cast_fp16 = mul(x = var_1644_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor x_145_cast_fp16 = mul(x = x_143_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1657_cast_fp16 = mul(x = x_145_cast_fp16, y = last_1_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor x_last_35_axes_0 = const()[name = string("x_last_35_axes_0"), val = tensor([-1])]; + bool x_last_35_keep_dims_0 = const()[name = string("x_last_35_keep_dims_0"), val = bool(true)]; + tensor x_last_35_cast_fp16 = reduce_sum(axes = x_last_35_axes_0, keep_dims = x_last_35_keep_dims_0, x = var_1657_cast_fp16)[name = string("x_last_35_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = x_145_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1662_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_35_cast_fp16)[name = string("op_1662_cast_fp16")]; + tensor input_273_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1662_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("replicate")]; + fp16 const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = fp16(0x0p+0)]; + tensor input_275_cast_fp16 = pad(constant_val = const_59_to_fp16, mode = input_275_mode_0, pad = input_275_pad_0, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")]; + string h_105_pad_type_0 = const()[name = string("h_105_pad_type_0"), val = string("valid")]; + int32 h_105_groups_0 = const()[name = string("h_105_groups_0"), val = int32(512)]; + tensor h_105_strides_0 = const()[name = string("h_105_strides_0"), val = tensor([1])]; + tensor h_105_pad_0 = const()[name = string("h_105_pad_0"), val = tensor([0, 0])]; + tensor h_105_dilations_0 = const()[name = string("h_105_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19698880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19700224))))[name = string("main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19700352)))]; + tensor h_105_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_105_dilations_0, groups = h_105_groups_0, pad = h_105_pad_0, pad_type = h_105_pad_type_0, strides = h_105_strides_0, weight = main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_275_cast_fp16)[name = string("h_105_cast_fp16")]; + tensor x_147_cast_fp16 = mul(x = h_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_277_perm_0 = const()[name = string("input_277_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1680_axes_0 = const()[name = string("op_1680_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19701440)))]; + tensor main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19702528)))]; + tensor input_277_cast_fp16 = transpose(perm = input_277_perm_0, x = x_147_cast_fp16)[name = string("transpose_43")]; + tensor var_1680_cast_fp16 = layer_norm(axes = var_1680_axes_0, beta = main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16, x = input_277_cast_fp16)[name = string("op_1680_cast_fp16")]; + tensor input_279_perm_0 = const()[name = string("input_279_perm_0"), val = tensor([0, 2, 1])]; + string h_107_pad_type_0 = const()[name = string("h_107_pad_type_0"), val = string("valid")]; + tensor h_107_strides_0 = const()[name = string("h_107_strides_0"), val = tensor([1])]; + tensor h_107_pad_0 = const()[name = string("h_107_pad_0"), val = tensor([0, 0])]; + tensor h_107_dilations_0 = const()[name = string("h_107_dilations_0"), val = tensor([1])]; + int32 h_107_groups_0 = const()[name = string("h_107_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19703616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20227968))))[name = string("main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20228096)))]; + tensor input_279_cast_fp16 = transpose(perm = input_279_perm_0, x = var_1680_cast_fp16)[name = string("transpose_42")]; + tensor h_107_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_107_dilations_0, groups = h_107_groups_0, pad = h_107_pad_0, pad_type = h_107_pad_type_0, strides = h_107_strides_0, weight = main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_279_cast_fp16)[name = string("h_107_cast_fp16")]; + string input_281_mode_0 = const()[name = string("input_281_mode_0"), val = string("EXACT")]; + tensor input_281_cast_fp16 = gelu(mode = input_281_mode_0, x = h_107_cast_fp16)[name = string("input_281_cast_fp16")]; + string h_109_pad_type_0 = const()[name = string("h_109_pad_type_0"), val = string("valid")]; + tensor h_109_strides_0 = const()[name = string("h_109_strides_0"), val = tensor([1])]; + tensor h_109_pad_0 = const()[name = string("h_109_pad_0"), val = tensor([0, 0])]; + tensor h_109_dilations_0 = const()[name = string("h_109_dilations_0"), val = tensor([1])]; + int32 h_109_groups_0 = const()[name = string("h_109_groups_0"), val = int32(1)]; + tensor op_1697_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20232256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20756608))))[name = string("op_1697_weight_0_to_fp16_palettized")]; + tensor var_1697_bias_0_to_fp16 = const()[name = string("op_1697_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20756736)))]; + tensor var_1697_cast_fp16 = conv(bias = var_1697_bias_0_to_fp16, dilations = h_109_dilations_0, groups = h_109_groups_0, pad = h_109_pad_0, pad_type = h_109_pad_type_0, strides = h_109_strides_0, weight = op_1697_weight_0_to_fp16_palettized, x = input_281_cast_fp16)[name = string("op_1697_cast_fp16")]; + tensor out_45_cast_fp16 = add(x = x_145_cast_fp16, y = var_1697_cast_fp16)[name = string("out_45_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = out_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor x_m_11_cast_fp16 = mul(x = x_149_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_11_cast_fp16")]; + tensor input_283_perm_0 = const()[name = string("input_283_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20757824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20823424))))[name = string("main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20823552)))]; + tensor input_283_cast_fp16 = transpose(perm = input_283_perm_0, x = x_149_cast_fp16)[name = string("transpose_41")]; + tensor linear_25_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_283_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_1713 = const()[name = string("op_1713"), val = tensor([2, 128, 2, 128])]; + tensor var_1714_cast_fp16 = reshape(shape = var_1713, x = linear_25_cast_fp16)[name = string("op_1714_cast_fp16")]; + tensor q_11_perm_0 = const()[name = string("q_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20824128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20856960))))[name = string("main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20857088)))]; + tensor linear_27_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1727 = const()[name = string("op_1727"), val = tensor([2, 50, 2, 128])]; + tensor var_1728_cast_fp16 = reshape(shape = var_1727, x = linear_27_cast_fp16)[name = string("op_1728_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1732_transpose_x_0 = const()[name = string("op_1732_transpose_x_0"), val = bool(false)]; + bool var_1732_transpose_y_0 = const()[name = string("op_1732_transpose_y_0"), val = bool(false)]; + tensor op_1731_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20857664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20870528))))[name = string("op_1731_to_fp16_palettized")]; + tensor q_11_cast_fp16 = transpose(perm = q_11_perm_0, x = var_1714_cast_fp16)[name = string("transpose_40")]; + tensor var_1732_cast_fp16 = matmul(transpose_x = var_1732_transpose_x_0, transpose_y = var_1732_transpose_y_0, x = q_11_cast_fp16, y = op_1731_to_fp16_palettized)[name = string("op_1732_cast_fp16")]; + fp16 _inversed_input_285_y_0_to_fp16 = const()[name = string("_inversed_input_285_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_285_cast_fp16 = mul(x = var_1732_cast_fp16, y = _inversed_input_285_y_0_to_fp16)[name = string("_inversed_input_285_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1228, x = _inversed_input_285_cast_fp16)[name = string("attn_15_cast_fp16")]; + tensor attn_17_cast_fp16 = mul(x = attn_15_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool var_1739_transpose_x_0 = const()[name = string("op_1739_transpose_x_0"), val = bool(false)]; + bool var_1739_transpose_y_0 = const()[name = string("op_1739_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_1728_cast_fp16)[name = string("transpose_39")]; + tensor var_1739_cast_fp16 = matmul(transpose_x = var_1739_transpose_x_0, transpose_y = var_1739_transpose_y_0, x = attn_17_cast_fp16, y = v_11_cast_fp16)[name = string("op_1739_cast_fp16")]; + tensor var_1740_perm_0 = const()[name = string("op_1740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1741 = const()[name = string("op_1741"), val = tensor([2, 128, 256])]; + tensor var_1740_cast_fp16 = transpose(perm = var_1740_perm_0, x = var_1739_cast_fp16)[name = string("transpose_38")]; + tensor input_287_cast_fp16 = reshape(shape = var_1741, x = var_1740_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20870656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20936256))))[name = string("main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20936384)))]; + tensor linear_28_cast_fp16 = linear(bias = main_blocks_2_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor out_47_perm_0 = const()[name = string("out_47_perm_0"), val = tensor([0, 2, 1])]; + tensor out_47_cast_fp16 = transpose(perm = out_47_perm_0, x = linear_28_cast_fp16)[name = string("transpose_37")]; + tensor var_1748_cast_fp16 = mul(x = out_47_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1748_cast_fp16")]; + tensor x_151_cast_fp16 = add(x = x_m_11_cast_fp16, y = var_1748_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_289_perm_0 = const()[name = string("input_289_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1755_axes_0 = const()[name = string("op_1755_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20937472)))]; + tensor main_blocks_2_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20938560)))]; + tensor input_289_cast_fp16 = transpose(perm = input_289_perm_0, x = x_151_cast_fp16)[name = string("transpose_36")]; + tensor var_1755_cast_fp16 = layer_norm(axes = var_1755_axes_0, beta = main_blocks_2_style_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_style_norm_norm_weight_to_fp16, x = input_289_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor var_1756_perm_0 = const()[name = string("op_1756_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1756_cast_fp16 = transpose(perm = var_1756_perm_0, x = var_1755_cast_fp16)[name = string("transpose_35")]; + tensor x_153_cast_fp16 = mul(x = var_1756_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_153_cast_fp16")]; + int32 var_1775 = const()[name = string("op_1775"), val = int32(-1)]; + tensor x_155_cast_fp16 = mul(x = x_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor var_1810_cast_fp16 = mul(x = x_155_cast_fp16, y = last_1_cast_fp16)[name = string("op_1810_cast_fp16")]; + tensor x_last_37_axes_0 = const()[name = string("x_last_37_axes_0"), val = tensor([-1])]; + bool x_last_37_keep_dims_0 = const()[name = string("x_last_37_keep_dims_0"), val = bool(true)]; + tensor x_last_37_cast_fp16 = reduce_sum(axes = x_last_37_axes_0, keep_dims = x_last_37_keep_dims_0, x = var_1810_cast_fp16)[name = string("x_last_37_cast_fp16")]; + tensor var_1813_cast_fp16 = mul(x = x_155_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1815_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_37_cast_fp16)[name = string("op_1815_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = var_1813_cast_fp16, y = var_1815_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_295_mode_0 = const()[name = string("input_295_mode_0"), val = string("replicate")]; + fp16 const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = fp16(0x0p+0)]; + tensor input_295_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")]; + string h_111_pad_type_0 = const()[name = string("h_111_pad_type_0"), val = string("valid")]; + int32 h_111_groups_0 = const()[name = string("h_111_groups_0"), val = int32(512)]; + tensor h_111_strides_0 = const()[name = string("h_111_strides_0"), val = tensor([1])]; + tensor h_111_pad_0 = const()[name = string("h_111_pad_0"), val = tensor([0, 0])]; + tensor h_111_dilations_0 = const()[name = string("h_111_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20939648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20940992))))[name = string("main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20941120)))]; + tensor h_111_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_111_dilations_0, groups = h_111_groups_0, pad = h_111_pad_0, pad_type = h_111_pad_type_0, strides = h_111_strides_0, weight = main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_295_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor x_157_cast_fp16 = mul(x = h_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor input_297_perm_0 = const()[name = string("input_297_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1833_axes_0 = const()[name = string("op_1833_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20942208)))]; + tensor main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20943296)))]; + fp16 var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_297_cast_fp16 = transpose(perm = input_297_perm_0, x = x_157_cast_fp16)[name = string("transpose_34")]; + tensor var_1833_cast_fp16 = layer_norm(axes = var_1833_axes_0, beta = main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16, x = input_297_cast_fp16)[name = string("op_1833_cast_fp16")]; + tensor input_299_perm_0 = const()[name = string("input_299_perm_0"), val = tensor([0, 2, 1])]; + string h_113_pad_type_0 = const()[name = string("h_113_pad_type_0"), val = string("valid")]; + tensor h_113_strides_0 = const()[name = string("h_113_strides_0"), val = tensor([1])]; + tensor h_113_pad_0 = const()[name = string("h_113_pad_0"), val = tensor([0, 0])]; + tensor h_113_dilations_0 = const()[name = string("h_113_dilations_0"), val = tensor([1])]; + int32 h_113_groups_0 = const()[name = string("h_113_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20944384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21468736))))[name = string("main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21468864)))]; + tensor input_299_cast_fp16 = transpose(perm = input_299_perm_0, x = var_1833_cast_fp16)[name = string("transpose_33")]; + tensor h_113_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_113_dilations_0, groups = h_113_groups_0, pad = h_113_pad_0, pad_type = h_113_pad_type_0, strides = h_113_strides_0, weight = main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("h_113_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = h_113_cast_fp16)[name = string("input_301_cast_fp16")]; + string h_115_pad_type_0 = const()[name = string("h_115_pad_type_0"), val = string("valid")]; + tensor h_115_strides_0 = const()[name = string("h_115_strides_0"), val = tensor([1])]; + tensor h_115_pad_0 = const()[name = string("h_115_pad_0"), val = tensor([0, 0])]; + tensor h_115_dilations_0 = const()[name = string("h_115_dilations_0"), val = tensor([1])]; + int32 h_115_groups_0 = const()[name = string("h_115_groups_0"), val = int32(1)]; + tensor op_1850_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21473024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21997376))))[name = string("op_1850_weight_0_to_fp16_palettized")]; + tensor var_1850_bias_0_to_fp16 = const()[name = string("op_1850_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21997504)))]; + tensor var_1850_cast_fp16 = conv(bias = var_1850_bias_0_to_fp16, dilations = h_115_dilations_0, groups = h_115_groups_0, pad = h_115_pad_0, pad_type = h_115_pad_type_0, strides = h_115_strides_0, weight = op_1850_weight_0_to_fp16_palettized, x = input_301_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor out_49_cast_fp16 = add(x = x_155_cast_fp16, y = var_1850_cast_fp16)[name = string("out_49_cast_fp16")]; + tensor x_159_cast_fp16 = mul(x = out_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor x_161_cast_fp16 = mul(x = x_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor var_1864_cast_fp16 = mul(x = x_161_cast_fp16, y = last_1_cast_fp16)[name = string("op_1864_cast_fp16")]; + tensor x_last_39_axes_0 = const()[name = string("x_last_39_axes_0"), val = tensor([-1])]; + bool x_last_39_keep_dims_0 = const()[name = string("x_last_39_keep_dims_0"), val = bool(true)]; + tensor x_last_39_cast_fp16 = reduce_sum(axes = x_last_39_axes_0, keep_dims = x_last_39_keep_dims_0, x = var_1864_cast_fp16)[name = string("x_last_39_cast_fp16")]; + tensor var_1867_cast_fp16 = mul(x = x_161_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_39_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor input_305_cast_fp16 = add(x = var_1867_cast_fp16, y = var_1869_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("replicate")]; + fp16 const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_66_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string h_117_pad_type_0 = const()[name = string("h_117_pad_type_0"), val = string("valid")]; + tensor h_117_dilations_0 = const()[name = string("h_117_dilations_0"), val = tensor([2])]; + int32 h_117_groups_0 = const()[name = string("h_117_groups_0"), val = int32(512)]; + tensor h_117_strides_0 = const()[name = string("h_117_strides_0"), val = tensor([1])]; + tensor h_117_pad_0 = const()[name = string("h_117_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21998592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21999936))))[name = string("main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22000064)))]; + tensor h_117_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_117_dilations_0, groups = h_117_groups_0, pad = h_117_pad_0, pad_type = h_117_pad_type_0, strides = h_117_strides_0, weight = main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_307_cast_fp16)[name = string("h_117_cast_fp16")]; + tensor x_163_cast_fp16 = mul(x = h_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1887_axes_0 = const()[name = string("op_1887_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22001152)))]; + tensor main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22002240)))]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_163_cast_fp16)[name = string("transpose_32")]; + tensor var_1887_cast_fp16 = layer_norm(axes = var_1887_axes_0, beta = main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16, x = input_309_cast_fp16)[name = string("op_1887_cast_fp16")]; + tensor input_311_perm_0 = const()[name = string("input_311_perm_0"), val = tensor([0, 2, 1])]; + string h_119_pad_type_0 = const()[name = string("h_119_pad_type_0"), val = string("valid")]; + tensor h_119_strides_0 = const()[name = string("h_119_strides_0"), val = tensor([1])]; + tensor h_119_pad_0 = const()[name = string("h_119_pad_0"), val = tensor([0, 0])]; + tensor h_119_dilations_0 = const()[name = string("h_119_dilations_0"), val = tensor([1])]; + int32 h_119_groups_0 = const()[name = string("h_119_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22003328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22527680))))[name = string("main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22527808)))]; + tensor input_311_cast_fp16 = transpose(perm = input_311_perm_0, x = var_1887_cast_fp16)[name = string("transpose_31")]; + tensor h_119_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_119_dilations_0, groups = h_119_groups_0, pad = h_119_pad_0, pad_type = h_119_pad_type_0, strides = h_119_strides_0, weight = main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_311_cast_fp16)[name = string("h_119_cast_fp16")]; + string input_313_mode_0 = const()[name = string("input_313_mode_0"), val = string("EXACT")]; + tensor input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = h_119_cast_fp16)[name = string("input_313_cast_fp16")]; + string h_121_pad_type_0 = const()[name = string("h_121_pad_type_0"), val = string("valid")]; + tensor h_121_strides_0 = const()[name = string("h_121_strides_0"), val = tensor([1])]; + tensor h_121_pad_0 = const()[name = string("h_121_pad_0"), val = tensor([0, 0])]; + tensor h_121_dilations_0 = const()[name = string("h_121_dilations_0"), val = tensor([1])]; + int32 h_121_groups_0 = const()[name = string("h_121_groups_0"), val = int32(1)]; + tensor op_1904_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22531968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23056320))))[name = string("op_1904_weight_0_to_fp16_palettized")]; + tensor var_1904_bias_0_to_fp16 = const()[name = string("op_1904_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23056448)))]; + tensor var_1904_cast_fp16 = conv(bias = var_1904_bias_0_to_fp16, dilations = h_121_dilations_0, groups = h_121_groups_0, pad = h_121_pad_0, pad_type = h_121_pad_type_0, strides = h_121_strides_0, weight = op_1904_weight_0_to_fp16_palettized, x = input_313_cast_fp16)[name = string("op_1904_cast_fp16")]; + tensor out_51_cast_fp16 = add(x = x_161_cast_fp16, y = var_1904_cast_fp16)[name = string("out_51_cast_fp16")]; + tensor x_165_cast_fp16 = mul(x = out_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor x_167_cast_fp16 = mul(x = x_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1918_cast_fp16 = mul(x = x_167_cast_fp16, y = last_1_cast_fp16)[name = string("op_1918_cast_fp16")]; + tensor x_last_41_axes_0 = const()[name = string("x_last_41_axes_0"), val = tensor([-1])]; + bool x_last_41_keep_dims_0 = const()[name = string("x_last_41_keep_dims_0"), val = bool(true)]; + tensor x_last_41_cast_fp16 = reduce_sum(axes = x_last_41_axes_0, keep_dims = x_last_41_keep_dims_0, x = var_1918_cast_fp16)[name = string("x_last_41_cast_fp16")]; + tensor var_1921_cast_fp16 = mul(x = x_167_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1921_cast_fp16")]; + tensor var_1923_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_41_cast_fp16)[name = string("op_1923_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = var_1921_cast_fp16, y = var_1923_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("replicate")]; + fp16 const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = fp16(0x0p+0)]; + tensor input_319_cast_fp16 = pad(constant_val = const_68_to_fp16, mode = input_319_mode_0, pad = input_319_pad_0, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string h_123_pad_type_0 = const()[name = string("h_123_pad_type_0"), val = string("valid")]; + tensor h_123_dilations_0 = const()[name = string("h_123_dilations_0"), val = tensor([4])]; + int32 h_123_groups_0 = const()[name = string("h_123_groups_0"), val = int32(512)]; + tensor h_123_strides_0 = const()[name = string("h_123_strides_0"), val = tensor([1])]; + tensor h_123_pad_0 = const()[name = string("h_123_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23057536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23058880))))[name = string("main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23059008)))]; + tensor h_123_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_123_dilations_0, groups = h_123_groups_0, pad = h_123_pad_0, pad_type = h_123_pad_type_0, strides = h_123_strides_0, weight = main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_319_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor x_169_cast_fp16 = mul(x = h_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1941_axes_0 = const()[name = string("op_1941_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23060096)))]; + tensor main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23061184)))]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_169_cast_fp16)[name = string("transpose_30")]; + tensor var_1941_cast_fp16 = layer_norm(axes = var_1941_axes_0, beta = main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16, x = input_321_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor([0, 2, 1])]; + string h_125_pad_type_0 = const()[name = string("h_125_pad_type_0"), val = string("valid")]; + tensor h_125_strides_0 = const()[name = string("h_125_strides_0"), val = tensor([1])]; + tensor h_125_pad_0 = const()[name = string("h_125_pad_0"), val = tensor([0, 0])]; + tensor h_125_dilations_0 = const()[name = string("h_125_dilations_0"), val = tensor([1])]; + int32 h_125_groups_0 = const()[name = string("h_125_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23062272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23586624))))[name = string("main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23586752)))]; + tensor input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = var_1941_cast_fp16)[name = string("transpose_29")]; + tensor h_125_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_125_dilations_0, groups = h_125_groups_0, pad = h_125_pad_0, pad_type = h_125_pad_type_0, strides = h_125_strides_0, weight = main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = string("h_125_cast_fp16")]; + string input_325_mode_0 = const()[name = string("input_325_mode_0"), val = string("EXACT")]; + tensor input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = h_125_cast_fp16)[name = string("input_325_cast_fp16")]; + string h_127_pad_type_0 = const()[name = string("h_127_pad_type_0"), val = string("valid")]; + tensor h_127_strides_0 = const()[name = string("h_127_strides_0"), val = tensor([1])]; + tensor h_127_pad_0 = const()[name = string("h_127_pad_0"), val = tensor([0, 0])]; + tensor h_127_dilations_0 = const()[name = string("h_127_dilations_0"), val = tensor([1])]; + int32 h_127_groups_0 = const()[name = string("h_127_groups_0"), val = int32(1)]; + tensor op_1958_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23590912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24115264))))[name = string("op_1958_weight_0_to_fp16_palettized")]; + tensor var_1958_bias_0_to_fp16 = const()[name = string("op_1958_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24115392)))]; + tensor var_1958_cast_fp16 = conv(bias = var_1958_bias_0_to_fp16, dilations = h_127_dilations_0, groups = h_127_groups_0, pad = h_127_pad_0, pad_type = h_127_pad_type_0, strides = h_127_strides_0, weight = op_1958_weight_0_to_fp16_palettized, x = input_325_cast_fp16)[name = string("op_1958_cast_fp16")]; + tensor out_53_cast_fp16 = add(x = x_167_cast_fp16, y = var_1958_cast_fp16)[name = string("out_53_cast_fp16")]; + tensor x_171_cast_fp16 = mul(x = out_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_171_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor var_1972_cast_fp16 = mul(x = x_173_cast_fp16, y = last_1_cast_fp16)[name = string("op_1972_cast_fp16")]; + tensor x_last_43_axes_0 = const()[name = string("x_last_43_axes_0"), val = tensor([-1])]; + bool x_last_43_keep_dims_0 = const()[name = string("x_last_43_keep_dims_0"), val = bool(true)]; + tensor x_last_43_cast_fp16 = reduce_sum(axes = x_last_43_axes_0, keep_dims = x_last_43_keep_dims_0, x = var_1972_cast_fp16)[name = string("x_last_43_cast_fp16")]; + tensor var_1975_cast_fp16 = mul(x = x_173_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1975_cast_fp16")]; + tensor var_1977_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_43_cast_fp16)[name = string("op_1977_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1977_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("replicate")]; + fp16 const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = fp16(0x0p+0)]; + tensor input_331_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = input_331_mode_0, pad = input_331_pad_0, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + string h_129_pad_type_0 = const()[name = string("h_129_pad_type_0"), val = string("valid")]; + tensor h_129_dilations_0 = const()[name = string("h_129_dilations_0"), val = tensor([8])]; + int32 h_129_groups_0 = const()[name = string("h_129_groups_0"), val = int32(512)]; + tensor h_129_strides_0 = const()[name = string("h_129_strides_0"), val = tensor([1])]; + tensor h_129_pad_0 = const()[name = string("h_129_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24116480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24117824))))[name = string("main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24117952)))]; + tensor h_129_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_129_dilations_0, groups = h_129_groups_0, pad = h_129_pad_0, pad_type = h_129_pad_type_0, strides = h_129_strides_0, weight = main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = string("h_129_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = h_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_333_perm_0 = const()[name = string("input_333_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1995_axes_0 = const()[name = string("op_1995_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24119040)))]; + tensor main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24120128)))]; + tensor input_333_cast_fp16 = transpose(perm = input_333_perm_0, x = x_175_cast_fp16)[name = string("transpose_28")]; + tensor var_1995_cast_fp16 = layer_norm(axes = var_1995_axes_0, beta = main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16, x = input_333_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor input_335_perm_0 = const()[name = string("input_335_perm_0"), val = tensor([0, 2, 1])]; + string h_131_pad_type_0 = const()[name = string("h_131_pad_type_0"), val = string("valid")]; + tensor h_131_strides_0 = const()[name = string("h_131_strides_0"), val = tensor([1])]; + tensor h_131_pad_0 = const()[name = string("h_131_pad_0"), val = tensor([0, 0])]; + tensor h_131_dilations_0 = const()[name = string("h_131_dilations_0"), val = tensor([1])]; + int32 h_131_groups_0 = const()[name = string("h_131_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24121216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24645568))))[name = string("main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24645696)))]; + tensor input_335_cast_fp16 = transpose(perm = input_335_perm_0, x = var_1995_cast_fp16)[name = string("transpose_27")]; + tensor h_131_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_131_dilations_0, groups = h_131_groups_0, pad = h_131_pad_0, pad_type = h_131_pad_type_0, strides = h_131_strides_0, weight = main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_335_cast_fp16)[name = string("h_131_cast_fp16")]; + string input_337_mode_0 = const()[name = string("input_337_mode_0"), val = string("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = h_131_cast_fp16)[name = string("input_337_cast_fp16")]; + string h_133_pad_type_0 = const()[name = string("h_133_pad_type_0"), val = string("valid")]; + tensor h_133_strides_0 = const()[name = string("h_133_strides_0"), val = tensor([1])]; + tensor h_133_pad_0 = const()[name = string("h_133_pad_0"), val = tensor([0, 0])]; + tensor h_133_dilations_0 = const()[name = string("h_133_dilations_0"), val = tensor([1])]; + int32 h_133_groups_0 = const()[name = string("h_133_groups_0"), val = int32(1)]; + tensor op_2012_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24649856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25174208))))[name = string("op_2012_weight_0_to_fp16_palettized")]; + tensor var_2012_bias_0_to_fp16 = const()[name = string("op_2012_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25174336)))]; + tensor var_2012_cast_fp16 = conv(bias = var_2012_bias_0_to_fp16, dilations = h_133_dilations_0, groups = h_133_groups_0, pad = h_133_pad_0, pad_type = h_133_pad_type_0, strides = h_133_strides_0, weight = op_2012_weight_0_to_fp16_palettized, x = input_337_cast_fp16)[name = string("op_2012_cast_fp16")]; + tensor out_55_cast_fp16 = add(x = x_173_cast_fp16, y = var_2012_cast_fp16)[name = string("out_55_cast_fp16")]; + tensor x_177_cast_fp16 = mul(x = out_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25175424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25191872))))[name = string("main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25192000)))]; + tensor linear_29_cast_fp16 = linear(bias = main_blocks_3_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor t_axes_0 = const()[name = string("t_axes_0"), val = tensor([-1])]; + tensor t_cast_fp16 = expand_dims(axes = t_axes_0, x = linear_29_cast_fp16)[name = string("t_cast_fp16")]; + tensor var_2022_cast_fp16 = add(x = x_177_cast_fp16, y = t_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor x_179_cast_fp16 = mul(x = var_2022_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor x_181_cast_fp16 = mul(x = x_179_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor var_2035_cast_fp16 = mul(x = x_181_cast_fp16, y = last_1_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor x_last_45_axes_0 = const()[name = string("x_last_45_axes_0"), val = tensor([-1])]; + bool x_last_45_keep_dims_0 = const()[name = string("x_last_45_keep_dims_0"), val = bool(true)]; + tensor x_last_45_cast_fp16 = reduce_sum(axes = x_last_45_axes_0, keep_dims = x_last_45_keep_dims_0, x = var_2035_cast_fp16)[name = string("x_last_45_cast_fp16")]; + tensor var_2038_cast_fp16 = mul(x = x_181_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2038_cast_fp16")]; + tensor var_2040_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_45_cast_fp16)[name = string("op_2040_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = var_2038_cast_fp16, y = var_2040_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_345_mode_0 = const()[name = string("input_345_mode_0"), val = string("replicate")]; + fp16 const_72_to_fp16 = const()[name = string("const_72_to_fp16"), val = fp16(0x0p+0)]; + tensor input_345_cast_fp16 = pad(constant_val = const_72_to_fp16, mode = input_345_mode_0, pad = input_345_pad_0, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + string h_135_pad_type_0 = const()[name = string("h_135_pad_type_0"), val = string("valid")]; + int32 h_135_groups_0 = const()[name = string("h_135_groups_0"), val = int32(512)]; + tensor h_135_strides_0 = const()[name = string("h_135_strides_0"), val = tensor([1])]; + tensor h_135_pad_0 = const()[name = string("h_135_pad_0"), val = tensor([0, 0])]; + tensor h_135_dilations_0 = const()[name = string("h_135_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25193088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25194432))))[name = string("main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25194560)))]; + tensor h_135_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_135_dilations_0, groups = h_135_groups_0, pad = h_135_pad_0, pad_type = h_135_pad_type_0, strides = h_135_strides_0, weight = main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_345_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor x_183_cast_fp16 = mul(x = h_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_183_cast_fp16")]; + tensor input_347_perm_0 = const()[name = string("input_347_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2058_axes_0 = const()[name = string("op_2058_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25195648)))]; + tensor main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25196736)))]; + tensor input_347_cast_fp16 = transpose(perm = input_347_perm_0, x = x_183_cast_fp16)[name = string("transpose_26")]; + tensor var_2058_cast_fp16 = layer_norm(axes = var_2058_axes_0, beta = main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16, x = input_347_cast_fp16)[name = string("op_2058_cast_fp16")]; + tensor input_349_perm_0 = const()[name = string("input_349_perm_0"), val = tensor([0, 2, 1])]; + string h_137_pad_type_0 = const()[name = string("h_137_pad_type_0"), val = string("valid")]; + tensor h_137_strides_0 = const()[name = string("h_137_strides_0"), val = tensor([1])]; + tensor h_137_pad_0 = const()[name = string("h_137_pad_0"), val = tensor([0, 0])]; + tensor h_137_dilations_0 = const()[name = string("h_137_dilations_0"), val = tensor([1])]; + int32 h_137_groups_0 = const()[name = string("h_137_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25197824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25722176))))[name = string("main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25722304)))]; + tensor input_349_cast_fp16 = transpose(perm = input_349_perm_0, x = var_2058_cast_fp16)[name = string("transpose_25")]; + tensor h_137_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_137_dilations_0, groups = h_137_groups_0, pad = h_137_pad_0, pad_type = h_137_pad_type_0, strides = h_137_strides_0, weight = main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_349_cast_fp16)[name = string("h_137_cast_fp16")]; + string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")]; + tensor input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = h_137_cast_fp16)[name = string("input_351_cast_fp16")]; + string h_139_pad_type_0 = const()[name = string("h_139_pad_type_0"), val = string("valid")]; + tensor h_139_strides_0 = const()[name = string("h_139_strides_0"), val = tensor([1])]; + tensor h_139_pad_0 = const()[name = string("h_139_pad_0"), val = tensor([0, 0])]; + tensor h_139_dilations_0 = const()[name = string("h_139_dilations_0"), val = tensor([1])]; + int32 h_139_groups_0 = const()[name = string("h_139_groups_0"), val = int32(1)]; + tensor op_2075_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25726464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26250816))))[name = string("op_2075_weight_0_to_fp16_palettized")]; + tensor var_2075_bias_0_to_fp16 = const()[name = string("op_2075_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26250944)))]; + tensor var_2075_cast_fp16 = conv(bias = var_2075_bias_0_to_fp16, dilations = h_139_dilations_0, groups = h_139_groups_0, pad = h_139_pad_0, pad_type = h_139_pad_type_0, strides = h_139_strides_0, weight = op_2075_weight_0_to_fp16_palettized, x = input_351_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor out_57_cast_fp16 = add(x = x_181_cast_fp16, y = var_2075_cast_fp16)[name = string("out_57_cast_fp16")]; + tensor x_185_cast_fp16 = mul(x = out_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor x_m_13_cast_fp16 = mul(x = x_185_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_13_cast_fp16")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26252032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26383168))))[name = string("main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26383296)))]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_185_cast_fp16)[name = string("transpose_24")]; + tensor linear_30_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_2093 = const()[name = string("op_2093"), val = tensor([2, 128, 8, 64])]; + tensor var_2094_cast_fp16 = reshape(shape = var_2093, x = linear_30_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor x_187_perm_0 = const()[name = string("x_187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26384384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26449984))))[name = string("main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26450112)))]; + tensor linear_31_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_2101 = const()[name = string("op_2101"), val = tensor([2, 128, 8, 64])]; + tensor var_2102_cast_fp16 = reshape(shape = var_2101, x = linear_31_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor x_189_perm_0 = const()[name = string("x_189_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26451200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26516800))))[name = string("main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26516928)))]; + tensor linear_32_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_2109 = const()[name = string("op_2109"), val = tensor([2, 128, 8, 64])]; + tensor var_2110_cast_fp16 = reshape(shape = var_2109, x = linear_32_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_13_begin_0 = const()[name = string("x_a_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_13_end_0 = const()[name = string("x_a_13_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_13_end_mask_0 = const()[name = string("x_a_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_187_cast_fp16 = transpose(perm = x_187_perm_0, x = var_2094_cast_fp16)[name = string("transpose_23")]; + tensor x_a_13_cast_fp16 = slice_by_index(begin = x_a_13_begin_0, end = x_a_13_end_0, end_mask = x_a_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_a_13_cast_fp16")]; + tensor x_b_13_begin_0 = const()[name = string("x_b_13_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_13_end_0 = const()[name = string("x_b_13_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_13_end_mask_0 = const()[name = string("x_b_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_13_cast_fp16 = slice_by_index(begin = x_b_13_begin_0, end = x_b_13_end_0, end_mask = x_b_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_b_13_cast_fp16")]; + tensor var_2138_cast_fp16 = mul(x = x_a_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2138_cast_fp16")]; + tensor var_2139_cast_fp16 = mul(x = x_b_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2139_cast_fp16")]; + tensor rot_a_13_cast_fp16 = sub(x = var_2138_cast_fp16, y = var_2139_cast_fp16)[name = string("rot_a_13_cast_fp16")]; + tensor var_2141_cast_fp16 = mul(x = x_a_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2141_cast_fp16")]; + tensor var_2142_cast_fp16 = mul(x = x_b_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2142_cast_fp16")]; + tensor rot_b_13_cast_fp16 = add(x = var_2141_cast_fp16, y = var_2142_cast_fp16)[name = string("rot_b_13_cast_fp16")]; + bool q_13_interleave_0 = const()[name = string("q_13_interleave_0"), val = bool(false)]; + tensor q_13_cast_fp16 = concat(axis = var_1775, interleave = q_13_interleave_0, values = (rot_a_13_cast_fp16, rot_b_13_cast_fp16))[name = string("q_13_cast_fp16")]; + tensor x_a_begin_0 = const()[name = string("x_a_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_end_0 = const()[name = string("x_a_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_end_mask_0 = const()[name = string("x_a_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_189_cast_fp16 = transpose(perm = x_189_perm_0, x = var_2102_cast_fp16)[name = string("transpose_22")]; + tensor x_a_cast_fp16 = slice_by_index(begin = x_a_begin_0, end = x_a_end_0, end_mask = x_a_end_mask_0, x = x_189_cast_fp16)[name = string("x_a_cast_fp16")]; + tensor x_b_begin_0 = const()[name = string("x_b_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_end_0 = const()[name = string("x_b_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_end_mask_0 = const()[name = string("x_b_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_cast_fp16 = slice_by_index(begin = x_b_begin_0, end = x_b_end_0, end_mask = x_b_end_mask_0, x = x_189_cast_fp16)[name = string("x_b_cast_fp16")]; + tensor var_2156_cast_fp16 = mul(x = x_a_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2156_cast_fp16")]; + tensor var_2157_cast_fp16 = mul(x = x_b_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2157_cast_fp16")]; + tensor rot_a_cast_fp16 = sub(x = var_2156_cast_fp16, y = var_2157_cast_fp16)[name = string("rot_a_cast_fp16")]; + tensor var_2159_cast_fp16 = mul(x = x_a_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2159_cast_fp16")]; + tensor var_2160_cast_fp16 = mul(x = x_b_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2160_cast_fp16")]; + tensor rot_b_cast_fp16 = add(x = var_2159_cast_fp16, y = var_2160_cast_fp16)[name = string("rot_b_cast_fp16")]; + bool k_13_interleave_0 = const()[name = string("k_13_interleave_0"), val = bool(false)]; + tensor k_13_cast_fp16 = concat(axis = var_1775, interleave = k_13_interleave_0, values = (rot_a_cast_fp16, rot_b_cast_fp16))[name = string("k_13_cast_fp16")]; + bool var_2165_transpose_x_1 = const()[name = string("op_2165_transpose_x_1"), val = bool(false)]; + bool var_2165_transpose_y_1 = const()[name = string("op_2165_transpose_y_1"), val = bool(true)]; + tensor var_2165_cast_fp16 = matmul(transpose_x = var_2165_transpose_x_1, transpose_y = var_2165_transpose_y_1, x = q_13_cast_fp16, y = k_13_cast_fp16)[name = string("op_2165_cast_fp16")]; + fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_cast_fp16 = mul(x = var_2165_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")]; + tensor input_359_cast_fp16 = sub(x = _inversed_scores_cast_fp16, y = var_530_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_1775, x = input_359_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool var_2174_transpose_x_0 = const()[name = string("op_2174_transpose_x_0"), val = bool(false)]; + bool var_2174_transpose_y_0 = const()[name = string("op_2174_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2110_cast_fp16)[name = string("transpose_21")]; + tensor var_2174_cast_fp16 = matmul(transpose_x = var_2174_transpose_x_0, transpose_y = var_2174_transpose_y_0, x = attn_19_cast_fp16, y = v_13_cast_fp16)[name = string("op_2174_cast_fp16")]; + tensor var_2175_perm_0 = const()[name = string("op_2175_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2176 = const()[name = string("op_2176"), val = tensor([2, 128, 512])]; + tensor var_2175_cast_fp16 = transpose(perm = var_2175_perm_0, x = var_2174_cast_fp16)[name = string("transpose_20")]; + tensor input_361_cast_fp16 = reshape(shape = var_2176, x = var_2175_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26518016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26649152))))[name = string("main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26649280)))]; + tensor linear_33_cast_fp16 = linear(bias = main_blocks_3_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor out_59_perm_0 = const()[name = string("out_59_perm_0"), val = tensor([0, 2, 1])]; + tensor out_59_cast_fp16 = transpose(perm = out_59_perm_0, x = linear_33_cast_fp16)[name = string("transpose_19")]; + tensor var_2183_cast_fp16 = mul(x = out_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_m_13_cast_fp16, y = var_2183_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor input_363_perm_0 = const()[name = string("input_363_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2190_axes_0 = const()[name = string("op_2190_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26650368)))]; + tensor main_blocks_3_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26651456)))]; + tensor input_363_cast_fp16 = transpose(perm = input_363_perm_0, x = x_191_cast_fp16)[name = string("transpose_18")]; + tensor var_2190_cast_fp16 = layer_norm(axes = var_2190_axes_0, beta = main_blocks_3_text_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_text_norm_norm_weight_to_fp16, x = input_363_cast_fp16)[name = string("op_2190_cast_fp16")]; + tensor var_2191_perm_0 = const()[name = string("op_2191_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2191_perm_0, x = var_2190_cast_fp16)[name = string("transpose_17")]; + tensor x_193_cast_fp16 = mul(x = var_2191_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_193_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2204_cast_fp16 = mul(x = x_195_cast_fp16, y = last_1_cast_fp16)[name = string("op_2204_cast_fp16")]; + tensor x_last_47_axes_0 = const()[name = string("x_last_47_axes_0"), val = tensor([-1])]; + bool x_last_47_keep_dims_0 = const()[name = string("x_last_47_keep_dims_0"), val = bool(true)]; + tensor x_last_47_cast_fp16 = reduce_sum(axes = x_last_47_axes_0, keep_dims = x_last_47_keep_dims_0, x = var_2204_cast_fp16)[name = string("x_last_47_cast_fp16")]; + tensor var_2207_cast_fp16 = mul(x = x_195_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2207_cast_fp16")]; + tensor var_2209_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_47_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor input_367_cast_fp16 = add(x = var_2207_cast_fp16, y = var_2209_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_369_mode_0 = const()[name = string("input_369_mode_0"), val = string("replicate")]; + fp16 const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = fp16(0x0p+0)]; + tensor input_369_cast_fp16 = pad(constant_val = const_79_to_fp16, mode = input_369_mode_0, pad = input_369_pad_0, x = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + string h_141_pad_type_0 = const()[name = string("h_141_pad_type_0"), val = string("valid")]; + int32 h_141_groups_0 = const()[name = string("h_141_groups_0"), val = int32(512)]; + tensor h_141_strides_0 = const()[name = string("h_141_strides_0"), val = tensor([1])]; + tensor h_141_pad_0 = const()[name = string("h_141_pad_0"), val = tensor([0, 0])]; + tensor h_141_dilations_0 = const()[name = string("h_141_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26652544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26653888))))[name = string("main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26654016)))]; + tensor h_141_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_141_dilations_0, groups = h_141_groups_0, pad = h_141_pad_0, pad_type = h_141_pad_type_0, strides = h_141_strides_0, weight = main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_369_cast_fp16)[name = string("h_141_cast_fp16")]; + tensor x_197_cast_fp16 = mul(x = h_141_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_371_perm_0 = const()[name = string("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2227_axes_0 = const()[name = string("op_2227_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26655104)))]; + tensor main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26656192)))]; + tensor input_371_cast_fp16 = transpose(perm = input_371_perm_0, x = x_197_cast_fp16)[name = string("transpose_16")]; + tensor var_2227_cast_fp16 = layer_norm(axes = var_2227_axes_0, beta = main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16, x = input_371_cast_fp16)[name = string("op_2227_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + string h_143_pad_type_0 = const()[name = string("h_143_pad_type_0"), val = string("valid")]; + tensor h_143_strides_0 = const()[name = string("h_143_strides_0"), val = tensor([1])]; + tensor h_143_pad_0 = const()[name = string("h_143_pad_0"), val = tensor([0, 0])]; + tensor h_143_dilations_0 = const()[name = string("h_143_dilations_0"), val = tensor([1])]; + int32 h_143_groups_0 = const()[name = string("h_143_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26657280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27181632))))[name = string("main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27181760)))]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = var_2227_cast_fp16)[name = string("transpose_15")]; + tensor h_143_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_143_dilations_0, groups = h_143_groups_0, pad = h_143_pad_0, pad_type = h_143_pad_type_0, strides = h_143_strides_0, weight = main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = string("h_143_cast_fp16")]; + string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")]; + tensor input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = h_143_cast_fp16)[name = string("input_375_cast_fp16")]; + string h_145_pad_type_0 = const()[name = string("h_145_pad_type_0"), val = string("valid")]; + tensor h_145_strides_0 = const()[name = string("h_145_strides_0"), val = tensor([1])]; + tensor h_145_pad_0 = const()[name = string("h_145_pad_0"), val = tensor([0, 0])]; + tensor h_145_dilations_0 = const()[name = string("h_145_dilations_0"), val = tensor([1])]; + int32 h_145_groups_0 = const()[name = string("h_145_groups_0"), val = int32(1)]; + tensor op_2244_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27185920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27710272))))[name = string("op_2244_weight_0_to_fp16_palettized")]; + tensor var_2244_bias_0_to_fp16 = const()[name = string("op_2244_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27710400)))]; + tensor var_2244_cast_fp16 = conv(bias = var_2244_bias_0_to_fp16, dilations = h_145_dilations_0, groups = h_145_groups_0, pad = h_145_pad_0, pad_type = h_145_pad_type_0, strides = h_145_strides_0, weight = op_2244_weight_0_to_fp16_palettized, x = input_375_cast_fp16)[name = string("op_2244_cast_fp16")]; + tensor out_61_cast_fp16 = add(x = x_195_cast_fp16, y = var_2244_cast_fp16)[name = string("out_61_cast_fp16")]; + tensor x_199_cast_fp16 = mul(x = out_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor x_m_cast_fp16 = mul(x = x_199_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_cast_fp16")]; + tensor input_377_perm_0 = const()[name = string("input_377_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27711488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27777088))))[name = string("main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27777216)))]; + tensor input_377_cast_fp16 = transpose(perm = input_377_perm_0, x = x_199_cast_fp16)[name = string("transpose_14")]; + tensor linear_34_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_2260 = const()[name = string("op_2260"), val = tensor([2, 128, 2, 128])]; + tensor var_2261_cast_fp16 = reshape(shape = var_2260, x = linear_34_cast_fp16)[name = string("op_2261_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27777792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27810624))))[name = string("main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27810752)))]; + tensor linear_36_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_2274 = const()[name = string("op_2274"), val = tensor([2, 50, 2, 128])]; + tensor var_2275_cast_fp16 = reshape(shape = var_2274, x = linear_36_cast_fp16)[name = string("op_2275_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_2279_transpose_x_0 = const()[name = string("op_2279_transpose_x_0"), val = bool(false)]; + bool var_2279_transpose_y_0 = const()[name = string("op_2279_transpose_y_0"), val = bool(false)]; + tensor op_2278_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27811328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27824192))))[name = string("op_2278_to_fp16_palettized")]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_2261_cast_fp16)[name = string("transpose_13")]; + tensor var_2279_cast_fp16 = matmul(transpose_x = var_2279_transpose_x_0, transpose_y = var_2279_transpose_y_0, x = q_cast_fp16, y = op_2278_to_fp16_palettized)[name = string("op_2279_cast_fp16")]; + fp16 _inversed_input_379_y_0_to_fp16 = const()[name = string("_inversed_input_379_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_379_cast_fp16 = mul(x = var_2279_cast_fp16, y = _inversed_input_379_y_0_to_fp16)[name = string("_inversed_input_379_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_1775, x = _inversed_input_379_cast_fp16)[name = string("attn_21_cast_fp16")]; + tensor attn_cast_fp16 = mul(x = attn_21_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_cast_fp16")]; + bool var_2286_transpose_x_0 = const()[name = string("op_2286_transpose_x_0"), val = bool(false)]; + bool var_2286_transpose_y_0 = const()[name = string("op_2286_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_2275_cast_fp16)[name = string("transpose_12")]; + tensor var_2286_cast_fp16 = matmul(transpose_x = var_2286_transpose_x_0, transpose_y = var_2286_transpose_y_0, x = attn_cast_fp16, y = v_15_cast_fp16)[name = string("op_2286_cast_fp16")]; + tensor var_2287_perm_0 = const()[name = string("op_2287_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2288 = const()[name = string("op_2288"), val = tensor([2, 128, 256])]; + tensor var_2287_cast_fp16 = transpose(perm = var_2287_perm_0, x = var_2286_cast_fp16)[name = string("transpose_11")]; + tensor input_381_cast_fp16 = reshape(shape = var_2288, x = var_2287_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27824320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27889920))))[name = string("main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27890048)))]; + tensor linear_37_cast_fp16 = linear(bias = main_blocks_3_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor out_63_perm_0 = const()[name = string("out_63_perm_0"), val = tensor([0, 2, 1])]; + tensor out_63_cast_fp16 = transpose(perm = out_63_perm_0, x = linear_37_cast_fp16)[name = string("transpose_10")]; + tensor var_2295_cast_fp16 = mul(x = out_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2295_cast_fp16")]; + tensor x_201_cast_fp16 = add(x = x_m_cast_fp16, y = var_2295_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_383_perm_0 = const()[name = string("input_383_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2302_axes_0 = const()[name = string("op_2302_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27891136)))]; + tensor main_blocks_3_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27892224)))]; + tensor input_383_cast_fp16 = transpose(perm = input_383_perm_0, x = x_201_cast_fp16)[name = string("transpose_9")]; + tensor var_2302_cast_fp16 = layer_norm(axes = var_2302_axes_0, beta = main_blocks_3_style_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_style_norm_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_perm_0 = const()[name = string("op_2303_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2303_cast_fp16 = transpose(perm = var_2303_perm_0, x = var_2302_cast_fp16)[name = string("transpose_8")]; + tensor x_203_cast_fp16 = mul(x = var_2303_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor x_205_cast_fp16 = mul(x = x_203_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor var_2330_cast_fp16 = mul(x = x_205_cast_fp16, y = last_1_cast_fp16)[name = string("op_2330_cast_fp16")]; + tensor x_last_49_axes_0 = const()[name = string("x_last_49_axes_0"), val = tensor([-1])]; + bool x_last_49_keep_dims_0 = const()[name = string("x_last_49_keep_dims_0"), val = bool(true)]; + tensor x_last_49_cast_fp16 = reduce_sum(axes = x_last_49_axes_0, keep_dims = x_last_49_keep_dims_0, x = var_2330_cast_fp16)[name = string("x_last_49_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = x_205_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor var_2335_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_49_cast_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2335_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_pad_0 = const()[name = string("input_389_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("replicate")]; + fp16 const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = fp16(0x0p+0)]; + tensor input_389_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = input_389_mode_0, pad = input_389_pad_0, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + string h_147_pad_type_0 = const()[name = string("h_147_pad_type_0"), val = string("valid")]; + int32 h_147_groups_0 = const()[name = string("h_147_groups_0"), val = int32(512)]; + tensor h_147_strides_0 = const()[name = string("h_147_strides_0"), val = tensor([1])]; + tensor h_147_pad_0 = const()[name = string("h_147_pad_0"), val = tensor([0, 0])]; + tensor h_147_dilations_0 = const()[name = string("h_147_dilations_0"), val = tensor([1])]; + tensor last_convnext_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27893312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894656))))[name = string("last_convnext_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894784)))]; + tensor h_147_cast_fp16 = conv(bias = last_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_147_dilations_0, groups = h_147_groups_0, pad = h_147_pad_0, pad_type = h_147_pad_type_0, strides = h_147_strides_0, weight = last_convnext_0_dwconv__conv_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor x_207_cast_fp16 = mul(x = h_147_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_391_perm_0 = const()[name = string("input_391_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2353_axes_0 = const()[name = string("op_2353_axes_0"), val = tensor([-1])]; + tensor last_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27895872)))]; + tensor last_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896960)))]; + fp16 var_2307_to_fp16 = const()[name = string("op_2307_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_391_cast_fp16 = transpose(perm = input_391_perm_0, x = x_207_cast_fp16)[name = string("transpose_7")]; + tensor var_2353_cast_fp16 = layer_norm(axes = var_2353_axes_0, beta = last_convnext_0_norm_norm_bias_to_fp16, epsilon = var_2307_to_fp16, gamma = last_convnext_0_norm_norm_weight_to_fp16, x = input_391_cast_fp16)[name = string("op_2353_cast_fp16")]; + tensor input_393_perm_0 = const()[name = string("input_393_perm_0"), val = tensor([0, 2, 1])]; + string h_149_pad_type_0 = const()[name = string("h_149_pad_type_0"), val = string("valid")]; + tensor h_149_strides_0 = const()[name = string("h_149_strides_0"), val = tensor([1])]; + tensor h_149_pad_0 = const()[name = string("h_149_pad_0"), val = tensor([0, 0])]; + tensor h_149_dilations_0 = const()[name = string("h_149_dilations_0"), val = tensor([1])]; + int32 h_149_groups_0 = const()[name = string("h_149_groups_0"), val = int32(1)]; + tensor last_convnext_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27898048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28422400))))[name = string("last_convnext_0_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28422528)))]; + tensor input_393_cast_fp16 = transpose(perm = input_393_perm_0, x = var_2353_cast_fp16)[name = string("transpose_6")]; + tensor h_149_cast_fp16 = conv(bias = last_convnext_0_pwconv1_bias_to_fp16, dilations = h_149_dilations_0, groups = h_149_groups_0, pad = h_149_pad_0, pad_type = h_149_pad_type_0, strides = h_149_strides_0, weight = last_convnext_0_pwconv1_weight_to_fp16_palettized, x = input_393_cast_fp16)[name = string("h_149_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = h_149_cast_fp16)[name = string("input_395_cast_fp16")]; + string h_151_pad_type_0 = const()[name = string("h_151_pad_type_0"), val = string("valid")]; + tensor h_151_strides_0 = const()[name = string("h_151_strides_0"), val = tensor([1])]; + tensor h_151_pad_0 = const()[name = string("h_151_pad_0"), val = tensor([0, 0])]; + tensor h_151_dilations_0 = const()[name = string("h_151_dilations_0"), val = tensor([1])]; + int32 h_151_groups_0 = const()[name = string("h_151_groups_0"), val = int32(1)]; + tensor op_2370_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28426688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28951040))))[name = string("op_2370_weight_0_to_fp16_palettized")]; + tensor var_2370_bias_0_to_fp16 = const()[name = string("op_2370_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28951168)))]; + tensor var_2370_cast_fp16 = conv(bias = var_2370_bias_0_to_fp16, dilations = h_151_dilations_0, groups = h_151_groups_0, pad = h_151_pad_0, pad_type = h_151_pad_type_0, strides = h_151_strides_0, weight = op_2370_weight_0_to_fp16_palettized, x = input_395_cast_fp16)[name = string("op_2370_cast_fp16")]; + tensor out_65_cast_fp16 = add(x = x_205_cast_fp16, y = var_2370_cast_fp16)[name = string("out_65_cast_fp16")]; + tensor x_209_cast_fp16 = mul(x = out_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor x_211_cast_fp16 = mul(x = x_209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_2398_cast_fp16 = mul(x = x_211_cast_fp16, y = last_1_cast_fp16)[name = string("op_2398_cast_fp16")]; + tensor x_last_51_axes_0 = const()[name = string("x_last_51_axes_0"), val = tensor([-1])]; + bool x_last_51_keep_dims_0 = const()[name = string("x_last_51_keep_dims_0"), val = bool(true)]; + tensor x_last_51_cast_fp16 = reduce_sum(axes = x_last_51_axes_0, keep_dims = x_last_51_keep_dims_0, x = var_2398_cast_fp16)[name = string("x_last_51_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = x_211_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_51_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor input_399_cast_fp16 = add(x = var_2401_cast_fp16, y = var_2403_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor input_401_pad_0 = const()[name = string("input_401_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_401_mode_0 = const()[name = string("input_401_mode_0"), val = string("replicate")]; + fp16 const_86_to_fp16 = const()[name = string("const_86_to_fp16"), val = fp16(0x0p+0)]; + tensor input_401_cast_fp16 = pad(constant_val = const_86_to_fp16, mode = input_401_mode_0, pad = input_401_pad_0, x = input_399_cast_fp16)[name = string("input_401_cast_fp16")]; + string h_153_pad_type_0 = const()[name = string("h_153_pad_type_0"), val = string("valid")]; + int32 h_153_groups_0 = const()[name = string("h_153_groups_0"), val = int32(512)]; + tensor h_153_strides_0 = const()[name = string("h_153_strides_0"), val = tensor([1])]; + tensor h_153_pad_0 = const()[name = string("h_153_pad_0"), val = tensor([0, 0])]; + tensor h_153_dilations_0 = const()[name = string("h_153_dilations_0"), val = tensor([1])]; + tensor last_convnext_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28952256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28953600))))[name = string("last_convnext_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28953728)))]; + tensor h_153_cast_fp16 = conv(bias = last_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_153_dilations_0, groups = h_153_groups_0, pad = h_153_pad_0, pad_type = h_153_pad_type_0, strides = h_153_strides_0, weight = last_convnext_1_dwconv__conv_weight_to_fp16_palettized, x = input_401_cast_fp16)[name = string("h_153_cast_fp16")]; + tensor x_213_cast_fp16 = mul(x = h_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_213_cast_fp16")]; + tensor input_403_perm_0 = const()[name = string("input_403_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2421_axes_0 = const()[name = string("op_2421_axes_0"), val = tensor([-1])]; + tensor last_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28954816)))]; + tensor last_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28955904)))]; + fp16 var_2375_to_fp16 = const()[name = string("op_2375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_403_cast_fp16 = transpose(perm = input_403_perm_0, x = x_213_cast_fp16)[name = string("transpose_5")]; + tensor var_2421_cast_fp16 = layer_norm(axes = var_2421_axes_0, beta = last_convnext_1_norm_norm_bias_to_fp16, epsilon = var_2375_to_fp16, gamma = last_convnext_1_norm_norm_weight_to_fp16, x = input_403_cast_fp16)[name = string("op_2421_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string h_155_pad_type_0 = const()[name = string("h_155_pad_type_0"), val = string("valid")]; + tensor h_155_strides_0 = const()[name = string("h_155_strides_0"), val = tensor([1])]; + tensor h_155_pad_0 = const()[name = string("h_155_pad_0"), val = tensor([0, 0])]; + tensor h_155_dilations_0 = const()[name = string("h_155_dilations_0"), val = tensor([1])]; + int32 h_155_groups_0 = const()[name = string("h_155_groups_0"), val = int32(1)]; + tensor last_convnext_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28956992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29481344))))[name = string("last_convnext_1_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29481472)))]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = var_2421_cast_fp16)[name = string("transpose_4")]; + tensor h_155_cast_fp16 = conv(bias = last_convnext_1_pwconv1_bias_to_fp16, dilations = h_155_dilations_0, groups = h_155_groups_0, pad = h_155_pad_0, pad_type = h_155_pad_type_0, strides = h_155_strides_0, weight = last_convnext_1_pwconv1_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = string("h_155_cast_fp16")]; + string input_407_mode_0 = const()[name = string("input_407_mode_0"), val = string("EXACT")]; + tensor input_407_cast_fp16 = gelu(mode = input_407_mode_0, x = h_155_cast_fp16)[name = string("input_407_cast_fp16")]; + string h_157_pad_type_0 = const()[name = string("h_157_pad_type_0"), val = string("valid")]; + tensor h_157_strides_0 = const()[name = string("h_157_strides_0"), val = tensor([1])]; + tensor h_157_pad_0 = const()[name = string("h_157_pad_0"), val = tensor([0, 0])]; + tensor h_157_dilations_0 = const()[name = string("h_157_dilations_0"), val = tensor([1])]; + int32 h_157_groups_0 = const()[name = string("h_157_groups_0"), val = int32(1)]; + tensor op_2438_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29485632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30009984))))[name = string("op_2438_weight_0_to_fp16_palettized")]; + tensor var_2438_bias_0_to_fp16 = const()[name = string("op_2438_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30010112)))]; + tensor var_2438_cast_fp16 = conv(bias = var_2438_bias_0_to_fp16, dilations = h_157_dilations_0, groups = h_157_groups_0, pad = h_157_pad_0, pad_type = h_157_pad_type_0, strides = h_157_strides_0, weight = op_2438_weight_0_to_fp16_palettized, x = input_407_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor out_67_cast_fp16 = add(x = x_211_cast_fp16, y = var_2438_cast_fp16)[name = string("out_67_cast_fp16")]; + tensor x_215_cast_fp16 = mul(x = out_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_215_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2466_cast_fp16 = mul(x = x_217_cast_fp16, y = last_1_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor x_last_53_axes_0 = const()[name = string("x_last_53_axes_0"), val = tensor([-1])]; + bool x_last_53_keep_dims_0 = const()[name = string("x_last_53_keep_dims_0"), val = bool(true)]; + tensor x_last_53_cast_fp16 = reduce_sum(axes = x_last_53_axes_0, keep_dims = x_last_53_keep_dims_0, x = var_2466_cast_fp16)[name = string("x_last_53_cast_fp16")]; + tensor var_2469_cast_fp16 = mul(x = x_217_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor var_2471_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_53_cast_fp16)[name = string("op_2471_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = var_2469_cast_fp16, y = var_2471_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("replicate")]; + fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)]; + tensor input_413_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = input_413_mode_0, pad = input_413_pad_0, x = input_411_cast_fp16)[name = string("input_413_cast_fp16")]; + string h_159_pad_type_0 = const()[name = string("h_159_pad_type_0"), val = string("valid")]; + int32 h_159_groups_0 = const()[name = string("h_159_groups_0"), val = int32(512)]; + tensor h_159_strides_0 = const()[name = string("h_159_strides_0"), val = tensor([1])]; + tensor h_159_pad_0 = const()[name = string("h_159_pad_0"), val = tensor([0, 0])]; + tensor h_159_dilations_0 = const()[name = string("h_159_dilations_0"), val = tensor([1])]; + tensor last_convnext_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30011200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30012544))))[name = string("last_convnext_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30012672)))]; + tensor h_159_cast_fp16 = conv(bias = last_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_159_dilations_0, groups = h_159_groups_0, pad = h_159_pad_0, pad_type = h_159_pad_type_0, strides = h_159_strides_0, weight = last_convnext_2_dwconv__conv_weight_to_fp16_palettized, x = input_413_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor x_219_cast_fp16 = mul(x = h_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_415_perm_0 = const()[name = string("input_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2489_axes_0 = const()[name = string("op_2489_axes_0"), val = tensor([-1])]; + tensor last_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30013760)))]; + tensor last_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30014848)))]; + fp16 var_2443_to_fp16 = const()[name = string("op_2443_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_415_cast_fp16 = transpose(perm = input_415_perm_0, x = x_219_cast_fp16)[name = string("transpose_3")]; + tensor var_2489_cast_fp16 = layer_norm(axes = var_2489_axes_0, beta = last_convnext_2_norm_norm_bias_to_fp16, epsilon = var_2443_to_fp16, gamma = last_convnext_2_norm_norm_weight_to_fp16, x = input_415_cast_fp16)[name = string("op_2489_cast_fp16")]; + tensor input_417_perm_0 = const()[name = string("input_417_perm_0"), val = tensor([0, 2, 1])]; + string h_161_pad_type_0 = const()[name = string("h_161_pad_type_0"), val = string("valid")]; + tensor h_161_strides_0 = const()[name = string("h_161_strides_0"), val = tensor([1])]; + tensor h_161_pad_0 = const()[name = string("h_161_pad_0"), val = tensor([0, 0])]; + tensor h_161_dilations_0 = const()[name = string("h_161_dilations_0"), val = tensor([1])]; + int32 h_161_groups_0 = const()[name = string("h_161_groups_0"), val = int32(1)]; + tensor last_convnext_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30015936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30540288))))[name = string("last_convnext_2_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30540416)))]; + tensor input_417_cast_fp16 = transpose(perm = input_417_perm_0, x = var_2489_cast_fp16)[name = string("transpose_2")]; + tensor h_161_cast_fp16 = conv(bias = last_convnext_2_pwconv1_bias_to_fp16, dilations = h_161_dilations_0, groups = h_161_groups_0, pad = h_161_pad_0, pad_type = h_161_pad_type_0, strides = h_161_strides_0, weight = last_convnext_2_pwconv1_weight_to_fp16_palettized, x = input_417_cast_fp16)[name = string("h_161_cast_fp16")]; + string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("EXACT")]; + tensor input_419_cast_fp16 = gelu(mode = input_419_mode_0, x = h_161_cast_fp16)[name = string("input_419_cast_fp16")]; + string h_163_pad_type_0 = const()[name = string("h_163_pad_type_0"), val = string("valid")]; + tensor h_163_strides_0 = const()[name = string("h_163_strides_0"), val = tensor([1])]; + tensor h_163_pad_0 = const()[name = string("h_163_pad_0"), val = tensor([0, 0])]; + tensor h_163_dilations_0 = const()[name = string("h_163_dilations_0"), val = tensor([1])]; + int32 h_163_groups_0 = const()[name = string("h_163_groups_0"), val = int32(1)]; + tensor op_2506_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30544576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31068928))))[name = string("op_2506_weight_0_to_fp16_palettized")]; + tensor var_2506_bias_0_to_fp16 = const()[name = string("op_2506_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31069056)))]; + tensor var_2506_cast_fp16 = conv(bias = var_2506_bias_0_to_fp16, dilations = h_163_dilations_0, groups = h_163_groups_0, pad = h_163_pad_0, pad_type = h_163_pad_type_0, strides = h_163_strides_0, weight = op_2506_weight_0_to_fp16_palettized, x = input_419_cast_fp16)[name = string("op_2506_cast_fp16")]; + tensor out_69_cast_fp16 = add(x = x_217_cast_fp16, y = var_2506_cast_fp16)[name = string("out_69_cast_fp16")]; + tensor x_221_cast_fp16 = mul(x = out_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor x_223_cast_fp16 = mul(x = x_221_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_2534_cast_fp16 = mul(x = x_223_cast_fp16, y = last_1_cast_fp16)[name = string("op_2534_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_2534_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_2537_cast_fp16 = mul(x = x_223_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2537_cast_fp16")]; + tensor var_2539_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_cast_fp16)[name = string("op_2539_cast_fp16")]; + tensor input_423_cast_fp16 = add(x = var_2537_cast_fp16, y = var_2539_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_425_mode_0 = const()[name = string("input_425_mode_0"), val = string("replicate")]; + fp16 const_90_to_fp16 = const()[name = string("const_90_to_fp16"), val = fp16(0x0p+0)]; + tensor input_425_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = input_425_mode_0, pad = input_425_pad_0, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")]; + string h_165_pad_type_0 = const()[name = string("h_165_pad_type_0"), val = string("valid")]; + int32 h_165_groups_0 = const()[name = string("h_165_groups_0"), val = int32(512)]; + tensor h_165_strides_0 = const()[name = string("h_165_strides_0"), val = tensor([1])]; + tensor h_165_pad_0 = const()[name = string("h_165_pad_0"), val = tensor([0, 0])]; + tensor h_165_dilations_0 = const()[name = string("h_165_dilations_0"), val = tensor([1])]; + tensor last_convnext_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31070144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31071488))))[name = string("last_convnext_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31071616)))]; + tensor h_165_cast_fp16 = conv(bias = last_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_165_dilations_0, groups = h_165_groups_0, pad = h_165_pad_0, pad_type = h_165_pad_type_0, strides = h_165_strides_0, weight = last_convnext_3_dwconv__conv_weight_to_fp16_palettized, x = input_425_cast_fp16)[name = string("h_165_cast_fp16")]; + tensor x_cast_fp16 = mul(x = h_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2557_axes_0 = const()[name = string("op_2557_axes_0"), val = tensor([-1])]; + tensor last_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31072704)))]; + tensor last_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31073792)))]; + fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_cast_fp16)[name = string("transpose_1")]; + tensor var_2557_cast_fp16 = layer_norm(axes = var_2557_axes_0, beta = last_convnext_3_norm_norm_bias_to_fp16, epsilon = var_2511_to_fp16, gamma = last_convnext_3_norm_norm_weight_to_fp16, x = input_427_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor input_429_perm_0 = const()[name = string("input_429_perm_0"), val = tensor([0, 2, 1])]; + string h_167_pad_type_0 = const()[name = string("h_167_pad_type_0"), val = string("valid")]; + tensor h_167_strides_0 = const()[name = string("h_167_strides_0"), val = tensor([1])]; + tensor h_167_pad_0 = const()[name = string("h_167_pad_0"), val = tensor([0, 0])]; + tensor h_167_dilations_0 = const()[name = string("h_167_dilations_0"), val = tensor([1])]; + int32 h_167_groups_0 = const()[name = string("h_167_groups_0"), val = int32(1)]; + tensor last_convnext_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31074880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31599232))))[name = string("last_convnext_3_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31599360)))]; + tensor input_429_cast_fp16 = transpose(perm = input_429_perm_0, x = var_2557_cast_fp16)[name = string("transpose_0")]; + tensor h_167_cast_fp16 = conv(bias = last_convnext_3_pwconv1_bias_to_fp16, dilations = h_167_dilations_0, groups = h_167_groups_0, pad = h_167_pad_0, pad_type = h_167_pad_type_0, strides = h_167_strides_0, weight = last_convnext_3_pwconv1_weight_to_fp16_palettized, x = input_429_cast_fp16)[name = string("h_167_cast_fp16")]; + string input_431_mode_0 = const()[name = string("input_431_mode_0"), val = string("EXACT")]; + tensor input_431_cast_fp16 = gelu(mode = input_431_mode_0, x = h_167_cast_fp16)[name = string("input_431_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor op_2574_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31603520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32127872))))[name = string("op_2574_weight_0_to_fp16_palettized")]; + tensor var_2574_bias_0_to_fp16 = const()[name = string("op_2574_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32128000)))]; + tensor var_2574_cast_fp16 = conv(bias = var_2574_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = op_2574_weight_0_to_fp16_palettized, x = input_431_cast_fp16)[name = string("op_2574_cast_fp16")]; + tensor out_cast_fp16 = add(x = x_223_cast_fp16, y = var_2574_cast_fp16)[name = string("out_cast_fp16")]; + tensor input_cast_fp16 = mul(x = out_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("input_cast_fp16")]; + string var_2587_pad_type_0 = const()[name = string("op_2587_pad_type_0"), val = string("valid")]; + tensor var_2587_strides_0 = const()[name = string("op_2587_strides_0"), val = tensor([1])]; + tensor var_2587_pad_0 = const()[name = string("op_2587_pad_0"), val = tensor([0, 0])]; + tensor var_2587_dilations_0 = const()[name = string("op_2587_dilations_0"), val = tensor([1])]; + int32 var_2587_groups_0 = const()[name = string("op_2587_groups_0"), val = int32(1)]; + tensor proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32129088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32166016))))[name = string("proj_out_weight_to_fp16_palettized")]; + tensor var_2587_cast_fp16 = conv(dilations = var_2587_dilations_0, groups = var_2587_groups_0, pad = var_2587_pad_0, pad_type = var_2587_pad_type_0, strides = var_2587_strides_0, weight = proj_out_weight_to_fp16_palettized, x = input_cast_fp16)[name = string("op_2587_cast_fp16")]; + tensor v_cast_fp16 = mul(x = var_2587_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("v_cast_fp16")]; + tensor cond_begin_0 = const()[name = string("cond_begin_0"), val = tensor([0, 0, 0])]; + tensor cond_end_0 = const()[name = string("cond_end_0"), val = tensor([1, 144, 128])]; + tensor cond_end_mask_0 = const()[name = string("cond_end_mask_0"), val = tensor([false, true, true])]; + tensor cond_cast_fp16 = slice_by_index(begin = cond_begin_0, end = cond_end_0, end_mask = cond_end_mask_0, x = v_cast_fp16)[name = string("cond_cast_fp16")]; + tensor uncond_begin_0 = const()[name = string("uncond_begin_0"), val = tensor([1, 0, 0])]; + tensor uncond_end_0 = const()[name = string("uncond_end_0"), val = tensor([2, 144, 128])]; + tensor uncond_end_mask_0 = const()[name = string("uncond_end_mask_0"), val = tensor([true, true, true])]; + tensor uncond_cast_fp16 = slice_by_index(begin = uncond_begin_0, end = uncond_end_0, end_mask = uncond_end_mask_0, x = v_cast_fp16)[name = string("uncond_cast_fp16")]; + fp32 var_2597_epsilon_0 = const()[name = string("op_2597_epsilon_0"), val = fp32(0x1.a36e2ep-14)]; + tensor var_2597_cast_fp16 = inverse(epsilon = var_2597_epsilon_0, x = total_step_to_fp16)[name = string("op_2597_cast_fp16")]; + tensor var_2603 = const()[name = string("op_2603"), val = tensor([-1, 1, 1])]; + tensor step_cast_fp16 = reshape(shape = var_2603, x = var_2597_cast_fp16)[name = string("step_cast_fp16")]; + fp16 var_2605_to_fp16 = const()[name = string("op_2605_to_fp16"), val = fp16(0x1p+2)]; + tensor var_2606_cast_fp16 = mul(x = cond_cast_fp16, y = var_2605_to_fp16)[name = string("op_2606_cast_fp16")]; + fp16 var_2607_to_fp16 = const()[name = string("op_2607_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_2608_cast_fp16 = mul(x = uncond_cast_fp16, y = var_2607_to_fp16)[name = string("op_2608_cast_fp16")]; + tensor var_2610_cast_fp16 = sub(x = var_2606_cast_fp16, y = var_2608_cast_fp16)[name = string("op_2610_cast_fp16")]; + tensor var_2611_cast_fp16 = mul(x = step_cast_fp16, y = var_2610_cast_fp16)[name = string("op_2611_cast_fp16")]; + tensor var_2613_cast_fp16 = add(x = noisy_latent_to_fp16, y = var_2611_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor var_2614_cast_fp16 = mul(x = var_2613_cast_fp16, y = latent_mask_to_fp16)[name = string("op_2614_cast_fp16")]; + string var_2614_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2614_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor denoised_latent = cast(dtype = var_2614_cast_fp16_to_fp32_dtype_0, x = var_2614_cast_fp16)[name = string("cast_0")]; + } -> (denoised_latent); +} \ No newline at end of file diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L128_int4.mlmodelc/weights/weight.bin b/VectorEstimatorVariants/VectorEstimator_v2_L128_int4.mlmodelc/weights/weight.bin new 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sha256:13cece5dc645c542ec2b26a45d9d4d1a417511eadcef491d265edfdad160fb7b +size 243 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L128_int6.mlmodelc/coremldata.bin b/VectorEstimatorVariants/VectorEstimator_v2_L128_int6.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..1815ba7d80859d86a9851fc9aa419cecd9b83eb5 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L128_int6.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e0e863f61bb384f8db2de50948dbdc391357945af6bce6deb327460cbe6625a7 +size 553 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L128_int6.mlmodelc/model.mil b/VectorEstimatorVariants/VectorEstimator_v2_L128_int6.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..18ce83410024c1422caa0a4eb51754a423d6376f --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L128_int6.mlmodelc/model.mil @@ -0,0 +1,2101 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor current_step, tensor latent_mask, tensor noisy_latent, tensor style_ttl, tensor text_emb, tensor text_mask, tensor total_step) { + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + bool text_emb_interleave_0 = const()[name = string("text_emb_interleave_0"), val = bool(false)]; + string text_emb_to_fp16_dtype_0 = const()[name = string("text_emb_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_uncond_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24704))))[name = string("text_uncond_to_fp16_palettized")]; + tensor text_emb_to_fp16 = cast(dtype = text_emb_to_fp16_dtype_0, x = text_emb)[name = string("cast_7")]; + tensor text_emb_cast_fp16 = concat(axis = var_39, interleave = text_emb_interleave_0, values = (text_emb_to_fp16, text_uncond_to_fp16_palettized))[name = string("text_emb_cast_fp16")]; + bool input_95_interleave_0 = const()[name = string("input_95_interleave_0"), val = bool(false)]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor uncond_masker_style_value_special_token_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34560))))[name = string("uncond_masker_style_value_special_token_to_fp16_palettized")]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_6")]; + tensor input_95_cast_fp16 = concat(axis = var_39, interleave = input_95_interleave_0, values = (style_ttl_to_fp16, uncond_masker_style_value_special_token_to_fp16_palettized))[name = string("input_95_cast_fp16")]; + int32 var_64 = const()[name = string("op_64"), val = int32(0)]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + string noisy_latent_to_fp16_dtype_0 = const()[name = string("noisy_latent_to_fp16_dtype_0"), val = string("fp16")]; + tensor noisy_latent_to_fp16 = cast(dtype = noisy_latent_to_fp16_dtype_0, x = noisy_latent)[name = string("cast_5")]; + tensor input_5_cast_fp16 = concat(axis = var_64, interleave = input_5_interleave_0, values = (noisy_latent_to_fp16, noisy_latent_to_fp16))[name = string("input_5_cast_fp16")]; + int32 var_67 = const()[name = string("op_67"), val = int32(0)]; + bool latent_mask_b_interleave_0 = const()[name = string("latent_mask_b_interleave_0"), val = bool(false)]; + string latent_mask_to_fp16_dtype_0 = const()[name = string("latent_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor latent_mask_to_fp16 = cast(dtype = latent_mask_to_fp16_dtype_0, x = latent_mask)[name = string("cast_4")]; + tensor latent_mask_b_cast_fp16 = concat(axis = var_67, interleave = latent_mask_b_interleave_0, values = (latent_mask_to_fp16, latent_mask_to_fp16))[name = string("latent_mask_b_cast_fp16")]; + int32 var_70 = const()[name = string("op_70"), val = int32(0)]; + bool text_mask_interleave_0 = const()[name = string("text_mask_interleave_0"), val = bool(false)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_3")]; + tensor text_mask_cast_fp16 = concat(axis = var_70, interleave = text_mask_interleave_0, values = (text_mask_to_fp16, text_mask_to_fp16))[name = string("text_mask_cast_fp16")]; + string current_step_to_fp16_dtype_0 = const()[name = string("current_step_to_fp16_dtype_0"), val = string("fp16")]; + string total_step_to_fp16_dtype_0 = const()[name = string("total_step_to_fp16_dtype_0"), val = string("fp16")]; + tensor current_step_to_fp16 = cast(dtype = current_step_to_fp16_dtype_0, x = current_step)[name = string("cast_1")]; + tensor total_step_to_fp16 = cast(dtype = total_step_to_fp16_dtype_0, x = total_step)[name = string("cast_2")]; + tensor t_1_cast_fp16 = real_div(x = current_step_to_fp16, y = total_step_to_fp16)[name = string("t_1_cast_fp16")]; + int32 var_74 = const()[name = string("op_74"), val = int32(0)]; + bool t_3_interleave_0 = const()[name = string("t_3_interleave_0"), val = bool(false)]; + tensor t_3_cast_fp16 = concat(axis = var_74, interleave = t_3_interleave_0, values = (t_1_cast_fp16, t_1_cast_fp16))[name = string("t_3_cast_fp16")]; + int32 var_78 = const()[name = string("op_78"), val = int32(-1)]; + tensor var_85 = const()[name = string("op_85"), val = tensor([-1, 1])]; + tensor var_86_cast_fp16 = reshape(shape = var_85, x = t_3_cast_fp16)[name = string("op_86_cast_fp16")]; + fp16 time_encoder_time_scale_to_fp16 = const()[name = string("time_encoder_time_scale_to_fp16"), val = fp16(0x1.f4p+9)]; + tensor t_scaled_cast_fp16 = mul(x = var_86_cast_fp16, y = time_encoder_time_scale_to_fp16)[name = string("t_scaled_cast_fp16")]; + tensor time_encoder_omegas_to_fp16 = const()[name = string("time_encoder_omegas_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34752)))]; + tensor phase_cast_fp16 = mul(x = t_scaled_cast_fp16, y = time_encoder_omegas_to_fp16)[name = string("phase_cast_fp16")]; + tensor var_89_cast_fp16 = sin(x = phase_cast_fp16)[name = string("op_89_cast_fp16")]; + tensor var_90_cast_fp16 = cos(x = phase_cast_fp16)[name = string("op_90_cast_fp16")]; + bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)]; + tensor input_1_cast_fp16 = concat(axis = var_78, interleave = input_1_interleave_0, values = (var_89_cast_fp16, var_90_cast_fp16))[name = string("input_1_cast_fp16")]; + tensor time_encoder_mlp_0_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47232))))[name = string("time_encoder_mlp_0_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_0_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47424)))]; + tensor linear_0_cast_fp16 = linear(bias = time_encoder_mlp_0_linear_bias_to_fp16, weight = time_encoder_mlp_0_linear_weight_to_fp16_palettized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_97_cast_fp16 = softplus(x = linear_0_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = tanh(x = var_97_cast_fp16)[name = string("op_98_cast_fp16")]; + tensor input_3_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_98_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor time_encoder_mlp_2_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60352))))[name = string("time_encoder_mlp_2_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_2_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60544)))]; + tensor linear_1_cast_fp16 = linear(bias = time_encoder_mlp_2_linear_bias_to_fp16, weight = time_encoder_mlp_2_linear_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor time_emb_axes_0 = const()[name = string("time_emb_axes_0"), val = tensor([-1])]; + tensor time_emb_cast_fp16 = expand_dims(axes = time_emb_axes_0, x = linear_1_cast_fp16)[name = string("time_emb_cast_fp16")]; + string var_115_pad_type_0 = const()[name = string("op_115_pad_type_0"), val = string("valid")]; + tensor var_115_strides_0 = const()[name = string("op_115_strides_0"), val = tensor([1])]; + tensor var_115_pad_0 = const()[name = string("op_115_pad_0"), val = tensor([0, 0])]; + tensor var_115_dilations_0 = const()[name = string("op_115_dilations_0"), val = tensor([1])]; + int32 var_115_groups_0 = const()[name = string("op_115_groups_0"), val = int32(1)]; + tensor proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116096))))[name = string("proj_in_weight_to_fp16_palettized")]; + tensor var_115_cast_fp16 = conv(dilations = var_115_dilations_0, groups = var_115_groups_0, pad = var_115_pad_0, pad_type = var_115_pad_type_0, strides = var_115_strides_0, weight = proj_in_weight_to_fp16_palettized, x = input_5_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = var_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_3_cast_fp16")]; + int32 var_134 = const()[name = string("op_134"), val = int32(-1)]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([0, 0, 1])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 128])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([true, true, true])]; + tensor input_7_cast_fp16 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, x = latent_mask_b_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_166_pad_0 = const()[name = string("op_166_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_166_mode_0 = const()[name = string("op_166_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_166_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_166_mode_0, pad = var_166_pad_0, x = input_7_cast_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_133_to_fp16 = const()[name = string("op_133_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = sub(x = var_133_to_fp16, y = var_166_cast_fp16)[name = string("op_167_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = latent_mask_b_cast_fp16, y = var_167_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_169_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_172_cast_fp16 = mul(x = x_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor var_173_cast_fp16 = sub(x = var_133_to_fp16, y = latent_mask_b_cast_fp16)[name = string("op_173_cast_fp16")]; + tensor var_174_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_174_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_174_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("replicate")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor input_11_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_11_mode_0, pad = input_11_pad_0, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(512)]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118272))))[name = string("main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118464)))]; + tensor h_3_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, 2, 1])]; + tensor var_192_axes_0 = const()[name = string("op_192_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119552)))]; + tensor main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120640)))]; + fp16 var_139_to_fp16 = const()[name = string("op_139_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_7_cast_fp16)[name = string("transpose_117")]; + tensor var_192_cast_fp16 = layer_norm(axes = var_192_axes_0, beta = main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16, x = input_13_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, 2, 1])]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908224))))[name = string("main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908416)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = var_192_cast_fp16)[name = string("transpose_116")]; + tensor h_5_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = string("h_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = h_5_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)]; + tensor op_209_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1699072))))[name = string("op_209_weight_0_to_fp16_palettized")]; + tensor var_209_bias_0_to_fp16 = const()[name = string("op_209_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1699264)))]; + tensor var_209_cast_fp16 = conv(bias = var_209_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = op_209_weight_0_to_fp16_palettized, x = input_17_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_209_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_223_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_223_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_226_cast_fp16 = mul(x = x_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_226_cast_fp16")]; + tensor var_228_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_228_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_226_cast_fp16, y = var_228_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")]; + fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)]; + tensor input_23_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([2])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(512)]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1700352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1702336))))[name = string("main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1702528)))]; + tensor h_9_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor var_246_axes_0 = const()[name = string("op_246_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1703616)))]; + tensor main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1704704)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_13_cast_fp16)[name = string("transpose_115")]; + tensor var_246_cast_fp16 = layer_norm(axes = var_246_axes_0, beta = main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_246_cast_fp16")]; + tensor input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor([0, 2, 1])]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1705792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2492288))))[name = string("main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2492480)))]; + tensor input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_246_cast_fp16)[name = string("transpose_114")]; + tensor h_11_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_27_cast_fp16)[name = string("h_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_11_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)]; + tensor op_263_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2496640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3283136))))[name = string("op_263_weight_0_to_fp16_palettized")]; + tensor var_263_bias_0_to_fp16 = const()[name = string("op_263_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3283328)))]; + tensor var_263_cast_fp16 = conv(bias = var_263_bias_0_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = op_263_weight_0_to_fp16_palettized, x = input_29_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_263_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_277_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_277_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_277_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_280_cast_fp16 = mul(x = x_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_280_cast_fp16")]; + tensor var_282_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_280_cast_fp16, y = var_282_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("replicate")]; + fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)]; + tensor input_35_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = input_35_mode_0, pad = input_35_pad_0, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([4])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(512)]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3284416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3286400))))[name = string("main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3286592)))]; + tensor h_15_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor var_300_axes_0 = const()[name = string("op_300_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3287680)))]; + tensor main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3288768)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_19_cast_fp16)[name = string("transpose_113")]; + tensor var_300_cast_fp16 = layer_norm(axes = var_300_axes_0, beta = main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16, x = input_37_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor([0, 2, 1])]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3289856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4076352))))[name = string("main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4076544)))]; + tensor input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_300_cast_fp16)[name = string("transpose_112")]; + tensor h_17_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = string("h_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = h_17_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)]; + tensor op_317_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4080704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4867200))))[name = string("op_317_weight_0_to_fp16_palettized")]; + tensor var_317_bias_0_to_fp16 = const()[name = string("op_317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4867392)))]; + tensor var_317_cast_fp16 = conv(bias = var_317_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = op_317_weight_0_to_fp16_palettized, x = input_41_cast_fp16)[name = string("op_317_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_317_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor var_331_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_331_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_331_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = x_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor var_336_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_336_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_334_cast_fp16, y = var_336_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")]; + fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([8])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(512)]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4868480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4870464))))[name = string("main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4870656)))]; + tensor h_21_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4871744)))]; + tensor main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4872832)))]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_25_cast_fp16)[name = string("transpose_111")]; + tensor var_354_cast_fp16 = layer_norm(axes = var_354_axes_0, beta = main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_354_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4873920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5660416))))[name = string("main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5660608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_354_cast_fp16)[name = string("transpose_110")]; + tensor h_23_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("h_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_23_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)]; + tensor op_371_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5664768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6451264))))[name = string("op_371_weight_0_to_fp16_palettized")]; + tensor var_371_bias_0_to_fp16 = const()[name = string("op_371_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6451456)))]; + tensor var_371_cast_fp16 = conv(bias = var_371_bias_0_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = op_371_weight_0_to_fp16_palettized, x = input_53_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_371_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor input_55_cast_fp16 = squeeze(axes = input_55_axes_0, x = time_emb_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6452544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6477184))))[name = string("main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6477376)))]; + tensor linear_2_cast_fp16 = linear(bias = main_blocks_0_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor t_5_axes_0 = const()[name = string("t_5_axes_0"), val = tensor([-1])]; + tensor t_5_cast_fp16 = expand_dims(axes = t_5_axes_0, x = linear_2_cast_fp16)[name = string("t_5_cast_fp16")]; + tensor var_381_cast_fp16 = add(x = x_27_cast_fp16, y = t_5_cast_fp16)[name = string("op_381_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_381_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_394_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_394_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_394_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_397_cast_fp16 = mul(x = x_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = var_397_cast_fp16, y = var_399_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("replicate")]; + fp16 const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = fp16(0x0p+0)]; + tensor input_61_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = input_61_mode_0, pad = input_61_pad_0, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(512)]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6478464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6480448))))[name = string("main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6480640)))]; + tensor h_27_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417_axes_0 = const()[name = string("op_417_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6481728)))]; + tensor main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6482816)))]; + tensor input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_33_cast_fp16)[name = string("transpose_109")]; + tensor var_417_cast_fp16 = layer_norm(axes = var_417_axes_0, beta = main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16, x = input_63_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6483904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7270400))))[name = string("main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7270592)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = var_417_cast_fp16)[name = string("transpose_108")]; + tensor h_29_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_65_cast_fp16)[name = string("h_29_cast_fp16")]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("EXACT")]; + tensor input_67_cast_fp16 = gelu(mode = input_67_mode_0, x = h_29_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)]; + tensor op_434_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7274752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8061248))))[name = string("op_434_weight_0_to_fp16_palettized")]; + tensor var_434_bias_0_to_fp16 = const()[name = string("op_434_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8061440)))]; + tensor var_434_cast_fp16 = conv(bias = var_434_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = op_434_weight_0_to_fp16_palettized, x = input_67_cast_fp16)[name = string("op_434_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_434_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_m_1_cast_fp16 = mul(x = x_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_1_cast_fp16")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8062528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8259200))))[name = string("main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8259392)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_35_cast_fp16)[name = string("transpose_107")]; + tensor linear_3_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([2, 128, 8, 64])]; + tensor var_453_cast_fp16 = reshape(shape = var_452, x = linear_3_cast_fp16)[name = string("op_453_cast_fp16")]; + tensor x_37_perm_0 = const()[name = string("x_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8260480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8358848))))[name = string("main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8359040)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = text_emb_cast_fp16)[name = string("transpose_106")]; + tensor linear_4_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([2, 128, 8, 64])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = linear_4_cast_fp16)[name = string("op_461_cast_fp16")]; + tensor x_39_perm_0 = const()[name = string("x_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8360128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8458496))))[name = string("main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8458688)))]; + tensor linear_5_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_468 = const()[name = string("op_468"), val = tensor([2, 128, 8, 64])]; + tensor var_469_cast_fp16 = reshape(shape = var_468, x = linear_5_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor divisor_1_axes_0 = const()[name = string("divisor_1_axes_0"), val = tensor([1, 2])]; + bool divisor_1_keep_dims_0 = const()[name = string("divisor_1_keep_dims_0"), val = bool(false)]; + tensor divisor_1_cast_fp16 = reduce_sum(axes = divisor_1_axes_0, keep_dims = divisor_1_keep_dims_0, x = latent_mask_b_cast_fp16)[name = string("divisor_1_cast_fp16")]; + tensor divisor_3_axes_0 = const()[name = string("divisor_3_axes_0"), val = tensor([1, 2])]; + bool divisor_3_keep_dims_0 = const()[name = string("divisor_3_keep_dims_0"), val = bool(false)]; + tensor divisor_3_cast_fp16 = reduce_sum(axes = divisor_3_axes_0, keep_dims = divisor_3_keep_dims_0, x = text_mask_cast_fp16)[name = string("divisor_3_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([-1, 1, 1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = divisor_1_cast_fp16)[name = string("op_488_cast_fp16")]; + tensor const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8459776)))]; + tensor scaled_1_cast_fp16 = real_div(x = const_16_to_fp16, y = var_488_cast_fp16)[name = string("scaled_1_cast_fp16")]; + tensor main_blocks_0_text_attn_theta_to_fp16 = const()[name = string("main_blocks_0_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8460352)))]; + tensor angles_1_cast_fp16 = mul(x = scaled_1_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_1_cast_fp16")]; + tensor var_491_cast_fp16 = cos(x = angles_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = var_491_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor var_493_cast_fp16 = sin(x = angles_1_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = var_493_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor x_a_1_begin_0 = const()[name = string("x_a_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_1_end_0 = const()[name = string("x_a_1_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_1_end_mask_0 = const()[name = string("x_a_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_37_cast_fp16 = transpose(perm = x_37_perm_0, x = var_453_cast_fp16)[name = string("transpose_105")]; + tensor x_a_1_cast_fp16 = slice_by_index(begin = x_a_1_begin_0, end = x_a_1_end_0, end_mask = x_a_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_a_1_cast_fp16")]; + tensor x_b_1_begin_0 = const()[name = string("x_b_1_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_1_end_0 = const()[name = string("x_b_1_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_1_end_mask_0 = const()[name = string("x_b_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_1_cast_fp16 = slice_by_index(begin = x_b_1_begin_0, end = x_b_1_end_0, end_mask = x_b_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_b_1_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = x_a_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor var_498_cast_fp16 = mul(x = x_b_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_498_cast_fp16")]; + tensor rot_a_1_cast_fp16 = sub(x = var_497_cast_fp16, y = var_498_cast_fp16)[name = string("rot_a_1_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = x_a_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = x_b_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor rot_b_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("rot_b_1_cast_fp16")]; + bool q_1_interleave_0 = const()[name = string("q_1_interleave_0"), val = bool(false)]; + tensor q_1_cast_fp16 = concat(axis = var_134, interleave = q_1_interleave_0, values = (rot_a_1_cast_fp16, rot_b_1_cast_fp16))[name = string("q_1_cast_fp16")]; + tensor var_505 = const()[name = string("op_505"), val = tensor([-1, 1, 1])]; + tensor var_506_cast_fp16 = reshape(shape = var_505, x = divisor_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor scaled_3_cast_fp16 = real_div(x = const_16_to_fp16, y = var_506_cast_fp16)[name = string("scaled_3_cast_fp16")]; + tensor angles_3_cast_fp16 = mul(x = scaled_3_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_3_cast_fp16")]; + tensor var_509_cast_fp16 = cos(x = angles_3_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor cos_3_axes_0 = const()[name = string("cos_3_axes_0"), val = tensor([1])]; + tensor cos_3_cast_fp16 = expand_dims(axes = cos_3_axes_0, x = var_509_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor var_511_cast_fp16 = sin(x = angles_3_cast_fp16)[name = string("op_511_cast_fp16")]; + tensor sin_3_axes_0 = const()[name = string("sin_3_axes_0"), val = tensor([1])]; + tensor sin_3_cast_fp16 = expand_dims(axes = sin_3_axes_0, x = var_511_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor x_a_3_begin_0 = const()[name = string("x_a_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_3_end_0 = const()[name = string("x_a_3_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_3_end_mask_0 = const()[name = string("x_a_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_39_cast_fp16 = transpose(perm = x_39_perm_0, x = var_461_cast_fp16)[name = string("transpose_104")]; + tensor x_a_3_cast_fp16 = slice_by_index(begin = x_a_3_begin_0, end = x_a_3_end_0, end_mask = x_a_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_a_3_cast_fp16")]; + tensor x_b_3_begin_0 = const()[name = string("x_b_3_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_3_end_0 = const()[name = string("x_b_3_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_3_end_mask_0 = const()[name = string("x_b_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_3_cast_fp16 = slice_by_index(begin = x_b_3_begin_0, end = x_b_3_end_0, end_mask = x_b_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_b_3_cast_fp16")]; + tensor var_515_cast_fp16 = mul(x = x_a_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_515_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = x_b_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor rot_a_3_cast_fp16 = sub(x = var_515_cast_fp16, y = var_516_cast_fp16)[name = string("rot_a_3_cast_fp16")]; + tensor var_518_cast_fp16 = mul(x = x_a_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = mul(x = x_b_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor rot_b_3_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = string("rot_b_3_cast_fp16")]; + bool k_1_interleave_0 = const()[name = string("k_1_interleave_0"), val = bool(false)]; + tensor k_1_cast_fp16 = concat(axis = var_134, interleave = k_1_interleave_0, values = (rot_a_3_cast_fp16, rot_b_3_cast_fp16))[name = string("k_1_cast_fp16")]; + bool var_524_transpose_x_1 = const()[name = string("op_524_transpose_x_1"), val = bool(false)]; + bool var_524_transpose_y_1 = const()[name = string("op_524_transpose_y_1"), val = bool(true)]; + tensor var_524_cast_fp16 = matmul(transpose_x = var_524_transpose_x_1, transpose_y = var_524_transpose_y_1, x = q_1_cast_fp16, y = k_1_cast_fp16)[name = string("op_524_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_524_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([2])]; + tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = text_mask_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528_cast_fp16 = sub(x = var_133_to_fp16, y = var_527_cast_fp16)[name = string("op_528_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_530_cast_fp16 = mul(x = var_528_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_75_cast_fp16 = sub(x = _inversed_scores_1_cast_fp16, y = var_530_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_134, x = input_75_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool var_533_transpose_x_0 = const()[name = string("op_533_transpose_x_0"), val = bool(false)]; + bool var_533_transpose_y_0 = const()[name = string("op_533_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_469_cast_fp16)[name = string("transpose_103")]; + tensor var_533_cast_fp16 = matmul(transpose_x = var_533_transpose_x_0, transpose_y = var_533_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("op_533_cast_fp16")]; + tensor var_534_perm_0 = const()[name = string("op_534_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_535 = const()[name = string("op_535"), val = tensor([2, 128, 512])]; + tensor var_534_cast_fp16 = transpose(perm = var_534_perm_0, x = var_533_cast_fp16)[name = string("transpose_102")]; + tensor input_77_cast_fp16 = reshape(shape = var_535, x = var_534_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8460480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8657152))))[name = string("main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8657344)))]; + tensor linear_6_cast_fp16 = linear(bias = main_blocks_0_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor out_11_perm_0 = const()[name = string("out_11_perm_0"), val = tensor([0, 2, 1])]; + tensor out_11_cast_fp16 = transpose(perm = out_11_perm_0, x = linear_6_cast_fp16)[name = string("transpose_101")]; + tensor var_542_cast_fp16 = mul(x = out_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_542_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = x_m_1_cast_fp16, y = var_542_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor var_549_axes_0 = const()[name = string("op_549_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8658432)))]; + tensor main_blocks_0_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8659520)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_41_cast_fp16)[name = string("transpose_100")]; + tensor var_549_cast_fp16 = layer_norm(axes = var_549_axes_0, beta = main_blocks_0_text_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_text_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor var_550_perm_0 = const()[name = string("op_550_perm_0"), val = tensor([0, 2, 1])]; + tensor var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = var_549_cast_fp16)[name = string("transpose_99")]; + tensor x_43_cast_fp16 = mul(x = var_550_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = x_45_cast_fp16, y = last_1_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor x_last_11_axes_0 = const()[name = string("x_last_11_axes_0"), val = tensor([-1])]; + bool x_last_11_keep_dims_0 = const()[name = string("x_last_11_keep_dims_0"), val = bool(true)]; + tensor x_last_11_cast_fp16 = reduce_sum(axes = x_last_11_axes_0, keep_dims = x_last_11_keep_dims_0, x = var_563_cast_fp16)[name = string("x_last_11_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = x_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor var_568_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_11_cast_fp16)[name = string("op_568_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = var_566_cast_fp16, y = var_568_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_85_mode_0 = const()[name = string("input_85_mode_0"), val = string("replicate")]; + fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)]; + tensor input_85_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(512)]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8660608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8662592))))[name = string("main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8662784)))]; + tensor h_33_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("h_33_cast_fp16")]; + tensor x_47_cast_fp16 = mul(x = h_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8663872)))]; + tensor main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8664960)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_47_cast_fp16)[name = string("transpose_98")]; + tensor var_586_cast_fp16 = layer_norm(axes = var_586_axes_0, beta = main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_586_cast_fp16")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8666048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9452544))))[name = string("main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9452736)))]; + tensor input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = var_586_cast_fp16)[name = string("transpose_97")]; + tensor h_35_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_89_cast_fp16)[name = string("h_35_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = h_35_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")]; + tensor h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor([1])]; + tensor h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor([0, 0])]; + tensor h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor([1])]; + int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)]; + tensor op_603_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9456896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10243392))))[name = string("op_603_weight_0_to_fp16_palettized")]; + tensor var_603_bias_0_to_fp16 = const()[name = string("op_603_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10243584)))]; + tensor var_603_cast_fp16 = conv(bias = var_603_bias_0_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = op_603_weight_0_to_fp16_palettized, x = input_91_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor out_13_cast_fp16 = add(x = x_45_cast_fp16, y = var_603_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_49_cast_fp16 = mul(x = out_13_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_m_3_cast_fp16 = mul(x = x_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_3_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10244672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10343040))))[name = string("main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10343232)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_49_cast_fp16)[name = string("transpose_96")]; + tensor linear_7_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([2, 128, 2, 128])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = linear_7_cast_fp16)[name = string("op_620_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10343808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10393024))))[name = string("main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10393216)))]; + tensor linear_9_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_633 = const()[name = string("op_633"), val = tensor([2, 50, 2, 128])]; + tensor var_634_cast_fp16 = reshape(shape = var_633, x = linear_9_cast_fp16)[name = string("op_634_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_638_transpose_x_0 = const()[name = string("op_638_transpose_x_0"), val = bool(false)]; + bool var_638_transpose_y_0 = const()[name = string("op_638_transpose_y_0"), val = bool(false)]; + tensor op_637_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10393792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10413056))))[name = string("op_637_to_fp16_palettized")]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_620_cast_fp16)[name = string("transpose_95")]; + tensor var_638_cast_fp16 = matmul(transpose_x = var_638_transpose_x_0, transpose_y = var_638_transpose_y_0, x = q_3_cast_fp16, y = op_637_to_fp16_palettized)[name = string("op_638_cast_fp16")]; + fp16 _inversed_input_97_y_0_to_fp16 = const()[name = string("_inversed_input_97_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_97_cast_fp16 = mul(x = var_638_cast_fp16, y = _inversed_input_97_y_0_to_fp16)[name = string("_inversed_input_97_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_134, x = _inversed_input_97_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_642_perm_0 = const()[name = string("op_642_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_1_axes_0 = const()[name = string("mask_1_axes_0"), val = tensor([1])]; + tensor var_642_cast_fp16 = transpose(perm = var_642_perm_0, x = latent_mask_b_cast_fp16)[name = string("transpose_94")]; + tensor mask_1_cast_fp16 = expand_dims(axes = mask_1_axes_0, x = var_642_cast_fp16)[name = string("mask_1_cast_fp16")]; + tensor attn_5_cast_fp16 = mul(x = attn_3_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool var_645_transpose_x_0 = const()[name = string("op_645_transpose_x_0"), val = bool(false)]; + bool var_645_transpose_y_0 = const()[name = string("op_645_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_634_cast_fp16)[name = string("transpose_93")]; + tensor var_645_cast_fp16 = matmul(transpose_x = var_645_transpose_x_0, transpose_y = var_645_transpose_y_0, x = attn_5_cast_fp16, y = v_3_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_646_perm_0 = const()[name = string("op_646_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_647 = const()[name = string("op_647"), val = tensor([2, 128, 256])]; + tensor var_646_cast_fp16 = transpose(perm = var_646_perm_0, x = var_645_cast_fp16)[name = string("transpose_92")]; + tensor input_99_cast_fp16 = reshape(shape = var_647, x = var_646_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10413248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10511616))))[name = string("main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10511808)))]; + tensor linear_10_cast_fp16 = linear(bias = main_blocks_0_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor out_15_perm_0 = const()[name = string("out_15_perm_0"), val = tensor([0, 2, 1])]; + tensor out_15_cast_fp16 = transpose(perm = out_15_perm_0, x = linear_10_cast_fp16)[name = string("transpose_91")]; + tensor var_654_cast_fp16 = mul(x = out_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor x_51_cast_fp16 = add(x = x_m_3_cast_fp16, y = var_654_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10512896)))]; + tensor main_blocks_0_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10513984)))]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_51_cast_fp16)[name = string("transpose_90")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = main_blocks_0_style_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_style_norm_norm_weight_to_fp16, x = input_101_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_662_perm_0 = const()[name = string("op_662_perm_0"), val = tensor([0, 2, 1])]; + tensor var_662_cast_fp16 = transpose(perm = var_662_perm_0, x = var_661_cast_fp16)[name = string("transpose_89")]; + tensor x_53_cast_fp16 = mul(x = var_662_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_53_cast_fp16")]; + int32 var_681 = const()[name = string("op_681"), val = int32(-1)]; + tensor x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = x_55_cast_fp16, y = last_1_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor x_last_13_axes_0 = const()[name = string("x_last_13_axes_0"), val = tensor([-1])]; + bool x_last_13_keep_dims_0 = const()[name = string("x_last_13_keep_dims_0"), val = bool(true)]; + tensor x_last_13_cast_fp16 = reduce_sum(axes = x_last_13_axes_0, keep_dims = x_last_13_keep_dims_0, x = var_716_cast_fp16)[name = string("x_last_13_cast_fp16")]; + tensor var_719_cast_fp16 = mul(x = x_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_721_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_13_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_719_cast_fp16, y = var_721_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("replicate")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(512)]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10515072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10517056))))[name = string("main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10517248)))]; + tensor h_39_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor x_57_cast_fp16 = mul(x = h_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_739_axes_0 = const()[name = string("op_739_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10518336)))]; + tensor main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10519424)))]; + fp16 var_686_to_fp16 = const()[name = string("op_686_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_57_cast_fp16)[name = string("transpose_88")]; + tensor var_739_cast_fp16 = layer_norm(axes = var_739_axes_0, beta = main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_739_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + string h_41_pad_type_0 = const()[name = string("h_41_pad_type_0"), val = string("valid")]; + tensor h_41_strides_0 = const()[name = string("h_41_strides_0"), val = tensor([1])]; + tensor h_41_pad_0 = const()[name = string("h_41_pad_0"), val = tensor([0, 0])]; + tensor h_41_dilations_0 = const()[name = string("h_41_dilations_0"), val = tensor([1])]; + int32 h_41_groups_0 = const()[name = string("h_41_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10520512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11307008))))[name = string("main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11307200)))]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = var_739_cast_fp16)[name = string("transpose_87")]; + tensor h_41_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_41_dilations_0, groups = h_41_groups_0, pad = h_41_pad_0, pad_type = h_41_pad_type_0, strides = h_41_strides_0, weight = main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = h_41_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor op_756_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11311360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12097856))))[name = string("op_756_weight_0_to_fp16_palettized")]; + tensor var_756_bias_0_to_fp16 = const()[name = string("op_756_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12098048)))]; + tensor var_756_cast_fp16 = conv(bias = var_756_bias_0_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = op_756_weight_0_to_fp16_palettized, x = input_113_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor out_17_cast_fp16 = add(x = x_55_cast_fp16, y = var_756_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_59_cast_fp16 = mul(x = out_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor x_61_cast_fp16 = mul(x = x_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_770_cast_fp16 = mul(x = x_61_cast_fp16, y = last_1_cast_fp16)[name = string("op_770_cast_fp16")]; + tensor x_last_15_axes_0 = const()[name = string("x_last_15_axes_0"), val = tensor([-1])]; + bool x_last_15_keep_dims_0 = const()[name = string("x_last_15_keep_dims_0"), val = bool(true)]; + tensor x_last_15_cast_fp16 = reduce_sum(axes = x_last_15_axes_0, keep_dims = x_last_15_keep_dims_0, x = var_770_cast_fp16)[name = string("x_last_15_cast_fp16")]; + tensor var_773_cast_fp16 = mul(x = x_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor var_775_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_15_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = var_773_cast_fp16, y = var_775_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("replicate")]; + fp16 const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = fp16(0x0p+0)]; + tensor input_119_cast_fp16 = pad(constant_val = const_26_to_fp16, mode = input_119_mode_0, pad = input_119_pad_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")]; + string h_45_pad_type_0 = const()[name = string("h_45_pad_type_0"), val = string("valid")]; + tensor h_45_dilations_0 = const()[name = string("h_45_dilations_0"), val = tensor([2])]; + int32 h_45_groups_0 = const()[name = string("h_45_groups_0"), val = int32(512)]; + tensor h_45_strides_0 = const()[name = string("h_45_strides_0"), val = tensor([1])]; + tensor h_45_pad_0 = const()[name = string("h_45_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12099136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12101120))))[name = string("main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12101312)))]; + tensor h_45_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_45_dilations_0, groups = h_45_groups_0, pad = h_45_pad_0, pad_type = h_45_pad_type_0, strides = h_45_strides_0, weight = main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = h_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12102400)))]; + tensor main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12103488)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_63_cast_fp16)[name = string("transpose_86")]; + tensor var_793_cast_fp16 = layer_norm(axes = var_793_axes_0, beta = main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor input_123_perm_0 = const()[name = string("input_123_perm_0"), val = tensor([0, 2, 1])]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12104576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12891072))))[name = string("main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12891264)))]; + tensor input_123_cast_fp16 = transpose(perm = input_123_perm_0, x = var_793_cast_fp16)[name = string("transpose_85")]; + tensor h_47_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = string("h_47_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = h_47_cast_fp16)[name = string("input_125_cast_fp16")]; + string h_49_pad_type_0 = const()[name = string("h_49_pad_type_0"), val = string("valid")]; + tensor h_49_strides_0 = const()[name = string("h_49_strides_0"), val = tensor([1])]; + tensor h_49_pad_0 = const()[name = string("h_49_pad_0"), val = tensor([0, 0])]; + tensor h_49_dilations_0 = const()[name = string("h_49_dilations_0"), val = tensor([1])]; + int32 h_49_groups_0 = const()[name = string("h_49_groups_0"), val = int32(1)]; + tensor op_810_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12895424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13681920))))[name = string("op_810_weight_0_to_fp16_palettized")]; + tensor var_810_bias_0_to_fp16 = const()[name = string("op_810_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13682112)))]; + tensor var_810_cast_fp16 = conv(bias = var_810_bias_0_to_fp16, dilations = h_49_dilations_0, groups = h_49_groups_0, pad = h_49_pad_0, pad_type = h_49_pad_type_0, strides = h_49_strides_0, weight = op_810_weight_0_to_fp16_palettized, x = input_125_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = x_61_cast_fp16, y = var_810_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor x_65_cast_fp16 = mul(x = out_19_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = x_67_cast_fp16, y = last_1_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_last_17_axes_0 = const()[name = string("x_last_17_axes_0"), val = tensor([-1])]; + bool x_last_17_keep_dims_0 = const()[name = string("x_last_17_keep_dims_0"), val = bool(true)]; + tensor x_last_17_cast_fp16 = reduce_sum(axes = x_last_17_axes_0, keep_dims = x_last_17_keep_dims_0, x = var_824_cast_fp16)[name = string("x_last_17_cast_fp16")]; + tensor var_827_cast_fp16 = mul(x = x_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_829_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_17_cast_fp16)[name = string("op_829_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = var_827_cast_fp16, y = var_829_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("replicate")]; + fp16 const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = fp16(0x0p+0)]; + tensor input_131_cast_fp16 = pad(constant_val = const_28_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + string h_51_pad_type_0 = const()[name = string("h_51_pad_type_0"), val = string("valid")]; + tensor h_51_dilations_0 = const()[name = string("h_51_dilations_0"), val = tensor([4])]; + int32 h_51_groups_0 = const()[name = string("h_51_groups_0"), val = int32(512)]; + tensor h_51_strides_0 = const()[name = string("h_51_strides_0"), val = tensor([1])]; + tensor h_51_pad_0 = const()[name = string("h_51_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13683200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13685184))))[name = string("main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13685376)))]; + tensor h_51_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_51_dilations_0, groups = h_51_groups_0, pad = h_51_pad_0, pad_type = h_51_pad_type_0, strides = h_51_strides_0, weight = main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor x_69_cast_fp16 = mul(x = h_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_847_axes_0 = const()[name = string("op_847_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13686464)))]; + tensor main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13687552)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_69_cast_fp16)[name = string("transpose_84")]; + tensor var_847_cast_fp16 = layer_norm(axes = var_847_axes_0, beta = main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_847_cast_fp16")]; + tensor input_135_perm_0 = const()[name = string("input_135_perm_0"), val = tensor([0, 2, 1])]; + string h_53_pad_type_0 = const()[name = string("h_53_pad_type_0"), val = string("valid")]; + tensor h_53_strides_0 = const()[name = string("h_53_strides_0"), val = tensor([1])]; + tensor h_53_pad_0 = const()[name = string("h_53_pad_0"), val = tensor([0, 0])]; + tensor h_53_dilations_0 = const()[name = string("h_53_dilations_0"), val = tensor([1])]; + int32 h_53_groups_0 = const()[name = string("h_53_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13688640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14475136))))[name = string("main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14475328)))]; + tensor input_135_cast_fp16 = transpose(perm = input_135_perm_0, x = var_847_cast_fp16)[name = string("transpose_83")]; + tensor h_53_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_53_dilations_0, groups = h_53_groups_0, pad = h_53_pad_0, pad_type = h_53_pad_type_0, strides = h_53_strides_0, weight = main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = string("h_53_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = h_53_cast_fp16)[name = string("input_137_cast_fp16")]; + string h_55_pad_type_0 = const()[name = string("h_55_pad_type_0"), val = string("valid")]; + tensor h_55_strides_0 = const()[name = string("h_55_strides_0"), val = tensor([1])]; + tensor h_55_pad_0 = const()[name = string("h_55_pad_0"), val = tensor([0, 0])]; + tensor h_55_dilations_0 = const()[name = string("h_55_dilations_0"), val = tensor([1])]; + int32 h_55_groups_0 = const()[name = string("h_55_groups_0"), val = int32(1)]; + tensor op_864_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14479488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15265984))))[name = string("op_864_weight_0_to_fp16_palettized")]; + tensor var_864_bias_0_to_fp16 = const()[name = string("op_864_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15266176)))]; + tensor var_864_cast_fp16 = conv(bias = var_864_bias_0_to_fp16, dilations = h_55_dilations_0, groups = h_55_groups_0, pad = h_55_pad_0, pad_type = h_55_pad_type_0, strides = h_55_strides_0, weight = op_864_weight_0_to_fp16_palettized, x = input_137_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor out_21_cast_fp16 = add(x = x_67_cast_fp16, y = var_864_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = out_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor x_73_cast_fp16 = mul(x = x_71_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_878_cast_fp16 = mul(x = x_73_cast_fp16, y = last_1_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor x_last_19_axes_0 = const()[name = string("x_last_19_axes_0"), val = tensor([-1])]; + bool x_last_19_keep_dims_0 = const()[name = string("x_last_19_keep_dims_0"), val = bool(true)]; + tensor x_last_19_cast_fp16 = reduce_sum(axes = x_last_19_axes_0, keep_dims = x_last_19_keep_dims_0, x = var_878_cast_fp16)[name = string("x_last_19_cast_fp16")]; + tensor var_881_cast_fp16 = mul(x = x_73_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_881_cast_fp16")]; + tensor var_883_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_19_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = var_881_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("replicate")]; + fp16 const_30_to_fp16 = const()[name = string("const_30_to_fp16"), val = fp16(0x0p+0)]; + tensor input_143_cast_fp16 = pad(constant_val = const_30_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + string h_57_pad_type_0 = const()[name = string("h_57_pad_type_0"), val = string("valid")]; + tensor h_57_dilations_0 = const()[name = string("h_57_dilations_0"), val = tensor([8])]; + int32 h_57_groups_0 = const()[name = string("h_57_groups_0"), val = int32(512)]; + tensor h_57_strides_0 = const()[name = string("h_57_strides_0"), val = tensor([1])]; + tensor h_57_pad_0 = const()[name = string("h_57_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15267264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15269248))))[name = string("main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15269440)))]; + tensor h_57_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_57_dilations_0, groups = h_57_groups_0, pad = h_57_pad_0, pad_type = h_57_pad_type_0, strides = h_57_strides_0, weight = main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = string("h_57_cast_fp16")]; + tensor x_75_cast_fp16 = mul(x = h_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_901_axes_0 = const()[name = string("op_901_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15270528)))]; + tensor main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15271616)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_75_cast_fp16)[name = string("transpose_82")]; + tensor var_901_cast_fp16 = layer_norm(axes = var_901_axes_0, beta = main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor input_147_perm_0 = const()[name = string("input_147_perm_0"), val = tensor([0, 2, 1])]; + string h_59_pad_type_0 = const()[name = string("h_59_pad_type_0"), val = string("valid")]; + tensor h_59_strides_0 = const()[name = string("h_59_strides_0"), val = tensor([1])]; + tensor h_59_pad_0 = const()[name = string("h_59_pad_0"), val = tensor([0, 0])]; + tensor h_59_dilations_0 = const()[name = string("h_59_dilations_0"), val = tensor([1])]; + int32 h_59_groups_0 = const()[name = string("h_59_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15272704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16059200))))[name = string("main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16059392)))]; + tensor input_147_cast_fp16 = transpose(perm = input_147_perm_0, x = var_901_cast_fp16)[name = string("transpose_81")]; + tensor h_59_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_59_dilations_0, groups = h_59_groups_0, pad = h_59_pad_0, pad_type = h_59_pad_type_0, strides = h_59_strides_0, weight = main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("h_59_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = h_59_cast_fp16)[name = string("input_149_cast_fp16")]; + string h_61_pad_type_0 = const()[name = string("h_61_pad_type_0"), val = string("valid")]; + tensor h_61_strides_0 = const()[name = string("h_61_strides_0"), val = tensor([1])]; + tensor h_61_pad_0 = const()[name = string("h_61_pad_0"), val = tensor([0, 0])]; + tensor h_61_dilations_0 = const()[name = string("h_61_dilations_0"), val = tensor([1])]; + int32 h_61_groups_0 = const()[name = string("h_61_groups_0"), val = int32(1)]; + tensor op_918_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16063552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16850048))))[name = string("op_918_weight_0_to_fp16_palettized")]; + tensor var_918_bias_0_to_fp16 = const()[name = string("op_918_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16850240)))]; + tensor var_918_cast_fp16 = conv(bias = var_918_bias_0_to_fp16, dilations = h_61_dilations_0, groups = h_61_groups_0, pad = h_61_pad_0, pad_type = h_61_pad_type_0, strides = h_61_strides_0, weight = op_918_weight_0_to_fp16_palettized, x = input_149_cast_fp16)[name = string("op_918_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = x_73_cast_fp16, y = var_918_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor x_77_cast_fp16 = mul(x = out_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16851328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16875968))))[name = string("main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16876160)))]; + tensor linear_11_cast_fp16 = linear(bias = main_blocks_1_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor t_7_axes_0 = const()[name = string("t_7_axes_0"), val = tensor([-1])]; + tensor t_7_cast_fp16 = expand_dims(axes = t_7_axes_0, x = linear_11_cast_fp16)[name = string("t_7_cast_fp16")]; + tensor var_928_cast_fp16 = add(x = x_77_cast_fp16, y = t_7_cast_fp16)[name = string("op_928_cast_fp16")]; + tensor x_79_cast_fp16 = mul(x = var_928_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor x_81_cast_fp16 = mul(x = x_79_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = x_81_cast_fp16, y = last_1_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor x_last_21_axes_0 = const()[name = string("x_last_21_axes_0"), val = tensor([-1])]; + bool x_last_21_keep_dims_0 = const()[name = string("x_last_21_keep_dims_0"), val = bool(true)]; + tensor x_last_21_cast_fp16 = reduce_sum(axes = x_last_21_axes_0, keep_dims = x_last_21_keep_dims_0, x = var_941_cast_fp16)[name = string("x_last_21_cast_fp16")]; + tensor var_944_cast_fp16 = mul(x = x_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor var_946_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_21_cast_fp16)[name = string("op_946_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = var_944_cast_fp16, y = var_946_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("replicate")]; + fp16 const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = fp16(0x0p+0)]; + tensor input_157_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string h_63_pad_type_0 = const()[name = string("h_63_pad_type_0"), val = string("valid")]; + int32 h_63_groups_0 = const()[name = string("h_63_groups_0"), val = int32(512)]; + tensor h_63_strides_0 = const()[name = string("h_63_strides_0"), val = tensor([1])]; + tensor h_63_pad_0 = const()[name = string("h_63_pad_0"), val = tensor([0, 0])]; + tensor h_63_dilations_0 = const()[name = string("h_63_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16877248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16879232))))[name = string("main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16879424)))]; + tensor h_63_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_63_dilations_0, groups = h_63_groups_0, pad = h_63_pad_0, pad_type = h_63_pad_type_0, strides = h_63_strides_0, weight = main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor x_83_cast_fp16 = mul(x = h_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor var_964_axes_0 = const()[name = string("op_964_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16880512)))]; + tensor main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16881600)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_83_cast_fp16)[name = string("transpose_80")]; + tensor var_964_cast_fp16 = layer_norm(axes = var_964_axes_0, beta = main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + string h_65_pad_type_0 = const()[name = string("h_65_pad_type_0"), val = string("valid")]; + tensor h_65_strides_0 = const()[name = string("h_65_strides_0"), val = tensor([1])]; + tensor h_65_pad_0 = const()[name = string("h_65_pad_0"), val = tensor([0, 0])]; + tensor h_65_dilations_0 = const()[name = string("h_65_dilations_0"), val = tensor([1])]; + int32 h_65_groups_0 = const()[name = string("h_65_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16882688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17669184))))[name = string("main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17669376)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = var_964_cast_fp16)[name = string("transpose_79")]; + tensor h_65_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_65_dilations_0, groups = h_65_groups_0, pad = h_65_pad_0, pad_type = h_65_pad_type_0, strides = h_65_strides_0, weight = main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = string("h_65_cast_fp16")]; + string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("EXACT")]; + tensor input_163_cast_fp16 = gelu(mode = input_163_mode_0, x = h_65_cast_fp16)[name = string("input_163_cast_fp16")]; + string h_67_pad_type_0 = const()[name = string("h_67_pad_type_0"), val = string("valid")]; + tensor h_67_strides_0 = const()[name = string("h_67_strides_0"), val = tensor([1])]; + tensor h_67_pad_0 = const()[name = string("h_67_pad_0"), val = tensor([0, 0])]; + tensor h_67_dilations_0 = const()[name = string("h_67_dilations_0"), val = tensor([1])]; + int32 h_67_groups_0 = const()[name = string("h_67_groups_0"), val = int32(1)]; + tensor op_981_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17673536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18460032))))[name = string("op_981_weight_0_to_fp16_palettized")]; + tensor var_981_bias_0_to_fp16 = const()[name = string("op_981_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18460224)))]; + tensor var_981_cast_fp16 = conv(bias = var_981_bias_0_to_fp16, dilations = h_67_dilations_0, groups = h_67_groups_0, pad = h_67_pad_0, pad_type = h_67_pad_type_0, strides = h_67_strides_0, weight = op_981_weight_0_to_fp16_palettized, x = input_163_cast_fp16)[name = string("op_981_cast_fp16")]; + tensor out_25_cast_fp16 = add(x = x_81_cast_fp16, y = var_981_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = out_25_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor x_m_5_cast_fp16 = mul(x = x_85_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_5_cast_fp16")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18461312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18657984))))[name = string("main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18658176)))]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_85_cast_fp16)[name = string("transpose_78")]; + tensor linear_12_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_999 = const()[name = string("op_999"), val = tensor([2, 128, 8, 64])]; + tensor var_1000_cast_fp16 = reshape(shape = var_999, x = linear_12_cast_fp16)[name = string("op_1000_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18659264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18757632))))[name = string("main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18757824)))]; + tensor linear_13_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_1007 = const()[name = string("op_1007"), val = tensor([2, 128, 8, 64])]; + tensor var_1008_cast_fp16 = reshape(shape = var_1007, x = linear_13_cast_fp16)[name = string("op_1008_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18758912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18857280))))[name = string("main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18857472)))]; + tensor linear_14_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_1015 = const()[name = string("op_1015"), val = tensor([2, 128, 8, 64])]; + tensor var_1016_cast_fp16 = reshape(shape = var_1015, x = linear_14_cast_fp16)[name = string("op_1016_cast_fp16")]; + tensor v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_5_begin_0 = const()[name = string("x_a_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_5_end_0 = const()[name = string("x_a_5_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_5_end_mask_0 = const()[name = string("x_a_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_1000_cast_fp16)[name = string("transpose_77")]; + tensor x_a_5_cast_fp16 = slice_by_index(begin = x_a_5_begin_0, end = x_a_5_end_0, end_mask = x_a_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_a_5_cast_fp16")]; + tensor x_b_5_begin_0 = const()[name = string("x_b_5_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_5_end_0 = const()[name = string("x_b_5_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_5_end_mask_0 = const()[name = string("x_b_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_5_cast_fp16 = slice_by_index(begin = x_b_5_begin_0, end = x_b_5_end_0, end_mask = x_b_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_b_5_cast_fp16")]; + tensor var_1044_cast_fp16 = mul(x = x_a_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1044_cast_fp16")]; + tensor var_1045_cast_fp16 = mul(x = x_b_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor rot_a_5_cast_fp16 = sub(x = var_1044_cast_fp16, y = var_1045_cast_fp16)[name = string("rot_a_5_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_a_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_cast_fp16 = mul(x = x_b_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1048_cast_fp16")]; + tensor rot_b_5_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("rot_b_5_cast_fp16")]; + bool q_5_interleave_0 = const()[name = string("q_5_interleave_0"), val = bool(false)]; + tensor q_5_cast_fp16 = concat(axis = var_681, interleave = q_5_interleave_0, values = (rot_a_5_cast_fp16, rot_b_5_cast_fp16))[name = string("q_5_cast_fp16")]; + tensor x_a_7_begin_0 = const()[name = string("x_a_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_7_end_0 = const()[name = string("x_a_7_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_7_end_mask_0 = const()[name = string("x_a_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_1008_cast_fp16)[name = string("transpose_76")]; + tensor x_a_7_cast_fp16 = slice_by_index(begin = x_a_7_begin_0, end = x_a_7_end_0, end_mask = x_a_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_a_7_cast_fp16")]; + tensor x_b_7_begin_0 = const()[name = string("x_b_7_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_7_end_0 = const()[name = string("x_b_7_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_7_end_mask_0 = const()[name = string("x_b_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_7_cast_fp16 = slice_by_index(begin = x_b_7_begin_0, end = x_b_7_end_0, end_mask = x_b_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_b_7_cast_fp16")]; + tensor var_1062_cast_fp16 = mul(x = x_a_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = x_b_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor rot_a_7_cast_fp16 = sub(x = var_1062_cast_fp16, y = var_1063_cast_fp16)[name = string("rot_a_7_cast_fp16")]; + tensor var_1065_cast_fp16 = mul(x = x_a_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1065_cast_fp16")]; + tensor var_1066_cast_fp16 = mul(x = x_b_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1066_cast_fp16")]; + tensor rot_b_7_cast_fp16 = add(x = var_1065_cast_fp16, y = var_1066_cast_fp16)[name = string("rot_b_7_cast_fp16")]; + bool k_5_interleave_0 = const()[name = string("k_5_interleave_0"), val = bool(false)]; + tensor k_5_cast_fp16 = concat(axis = var_681, interleave = k_5_interleave_0, values = (rot_a_7_cast_fp16, rot_b_7_cast_fp16))[name = string("k_5_cast_fp16")]; + bool var_1071_transpose_x_1 = const()[name = string("op_1071_transpose_x_1"), val = bool(false)]; + bool var_1071_transpose_y_1 = const()[name = string("op_1071_transpose_y_1"), val = bool(true)]; + tensor var_1071_cast_fp16 = matmul(transpose_x = var_1071_transpose_x_1, transpose_y = var_1071_transpose_y_1, x = q_5_cast_fp16, y = k_5_cast_fp16)[name = string("op_1071_cast_fp16")]; + fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_3_cast_fp16 = mul(x = var_1071_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")]; + tensor input_171_cast_fp16 = sub(x = _inversed_scores_3_cast_fp16, y = var_530_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_681, x = input_171_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool var_1080_transpose_x_0 = const()[name = string("op_1080_transpose_x_0"), val = bool(false)]; + bool var_1080_transpose_y_0 = const()[name = string("op_1080_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1016_cast_fp16)[name = string("transpose_75")]; + tensor var_1080_cast_fp16 = matmul(transpose_x = var_1080_transpose_x_0, transpose_y = var_1080_transpose_y_0, x = attn_7_cast_fp16, y = v_5_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_perm_0 = const()[name = string("op_1081_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1082 = const()[name = string("op_1082"), val = tensor([2, 128, 512])]; + tensor var_1081_cast_fp16 = transpose(perm = var_1081_perm_0, x = var_1080_cast_fp16)[name = string("transpose_74")]; + tensor input_173_cast_fp16 = reshape(shape = var_1082, x = var_1081_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18858560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19055232))))[name = string("main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19055424)))]; + tensor linear_15_cast_fp16 = linear(bias = main_blocks_1_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor out_27_perm_0 = const()[name = string("out_27_perm_0"), val = tensor([0, 2, 1])]; + tensor out_27_cast_fp16 = transpose(perm = out_27_perm_0, x = linear_15_cast_fp16)[name = string("transpose_73")]; + tensor var_1089_cast_fp16 = mul(x = out_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor x_91_cast_fp16 = add(x = x_m_5_cast_fp16, y = var_1089_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_175_perm_0 = const()[name = string("input_175_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1096_axes_0 = const()[name = string("op_1096_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19056512)))]; + tensor main_blocks_1_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19057600)))]; + tensor input_175_cast_fp16 = transpose(perm = input_175_perm_0, x = x_91_cast_fp16)[name = string("transpose_72")]; + tensor var_1096_cast_fp16 = layer_norm(axes = var_1096_axes_0, beta = main_blocks_1_text_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_text_norm_norm_weight_to_fp16, x = input_175_cast_fp16)[name = string("op_1096_cast_fp16")]; + tensor var_1097_perm_0 = const()[name = string("op_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1097_cast_fp16 = transpose(perm = var_1097_perm_0, x = var_1096_cast_fp16)[name = string("transpose_71")]; + tensor x_93_cast_fp16 = mul(x = var_1097_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor x_95_cast_fp16 = mul(x = x_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_1110_cast_fp16 = mul(x = x_95_cast_fp16, y = last_1_cast_fp16)[name = string("op_1110_cast_fp16")]; + tensor x_last_23_axes_0 = const()[name = string("x_last_23_axes_0"), val = tensor([-1])]; + bool x_last_23_keep_dims_0 = const()[name = string("x_last_23_keep_dims_0"), val = bool(true)]; + tensor x_last_23_cast_fp16 = reduce_sum(axes = x_last_23_axes_0, keep_dims = x_last_23_keep_dims_0, x = var_1110_cast_fp16)[name = string("x_last_23_cast_fp16")]; + tensor var_1113_cast_fp16 = mul(x = x_95_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1115_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_23_cast_fp16)[name = string("op_1115_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = var_1113_cast_fp16, y = var_1115_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("replicate")]; + fp16 const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = fp16(0x0p+0)]; + tensor input_181_cast_fp16 = pad(constant_val = const_39_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + string h_69_pad_type_0 = const()[name = string("h_69_pad_type_0"), val = string("valid")]; + int32 h_69_groups_0 = const()[name = string("h_69_groups_0"), val = int32(512)]; + tensor h_69_strides_0 = const()[name = string("h_69_strides_0"), val = tensor([1])]; + tensor h_69_pad_0 = const()[name = string("h_69_pad_0"), val = tensor([0, 0])]; + tensor h_69_dilations_0 = const()[name = string("h_69_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19058688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19060672))))[name = string("main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19060864)))]; + tensor h_69_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_69_dilations_0, groups = h_69_groups_0, pad = h_69_pad_0, pad_type = h_69_pad_type_0, strides = h_69_strides_0, weight = main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = string("h_69_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = h_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_183_perm_0 = const()[name = string("input_183_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1133_axes_0 = const()[name = string("op_1133_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19061952)))]; + tensor main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19063040)))]; + tensor input_183_cast_fp16 = transpose(perm = input_183_perm_0, x = x_97_cast_fp16)[name = string("transpose_70")]; + tensor var_1133_cast_fp16 = layer_norm(axes = var_1133_axes_0, beta = main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16, x = input_183_cast_fp16)[name = string("op_1133_cast_fp16")]; + tensor input_185_perm_0 = const()[name = string("input_185_perm_0"), val = tensor([0, 2, 1])]; + string h_71_pad_type_0 = const()[name = string("h_71_pad_type_0"), val = string("valid")]; + tensor h_71_strides_0 = const()[name = string("h_71_strides_0"), val = tensor([1])]; + tensor h_71_pad_0 = const()[name = string("h_71_pad_0"), val = tensor([0, 0])]; + tensor h_71_dilations_0 = const()[name = string("h_71_dilations_0"), val = tensor([1])]; + int32 h_71_groups_0 = const()[name = string("h_71_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19064128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19850624))))[name = string("main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19850816)))]; + tensor input_185_cast_fp16 = transpose(perm = input_185_perm_0, x = var_1133_cast_fp16)[name = string("transpose_69")]; + tensor h_71_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_71_dilations_0, groups = h_71_groups_0, pad = h_71_pad_0, pad_type = h_71_pad_type_0, strides = h_71_strides_0, weight = main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = string("h_71_cast_fp16")]; + string input_187_mode_0 = const()[name = string("input_187_mode_0"), val = string("EXACT")]; + tensor input_187_cast_fp16 = gelu(mode = input_187_mode_0, x = h_71_cast_fp16)[name = string("input_187_cast_fp16")]; + string h_73_pad_type_0 = const()[name = string("h_73_pad_type_0"), val = string("valid")]; + tensor h_73_strides_0 = const()[name = string("h_73_strides_0"), val = tensor([1])]; + tensor h_73_pad_0 = const()[name = string("h_73_pad_0"), val = tensor([0, 0])]; + tensor h_73_dilations_0 = const()[name = string("h_73_dilations_0"), val = tensor([1])]; + int32 h_73_groups_0 = const()[name = string("h_73_groups_0"), val = int32(1)]; + tensor op_1150_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19854976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20641472))))[name = string("op_1150_weight_0_to_fp16_palettized")]; + tensor var_1150_bias_0_to_fp16 = const()[name = string("op_1150_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20641664)))]; + tensor var_1150_cast_fp16 = conv(bias = var_1150_bias_0_to_fp16, dilations = h_73_dilations_0, groups = h_73_groups_0, pad = h_73_pad_0, pad_type = h_73_pad_type_0, strides = h_73_strides_0, weight = op_1150_weight_0_to_fp16_palettized, x = input_187_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor out_29_cast_fp16 = add(x = x_95_cast_fp16, y = var_1150_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor x_99_cast_fp16 = mul(x = out_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor x_m_7_cast_fp16 = mul(x = x_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_7_cast_fp16")]; + tensor input_189_perm_0 = const()[name = string("input_189_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20642752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741120))))[name = string("main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741312)))]; + tensor input_189_cast_fp16 = transpose(perm = input_189_perm_0, x = x_99_cast_fp16)[name = string("transpose_68")]; + tensor linear_16_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_1166 = const()[name = string("op_1166"), val = tensor([2, 128, 2, 128])]; + tensor var_1167_cast_fp16 = reshape(shape = var_1166, x = linear_16_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20791104))))[name = string("main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20791296)))]; + tensor linear_18_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_1180 = const()[name = string("op_1180"), val = tensor([2, 50, 2, 128])]; + tensor var_1181_cast_fp16 = reshape(shape = var_1180, x = linear_18_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1185_transpose_x_0 = const()[name = string("op_1185_transpose_x_0"), val = bool(false)]; + bool var_1185_transpose_y_0 = const()[name = string("op_1185_transpose_y_0"), val = bool(false)]; + tensor op_1184_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20791872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20811136))))[name = string("op_1184_to_fp16_palettized")]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1167_cast_fp16)[name = string("transpose_67")]; + tensor var_1185_cast_fp16 = matmul(transpose_x = var_1185_transpose_x_0, transpose_y = var_1185_transpose_y_0, x = q_7_cast_fp16, y = op_1184_to_fp16_palettized)[name = string("op_1185_cast_fp16")]; + fp16 _inversed_input_191_y_0_to_fp16 = const()[name = string("_inversed_input_191_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_191_cast_fp16 = mul(x = var_1185_cast_fp16, y = _inversed_input_191_y_0_to_fp16)[name = string("_inversed_input_191_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_681, x = _inversed_input_191_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor attn_11_cast_fp16 = mul(x = attn_9_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool var_1192_transpose_x_0 = const()[name = string("op_1192_transpose_x_0"), val = bool(false)]; + bool var_1192_transpose_y_0 = const()[name = string("op_1192_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1181_cast_fp16)[name = string("transpose_66")]; + tensor var_1192_cast_fp16 = matmul(transpose_x = var_1192_transpose_x_0, transpose_y = var_1192_transpose_y_0, x = attn_11_cast_fp16, y = v_7_cast_fp16)[name = string("op_1192_cast_fp16")]; + tensor var_1193_perm_0 = const()[name = string("op_1193_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1194 = const()[name = string("op_1194"), val = tensor([2, 128, 256])]; + tensor var_1193_cast_fp16 = transpose(perm = var_1193_perm_0, x = var_1192_cast_fp16)[name = string("transpose_65")]; + tensor input_193_cast_fp16 = reshape(shape = var_1194, x = var_1193_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20811328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20909696))))[name = string("main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20909888)))]; + tensor linear_19_cast_fp16 = linear(bias = main_blocks_1_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_193_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor out_31_perm_0 = const()[name = string("out_31_perm_0"), val = tensor([0, 2, 1])]; + tensor out_31_cast_fp16 = transpose(perm = out_31_perm_0, x = linear_19_cast_fp16)[name = string("transpose_64")]; + tensor var_1201_cast_fp16 = mul(x = out_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1201_cast_fp16")]; + tensor x_101_cast_fp16 = add(x = x_m_7_cast_fp16, y = var_1201_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_195_perm_0 = const()[name = string("input_195_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1208_axes_0 = const()[name = string("op_1208_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20910976)))]; + tensor main_blocks_1_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20912064)))]; + tensor input_195_cast_fp16 = transpose(perm = input_195_perm_0, x = x_101_cast_fp16)[name = string("transpose_63")]; + tensor var_1208_cast_fp16 = layer_norm(axes = var_1208_axes_0, beta = main_blocks_1_style_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_style_norm_norm_weight_to_fp16, x = input_195_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_perm_0 = const()[name = string("op_1209_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1209_cast_fp16 = transpose(perm = var_1209_perm_0, x = var_1208_cast_fp16)[name = string("transpose_62")]; + tensor x_103_cast_fp16 = mul(x = var_1209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_103_cast_fp16")]; + int32 var_1228 = const()[name = string("op_1228"), val = int32(-1)]; + tensor x_105_cast_fp16 = mul(x = x_103_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_1263_cast_fp16 = mul(x = x_105_cast_fp16, y = last_1_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor x_last_25_axes_0 = const()[name = string("x_last_25_axes_0"), val = tensor([-1])]; + bool x_last_25_keep_dims_0 = const()[name = string("x_last_25_keep_dims_0"), val = bool(true)]; + tensor x_last_25_cast_fp16 = reduce_sum(axes = x_last_25_axes_0, keep_dims = x_last_25_keep_dims_0, x = var_1263_cast_fp16)[name = string("x_last_25_cast_fp16")]; + tensor var_1266_cast_fp16 = mul(x = x_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1266_cast_fp16")]; + tensor var_1268_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_25_cast_fp16)[name = string("op_1268_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1266_cast_fp16, y = var_1268_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_201_mode_0 = const()[name = string("input_201_mode_0"), val = string("replicate")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_201_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")]; + string h_75_pad_type_0 = const()[name = string("h_75_pad_type_0"), val = string("valid")]; + int32 h_75_groups_0 = const()[name = string("h_75_groups_0"), val = int32(512)]; + tensor h_75_strides_0 = const()[name = string("h_75_strides_0"), val = tensor([1])]; + tensor h_75_pad_0 = const()[name = string("h_75_pad_0"), val = tensor([0, 0])]; + tensor h_75_dilations_0 = const()[name = string("h_75_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20913152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20915136))))[name = string("main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20915328)))]; + tensor h_75_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_75_dilations_0, groups = h_75_groups_0, pad = h_75_pad_0, pad_type = h_75_pad_type_0, strides = h_75_strides_0, weight = main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_201_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = h_75_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_203_perm_0 = const()[name = string("input_203_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1286_axes_0 = const()[name = string("op_1286_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20916416)))]; + tensor main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20917504)))]; + fp16 var_1233_to_fp16 = const()[name = string("op_1233_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_203_cast_fp16 = transpose(perm = input_203_perm_0, x = x_107_cast_fp16)[name = string("transpose_61")]; + tensor var_1286_cast_fp16 = layer_norm(axes = var_1286_axes_0, beta = main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16, x = input_203_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string h_77_pad_type_0 = const()[name = string("h_77_pad_type_0"), val = string("valid")]; + tensor h_77_strides_0 = const()[name = string("h_77_strides_0"), val = tensor([1])]; + tensor h_77_pad_0 = const()[name = string("h_77_pad_0"), val = tensor([0, 0])]; + tensor h_77_dilations_0 = const()[name = string("h_77_dilations_0"), val = tensor([1])]; + int32 h_77_groups_0 = const()[name = string("h_77_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20918592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21705088))))[name = string("main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21705280)))]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = var_1286_cast_fp16)[name = string("transpose_60")]; + tensor h_77_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_77_dilations_0, groups = h_77_groups_0, pad = h_77_pad_0, pad_type = h_77_pad_type_0, strides = h_77_strides_0, weight = main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_205_cast_fp16)[name = string("h_77_cast_fp16")]; + string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")]; + tensor input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = h_77_cast_fp16)[name = string("input_207_cast_fp16")]; + string h_79_pad_type_0 = const()[name = string("h_79_pad_type_0"), val = string("valid")]; + tensor h_79_strides_0 = const()[name = string("h_79_strides_0"), val = tensor([1])]; + tensor h_79_pad_0 = const()[name = string("h_79_pad_0"), val = tensor([0, 0])]; + tensor h_79_dilations_0 = const()[name = string("h_79_dilations_0"), val = tensor([1])]; + int32 h_79_groups_0 = const()[name = string("h_79_groups_0"), val = int32(1)]; + tensor op_1303_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21709440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22495936))))[name = string("op_1303_weight_0_to_fp16_palettized")]; + tensor var_1303_bias_0_to_fp16 = const()[name = string("op_1303_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22496128)))]; + tensor var_1303_cast_fp16 = conv(bias = var_1303_bias_0_to_fp16, dilations = h_79_dilations_0, groups = h_79_groups_0, pad = h_79_pad_0, pad_type = h_79_pad_type_0, strides = h_79_strides_0, weight = op_1303_weight_0_to_fp16_palettized, x = input_207_cast_fp16)[name = string("op_1303_cast_fp16")]; + tensor out_33_cast_fp16 = add(x = x_105_cast_fp16, y = var_1303_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor x_109_cast_fp16 = mul(x = out_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor x_111_cast_fp16 = mul(x = x_109_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_1317_cast_fp16 = mul(x = x_111_cast_fp16, y = last_1_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor x_last_27_axes_0 = const()[name = string("x_last_27_axes_0"), val = tensor([-1])]; + bool x_last_27_keep_dims_0 = const()[name = string("x_last_27_keep_dims_0"), val = bool(true)]; + tensor x_last_27_cast_fp16 = reduce_sum(axes = x_last_27_axes_0, keep_dims = x_last_27_keep_dims_0, x = var_1317_cast_fp16)[name = string("x_last_27_cast_fp16")]; + tensor var_1320_cast_fp16 = mul(x = x_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1322_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_27_cast_fp16)[name = string("op_1322_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1320_cast_fp16, y = var_1322_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("replicate")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor input_213_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = input_213_mode_0, pad = input_213_pad_0, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + string h_81_pad_type_0 = const()[name = string("h_81_pad_type_0"), val = string("valid")]; + tensor h_81_dilations_0 = const()[name = string("h_81_dilations_0"), val = tensor([2])]; + int32 h_81_groups_0 = const()[name = string("h_81_groups_0"), val = int32(512)]; + tensor h_81_strides_0 = const()[name = string("h_81_strides_0"), val = tensor([1])]; + tensor h_81_pad_0 = const()[name = string("h_81_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22497216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22499200))))[name = string("main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22499392)))]; + tensor h_81_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_81_dilations_0, groups = h_81_groups_0, pad = h_81_pad_0, pad_type = h_81_pad_type_0, strides = h_81_strides_0, weight = main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_213_cast_fp16)[name = string("h_81_cast_fp16")]; + tensor x_113_cast_fp16 = mul(x = h_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor input_215_perm_0 = const()[name = string("input_215_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1340_axes_0 = const()[name = string("op_1340_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22500480)))]; + tensor main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22501568)))]; + tensor input_215_cast_fp16 = transpose(perm = input_215_perm_0, x = x_113_cast_fp16)[name = string("transpose_59")]; + tensor var_1340_cast_fp16 = layer_norm(axes = var_1340_axes_0, beta = main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16, x = input_215_cast_fp16)[name = string("op_1340_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + string h_83_pad_type_0 = const()[name = string("h_83_pad_type_0"), val = string("valid")]; + tensor h_83_strides_0 = const()[name = string("h_83_strides_0"), val = tensor([1])]; + tensor h_83_pad_0 = const()[name = string("h_83_pad_0"), val = tensor([0, 0])]; + tensor h_83_dilations_0 = const()[name = string("h_83_dilations_0"), val = tensor([1])]; + int32 h_83_groups_0 = const()[name = string("h_83_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22502656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23289152))))[name = string("main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23289344)))]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = var_1340_cast_fp16)[name = string("transpose_58")]; + tensor h_83_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_83_dilations_0, groups = h_83_groups_0, pad = h_83_pad_0, pad_type = h_83_pad_type_0, strides = h_83_strides_0, weight = main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = string("h_83_cast_fp16")]; + string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")]; + tensor input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = h_83_cast_fp16)[name = string("input_219_cast_fp16")]; + string h_85_pad_type_0 = const()[name = string("h_85_pad_type_0"), val = string("valid")]; + tensor h_85_strides_0 = const()[name = string("h_85_strides_0"), val = tensor([1])]; + tensor h_85_pad_0 = const()[name = string("h_85_pad_0"), val = tensor([0, 0])]; + tensor h_85_dilations_0 = const()[name = string("h_85_dilations_0"), val = tensor([1])]; + int32 h_85_groups_0 = const()[name = string("h_85_groups_0"), val = int32(1)]; + tensor op_1357_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23293504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24080000))))[name = string("op_1357_weight_0_to_fp16_palettized")]; + tensor var_1357_bias_0_to_fp16 = const()[name = string("op_1357_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24080192)))]; + tensor var_1357_cast_fp16 = conv(bias = var_1357_bias_0_to_fp16, dilations = h_85_dilations_0, groups = h_85_groups_0, pad = h_85_pad_0, pad_type = h_85_pad_type_0, strides = h_85_strides_0, weight = op_1357_weight_0_to_fp16_palettized, x = input_219_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor out_35_cast_fp16 = add(x = x_111_cast_fp16, y = var_1357_cast_fp16)[name = string("out_35_cast_fp16")]; + tensor x_115_cast_fp16 = mul(x = out_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_cast_fp16 = mul(x = x_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1371_cast_fp16 = mul(x = x_117_cast_fp16, y = last_1_cast_fp16)[name = string("op_1371_cast_fp16")]; + tensor x_last_29_axes_0 = const()[name = string("x_last_29_axes_0"), val = tensor([-1])]; + bool x_last_29_keep_dims_0 = const()[name = string("x_last_29_keep_dims_0"), val = bool(true)]; + tensor x_last_29_cast_fp16 = reduce_sum(axes = x_last_29_axes_0, keep_dims = x_last_29_keep_dims_0, x = var_1371_cast_fp16)[name = string("x_last_29_cast_fp16")]; + tensor var_1374_cast_fp16 = mul(x = x_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_29_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1376_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_225_mode_0 = const()[name = string("input_225_mode_0"), val = string("replicate")]; + fp16 const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = fp16(0x0p+0)]; + tensor input_225_cast_fp16 = pad(constant_val = const_48_to_fp16, mode = input_225_mode_0, pad = input_225_pad_0, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")]; + string h_87_pad_type_0 = const()[name = string("h_87_pad_type_0"), val = string("valid")]; + tensor h_87_dilations_0 = const()[name = string("h_87_dilations_0"), val = tensor([4])]; + int32 h_87_groups_0 = const()[name = string("h_87_groups_0"), val = int32(512)]; + tensor h_87_strides_0 = const()[name = string("h_87_strides_0"), val = tensor([1])]; + tensor h_87_pad_0 = const()[name = string("h_87_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24081280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24083264))))[name = string("main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24083456)))]; + tensor h_87_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_87_dilations_0, groups = h_87_groups_0, pad = h_87_pad_0, pad_type = h_87_pad_type_0, strides = h_87_strides_0, weight = main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor x_119_cast_fp16 = mul(x = h_87_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_227_perm_0 = const()[name = string("input_227_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1394_axes_0 = const()[name = string("op_1394_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24084544)))]; + tensor main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24085632)))]; + tensor input_227_cast_fp16 = transpose(perm = input_227_perm_0, x = x_119_cast_fp16)[name = string("transpose_57")]; + tensor var_1394_cast_fp16 = layer_norm(axes = var_1394_axes_0, beta = main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16, x = input_227_cast_fp16)[name = string("op_1394_cast_fp16")]; + tensor input_229_perm_0 = const()[name = string("input_229_perm_0"), val = tensor([0, 2, 1])]; + string h_89_pad_type_0 = const()[name = string("h_89_pad_type_0"), val = string("valid")]; + tensor h_89_strides_0 = const()[name = string("h_89_strides_0"), val = tensor([1])]; + tensor h_89_pad_0 = const()[name = string("h_89_pad_0"), val = tensor([0, 0])]; + tensor h_89_dilations_0 = const()[name = string("h_89_dilations_0"), val = tensor([1])]; + int32 h_89_groups_0 = const()[name = string("h_89_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24086720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24873216))))[name = string("main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24873408)))]; + tensor input_229_cast_fp16 = transpose(perm = input_229_perm_0, x = var_1394_cast_fp16)[name = string("transpose_56")]; + tensor h_89_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_89_dilations_0, groups = h_89_groups_0, pad = h_89_pad_0, pad_type = h_89_pad_type_0, strides = h_89_strides_0, weight = main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = string("h_89_cast_fp16")]; + string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")]; + tensor input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = h_89_cast_fp16)[name = string("input_231_cast_fp16")]; + string h_91_pad_type_0 = const()[name = string("h_91_pad_type_0"), val = string("valid")]; + tensor h_91_strides_0 = const()[name = string("h_91_strides_0"), val = tensor([1])]; + tensor h_91_pad_0 = const()[name = string("h_91_pad_0"), val = tensor([0, 0])]; + tensor h_91_dilations_0 = const()[name = string("h_91_dilations_0"), val = tensor([1])]; + int32 h_91_groups_0 = const()[name = string("h_91_groups_0"), val = int32(1)]; + tensor op_1411_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24877568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25664064))))[name = string("op_1411_weight_0_to_fp16_palettized")]; + tensor var_1411_bias_0_to_fp16 = const()[name = string("op_1411_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25664256)))]; + tensor var_1411_cast_fp16 = conv(bias = var_1411_bias_0_to_fp16, dilations = h_91_dilations_0, groups = h_91_groups_0, pad = h_91_pad_0, pad_type = h_91_pad_type_0, strides = h_91_strides_0, weight = op_1411_weight_0_to_fp16_palettized, x = input_231_cast_fp16)[name = string("op_1411_cast_fp16")]; + tensor out_37_cast_fp16 = add(x = x_117_cast_fp16, y = var_1411_cast_fp16)[name = string("out_37_cast_fp16")]; + tensor x_121_cast_fp16 = mul(x = out_37_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_121_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1425_cast_fp16 = mul(x = x_123_cast_fp16, y = last_1_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor x_last_31_axes_0 = const()[name = string("x_last_31_axes_0"), val = tensor([-1])]; + bool x_last_31_keep_dims_0 = const()[name = string("x_last_31_keep_dims_0"), val = bool(true)]; + tensor x_last_31_cast_fp16 = reduce_sum(axes = x_last_31_axes_0, keep_dims = x_last_31_keep_dims_0, x = var_1425_cast_fp16)[name = string("x_last_31_cast_fp16")]; + tensor var_1428_cast_fp16 = mul(x = x_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_31_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = var_1428_cast_fp16, y = var_1430_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("replicate")]; + fp16 const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = fp16(0x0p+0)]; + tensor input_237_cast_fp16 = pad(constant_val = const_50_to_fp16, mode = input_237_mode_0, pad = input_237_pad_0, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + string h_93_pad_type_0 = const()[name = string("h_93_pad_type_0"), val = string("valid")]; + tensor h_93_dilations_0 = const()[name = string("h_93_dilations_0"), val = tensor([8])]; + int32 h_93_groups_0 = const()[name = string("h_93_groups_0"), val = int32(512)]; + tensor h_93_strides_0 = const()[name = string("h_93_strides_0"), val = tensor([1])]; + tensor h_93_pad_0 = const()[name = string("h_93_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25665344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25667328))))[name = string("main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25667520)))]; + tensor h_93_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_93_dilations_0, groups = h_93_groups_0, pad = h_93_pad_0, pad_type = h_93_pad_type_0, strides = h_93_strides_0, weight = main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = string("h_93_cast_fp16")]; + tensor x_125_cast_fp16 = mul(x = h_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_239_perm_0 = const()[name = string("input_239_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1448_axes_0 = const()[name = string("op_1448_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25668608)))]; + tensor main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25669696)))]; + tensor input_239_cast_fp16 = transpose(perm = input_239_perm_0, x = x_125_cast_fp16)[name = string("transpose_55")]; + tensor var_1448_cast_fp16 = layer_norm(axes = var_1448_axes_0, beta = main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16, x = input_239_cast_fp16)[name = string("op_1448_cast_fp16")]; + tensor input_241_perm_0 = const()[name = string("input_241_perm_0"), val = tensor([0, 2, 1])]; + string h_95_pad_type_0 = const()[name = string("h_95_pad_type_0"), val = string("valid")]; + tensor h_95_strides_0 = const()[name = string("h_95_strides_0"), val = tensor([1])]; + tensor h_95_pad_0 = const()[name = string("h_95_pad_0"), val = tensor([0, 0])]; + tensor h_95_dilations_0 = const()[name = string("h_95_dilations_0"), val = tensor([1])]; + int32 h_95_groups_0 = const()[name = string("h_95_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25670784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26457280))))[name = string("main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26457472)))]; + tensor input_241_cast_fp16 = transpose(perm = input_241_perm_0, x = var_1448_cast_fp16)[name = string("transpose_54")]; + tensor h_95_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_95_dilations_0, groups = h_95_groups_0, pad = h_95_pad_0, pad_type = h_95_pad_type_0, strides = h_95_strides_0, weight = main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_241_cast_fp16)[name = string("h_95_cast_fp16")]; + string input_243_mode_0 = const()[name = string("input_243_mode_0"), val = string("EXACT")]; + tensor input_243_cast_fp16 = gelu(mode = input_243_mode_0, x = h_95_cast_fp16)[name = string("input_243_cast_fp16")]; + string h_97_pad_type_0 = const()[name = string("h_97_pad_type_0"), val = string("valid")]; + tensor h_97_strides_0 = const()[name = string("h_97_strides_0"), val = tensor([1])]; + tensor h_97_pad_0 = const()[name = string("h_97_pad_0"), val = tensor([0, 0])]; + tensor h_97_dilations_0 = const()[name = string("h_97_dilations_0"), val = tensor([1])]; + int32 h_97_groups_0 = const()[name = string("h_97_groups_0"), val = int32(1)]; + tensor op_1465_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26461632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27248128))))[name = string("op_1465_weight_0_to_fp16_palettized")]; + tensor var_1465_bias_0_to_fp16 = const()[name = string("op_1465_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27248320)))]; + tensor var_1465_cast_fp16 = conv(bias = var_1465_bias_0_to_fp16, dilations = h_97_dilations_0, groups = h_97_groups_0, pad = h_97_pad_0, pad_type = h_97_pad_type_0, strides = h_97_strides_0, weight = op_1465_weight_0_to_fp16_palettized, x = input_243_cast_fp16)[name = string("op_1465_cast_fp16")]; + tensor out_39_cast_fp16 = add(x = x_123_cast_fp16, y = var_1465_cast_fp16)[name = string("out_39_cast_fp16")]; + tensor x_127_cast_fp16 = mul(x = out_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27249408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27274048))))[name = string("main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27274240)))]; + tensor linear_20_cast_fp16 = linear(bias = main_blocks_2_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor t_9_axes_0 = const()[name = string("t_9_axes_0"), val = tensor([-1])]; + tensor t_9_cast_fp16 = expand_dims(axes = t_9_axes_0, x = linear_20_cast_fp16)[name = string("t_9_cast_fp16")]; + tensor var_1475_cast_fp16 = add(x = x_127_cast_fp16, y = t_9_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = var_1475_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor x_131_cast_fp16 = mul(x = x_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor var_1488_cast_fp16 = mul(x = x_131_cast_fp16, y = last_1_cast_fp16)[name = string("op_1488_cast_fp16")]; + tensor x_last_33_axes_0 = const()[name = string("x_last_33_axes_0"), val = tensor([-1])]; + bool x_last_33_keep_dims_0 = const()[name = string("x_last_33_keep_dims_0"), val = bool(true)]; + tensor x_last_33_cast_fp16 = reduce_sum(axes = x_last_33_axes_0, keep_dims = x_last_33_keep_dims_0, x = var_1488_cast_fp16)[name = string("x_last_33_cast_fp16")]; + tensor var_1491_cast_fp16 = mul(x = x_131_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1491_cast_fp16")]; + tensor var_1493_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_33_cast_fp16)[name = string("op_1493_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = var_1491_cast_fp16, y = var_1493_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_251_mode_0 = const()[name = string("input_251_mode_0"), val = string("replicate")]; + fp16 const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = fp16(0x0p+0)]; + tensor input_251_cast_fp16 = pad(constant_val = const_52_to_fp16, mode = input_251_mode_0, pad = input_251_pad_0, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + string h_99_pad_type_0 = const()[name = string("h_99_pad_type_0"), val = string("valid")]; + int32 h_99_groups_0 = const()[name = string("h_99_groups_0"), val = int32(512)]; + tensor h_99_strides_0 = const()[name = string("h_99_strides_0"), val = tensor([1])]; + tensor h_99_pad_0 = const()[name = string("h_99_pad_0"), val = tensor([0, 0])]; + tensor h_99_dilations_0 = const()[name = string("h_99_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27275328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27277312))))[name = string("main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27277504)))]; + tensor h_99_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_99_dilations_0, groups = h_99_groups_0, pad = h_99_pad_0, pad_type = h_99_pad_type_0, strides = h_99_strides_0, weight = main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor x_133_cast_fp16 = mul(x = h_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_253_perm_0 = const()[name = string("input_253_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1511_axes_0 = const()[name = string("op_1511_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27278592)))]; + tensor main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27279680)))]; + tensor input_253_cast_fp16 = transpose(perm = input_253_perm_0, x = x_133_cast_fp16)[name = string("transpose_53")]; + tensor var_1511_cast_fp16 = layer_norm(axes = var_1511_axes_0, beta = main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16, x = input_253_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor input_255_perm_0 = const()[name = string("input_255_perm_0"), val = tensor([0, 2, 1])]; + string h_101_pad_type_0 = const()[name = string("h_101_pad_type_0"), val = string("valid")]; + tensor h_101_strides_0 = const()[name = string("h_101_strides_0"), val = tensor([1])]; + tensor h_101_pad_0 = const()[name = string("h_101_pad_0"), val = tensor([0, 0])]; + tensor h_101_dilations_0 = const()[name = string("h_101_dilations_0"), val = tensor([1])]; + int32 h_101_groups_0 = const()[name = string("h_101_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27280768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28067264))))[name = string("main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28067456)))]; + tensor input_255_cast_fp16 = transpose(perm = input_255_perm_0, x = var_1511_cast_fp16)[name = string("transpose_52")]; + tensor h_101_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_101_dilations_0, groups = h_101_groups_0, pad = h_101_pad_0, pad_type = h_101_pad_type_0, strides = h_101_strides_0, weight = main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_255_cast_fp16)[name = string("h_101_cast_fp16")]; + string input_257_mode_0 = const()[name = string("input_257_mode_0"), val = string("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = h_101_cast_fp16)[name = string("input_257_cast_fp16")]; + string h_103_pad_type_0 = const()[name = string("h_103_pad_type_0"), val = string("valid")]; + tensor h_103_strides_0 = const()[name = string("h_103_strides_0"), val = tensor([1])]; + tensor h_103_pad_0 = const()[name = string("h_103_pad_0"), val = tensor([0, 0])]; + tensor h_103_dilations_0 = const()[name = string("h_103_dilations_0"), val = tensor([1])]; + int32 h_103_groups_0 = const()[name = string("h_103_groups_0"), val = int32(1)]; + tensor op_1528_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28071616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28858112))))[name = string("op_1528_weight_0_to_fp16_palettized")]; + tensor var_1528_bias_0_to_fp16 = const()[name = string("op_1528_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28858304)))]; + tensor var_1528_cast_fp16 = conv(bias = var_1528_bias_0_to_fp16, dilations = h_103_dilations_0, groups = h_103_groups_0, pad = h_103_pad_0, pad_type = h_103_pad_type_0, strides = h_103_strides_0, weight = op_1528_weight_0_to_fp16_palettized, x = input_257_cast_fp16)[name = string("op_1528_cast_fp16")]; + tensor out_41_cast_fp16 = add(x = x_131_cast_fp16, y = var_1528_cast_fp16)[name = string("out_41_cast_fp16")]; + tensor x_135_cast_fp16 = mul(x = out_41_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor x_m_9_cast_fp16 = mul(x = x_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_9_cast_fp16")]; + tensor input_259_perm_0 = const()[name = string("input_259_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28859392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29056064))))[name = string("main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29056256)))]; + tensor input_259_cast_fp16 = transpose(perm = input_259_perm_0, x = x_135_cast_fp16)[name = string("transpose_51")]; + tensor linear_21_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_259_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_1546 = const()[name = string("op_1546"), val = tensor([2, 128, 8, 64])]; + tensor var_1547_cast_fp16 = reshape(shape = var_1546, x = linear_21_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor x_137_perm_0 = const()[name = string("x_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29057344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29155712))))[name = string("main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29155904)))]; + tensor linear_22_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_1554 = const()[name = string("op_1554"), val = tensor([2, 128, 8, 64])]; + tensor var_1555_cast_fp16 = reshape(shape = var_1554, x = linear_22_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_139_perm_0 = const()[name = string("x_139_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29156992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29255360))))[name = string("main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29255552)))]; + tensor linear_23_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_1562 = const()[name = string("op_1562"), val = tensor([2, 128, 8, 64])]; + tensor var_1563_cast_fp16 = reshape(shape = var_1562, x = linear_23_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_9_begin_0 = const()[name = string("x_a_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_9_end_0 = const()[name = string("x_a_9_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_9_end_mask_0 = const()[name = string("x_a_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_137_cast_fp16 = transpose(perm = x_137_perm_0, x = var_1547_cast_fp16)[name = string("transpose_50")]; + tensor x_a_9_cast_fp16 = slice_by_index(begin = x_a_9_begin_0, end = x_a_9_end_0, end_mask = x_a_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_a_9_cast_fp16")]; + tensor x_b_9_begin_0 = const()[name = string("x_b_9_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_9_end_0 = const()[name = string("x_b_9_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_9_end_mask_0 = const()[name = string("x_b_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_9_cast_fp16 = slice_by_index(begin = x_b_9_begin_0, end = x_b_9_end_0, end_mask = x_b_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_b_9_cast_fp16")]; + tensor var_1591_cast_fp16 = mul(x = x_a_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1591_cast_fp16")]; + tensor var_1592_cast_fp16 = mul(x = x_b_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor rot_a_9_cast_fp16 = sub(x = var_1591_cast_fp16, y = var_1592_cast_fp16)[name = string("rot_a_9_cast_fp16")]; + tensor var_1594_cast_fp16 = mul(x = x_a_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595_cast_fp16 = mul(x = x_b_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1595_cast_fp16")]; + tensor rot_b_9_cast_fp16 = add(x = var_1594_cast_fp16, y = var_1595_cast_fp16)[name = string("rot_b_9_cast_fp16")]; + bool q_9_interleave_0 = const()[name = string("q_9_interleave_0"), val = bool(false)]; + tensor q_9_cast_fp16 = concat(axis = var_1228, interleave = q_9_interleave_0, values = (rot_a_9_cast_fp16, rot_b_9_cast_fp16))[name = string("q_9_cast_fp16")]; + tensor x_a_11_begin_0 = const()[name = string("x_a_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_11_end_0 = const()[name = string("x_a_11_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_11_end_mask_0 = const()[name = string("x_a_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_139_cast_fp16 = transpose(perm = x_139_perm_0, x = var_1555_cast_fp16)[name = string("transpose_49")]; + tensor x_a_11_cast_fp16 = slice_by_index(begin = x_a_11_begin_0, end = x_a_11_end_0, end_mask = x_a_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_a_11_cast_fp16")]; + tensor x_b_11_begin_0 = const()[name = string("x_b_11_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_11_end_0 = const()[name = string("x_b_11_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_11_end_mask_0 = const()[name = string("x_b_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_11_cast_fp16 = slice_by_index(begin = x_b_11_begin_0, end = x_b_11_end_0, end_mask = x_b_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_b_11_cast_fp16")]; + tensor var_1609_cast_fp16 = mul(x = x_a_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor var_1610_cast_fp16 = mul(x = x_b_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor rot_a_11_cast_fp16 = sub(x = var_1609_cast_fp16, y = var_1610_cast_fp16)[name = string("rot_a_11_cast_fp16")]; + tensor var_1612_cast_fp16 = mul(x = x_a_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1612_cast_fp16")]; + tensor var_1613_cast_fp16 = mul(x = x_b_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1613_cast_fp16")]; + tensor rot_b_11_cast_fp16 = add(x = var_1612_cast_fp16, y = var_1613_cast_fp16)[name = string("rot_b_11_cast_fp16")]; + bool k_9_interleave_0 = const()[name = string("k_9_interleave_0"), val = bool(false)]; + tensor k_9_cast_fp16 = concat(axis = var_1228, interleave = k_9_interleave_0, values = (rot_a_11_cast_fp16, rot_b_11_cast_fp16))[name = string("k_9_cast_fp16")]; + bool var_1618_transpose_x_1 = const()[name = string("op_1618_transpose_x_1"), val = bool(false)]; + bool var_1618_transpose_y_1 = const()[name = string("op_1618_transpose_y_1"), val = bool(true)]; + tensor var_1618_cast_fp16 = matmul(transpose_x = var_1618_transpose_x_1, transpose_y = var_1618_transpose_y_1, x = q_9_cast_fp16, y = k_9_cast_fp16)[name = string("op_1618_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_1618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor input_265_cast_fp16 = sub(x = _inversed_scores_5_cast_fp16, y = var_530_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1228, x = input_265_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool var_1627_transpose_x_0 = const()[name = string("op_1627_transpose_x_0"), val = bool(false)]; + bool var_1627_transpose_y_0 = const()[name = string("op_1627_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1563_cast_fp16)[name = string("transpose_48")]; + tensor var_1627_cast_fp16 = matmul(transpose_x = var_1627_transpose_x_0, transpose_y = var_1627_transpose_y_0, x = attn_13_cast_fp16, y = v_9_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor var_1628_perm_0 = const()[name = string("op_1628_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1629 = const()[name = string("op_1629"), val = tensor([2, 128, 512])]; + tensor var_1628_cast_fp16 = transpose(perm = var_1628_perm_0, x = var_1627_cast_fp16)[name = string("transpose_47")]; + tensor input_267_cast_fp16 = reshape(shape = var_1629, x = var_1628_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29256640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29453312))))[name = string("main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29453504)))]; + tensor linear_24_cast_fp16 = linear(bias = main_blocks_2_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor out_43_perm_0 = const()[name = string("out_43_perm_0"), val = tensor([0, 2, 1])]; + tensor out_43_cast_fp16 = transpose(perm = out_43_perm_0, x = linear_24_cast_fp16)[name = string("transpose_46")]; + tensor var_1636_cast_fp16 = mul(x = out_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1636_cast_fp16")]; + tensor x_141_cast_fp16 = add(x = x_m_9_cast_fp16, y = var_1636_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1643_axes_0 = const()[name = string("op_1643_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29454592)))]; + tensor main_blocks_2_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29455680)))]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_141_cast_fp16)[name = string("transpose_45")]; + tensor var_1643_cast_fp16 = layer_norm(axes = var_1643_axes_0, beta = main_blocks_2_text_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_text_norm_norm_weight_to_fp16, x = input_269_cast_fp16)[name = string("op_1643_cast_fp16")]; + tensor var_1644_perm_0 = const()[name = string("op_1644_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1644_cast_fp16 = transpose(perm = var_1644_perm_0, x = var_1643_cast_fp16)[name = string("transpose_44")]; + tensor x_143_cast_fp16 = mul(x = var_1644_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor x_145_cast_fp16 = mul(x = x_143_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1657_cast_fp16 = mul(x = x_145_cast_fp16, y = last_1_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor x_last_35_axes_0 = const()[name = string("x_last_35_axes_0"), val = tensor([-1])]; + bool x_last_35_keep_dims_0 = const()[name = string("x_last_35_keep_dims_0"), val = bool(true)]; + tensor x_last_35_cast_fp16 = reduce_sum(axes = x_last_35_axes_0, keep_dims = x_last_35_keep_dims_0, x = var_1657_cast_fp16)[name = string("x_last_35_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = x_145_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1662_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_35_cast_fp16)[name = string("op_1662_cast_fp16")]; + tensor input_273_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1662_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("replicate")]; + fp16 const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = fp16(0x0p+0)]; + tensor input_275_cast_fp16 = pad(constant_val = const_59_to_fp16, mode = input_275_mode_0, pad = input_275_pad_0, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")]; + string h_105_pad_type_0 = const()[name = string("h_105_pad_type_0"), val = string("valid")]; + int32 h_105_groups_0 = const()[name = string("h_105_groups_0"), val = int32(512)]; + tensor h_105_strides_0 = const()[name = string("h_105_strides_0"), val = tensor([1])]; + tensor h_105_pad_0 = const()[name = string("h_105_pad_0"), val = tensor([0, 0])]; + tensor h_105_dilations_0 = const()[name = string("h_105_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29456768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29458752))))[name = string("main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29458944)))]; + tensor h_105_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_105_dilations_0, groups = h_105_groups_0, pad = h_105_pad_0, pad_type = h_105_pad_type_0, strides = h_105_strides_0, weight = main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_275_cast_fp16)[name = string("h_105_cast_fp16")]; + tensor x_147_cast_fp16 = mul(x = h_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_277_perm_0 = const()[name = string("input_277_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1680_axes_0 = const()[name = string("op_1680_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29460032)))]; + tensor main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29461120)))]; + tensor input_277_cast_fp16 = transpose(perm = input_277_perm_0, x = x_147_cast_fp16)[name = string("transpose_43")]; + tensor var_1680_cast_fp16 = layer_norm(axes = var_1680_axes_0, beta = main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16, x = input_277_cast_fp16)[name = string("op_1680_cast_fp16")]; + tensor input_279_perm_0 = const()[name = string("input_279_perm_0"), val = tensor([0, 2, 1])]; + string h_107_pad_type_0 = const()[name = string("h_107_pad_type_0"), val = string("valid")]; + tensor h_107_strides_0 = const()[name = string("h_107_strides_0"), val = tensor([1])]; + tensor h_107_pad_0 = const()[name = string("h_107_pad_0"), val = tensor([0, 0])]; + tensor h_107_dilations_0 = const()[name = string("h_107_dilations_0"), val = tensor([1])]; + int32 h_107_groups_0 = const()[name = string("h_107_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29462208))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30248704))))[name = string("main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30248896)))]; + tensor input_279_cast_fp16 = transpose(perm = input_279_perm_0, x = var_1680_cast_fp16)[name = string("transpose_42")]; + tensor h_107_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_107_dilations_0, groups = h_107_groups_0, pad = h_107_pad_0, pad_type = h_107_pad_type_0, strides = h_107_strides_0, weight = main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_279_cast_fp16)[name = string("h_107_cast_fp16")]; + string input_281_mode_0 = const()[name = string("input_281_mode_0"), val = string("EXACT")]; + tensor input_281_cast_fp16 = gelu(mode = input_281_mode_0, x = h_107_cast_fp16)[name = string("input_281_cast_fp16")]; + string h_109_pad_type_0 = const()[name = string("h_109_pad_type_0"), val = string("valid")]; + tensor h_109_strides_0 = const()[name = string("h_109_strides_0"), val = tensor([1])]; + tensor h_109_pad_0 = const()[name = string("h_109_pad_0"), val = tensor([0, 0])]; + tensor h_109_dilations_0 = const()[name = string("h_109_dilations_0"), val = tensor([1])]; + int32 h_109_groups_0 = const()[name = string("h_109_groups_0"), val = int32(1)]; + tensor op_1697_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30253056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31039552))))[name = string("op_1697_weight_0_to_fp16_palettized")]; + tensor var_1697_bias_0_to_fp16 = const()[name = string("op_1697_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31039744)))]; + tensor var_1697_cast_fp16 = conv(bias = var_1697_bias_0_to_fp16, dilations = h_109_dilations_0, groups = h_109_groups_0, pad = h_109_pad_0, pad_type = h_109_pad_type_0, strides = h_109_strides_0, weight = op_1697_weight_0_to_fp16_palettized, x = input_281_cast_fp16)[name = string("op_1697_cast_fp16")]; + tensor out_45_cast_fp16 = add(x = x_145_cast_fp16, y = var_1697_cast_fp16)[name = string("out_45_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = out_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor x_m_11_cast_fp16 = mul(x = x_149_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_11_cast_fp16")]; + tensor input_283_perm_0 = const()[name = string("input_283_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31040832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31139200))))[name = string("main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31139392)))]; + tensor input_283_cast_fp16 = transpose(perm = input_283_perm_0, x = x_149_cast_fp16)[name = string("transpose_41")]; + tensor linear_25_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_283_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_1713 = const()[name = string("op_1713"), val = tensor([2, 128, 2, 128])]; + tensor var_1714_cast_fp16 = reshape(shape = var_1713, x = linear_25_cast_fp16)[name = string("op_1714_cast_fp16")]; + tensor q_11_perm_0 = const()[name = string("q_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31139968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31189184))))[name = string("main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31189376)))]; + tensor linear_27_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1727 = const()[name = string("op_1727"), val = tensor([2, 50, 2, 128])]; + tensor var_1728_cast_fp16 = reshape(shape = var_1727, x = linear_27_cast_fp16)[name = string("op_1728_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1732_transpose_x_0 = const()[name = string("op_1732_transpose_x_0"), val = bool(false)]; + bool var_1732_transpose_y_0 = const()[name = string("op_1732_transpose_y_0"), val = bool(false)]; + tensor op_1731_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31189952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31209216))))[name = string("op_1731_to_fp16_palettized")]; + tensor q_11_cast_fp16 = transpose(perm = q_11_perm_0, x = var_1714_cast_fp16)[name = string("transpose_40")]; + tensor var_1732_cast_fp16 = matmul(transpose_x = var_1732_transpose_x_0, transpose_y = var_1732_transpose_y_0, x = q_11_cast_fp16, y = op_1731_to_fp16_palettized)[name = string("op_1732_cast_fp16")]; + fp16 _inversed_input_285_y_0_to_fp16 = const()[name = string("_inversed_input_285_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_285_cast_fp16 = mul(x = var_1732_cast_fp16, y = _inversed_input_285_y_0_to_fp16)[name = string("_inversed_input_285_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1228, x = _inversed_input_285_cast_fp16)[name = string("attn_15_cast_fp16")]; + tensor attn_17_cast_fp16 = mul(x = attn_15_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool var_1739_transpose_x_0 = const()[name = string("op_1739_transpose_x_0"), val = bool(false)]; + bool var_1739_transpose_y_0 = const()[name = string("op_1739_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_1728_cast_fp16)[name = string("transpose_39")]; + tensor var_1739_cast_fp16 = matmul(transpose_x = var_1739_transpose_x_0, transpose_y = var_1739_transpose_y_0, x = attn_17_cast_fp16, y = v_11_cast_fp16)[name = string("op_1739_cast_fp16")]; + tensor var_1740_perm_0 = const()[name = string("op_1740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1741 = const()[name = string("op_1741"), val = tensor([2, 128, 256])]; + tensor var_1740_cast_fp16 = transpose(perm = var_1740_perm_0, x = var_1739_cast_fp16)[name = string("transpose_38")]; + tensor input_287_cast_fp16 = reshape(shape = var_1741, x = var_1740_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31209408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31307776))))[name = string("main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31307968)))]; + tensor linear_28_cast_fp16 = linear(bias = main_blocks_2_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor out_47_perm_0 = const()[name = string("out_47_perm_0"), val = tensor([0, 2, 1])]; + tensor out_47_cast_fp16 = transpose(perm = out_47_perm_0, x = linear_28_cast_fp16)[name = string("transpose_37")]; + tensor var_1748_cast_fp16 = mul(x = out_47_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1748_cast_fp16")]; + tensor x_151_cast_fp16 = add(x = x_m_11_cast_fp16, y = var_1748_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_289_perm_0 = const()[name = string("input_289_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1755_axes_0 = const()[name = string("op_1755_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31309056)))]; + tensor main_blocks_2_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31310144)))]; + tensor input_289_cast_fp16 = transpose(perm = input_289_perm_0, x = x_151_cast_fp16)[name = string("transpose_36")]; + tensor var_1755_cast_fp16 = layer_norm(axes = var_1755_axes_0, beta = main_blocks_2_style_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_style_norm_norm_weight_to_fp16, x = input_289_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor var_1756_perm_0 = const()[name = string("op_1756_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1756_cast_fp16 = transpose(perm = var_1756_perm_0, x = var_1755_cast_fp16)[name = string("transpose_35")]; + tensor x_153_cast_fp16 = mul(x = var_1756_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_153_cast_fp16")]; + int32 var_1775 = const()[name = string("op_1775"), val = int32(-1)]; + tensor x_155_cast_fp16 = mul(x = x_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor var_1810_cast_fp16 = mul(x = x_155_cast_fp16, y = last_1_cast_fp16)[name = string("op_1810_cast_fp16")]; + tensor x_last_37_axes_0 = const()[name = string("x_last_37_axes_0"), val = tensor([-1])]; + bool x_last_37_keep_dims_0 = const()[name = string("x_last_37_keep_dims_0"), val = bool(true)]; + tensor x_last_37_cast_fp16 = reduce_sum(axes = x_last_37_axes_0, keep_dims = x_last_37_keep_dims_0, x = var_1810_cast_fp16)[name = string("x_last_37_cast_fp16")]; + tensor var_1813_cast_fp16 = mul(x = x_155_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1815_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_37_cast_fp16)[name = string("op_1815_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = var_1813_cast_fp16, y = var_1815_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_295_mode_0 = const()[name = string("input_295_mode_0"), val = string("replicate")]; + fp16 const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = fp16(0x0p+0)]; + tensor input_295_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")]; + string h_111_pad_type_0 = const()[name = string("h_111_pad_type_0"), val = string("valid")]; + int32 h_111_groups_0 = const()[name = string("h_111_groups_0"), val = int32(512)]; + tensor h_111_strides_0 = const()[name = string("h_111_strides_0"), val = tensor([1])]; + tensor h_111_pad_0 = const()[name = string("h_111_pad_0"), val = tensor([0, 0])]; + tensor h_111_dilations_0 = const()[name = string("h_111_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31311232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31313216))))[name = string("main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31313408)))]; + tensor h_111_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_111_dilations_0, groups = h_111_groups_0, pad = h_111_pad_0, pad_type = h_111_pad_type_0, strides = h_111_strides_0, weight = main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_295_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor x_157_cast_fp16 = mul(x = h_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor input_297_perm_0 = const()[name = string("input_297_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1833_axes_0 = const()[name = string("op_1833_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31314496)))]; + tensor main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31315584)))]; + fp16 var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_297_cast_fp16 = transpose(perm = input_297_perm_0, x = x_157_cast_fp16)[name = string("transpose_34")]; + tensor var_1833_cast_fp16 = layer_norm(axes = var_1833_axes_0, beta = main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16, x = input_297_cast_fp16)[name = string("op_1833_cast_fp16")]; + tensor input_299_perm_0 = const()[name = string("input_299_perm_0"), val = tensor([0, 2, 1])]; + string h_113_pad_type_0 = const()[name = string("h_113_pad_type_0"), val = string("valid")]; + tensor h_113_strides_0 = const()[name = string("h_113_strides_0"), val = tensor([1])]; + tensor h_113_pad_0 = const()[name = string("h_113_pad_0"), val = tensor([0, 0])]; + tensor h_113_dilations_0 = const()[name = string("h_113_dilations_0"), val = tensor([1])]; + int32 h_113_groups_0 = const()[name = string("h_113_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31316672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32103168))))[name = string("main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32103360)))]; + tensor input_299_cast_fp16 = transpose(perm = input_299_perm_0, x = var_1833_cast_fp16)[name = string("transpose_33")]; + tensor h_113_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_113_dilations_0, groups = h_113_groups_0, pad = h_113_pad_0, pad_type = h_113_pad_type_0, strides = h_113_strides_0, weight = main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("h_113_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = h_113_cast_fp16)[name = string("input_301_cast_fp16")]; + string h_115_pad_type_0 = const()[name = string("h_115_pad_type_0"), val = string("valid")]; + tensor h_115_strides_0 = const()[name = string("h_115_strides_0"), val = tensor([1])]; + tensor h_115_pad_0 = const()[name = string("h_115_pad_0"), val = tensor([0, 0])]; + tensor h_115_dilations_0 = const()[name = string("h_115_dilations_0"), val = tensor([1])]; + int32 h_115_groups_0 = const()[name = string("h_115_groups_0"), val = int32(1)]; + tensor op_1850_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32107520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32894016))))[name = string("op_1850_weight_0_to_fp16_palettized")]; + tensor var_1850_bias_0_to_fp16 = const()[name = string("op_1850_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32894208)))]; + tensor var_1850_cast_fp16 = conv(bias = var_1850_bias_0_to_fp16, dilations = h_115_dilations_0, groups = h_115_groups_0, pad = h_115_pad_0, pad_type = h_115_pad_type_0, strides = h_115_strides_0, weight = op_1850_weight_0_to_fp16_palettized, x = input_301_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor out_49_cast_fp16 = add(x = x_155_cast_fp16, y = var_1850_cast_fp16)[name = string("out_49_cast_fp16")]; + tensor x_159_cast_fp16 = mul(x = out_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor x_161_cast_fp16 = mul(x = x_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor var_1864_cast_fp16 = mul(x = x_161_cast_fp16, y = last_1_cast_fp16)[name = string("op_1864_cast_fp16")]; + tensor x_last_39_axes_0 = const()[name = string("x_last_39_axes_0"), val = tensor([-1])]; + bool x_last_39_keep_dims_0 = const()[name = string("x_last_39_keep_dims_0"), val = bool(true)]; + tensor x_last_39_cast_fp16 = reduce_sum(axes = x_last_39_axes_0, keep_dims = x_last_39_keep_dims_0, x = var_1864_cast_fp16)[name = string("x_last_39_cast_fp16")]; + tensor var_1867_cast_fp16 = mul(x = x_161_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_39_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor input_305_cast_fp16 = add(x = var_1867_cast_fp16, y = var_1869_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("replicate")]; + fp16 const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_66_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string h_117_pad_type_0 = const()[name = string("h_117_pad_type_0"), val = string("valid")]; + tensor h_117_dilations_0 = const()[name = string("h_117_dilations_0"), val = tensor([2])]; + int32 h_117_groups_0 = const()[name = string("h_117_groups_0"), val = int32(512)]; + tensor h_117_strides_0 = const()[name = string("h_117_strides_0"), val = tensor([1])]; + tensor h_117_pad_0 = const()[name = string("h_117_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32895296))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32897280))))[name = string("main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32897472)))]; + tensor h_117_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_117_dilations_0, groups = h_117_groups_0, pad = h_117_pad_0, pad_type = h_117_pad_type_0, strides = h_117_strides_0, weight = main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_307_cast_fp16)[name = string("h_117_cast_fp16")]; + tensor x_163_cast_fp16 = mul(x = h_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1887_axes_0 = const()[name = string("op_1887_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32898560)))]; + tensor main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32899648)))]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_163_cast_fp16)[name = string("transpose_32")]; + tensor var_1887_cast_fp16 = layer_norm(axes = var_1887_axes_0, beta = main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16, x = input_309_cast_fp16)[name = string("op_1887_cast_fp16")]; + tensor input_311_perm_0 = const()[name = string("input_311_perm_0"), val = tensor([0, 2, 1])]; + string h_119_pad_type_0 = const()[name = string("h_119_pad_type_0"), val = string("valid")]; + tensor h_119_strides_0 = const()[name = string("h_119_strides_0"), val = tensor([1])]; + tensor h_119_pad_0 = const()[name = string("h_119_pad_0"), val = tensor([0, 0])]; + tensor h_119_dilations_0 = const()[name = string("h_119_dilations_0"), val = tensor([1])]; + int32 h_119_groups_0 = const()[name = string("h_119_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32900736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33687232))))[name = string("main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33687424)))]; + tensor input_311_cast_fp16 = transpose(perm = input_311_perm_0, x = var_1887_cast_fp16)[name = string("transpose_31")]; + tensor h_119_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_119_dilations_0, groups = h_119_groups_0, pad = h_119_pad_0, pad_type = h_119_pad_type_0, strides = h_119_strides_0, weight = main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_311_cast_fp16)[name = string("h_119_cast_fp16")]; + string input_313_mode_0 = const()[name = string("input_313_mode_0"), val = string("EXACT")]; + tensor input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = h_119_cast_fp16)[name = string("input_313_cast_fp16")]; + string h_121_pad_type_0 = const()[name = string("h_121_pad_type_0"), val = string("valid")]; + tensor h_121_strides_0 = const()[name = string("h_121_strides_0"), val = tensor([1])]; + tensor h_121_pad_0 = const()[name = string("h_121_pad_0"), val = tensor([0, 0])]; + tensor h_121_dilations_0 = const()[name = string("h_121_dilations_0"), val = tensor([1])]; + int32 h_121_groups_0 = const()[name = string("h_121_groups_0"), val = int32(1)]; + tensor op_1904_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33691584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34478080))))[name = string("op_1904_weight_0_to_fp16_palettized")]; + tensor var_1904_bias_0_to_fp16 = const()[name = string("op_1904_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34478272)))]; + tensor var_1904_cast_fp16 = conv(bias = var_1904_bias_0_to_fp16, dilations = h_121_dilations_0, groups = h_121_groups_0, pad = h_121_pad_0, pad_type = h_121_pad_type_0, strides = h_121_strides_0, weight = op_1904_weight_0_to_fp16_palettized, x = input_313_cast_fp16)[name = string("op_1904_cast_fp16")]; + tensor out_51_cast_fp16 = add(x = x_161_cast_fp16, y = var_1904_cast_fp16)[name = string("out_51_cast_fp16")]; + tensor x_165_cast_fp16 = mul(x = out_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor x_167_cast_fp16 = mul(x = x_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1918_cast_fp16 = mul(x = x_167_cast_fp16, y = last_1_cast_fp16)[name = string("op_1918_cast_fp16")]; + tensor x_last_41_axes_0 = const()[name = string("x_last_41_axes_0"), val = tensor([-1])]; + bool x_last_41_keep_dims_0 = const()[name = string("x_last_41_keep_dims_0"), val = bool(true)]; + tensor x_last_41_cast_fp16 = reduce_sum(axes = x_last_41_axes_0, keep_dims = x_last_41_keep_dims_0, x = var_1918_cast_fp16)[name = string("x_last_41_cast_fp16")]; + tensor var_1921_cast_fp16 = mul(x = x_167_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1921_cast_fp16")]; + tensor var_1923_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_41_cast_fp16)[name = string("op_1923_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = var_1921_cast_fp16, y = var_1923_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("replicate")]; + fp16 const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = fp16(0x0p+0)]; + tensor input_319_cast_fp16 = pad(constant_val = const_68_to_fp16, mode = input_319_mode_0, pad = input_319_pad_0, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string h_123_pad_type_0 = const()[name = string("h_123_pad_type_0"), val = string("valid")]; + tensor h_123_dilations_0 = const()[name = string("h_123_dilations_0"), val = tensor([4])]; + int32 h_123_groups_0 = const()[name = string("h_123_groups_0"), val = int32(512)]; + tensor h_123_strides_0 = const()[name = string("h_123_strides_0"), val = tensor([1])]; + tensor h_123_pad_0 = const()[name = string("h_123_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34479360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34481344))))[name = string("main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34481536)))]; + tensor h_123_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_123_dilations_0, groups = h_123_groups_0, pad = h_123_pad_0, pad_type = h_123_pad_type_0, strides = h_123_strides_0, weight = main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_319_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor x_169_cast_fp16 = mul(x = h_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1941_axes_0 = const()[name = string("op_1941_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34482624)))]; + tensor main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34483712)))]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_169_cast_fp16)[name = string("transpose_30")]; + tensor var_1941_cast_fp16 = layer_norm(axes = var_1941_axes_0, beta = main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16, x = input_321_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor([0, 2, 1])]; + string h_125_pad_type_0 = const()[name = string("h_125_pad_type_0"), val = string("valid")]; + tensor h_125_strides_0 = const()[name = string("h_125_strides_0"), val = tensor([1])]; + tensor h_125_pad_0 = const()[name = string("h_125_pad_0"), val = tensor([0, 0])]; + tensor h_125_dilations_0 = const()[name = string("h_125_dilations_0"), val = tensor([1])]; + int32 h_125_groups_0 = const()[name = string("h_125_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34484800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35271296))))[name = string("main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35271488)))]; + tensor input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = var_1941_cast_fp16)[name = string("transpose_29")]; + tensor h_125_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_125_dilations_0, groups = h_125_groups_0, pad = h_125_pad_0, pad_type = h_125_pad_type_0, strides = h_125_strides_0, weight = main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = string("h_125_cast_fp16")]; + string input_325_mode_0 = const()[name = string("input_325_mode_0"), val = string("EXACT")]; + tensor input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = h_125_cast_fp16)[name = string("input_325_cast_fp16")]; + string h_127_pad_type_0 = const()[name = string("h_127_pad_type_0"), val = string("valid")]; + tensor h_127_strides_0 = const()[name = string("h_127_strides_0"), val = tensor([1])]; + tensor h_127_pad_0 = const()[name = string("h_127_pad_0"), val = tensor([0, 0])]; + tensor h_127_dilations_0 = const()[name = string("h_127_dilations_0"), val = tensor([1])]; + int32 h_127_groups_0 = const()[name = string("h_127_groups_0"), val = int32(1)]; + tensor op_1958_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35275648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36062144))))[name = string("op_1958_weight_0_to_fp16_palettized")]; + tensor var_1958_bias_0_to_fp16 = const()[name = string("op_1958_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36062336)))]; + tensor var_1958_cast_fp16 = conv(bias = var_1958_bias_0_to_fp16, dilations = h_127_dilations_0, groups = h_127_groups_0, pad = h_127_pad_0, pad_type = h_127_pad_type_0, strides = h_127_strides_0, weight = op_1958_weight_0_to_fp16_palettized, x = input_325_cast_fp16)[name = string("op_1958_cast_fp16")]; + tensor out_53_cast_fp16 = add(x = x_167_cast_fp16, y = var_1958_cast_fp16)[name = string("out_53_cast_fp16")]; + tensor x_171_cast_fp16 = mul(x = out_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_171_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor var_1972_cast_fp16 = mul(x = x_173_cast_fp16, y = last_1_cast_fp16)[name = string("op_1972_cast_fp16")]; + tensor x_last_43_axes_0 = const()[name = string("x_last_43_axes_0"), val = tensor([-1])]; + bool x_last_43_keep_dims_0 = const()[name = string("x_last_43_keep_dims_0"), val = bool(true)]; + tensor x_last_43_cast_fp16 = reduce_sum(axes = x_last_43_axes_0, keep_dims = x_last_43_keep_dims_0, x = var_1972_cast_fp16)[name = string("x_last_43_cast_fp16")]; + tensor var_1975_cast_fp16 = mul(x = x_173_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1975_cast_fp16")]; + tensor var_1977_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_43_cast_fp16)[name = string("op_1977_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1977_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("replicate")]; + fp16 const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = fp16(0x0p+0)]; + tensor input_331_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = input_331_mode_0, pad = input_331_pad_0, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + string h_129_pad_type_0 = const()[name = string("h_129_pad_type_0"), val = string("valid")]; + tensor h_129_dilations_0 = const()[name = string("h_129_dilations_0"), val = tensor([8])]; + int32 h_129_groups_0 = const()[name = string("h_129_groups_0"), val = int32(512)]; + tensor h_129_strides_0 = const()[name = string("h_129_strides_0"), val = tensor([1])]; + tensor h_129_pad_0 = const()[name = string("h_129_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36063424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36065408))))[name = string("main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36065600)))]; + tensor h_129_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_129_dilations_0, groups = h_129_groups_0, pad = h_129_pad_0, pad_type = h_129_pad_type_0, strides = h_129_strides_0, weight = main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = string("h_129_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = h_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_333_perm_0 = const()[name = string("input_333_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1995_axes_0 = const()[name = string("op_1995_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36066688)))]; + tensor main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36067776)))]; + tensor input_333_cast_fp16 = transpose(perm = input_333_perm_0, x = x_175_cast_fp16)[name = string("transpose_28")]; + tensor var_1995_cast_fp16 = layer_norm(axes = var_1995_axes_0, beta = main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16, x = input_333_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor input_335_perm_0 = const()[name = string("input_335_perm_0"), val = tensor([0, 2, 1])]; + string h_131_pad_type_0 = const()[name = string("h_131_pad_type_0"), val = string("valid")]; + tensor h_131_strides_0 = const()[name = string("h_131_strides_0"), val = tensor([1])]; + tensor h_131_pad_0 = const()[name = string("h_131_pad_0"), val = tensor([0, 0])]; + tensor h_131_dilations_0 = const()[name = string("h_131_dilations_0"), val = tensor([1])]; + int32 h_131_groups_0 = const()[name = string("h_131_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36068864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36855360))))[name = string("main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36855552)))]; + tensor input_335_cast_fp16 = transpose(perm = input_335_perm_0, x = var_1995_cast_fp16)[name = string("transpose_27")]; + tensor h_131_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_131_dilations_0, groups = h_131_groups_0, pad = h_131_pad_0, pad_type = h_131_pad_type_0, strides = h_131_strides_0, weight = main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_335_cast_fp16)[name = string("h_131_cast_fp16")]; + string input_337_mode_0 = const()[name = string("input_337_mode_0"), val = string("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = h_131_cast_fp16)[name = string("input_337_cast_fp16")]; + string h_133_pad_type_0 = const()[name = string("h_133_pad_type_0"), val = string("valid")]; + tensor h_133_strides_0 = const()[name = string("h_133_strides_0"), val = tensor([1])]; + tensor h_133_pad_0 = const()[name = string("h_133_pad_0"), val = tensor([0, 0])]; + tensor h_133_dilations_0 = const()[name = string("h_133_dilations_0"), val = tensor([1])]; + int32 h_133_groups_0 = const()[name = string("h_133_groups_0"), val = int32(1)]; + tensor op_2012_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36859712))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37646208))))[name = string("op_2012_weight_0_to_fp16_palettized")]; + tensor var_2012_bias_0_to_fp16 = const()[name = string("op_2012_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37646400)))]; + tensor var_2012_cast_fp16 = conv(bias = var_2012_bias_0_to_fp16, dilations = h_133_dilations_0, groups = h_133_groups_0, pad = h_133_pad_0, pad_type = h_133_pad_type_0, strides = h_133_strides_0, weight = op_2012_weight_0_to_fp16_palettized, x = input_337_cast_fp16)[name = string("op_2012_cast_fp16")]; + tensor out_55_cast_fp16 = add(x = x_173_cast_fp16, y = var_2012_cast_fp16)[name = string("out_55_cast_fp16")]; + tensor x_177_cast_fp16 = mul(x = out_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37647488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37672128))))[name = string("main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37672320)))]; + tensor linear_29_cast_fp16 = linear(bias = main_blocks_3_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor t_axes_0 = const()[name = string("t_axes_0"), val = tensor([-1])]; + tensor t_cast_fp16 = expand_dims(axes = t_axes_0, x = linear_29_cast_fp16)[name = string("t_cast_fp16")]; + tensor var_2022_cast_fp16 = add(x = x_177_cast_fp16, y = t_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor x_179_cast_fp16 = mul(x = var_2022_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor x_181_cast_fp16 = mul(x = x_179_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor var_2035_cast_fp16 = mul(x = x_181_cast_fp16, y = last_1_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor x_last_45_axes_0 = const()[name = string("x_last_45_axes_0"), val = tensor([-1])]; + bool x_last_45_keep_dims_0 = const()[name = string("x_last_45_keep_dims_0"), val = bool(true)]; + tensor x_last_45_cast_fp16 = reduce_sum(axes = x_last_45_axes_0, keep_dims = x_last_45_keep_dims_0, x = var_2035_cast_fp16)[name = string("x_last_45_cast_fp16")]; + tensor var_2038_cast_fp16 = mul(x = x_181_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2038_cast_fp16")]; + tensor var_2040_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_45_cast_fp16)[name = string("op_2040_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = var_2038_cast_fp16, y = var_2040_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_345_mode_0 = const()[name = string("input_345_mode_0"), val = string("replicate")]; + fp16 const_72_to_fp16 = const()[name = string("const_72_to_fp16"), val = fp16(0x0p+0)]; + tensor input_345_cast_fp16 = pad(constant_val = const_72_to_fp16, mode = input_345_mode_0, pad = input_345_pad_0, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + string h_135_pad_type_0 = const()[name = string("h_135_pad_type_0"), val = string("valid")]; + int32 h_135_groups_0 = const()[name = string("h_135_groups_0"), val = int32(512)]; + tensor h_135_strides_0 = const()[name = string("h_135_strides_0"), val = tensor([1])]; + tensor h_135_pad_0 = const()[name = string("h_135_pad_0"), val = tensor([0, 0])]; + tensor h_135_dilations_0 = const()[name = string("h_135_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37673408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37675392))))[name = string("main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37675584)))]; + tensor h_135_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_135_dilations_0, groups = h_135_groups_0, pad = h_135_pad_0, pad_type = h_135_pad_type_0, strides = h_135_strides_0, weight = main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_345_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor x_183_cast_fp16 = mul(x = h_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_183_cast_fp16")]; + tensor input_347_perm_0 = const()[name = string("input_347_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2058_axes_0 = const()[name = string("op_2058_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37676672)))]; + tensor main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37677760)))]; + tensor input_347_cast_fp16 = transpose(perm = input_347_perm_0, x = x_183_cast_fp16)[name = string("transpose_26")]; + tensor var_2058_cast_fp16 = layer_norm(axes = var_2058_axes_0, beta = main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16, x = input_347_cast_fp16)[name = string("op_2058_cast_fp16")]; + tensor input_349_perm_0 = const()[name = string("input_349_perm_0"), val = tensor([0, 2, 1])]; + string h_137_pad_type_0 = const()[name = string("h_137_pad_type_0"), val = string("valid")]; + tensor h_137_strides_0 = const()[name = string("h_137_strides_0"), val = tensor([1])]; + tensor h_137_pad_0 = const()[name = string("h_137_pad_0"), val = tensor([0, 0])]; + tensor h_137_dilations_0 = const()[name = string("h_137_dilations_0"), val = tensor([1])]; + int32 h_137_groups_0 = const()[name = string("h_137_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37678848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38465344))))[name = string("main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38465536)))]; + tensor input_349_cast_fp16 = transpose(perm = input_349_perm_0, x = var_2058_cast_fp16)[name = string("transpose_25")]; + tensor h_137_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_137_dilations_0, groups = h_137_groups_0, pad = h_137_pad_0, pad_type = h_137_pad_type_0, strides = h_137_strides_0, weight = main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_349_cast_fp16)[name = string("h_137_cast_fp16")]; + string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")]; + tensor input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = h_137_cast_fp16)[name = string("input_351_cast_fp16")]; + string h_139_pad_type_0 = const()[name = string("h_139_pad_type_0"), val = string("valid")]; + tensor h_139_strides_0 = const()[name = string("h_139_strides_0"), val = tensor([1])]; + tensor h_139_pad_0 = const()[name = string("h_139_pad_0"), val = tensor([0, 0])]; + tensor h_139_dilations_0 = const()[name = string("h_139_dilations_0"), val = tensor([1])]; + int32 h_139_groups_0 = const()[name = string("h_139_groups_0"), val = int32(1)]; + tensor op_2075_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38469696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39256192))))[name = string("op_2075_weight_0_to_fp16_palettized")]; + tensor var_2075_bias_0_to_fp16 = const()[name = string("op_2075_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39256384)))]; + tensor var_2075_cast_fp16 = conv(bias = var_2075_bias_0_to_fp16, dilations = h_139_dilations_0, groups = h_139_groups_0, pad = h_139_pad_0, pad_type = h_139_pad_type_0, strides = h_139_strides_0, weight = op_2075_weight_0_to_fp16_palettized, x = input_351_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor out_57_cast_fp16 = add(x = x_181_cast_fp16, y = var_2075_cast_fp16)[name = string("out_57_cast_fp16")]; + tensor x_185_cast_fp16 = mul(x = out_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor x_m_13_cast_fp16 = mul(x = x_185_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_13_cast_fp16")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39257472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454144))))[name = string("main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454336)))]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_185_cast_fp16)[name = string("transpose_24")]; + tensor linear_30_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_2093 = const()[name = string("op_2093"), val = tensor([2, 128, 8, 64])]; + tensor var_2094_cast_fp16 = reshape(shape = var_2093, x = linear_30_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor x_187_perm_0 = const()[name = string("x_187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39455424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39553792))))[name = string("main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39553984)))]; + tensor linear_31_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_2101 = const()[name = string("op_2101"), val = tensor([2, 128, 8, 64])]; + tensor var_2102_cast_fp16 = reshape(shape = var_2101, x = linear_31_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor x_189_perm_0 = const()[name = string("x_189_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39555072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39653440))))[name = string("main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39653632)))]; + tensor linear_32_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_2109 = const()[name = string("op_2109"), val = tensor([2, 128, 8, 64])]; + tensor var_2110_cast_fp16 = reshape(shape = var_2109, x = linear_32_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_13_begin_0 = const()[name = string("x_a_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_13_end_0 = const()[name = string("x_a_13_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_13_end_mask_0 = const()[name = string("x_a_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_187_cast_fp16 = transpose(perm = x_187_perm_0, x = var_2094_cast_fp16)[name = string("transpose_23")]; + tensor x_a_13_cast_fp16 = slice_by_index(begin = x_a_13_begin_0, end = x_a_13_end_0, end_mask = x_a_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_a_13_cast_fp16")]; + tensor x_b_13_begin_0 = const()[name = string("x_b_13_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_13_end_0 = const()[name = string("x_b_13_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_13_end_mask_0 = const()[name = string("x_b_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_13_cast_fp16 = slice_by_index(begin = x_b_13_begin_0, end = x_b_13_end_0, end_mask = x_b_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_b_13_cast_fp16")]; + tensor var_2138_cast_fp16 = mul(x = x_a_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2138_cast_fp16")]; + tensor var_2139_cast_fp16 = mul(x = x_b_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2139_cast_fp16")]; + tensor rot_a_13_cast_fp16 = sub(x = var_2138_cast_fp16, y = var_2139_cast_fp16)[name = string("rot_a_13_cast_fp16")]; + tensor var_2141_cast_fp16 = mul(x = x_a_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2141_cast_fp16")]; + tensor var_2142_cast_fp16 = mul(x = x_b_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2142_cast_fp16")]; + tensor rot_b_13_cast_fp16 = add(x = var_2141_cast_fp16, y = var_2142_cast_fp16)[name = string("rot_b_13_cast_fp16")]; + bool q_13_interleave_0 = const()[name = string("q_13_interleave_0"), val = bool(false)]; + tensor q_13_cast_fp16 = concat(axis = var_1775, interleave = q_13_interleave_0, values = (rot_a_13_cast_fp16, rot_b_13_cast_fp16))[name = string("q_13_cast_fp16")]; + tensor x_a_begin_0 = const()[name = string("x_a_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_end_0 = const()[name = string("x_a_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_end_mask_0 = const()[name = string("x_a_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_189_cast_fp16 = transpose(perm = x_189_perm_0, x = var_2102_cast_fp16)[name = string("transpose_22")]; + tensor x_a_cast_fp16 = slice_by_index(begin = x_a_begin_0, end = x_a_end_0, end_mask = x_a_end_mask_0, x = x_189_cast_fp16)[name = string("x_a_cast_fp16")]; + tensor x_b_begin_0 = const()[name = string("x_b_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_end_0 = const()[name = string("x_b_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_end_mask_0 = const()[name = string("x_b_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_cast_fp16 = slice_by_index(begin = x_b_begin_0, end = x_b_end_0, end_mask = x_b_end_mask_0, x = x_189_cast_fp16)[name = string("x_b_cast_fp16")]; + tensor var_2156_cast_fp16 = mul(x = x_a_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2156_cast_fp16")]; + tensor var_2157_cast_fp16 = mul(x = x_b_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2157_cast_fp16")]; + tensor rot_a_cast_fp16 = sub(x = var_2156_cast_fp16, y = var_2157_cast_fp16)[name = string("rot_a_cast_fp16")]; + tensor var_2159_cast_fp16 = mul(x = x_a_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2159_cast_fp16")]; + tensor var_2160_cast_fp16 = mul(x = x_b_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2160_cast_fp16")]; + tensor rot_b_cast_fp16 = add(x = var_2159_cast_fp16, y = var_2160_cast_fp16)[name = string("rot_b_cast_fp16")]; + bool k_13_interleave_0 = const()[name = string("k_13_interleave_0"), val = bool(false)]; + tensor k_13_cast_fp16 = concat(axis = var_1775, interleave = k_13_interleave_0, values = (rot_a_cast_fp16, rot_b_cast_fp16))[name = string("k_13_cast_fp16")]; + bool var_2165_transpose_x_1 = const()[name = string("op_2165_transpose_x_1"), val = bool(false)]; + bool var_2165_transpose_y_1 = const()[name = string("op_2165_transpose_y_1"), val = bool(true)]; + tensor var_2165_cast_fp16 = matmul(transpose_x = var_2165_transpose_x_1, transpose_y = var_2165_transpose_y_1, x = q_13_cast_fp16, y = k_13_cast_fp16)[name = string("op_2165_cast_fp16")]; + fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_cast_fp16 = mul(x = var_2165_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")]; + tensor input_359_cast_fp16 = sub(x = _inversed_scores_cast_fp16, y = var_530_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_1775, x = input_359_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool var_2174_transpose_x_0 = const()[name = string("op_2174_transpose_x_0"), val = bool(false)]; + bool var_2174_transpose_y_0 = const()[name = string("op_2174_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2110_cast_fp16)[name = string("transpose_21")]; + tensor var_2174_cast_fp16 = matmul(transpose_x = var_2174_transpose_x_0, transpose_y = var_2174_transpose_y_0, x = attn_19_cast_fp16, y = v_13_cast_fp16)[name = string("op_2174_cast_fp16")]; + tensor var_2175_perm_0 = const()[name = string("op_2175_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2176 = const()[name = string("op_2176"), val = tensor([2, 128, 512])]; + tensor var_2175_cast_fp16 = transpose(perm = var_2175_perm_0, x = var_2174_cast_fp16)[name = string("transpose_20")]; + tensor input_361_cast_fp16 = reshape(shape = var_2176, x = var_2175_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39654720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39851392))))[name = string("main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39851584)))]; + tensor linear_33_cast_fp16 = linear(bias = main_blocks_3_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor out_59_perm_0 = const()[name = string("out_59_perm_0"), val = tensor([0, 2, 1])]; + tensor out_59_cast_fp16 = transpose(perm = out_59_perm_0, x = linear_33_cast_fp16)[name = string("transpose_19")]; + tensor var_2183_cast_fp16 = mul(x = out_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_m_13_cast_fp16, y = var_2183_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor input_363_perm_0 = const()[name = string("input_363_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2190_axes_0 = const()[name = string("op_2190_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39852672)))]; + tensor main_blocks_3_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39853760)))]; + tensor input_363_cast_fp16 = transpose(perm = input_363_perm_0, x = x_191_cast_fp16)[name = string("transpose_18")]; + tensor var_2190_cast_fp16 = layer_norm(axes = var_2190_axes_0, beta = main_blocks_3_text_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_text_norm_norm_weight_to_fp16, x = input_363_cast_fp16)[name = string("op_2190_cast_fp16")]; + tensor var_2191_perm_0 = const()[name = string("op_2191_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2191_perm_0, x = var_2190_cast_fp16)[name = string("transpose_17")]; + tensor x_193_cast_fp16 = mul(x = var_2191_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_193_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2204_cast_fp16 = mul(x = x_195_cast_fp16, y = last_1_cast_fp16)[name = string("op_2204_cast_fp16")]; + tensor x_last_47_axes_0 = const()[name = string("x_last_47_axes_0"), val = tensor([-1])]; + bool x_last_47_keep_dims_0 = const()[name = string("x_last_47_keep_dims_0"), val = bool(true)]; + tensor x_last_47_cast_fp16 = reduce_sum(axes = x_last_47_axes_0, keep_dims = x_last_47_keep_dims_0, x = var_2204_cast_fp16)[name = string("x_last_47_cast_fp16")]; + tensor var_2207_cast_fp16 = mul(x = x_195_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2207_cast_fp16")]; + tensor var_2209_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_47_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor input_367_cast_fp16 = add(x = var_2207_cast_fp16, y = var_2209_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_369_mode_0 = const()[name = string("input_369_mode_0"), val = string("replicate")]; + fp16 const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = fp16(0x0p+0)]; + tensor input_369_cast_fp16 = pad(constant_val = const_79_to_fp16, mode = input_369_mode_0, pad = input_369_pad_0, x = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + string h_141_pad_type_0 = const()[name = string("h_141_pad_type_0"), val = string("valid")]; + int32 h_141_groups_0 = const()[name = string("h_141_groups_0"), val = int32(512)]; + tensor h_141_strides_0 = const()[name = string("h_141_strides_0"), val = tensor([1])]; + tensor h_141_pad_0 = const()[name = string("h_141_pad_0"), val = tensor([0, 0])]; + tensor h_141_dilations_0 = const()[name = string("h_141_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39854848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39856832))))[name = string("main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39857024)))]; + tensor h_141_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_141_dilations_0, groups = h_141_groups_0, pad = h_141_pad_0, pad_type = h_141_pad_type_0, strides = h_141_strides_0, weight = main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_369_cast_fp16)[name = string("h_141_cast_fp16")]; + tensor x_197_cast_fp16 = mul(x = h_141_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_371_perm_0 = const()[name = string("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2227_axes_0 = const()[name = string("op_2227_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39858112)))]; + tensor main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39859200)))]; + tensor input_371_cast_fp16 = transpose(perm = input_371_perm_0, x = x_197_cast_fp16)[name = string("transpose_16")]; + tensor var_2227_cast_fp16 = layer_norm(axes = var_2227_axes_0, beta = main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16, x = input_371_cast_fp16)[name = string("op_2227_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + string h_143_pad_type_0 = const()[name = string("h_143_pad_type_0"), val = string("valid")]; + tensor h_143_strides_0 = const()[name = string("h_143_strides_0"), val = tensor([1])]; + tensor h_143_pad_0 = const()[name = string("h_143_pad_0"), val = tensor([0, 0])]; + tensor h_143_dilations_0 = const()[name = string("h_143_dilations_0"), val = tensor([1])]; + int32 h_143_groups_0 = const()[name = string("h_143_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39860288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40646784))))[name = string("main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40646976)))]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = var_2227_cast_fp16)[name = string("transpose_15")]; + tensor h_143_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_143_dilations_0, groups = h_143_groups_0, pad = h_143_pad_0, pad_type = h_143_pad_type_0, strides = h_143_strides_0, weight = main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = string("h_143_cast_fp16")]; + string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")]; + tensor input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = h_143_cast_fp16)[name = string("input_375_cast_fp16")]; + string h_145_pad_type_0 = const()[name = string("h_145_pad_type_0"), val = string("valid")]; + tensor h_145_strides_0 = const()[name = string("h_145_strides_0"), val = tensor([1])]; + tensor h_145_pad_0 = const()[name = string("h_145_pad_0"), val = tensor([0, 0])]; + tensor h_145_dilations_0 = const()[name = string("h_145_dilations_0"), val = tensor([1])]; + int32 h_145_groups_0 = const()[name = string("h_145_groups_0"), val = int32(1)]; + tensor op_2244_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40651136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41437632))))[name = string("op_2244_weight_0_to_fp16_palettized")]; + tensor var_2244_bias_0_to_fp16 = const()[name = string("op_2244_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41437824)))]; + tensor var_2244_cast_fp16 = conv(bias = var_2244_bias_0_to_fp16, dilations = h_145_dilations_0, groups = h_145_groups_0, pad = h_145_pad_0, pad_type = h_145_pad_type_0, strides = h_145_strides_0, weight = op_2244_weight_0_to_fp16_palettized, x = input_375_cast_fp16)[name = string("op_2244_cast_fp16")]; + tensor out_61_cast_fp16 = add(x = x_195_cast_fp16, y = var_2244_cast_fp16)[name = string("out_61_cast_fp16")]; + tensor x_199_cast_fp16 = mul(x = out_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor x_m_cast_fp16 = mul(x = x_199_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_cast_fp16")]; + tensor input_377_perm_0 = const()[name = string("input_377_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41438912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41537280))))[name = string("main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41537472)))]; + tensor input_377_cast_fp16 = transpose(perm = input_377_perm_0, x = x_199_cast_fp16)[name = string("transpose_14")]; + tensor linear_34_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_2260 = const()[name = string("op_2260"), val = tensor([2, 128, 2, 128])]; + tensor var_2261_cast_fp16 = reshape(shape = var_2260, x = linear_34_cast_fp16)[name = string("op_2261_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41538048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41587264))))[name = string("main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41587456)))]; + tensor linear_36_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_2274 = const()[name = string("op_2274"), val = tensor([2, 50, 2, 128])]; + tensor var_2275_cast_fp16 = reshape(shape = var_2274, x = linear_36_cast_fp16)[name = string("op_2275_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_2279_transpose_x_0 = const()[name = string("op_2279_transpose_x_0"), val = bool(false)]; + bool var_2279_transpose_y_0 = const()[name = string("op_2279_transpose_y_0"), val = bool(false)]; + tensor op_2278_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41588032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41607296))))[name = string("op_2278_to_fp16_palettized")]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_2261_cast_fp16)[name = string("transpose_13")]; + tensor var_2279_cast_fp16 = matmul(transpose_x = var_2279_transpose_x_0, transpose_y = var_2279_transpose_y_0, x = q_cast_fp16, y = op_2278_to_fp16_palettized)[name = string("op_2279_cast_fp16")]; + fp16 _inversed_input_379_y_0_to_fp16 = const()[name = string("_inversed_input_379_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_379_cast_fp16 = mul(x = var_2279_cast_fp16, y = _inversed_input_379_y_0_to_fp16)[name = string("_inversed_input_379_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_1775, x = _inversed_input_379_cast_fp16)[name = string("attn_21_cast_fp16")]; + tensor attn_cast_fp16 = mul(x = attn_21_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_cast_fp16")]; + bool var_2286_transpose_x_0 = const()[name = string("op_2286_transpose_x_0"), val = bool(false)]; + bool var_2286_transpose_y_0 = const()[name = string("op_2286_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_2275_cast_fp16)[name = string("transpose_12")]; + tensor var_2286_cast_fp16 = matmul(transpose_x = var_2286_transpose_x_0, transpose_y = var_2286_transpose_y_0, x = attn_cast_fp16, y = v_15_cast_fp16)[name = string("op_2286_cast_fp16")]; + tensor var_2287_perm_0 = const()[name = string("op_2287_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2288 = const()[name = string("op_2288"), val = tensor([2, 128, 256])]; + tensor var_2287_cast_fp16 = transpose(perm = var_2287_perm_0, x = var_2286_cast_fp16)[name = string("transpose_11")]; + tensor input_381_cast_fp16 = reshape(shape = var_2288, x = var_2287_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41607488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41705856))))[name = string("main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41706048)))]; + tensor linear_37_cast_fp16 = linear(bias = main_blocks_3_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor out_63_perm_0 = const()[name = string("out_63_perm_0"), val = tensor([0, 2, 1])]; + tensor out_63_cast_fp16 = transpose(perm = out_63_perm_0, x = linear_37_cast_fp16)[name = string("transpose_10")]; + tensor var_2295_cast_fp16 = mul(x = out_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2295_cast_fp16")]; + tensor x_201_cast_fp16 = add(x = x_m_cast_fp16, y = var_2295_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_383_perm_0 = const()[name = string("input_383_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2302_axes_0 = const()[name = string("op_2302_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41707136)))]; + tensor main_blocks_3_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41708224)))]; + tensor input_383_cast_fp16 = transpose(perm = input_383_perm_0, x = x_201_cast_fp16)[name = string("transpose_9")]; + tensor var_2302_cast_fp16 = layer_norm(axes = var_2302_axes_0, beta = main_blocks_3_style_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_style_norm_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_perm_0 = const()[name = string("op_2303_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2303_cast_fp16 = transpose(perm = var_2303_perm_0, x = var_2302_cast_fp16)[name = string("transpose_8")]; + tensor x_203_cast_fp16 = mul(x = var_2303_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor x_205_cast_fp16 = mul(x = x_203_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor var_2330_cast_fp16 = mul(x = x_205_cast_fp16, y = last_1_cast_fp16)[name = string("op_2330_cast_fp16")]; + tensor x_last_49_axes_0 = const()[name = string("x_last_49_axes_0"), val = tensor([-1])]; + bool x_last_49_keep_dims_0 = const()[name = string("x_last_49_keep_dims_0"), val = bool(true)]; + tensor x_last_49_cast_fp16 = reduce_sum(axes = x_last_49_axes_0, keep_dims = x_last_49_keep_dims_0, x = var_2330_cast_fp16)[name = string("x_last_49_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = x_205_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor var_2335_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_49_cast_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2335_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_pad_0 = const()[name = string("input_389_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("replicate")]; + fp16 const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = fp16(0x0p+0)]; + tensor input_389_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = input_389_mode_0, pad = input_389_pad_0, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + string h_147_pad_type_0 = const()[name = string("h_147_pad_type_0"), val = string("valid")]; + int32 h_147_groups_0 = const()[name = string("h_147_groups_0"), val = int32(512)]; + tensor h_147_strides_0 = const()[name = string("h_147_strides_0"), val = tensor([1])]; + tensor h_147_pad_0 = const()[name = string("h_147_pad_0"), val = tensor([0, 0])]; + tensor h_147_dilations_0 = const()[name = string("h_147_dilations_0"), val = tensor([1])]; + tensor last_convnext_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41709312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41711296))))[name = string("last_convnext_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41711488)))]; + tensor h_147_cast_fp16 = conv(bias = last_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_147_dilations_0, groups = h_147_groups_0, pad = h_147_pad_0, pad_type = h_147_pad_type_0, strides = h_147_strides_0, weight = last_convnext_0_dwconv__conv_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor x_207_cast_fp16 = mul(x = h_147_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_391_perm_0 = const()[name = string("input_391_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2353_axes_0 = const()[name = string("op_2353_axes_0"), val = tensor([-1])]; + tensor last_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41712576)))]; + tensor last_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41713664)))]; + fp16 var_2307_to_fp16 = const()[name = string("op_2307_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_391_cast_fp16 = transpose(perm = input_391_perm_0, x = x_207_cast_fp16)[name = string("transpose_7")]; + tensor var_2353_cast_fp16 = layer_norm(axes = var_2353_axes_0, beta = last_convnext_0_norm_norm_bias_to_fp16, epsilon = var_2307_to_fp16, gamma = last_convnext_0_norm_norm_weight_to_fp16, x = input_391_cast_fp16)[name = string("op_2353_cast_fp16")]; + tensor input_393_perm_0 = const()[name = string("input_393_perm_0"), val = tensor([0, 2, 1])]; + string h_149_pad_type_0 = const()[name = string("h_149_pad_type_0"), val = string("valid")]; + tensor h_149_strides_0 = const()[name = string("h_149_strides_0"), val = tensor([1])]; + tensor h_149_pad_0 = const()[name = string("h_149_pad_0"), val = tensor([0, 0])]; + tensor h_149_dilations_0 = const()[name = string("h_149_dilations_0"), val = tensor([1])]; + int32 h_149_groups_0 = const()[name = string("h_149_groups_0"), val = int32(1)]; + tensor last_convnext_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41714752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42501248))))[name = string("last_convnext_0_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42501440)))]; + tensor input_393_cast_fp16 = transpose(perm = input_393_perm_0, x = var_2353_cast_fp16)[name = string("transpose_6")]; + tensor h_149_cast_fp16 = conv(bias = last_convnext_0_pwconv1_bias_to_fp16, dilations = h_149_dilations_0, groups = h_149_groups_0, pad = h_149_pad_0, pad_type = h_149_pad_type_0, strides = h_149_strides_0, weight = last_convnext_0_pwconv1_weight_to_fp16_palettized, x = input_393_cast_fp16)[name = string("h_149_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = h_149_cast_fp16)[name = string("input_395_cast_fp16")]; + string h_151_pad_type_0 = const()[name = string("h_151_pad_type_0"), val = string("valid")]; + tensor h_151_strides_0 = const()[name = string("h_151_strides_0"), val = tensor([1])]; + tensor h_151_pad_0 = const()[name = string("h_151_pad_0"), val = tensor([0, 0])]; + tensor h_151_dilations_0 = const()[name = string("h_151_dilations_0"), val = tensor([1])]; + int32 h_151_groups_0 = const()[name = string("h_151_groups_0"), val = int32(1)]; + tensor op_2370_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42505600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43292096))))[name = string("op_2370_weight_0_to_fp16_palettized")]; + tensor var_2370_bias_0_to_fp16 = const()[name = string("op_2370_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43292288)))]; + tensor var_2370_cast_fp16 = conv(bias = var_2370_bias_0_to_fp16, dilations = h_151_dilations_0, groups = h_151_groups_0, pad = h_151_pad_0, pad_type = h_151_pad_type_0, strides = h_151_strides_0, weight = op_2370_weight_0_to_fp16_palettized, x = input_395_cast_fp16)[name = string("op_2370_cast_fp16")]; + tensor out_65_cast_fp16 = add(x = x_205_cast_fp16, y = var_2370_cast_fp16)[name = string("out_65_cast_fp16")]; + tensor x_209_cast_fp16 = mul(x = out_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor x_211_cast_fp16 = mul(x = x_209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_2398_cast_fp16 = mul(x = x_211_cast_fp16, y = last_1_cast_fp16)[name = string("op_2398_cast_fp16")]; + tensor x_last_51_axes_0 = const()[name = string("x_last_51_axes_0"), val = tensor([-1])]; + bool x_last_51_keep_dims_0 = const()[name = string("x_last_51_keep_dims_0"), val = bool(true)]; + tensor x_last_51_cast_fp16 = reduce_sum(axes = x_last_51_axes_0, keep_dims = x_last_51_keep_dims_0, x = var_2398_cast_fp16)[name = string("x_last_51_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = x_211_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_51_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor input_399_cast_fp16 = add(x = var_2401_cast_fp16, y = var_2403_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor input_401_pad_0 = const()[name = string("input_401_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_401_mode_0 = const()[name = string("input_401_mode_0"), val = string("replicate")]; + fp16 const_86_to_fp16 = const()[name = string("const_86_to_fp16"), val = fp16(0x0p+0)]; + tensor input_401_cast_fp16 = pad(constant_val = const_86_to_fp16, mode = input_401_mode_0, pad = input_401_pad_0, x = input_399_cast_fp16)[name = string("input_401_cast_fp16")]; + string h_153_pad_type_0 = const()[name = string("h_153_pad_type_0"), val = string("valid")]; + int32 h_153_groups_0 = const()[name = string("h_153_groups_0"), val = int32(512)]; + tensor h_153_strides_0 = const()[name = string("h_153_strides_0"), val = tensor([1])]; + tensor h_153_pad_0 = const()[name = string("h_153_pad_0"), val = tensor([0, 0])]; + tensor h_153_dilations_0 = const()[name = string("h_153_dilations_0"), val = tensor([1])]; + tensor last_convnext_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43293376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43295360))))[name = string("last_convnext_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43295552)))]; + tensor h_153_cast_fp16 = conv(bias = last_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_153_dilations_0, groups = h_153_groups_0, pad = h_153_pad_0, pad_type = h_153_pad_type_0, strides = h_153_strides_0, weight = last_convnext_1_dwconv__conv_weight_to_fp16_palettized, x = input_401_cast_fp16)[name = string("h_153_cast_fp16")]; + tensor x_213_cast_fp16 = mul(x = h_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_213_cast_fp16")]; + tensor input_403_perm_0 = const()[name = string("input_403_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2421_axes_0 = const()[name = string("op_2421_axes_0"), val = tensor([-1])]; + tensor last_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43296640)))]; + tensor last_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43297728)))]; + fp16 var_2375_to_fp16 = const()[name = string("op_2375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_403_cast_fp16 = transpose(perm = input_403_perm_0, x = x_213_cast_fp16)[name = string("transpose_5")]; + tensor var_2421_cast_fp16 = layer_norm(axes = var_2421_axes_0, beta = last_convnext_1_norm_norm_bias_to_fp16, epsilon = var_2375_to_fp16, gamma = last_convnext_1_norm_norm_weight_to_fp16, x = input_403_cast_fp16)[name = string("op_2421_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string h_155_pad_type_0 = const()[name = string("h_155_pad_type_0"), val = string("valid")]; + tensor h_155_strides_0 = const()[name = string("h_155_strides_0"), val = tensor([1])]; + tensor h_155_pad_0 = const()[name = string("h_155_pad_0"), val = tensor([0, 0])]; + tensor h_155_dilations_0 = const()[name = string("h_155_dilations_0"), val = tensor([1])]; + int32 h_155_groups_0 = const()[name = string("h_155_groups_0"), val = int32(1)]; + tensor last_convnext_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43298816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44085312))))[name = string("last_convnext_1_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44085504)))]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = var_2421_cast_fp16)[name = string("transpose_4")]; + tensor h_155_cast_fp16 = conv(bias = last_convnext_1_pwconv1_bias_to_fp16, dilations = h_155_dilations_0, groups = h_155_groups_0, pad = h_155_pad_0, pad_type = h_155_pad_type_0, strides = h_155_strides_0, weight = last_convnext_1_pwconv1_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = string("h_155_cast_fp16")]; + string input_407_mode_0 = const()[name = string("input_407_mode_0"), val = string("EXACT")]; + tensor input_407_cast_fp16 = gelu(mode = input_407_mode_0, x = h_155_cast_fp16)[name = string("input_407_cast_fp16")]; + string h_157_pad_type_0 = const()[name = string("h_157_pad_type_0"), val = string("valid")]; + tensor h_157_strides_0 = const()[name = string("h_157_strides_0"), val = tensor([1])]; + tensor h_157_pad_0 = const()[name = string("h_157_pad_0"), val = tensor([0, 0])]; + tensor h_157_dilations_0 = const()[name = string("h_157_dilations_0"), val = tensor([1])]; + int32 h_157_groups_0 = const()[name = string("h_157_groups_0"), val = int32(1)]; + tensor op_2438_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44089664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44876160))))[name = string("op_2438_weight_0_to_fp16_palettized")]; + tensor var_2438_bias_0_to_fp16 = const()[name = string("op_2438_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44876352)))]; + tensor var_2438_cast_fp16 = conv(bias = var_2438_bias_0_to_fp16, dilations = h_157_dilations_0, groups = h_157_groups_0, pad = h_157_pad_0, pad_type = h_157_pad_type_0, strides = h_157_strides_0, weight = op_2438_weight_0_to_fp16_palettized, x = input_407_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor out_67_cast_fp16 = add(x = x_211_cast_fp16, y = var_2438_cast_fp16)[name = string("out_67_cast_fp16")]; + tensor x_215_cast_fp16 = mul(x = out_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_215_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2466_cast_fp16 = mul(x = x_217_cast_fp16, y = last_1_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor x_last_53_axes_0 = const()[name = string("x_last_53_axes_0"), val = tensor([-1])]; + bool x_last_53_keep_dims_0 = const()[name = string("x_last_53_keep_dims_0"), val = bool(true)]; + tensor x_last_53_cast_fp16 = reduce_sum(axes = x_last_53_axes_0, keep_dims = x_last_53_keep_dims_0, x = var_2466_cast_fp16)[name = string("x_last_53_cast_fp16")]; + tensor var_2469_cast_fp16 = mul(x = x_217_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor var_2471_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_53_cast_fp16)[name = string("op_2471_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = var_2469_cast_fp16, y = var_2471_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("replicate")]; + fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)]; + tensor input_413_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = input_413_mode_0, pad = input_413_pad_0, x = input_411_cast_fp16)[name = string("input_413_cast_fp16")]; + string h_159_pad_type_0 = const()[name = string("h_159_pad_type_0"), val = string("valid")]; + int32 h_159_groups_0 = const()[name = string("h_159_groups_0"), val = int32(512)]; + tensor h_159_strides_0 = const()[name = string("h_159_strides_0"), val = tensor([1])]; + tensor h_159_pad_0 = const()[name = string("h_159_pad_0"), val = tensor([0, 0])]; + tensor h_159_dilations_0 = const()[name = string("h_159_dilations_0"), val = tensor([1])]; + tensor last_convnext_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44877440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44879424))))[name = string("last_convnext_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44879616)))]; + tensor h_159_cast_fp16 = conv(bias = last_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_159_dilations_0, groups = h_159_groups_0, pad = h_159_pad_0, pad_type = h_159_pad_type_0, strides = h_159_strides_0, weight = last_convnext_2_dwconv__conv_weight_to_fp16_palettized, x = input_413_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor x_219_cast_fp16 = mul(x = h_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_415_perm_0 = const()[name = string("input_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2489_axes_0 = const()[name = string("op_2489_axes_0"), val = tensor([-1])]; + tensor last_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44880704)))]; + tensor last_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44881792)))]; + fp16 var_2443_to_fp16 = const()[name = string("op_2443_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_415_cast_fp16 = transpose(perm = input_415_perm_0, x = x_219_cast_fp16)[name = string("transpose_3")]; + tensor var_2489_cast_fp16 = layer_norm(axes = var_2489_axes_0, beta = last_convnext_2_norm_norm_bias_to_fp16, epsilon = var_2443_to_fp16, gamma = last_convnext_2_norm_norm_weight_to_fp16, x = input_415_cast_fp16)[name = string("op_2489_cast_fp16")]; + tensor input_417_perm_0 = const()[name = string("input_417_perm_0"), val = tensor([0, 2, 1])]; + string h_161_pad_type_0 = const()[name = string("h_161_pad_type_0"), val = string("valid")]; + tensor h_161_strides_0 = const()[name = string("h_161_strides_0"), val = tensor([1])]; + tensor h_161_pad_0 = const()[name = string("h_161_pad_0"), val = tensor([0, 0])]; + tensor h_161_dilations_0 = const()[name = string("h_161_dilations_0"), val = tensor([1])]; + int32 h_161_groups_0 = const()[name = string("h_161_groups_0"), val = int32(1)]; + tensor last_convnext_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44882880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45669376))))[name = string("last_convnext_2_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45669568)))]; + tensor input_417_cast_fp16 = transpose(perm = input_417_perm_0, x = var_2489_cast_fp16)[name = string("transpose_2")]; + tensor h_161_cast_fp16 = conv(bias = last_convnext_2_pwconv1_bias_to_fp16, dilations = h_161_dilations_0, groups = h_161_groups_0, pad = h_161_pad_0, pad_type = h_161_pad_type_0, strides = h_161_strides_0, weight = last_convnext_2_pwconv1_weight_to_fp16_palettized, x = input_417_cast_fp16)[name = string("h_161_cast_fp16")]; + string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("EXACT")]; + tensor input_419_cast_fp16 = gelu(mode = input_419_mode_0, x = h_161_cast_fp16)[name = string("input_419_cast_fp16")]; + string h_163_pad_type_0 = const()[name = string("h_163_pad_type_0"), val = string("valid")]; + tensor h_163_strides_0 = const()[name = string("h_163_strides_0"), val = tensor([1])]; + tensor h_163_pad_0 = const()[name = string("h_163_pad_0"), val = tensor([0, 0])]; + tensor h_163_dilations_0 = const()[name = string("h_163_dilations_0"), val = tensor([1])]; + int32 h_163_groups_0 = const()[name = string("h_163_groups_0"), val = int32(1)]; + tensor op_2506_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45673728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46460224))))[name = string("op_2506_weight_0_to_fp16_palettized")]; + tensor var_2506_bias_0_to_fp16 = const()[name = string("op_2506_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46460416)))]; + tensor var_2506_cast_fp16 = conv(bias = var_2506_bias_0_to_fp16, dilations = h_163_dilations_0, groups = h_163_groups_0, pad = h_163_pad_0, pad_type = h_163_pad_type_0, strides = h_163_strides_0, weight = op_2506_weight_0_to_fp16_palettized, x = input_419_cast_fp16)[name = string("op_2506_cast_fp16")]; + tensor out_69_cast_fp16 = add(x = x_217_cast_fp16, y = var_2506_cast_fp16)[name = string("out_69_cast_fp16")]; + tensor x_221_cast_fp16 = mul(x = out_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor x_223_cast_fp16 = mul(x = x_221_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_2534_cast_fp16 = mul(x = x_223_cast_fp16, y = last_1_cast_fp16)[name = string("op_2534_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_2534_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_2537_cast_fp16 = mul(x = x_223_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2537_cast_fp16")]; + tensor var_2539_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_cast_fp16)[name = string("op_2539_cast_fp16")]; + tensor input_423_cast_fp16 = add(x = var_2537_cast_fp16, y = var_2539_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_425_mode_0 = const()[name = string("input_425_mode_0"), val = string("replicate")]; + fp16 const_90_to_fp16 = const()[name = string("const_90_to_fp16"), val = fp16(0x0p+0)]; + tensor input_425_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = input_425_mode_0, pad = input_425_pad_0, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")]; + string h_165_pad_type_0 = const()[name = string("h_165_pad_type_0"), val = string("valid")]; + int32 h_165_groups_0 = const()[name = string("h_165_groups_0"), val = int32(512)]; + tensor h_165_strides_0 = const()[name = string("h_165_strides_0"), val = tensor([1])]; + tensor h_165_pad_0 = const()[name = string("h_165_pad_0"), val = tensor([0, 0])]; + tensor h_165_dilations_0 = const()[name = string("h_165_dilations_0"), val = tensor([1])]; + tensor last_convnext_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46461504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46463488))))[name = string("last_convnext_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46463680)))]; + tensor h_165_cast_fp16 = conv(bias = last_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_165_dilations_0, groups = h_165_groups_0, pad = h_165_pad_0, pad_type = h_165_pad_type_0, strides = h_165_strides_0, weight = last_convnext_3_dwconv__conv_weight_to_fp16_palettized, x = input_425_cast_fp16)[name = string("h_165_cast_fp16")]; + tensor x_cast_fp16 = mul(x = h_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2557_axes_0 = const()[name = string("op_2557_axes_0"), val = tensor([-1])]; + tensor last_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46464768)))]; + tensor last_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46465856)))]; + fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_cast_fp16)[name = string("transpose_1")]; + tensor var_2557_cast_fp16 = layer_norm(axes = var_2557_axes_0, beta = last_convnext_3_norm_norm_bias_to_fp16, epsilon = var_2511_to_fp16, gamma = last_convnext_3_norm_norm_weight_to_fp16, x = input_427_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor input_429_perm_0 = const()[name = string("input_429_perm_0"), val = tensor([0, 2, 1])]; + string h_167_pad_type_0 = const()[name = string("h_167_pad_type_0"), val = string("valid")]; + tensor h_167_strides_0 = const()[name = string("h_167_strides_0"), val = tensor([1])]; + tensor h_167_pad_0 = const()[name = string("h_167_pad_0"), val = tensor([0, 0])]; + tensor h_167_dilations_0 = const()[name = string("h_167_dilations_0"), val = tensor([1])]; + int32 h_167_groups_0 = const()[name = string("h_167_groups_0"), val = int32(1)]; + tensor last_convnext_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46466944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47253440))))[name = string("last_convnext_3_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47253632)))]; + tensor input_429_cast_fp16 = transpose(perm = input_429_perm_0, x = var_2557_cast_fp16)[name = string("transpose_0")]; + tensor h_167_cast_fp16 = conv(bias = last_convnext_3_pwconv1_bias_to_fp16, dilations = h_167_dilations_0, groups = h_167_groups_0, pad = h_167_pad_0, pad_type = h_167_pad_type_0, strides = h_167_strides_0, weight = last_convnext_3_pwconv1_weight_to_fp16_palettized, x = input_429_cast_fp16)[name = string("h_167_cast_fp16")]; + string input_431_mode_0 = const()[name = string("input_431_mode_0"), val = string("EXACT")]; + tensor input_431_cast_fp16 = gelu(mode = input_431_mode_0, x = h_167_cast_fp16)[name = string("input_431_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor op_2574_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47257792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48044288))))[name = string("op_2574_weight_0_to_fp16_palettized")]; + tensor var_2574_bias_0_to_fp16 = const()[name = string("op_2574_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48044480)))]; + tensor var_2574_cast_fp16 = conv(bias = var_2574_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = op_2574_weight_0_to_fp16_palettized, x = input_431_cast_fp16)[name = string("op_2574_cast_fp16")]; + tensor out_cast_fp16 = add(x = x_223_cast_fp16, y = var_2574_cast_fp16)[name = string("out_cast_fp16")]; + tensor input_cast_fp16 = mul(x = out_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("input_cast_fp16")]; + string var_2587_pad_type_0 = const()[name = string("op_2587_pad_type_0"), val = string("valid")]; + tensor var_2587_strides_0 = const()[name = string("op_2587_strides_0"), val = tensor([1])]; + tensor var_2587_pad_0 = const()[name = string("op_2587_pad_0"), val = tensor([0, 0])]; + tensor var_2587_dilations_0 = const()[name = string("op_2587_dilations_0"), val = tensor([1])]; + int32 var_2587_groups_0 = const()[name = string("op_2587_groups_0"), val = int32(1)]; + tensor proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48045568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48100928))))[name = string("proj_out_weight_to_fp16_palettized")]; + tensor var_2587_cast_fp16 = conv(dilations = var_2587_dilations_0, groups = var_2587_groups_0, pad = var_2587_pad_0, pad_type = var_2587_pad_type_0, strides = var_2587_strides_0, weight = proj_out_weight_to_fp16_palettized, x = input_cast_fp16)[name = string("op_2587_cast_fp16")]; + tensor v_cast_fp16 = mul(x = var_2587_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("v_cast_fp16")]; + tensor cond_begin_0 = const()[name = string("cond_begin_0"), val = tensor([0, 0, 0])]; + tensor cond_end_0 = const()[name = string("cond_end_0"), val = tensor([1, 144, 128])]; + tensor cond_end_mask_0 = const()[name = string("cond_end_mask_0"), val = tensor([false, true, true])]; + tensor cond_cast_fp16 = slice_by_index(begin = cond_begin_0, end = cond_end_0, end_mask = cond_end_mask_0, x = v_cast_fp16)[name = string("cond_cast_fp16")]; + tensor uncond_begin_0 = const()[name = string("uncond_begin_0"), val = tensor([1, 0, 0])]; + tensor uncond_end_0 = const()[name = string("uncond_end_0"), val = tensor([2, 144, 128])]; + tensor uncond_end_mask_0 = const()[name = string("uncond_end_mask_0"), val = tensor([true, true, true])]; + tensor uncond_cast_fp16 = slice_by_index(begin = uncond_begin_0, end = uncond_end_0, end_mask = uncond_end_mask_0, x = v_cast_fp16)[name = string("uncond_cast_fp16")]; + fp32 var_2597_epsilon_0 = const()[name = string("op_2597_epsilon_0"), val = fp32(0x1.a36e2ep-14)]; + tensor var_2597_cast_fp16 = inverse(epsilon = var_2597_epsilon_0, x = total_step_to_fp16)[name = string("op_2597_cast_fp16")]; + tensor var_2603 = const()[name = string("op_2603"), val = tensor([-1, 1, 1])]; + tensor step_cast_fp16 = reshape(shape = var_2603, x = var_2597_cast_fp16)[name = string("step_cast_fp16")]; + fp16 var_2605_to_fp16 = const()[name = string("op_2605_to_fp16"), val = fp16(0x1p+2)]; + tensor var_2606_cast_fp16 = mul(x = cond_cast_fp16, y = var_2605_to_fp16)[name = string("op_2606_cast_fp16")]; + fp16 var_2607_to_fp16 = const()[name = string("op_2607_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_2608_cast_fp16 = mul(x = uncond_cast_fp16, y = var_2607_to_fp16)[name = string("op_2608_cast_fp16")]; + tensor var_2610_cast_fp16 = sub(x = var_2606_cast_fp16, y = var_2608_cast_fp16)[name = string("op_2610_cast_fp16")]; + tensor var_2611_cast_fp16 = mul(x = step_cast_fp16, y = var_2610_cast_fp16)[name = string("op_2611_cast_fp16")]; + tensor var_2613_cast_fp16 = add(x = noisy_latent_to_fp16, y = var_2611_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor var_2614_cast_fp16 = mul(x = var_2613_cast_fp16, y = latent_mask_to_fp16)[name = string("op_2614_cast_fp16")]; + string var_2614_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2614_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor denoised_latent = cast(dtype = var_2614_cast_fp16_to_fp32_dtype_0, x = var_2614_cast_fp16)[name = string("cast_0")]; + } -> (denoised_latent); +} \ No newline at end of file diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L128_int6.mlmodelc/weights/weight.bin b/VectorEstimatorVariants/VectorEstimator_v2_L128_int6.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..ea923f53a717c029c3b3602e4a6a6cc5ea581c8f --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L128_int6.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72b5e863e923e8dd37071f9bcc2cb17f7e42b4565306bbf9169f5b7ba9b43935 +size 48101120 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L128_int6.mlpackage/Data/com.apple.CoreML/model.mlmodel 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sha256:feeaddf0c93ab0367414cc93c370e4918becfc3c016c671a72f16ea20fe88239 +size 553 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L128_int8.mlmodelc/model.mil b/VectorEstimatorVariants/VectorEstimator_v2_L128_int8.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..d21d4c502b50b946fae1e8683d028d35a0de38ec --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L128_int8.mlmodelc/model.mil @@ -0,0 +1,2101 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor current_step, tensor latent_mask, tensor noisy_latent, tensor style_ttl, tensor text_emb, tensor text_mask, tensor total_step) { + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + bool text_emb_interleave_0 = const()[name = string("text_emb_interleave_0"), val = bool(false)]; + string text_emb_to_fp16_dtype_0 = const()[name = string("text_emb_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_uncond_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor([[[0x1.9b8p-10]]]))[name = string("text_uncond_to_fp16_quantized")]; + tensor text_emb_to_fp16 = cast(dtype = text_emb_to_fp16_dtype_0, x = text_emb)[name = string("cast_7")]; + tensor text_emb_cast_fp16 = concat(axis = var_39, interleave = text_emb_interleave_0, values = (text_emb_to_fp16, text_uncond_to_fp16_quantized))[name = string("text_emb_cast_fp16")]; + bool input_95_interleave_0 = const()[name = string("input_95_interleave_0"), val = bool(false)]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor uncond_masker_style_value_special_token_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32896))), scale = tensor([[[0x1.bb4p-9]]]))[name = string("uncond_masker_style_value_special_token_to_fp16_quantized")]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_6")]; + tensor input_95_cast_fp16 = concat(axis = var_39, interleave = input_95_interleave_0, values = (style_ttl_to_fp16, uncond_masker_style_value_special_token_to_fp16_quantized))[name = string("input_95_cast_fp16")]; + int32 var_64 = const()[name = string("op_64"), val = int32(0)]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + string noisy_latent_to_fp16_dtype_0 = const()[name = string("noisy_latent_to_fp16_dtype_0"), val = string("fp16")]; + tensor noisy_latent_to_fp16 = cast(dtype = noisy_latent_to_fp16_dtype_0, x = noisy_latent)[name = string("cast_5")]; + tensor input_5_cast_fp16 = concat(axis = var_64, interleave = input_5_interleave_0, values = (noisy_latent_to_fp16, noisy_latent_to_fp16))[name = string("input_5_cast_fp16")]; + int32 var_67 = const()[name = string("op_67"), val = int32(0)]; + bool latent_mask_b_interleave_0 = const()[name = string("latent_mask_b_interleave_0"), val = bool(false)]; + string latent_mask_to_fp16_dtype_0 = const()[name = string("latent_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor latent_mask_to_fp16 = cast(dtype = latent_mask_to_fp16_dtype_0, x = latent_mask)[name = string("cast_4")]; + tensor latent_mask_b_cast_fp16 = concat(axis = var_67, interleave = latent_mask_b_interleave_0, values = (latent_mask_to_fp16, latent_mask_to_fp16))[name = string("latent_mask_b_cast_fp16")]; + int32 var_70 = const()[name = string("op_70"), val = int32(0)]; + bool text_mask_interleave_0 = const()[name = string("text_mask_interleave_0"), val = bool(false)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_3")]; + tensor text_mask_cast_fp16 = concat(axis = var_70, interleave = text_mask_interleave_0, values = (text_mask_to_fp16, text_mask_to_fp16))[name = string("text_mask_cast_fp16")]; + string current_step_to_fp16_dtype_0 = const()[name = string("current_step_to_fp16_dtype_0"), val = string("fp16")]; + string total_step_to_fp16_dtype_0 = const()[name = string("total_step_to_fp16_dtype_0"), val = string("fp16")]; + tensor current_step_to_fp16 = cast(dtype = current_step_to_fp16_dtype_0, x = current_step)[name = string("cast_1")]; + tensor total_step_to_fp16 = cast(dtype = total_step_to_fp16_dtype_0, x = total_step)[name = string("cast_2")]; + tensor t_1_cast_fp16 = real_div(x = current_step_to_fp16, y = total_step_to_fp16)[name = string("t_1_cast_fp16")]; + int32 var_74 = const()[name = string("op_74"), val = int32(0)]; + bool t_3_interleave_0 = const()[name = string("t_3_interleave_0"), val = bool(false)]; + tensor t_3_cast_fp16 = concat(axis = var_74, interleave = t_3_interleave_0, values = (t_1_cast_fp16, t_1_cast_fp16))[name = string("t_3_cast_fp16")]; + int32 var_78 = const()[name = string("op_78"), val = int32(-1)]; + tensor var_85 = const()[name = string("op_85"), val = tensor([-1, 1])]; + tensor var_86_cast_fp16 = reshape(shape = var_85, x = t_3_cast_fp16)[name = string("op_86_cast_fp16")]; + fp16 time_encoder_time_scale_to_fp16 = const()[name = string("time_encoder_time_scale_to_fp16"), val = fp16(0x1.f4p+9)]; + tensor t_scaled_cast_fp16 = mul(x = var_86_cast_fp16, y = time_encoder_time_scale_to_fp16)[name = string("t_scaled_cast_fp16")]; + tensor time_encoder_omegas_to_fp16 = const()[name = string("time_encoder_omegas_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45760)))]; + tensor phase_cast_fp16 = mul(x = t_scaled_cast_fp16, y = time_encoder_omegas_to_fp16)[name = string("phase_cast_fp16")]; + tensor var_89_cast_fp16 = sin(x = phase_cast_fp16)[name = string("op_89_cast_fp16")]; + tensor var_90_cast_fp16 = cos(x = phase_cast_fp16)[name = string("op_90_cast_fp16")]; + bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)]; + tensor input_1_cast_fp16 = concat(axis = var_78, interleave = input_1_interleave_0, values = (var_89_cast_fp16, var_90_cast_fp16))[name = string("input_1_cast_fp16")]; + tensor time_encoder_mlp_0_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62336))))[name = string("time_encoder_mlp_0_linear_weight_to_fp16_quantized")]; + tensor time_encoder_mlp_0_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62912)))]; + tensor linear_0_cast_fp16 = linear(bias = time_encoder_mlp_0_linear_bias_to_fp16, weight = time_encoder_mlp_0_linear_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_97_cast_fp16 = softplus(x = linear_0_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = tanh(x = var_97_cast_fp16)[name = string("op_98_cast_fp16")]; + tensor input_3_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_98_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor time_encoder_mlp_2_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79936))))[name = string("time_encoder_mlp_2_linear_weight_to_fp16_quantized")]; + tensor time_encoder_mlp_2_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80128)))]; + tensor linear_1_cast_fp16 = linear(bias = time_encoder_mlp_2_linear_bias_to_fp16, weight = time_encoder_mlp_2_linear_weight_to_fp16_quantized, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor time_emb_axes_0 = const()[name = string("time_emb_axes_0"), val = tensor([-1])]; + tensor time_emb_cast_fp16 = expand_dims(axes = time_emb_axes_0, x = linear_1_cast_fp16)[name = string("time_emb_cast_fp16")]; + string var_115_pad_type_0 = const()[name = string("op_115_pad_type_0"), val = string("valid")]; + tensor var_115_strides_0 = const()[name = string("op_115_strides_0"), val = tensor([1])]; + tensor var_115_pad_0 = const()[name = string("op_115_pad_0"), val = tensor([0, 0])]; + tensor var_115_dilations_0 = const()[name = string("op_115_dilations_0"), val = tensor([1])]; + int32 var_115_groups_0 = const()[name = string("op_115_groups_0"), val = int32(1)]; + tensor proj_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154112))))[name = string("proj_in_weight_to_fp16_quantized")]; + tensor var_115_cast_fp16 = conv(dilations = var_115_dilations_0, groups = var_115_groups_0, pad = var_115_pad_0, pad_type = var_115_pad_type_0, strides = var_115_strides_0, weight = proj_in_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = var_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_3_cast_fp16")]; + int32 var_134 = const()[name = string("op_134"), val = int32(-1)]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([0, 0, 1])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 128])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([true, true, true])]; + tensor input_7_cast_fp16 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, x = latent_mask_b_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_166_pad_0 = const()[name = string("op_166_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_166_mode_0 = const()[name = string("op_166_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_166_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_166_mode_0, pad = var_166_pad_0, x = input_7_cast_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_133_to_fp16 = const()[name = string("op_133_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = sub(x = var_133_to_fp16, y = var_166_cast_fp16)[name = string("op_167_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = latent_mask_b_cast_fp16, y = var_167_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_169_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_172_cast_fp16 = mul(x = x_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor var_173_cast_fp16 = sub(x = var_133_to_fp16, y = latent_mask_b_cast_fp16)[name = string("op_173_cast_fp16")]; + tensor var_174_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_174_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_174_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("replicate")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor input_11_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_11_mode_0, pad = input_11_pad_0, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(512)]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157824))))[name = string("main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158912)))]; + tensor h_3_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, 2, 1])]; + tensor var_192_axes_0 = const()[name = string("op_192_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160000)))]; + tensor main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161088)))]; + fp16 var_139_to_fp16 = const()[name = string("op_139_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_7_cast_fp16)[name = string("transpose_117")]; + tensor var_192_cast_fp16 = layer_norm(axes = var_192_axes_0, beta = main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16, x = input_13_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, 2, 1])]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1210816))))[name = string("main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214976)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = var_192_cast_fp16)[name = string("transpose_116")]; + tensor h_5_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("h_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = h_5_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)]; + tensor op_209_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1219136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2267776))))[name = string("op_209_weight_0_to_fp16_quantized")]; + tensor var_209_bias_0_to_fp16 = const()[name = string("op_209_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2268864)))]; + tensor var_209_cast_fp16 = conv(bias = var_209_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = op_209_weight_0_to_fp16_quantized, x = input_17_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_209_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_223_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_223_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_226_cast_fp16 = mul(x = x_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_226_cast_fp16")]; + tensor var_228_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_228_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_226_cast_fp16, y = var_228_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")]; + fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)]; + tensor input_23_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([2])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(512)]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2269952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2272576))))[name = string("main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2273664)))]; + tensor h_9_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor var_246_axes_0 = const()[name = string("op_246_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2274752)))]; + tensor main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2275840)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_13_cast_fp16)[name = string("transpose_115")]; + tensor var_246_cast_fp16 = layer_norm(axes = var_246_axes_0, beta = main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_246_cast_fp16")]; + tensor input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor([0, 2, 1])]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2276928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3325568))))[name = string("main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3329728)))]; + tensor input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_246_cast_fp16)[name = string("transpose_114")]; + tensor h_11_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("h_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_11_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)]; + tensor op_263_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3333888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4382528))))[name = string("op_263_weight_0_to_fp16_quantized")]; + tensor var_263_bias_0_to_fp16 = const()[name = string("op_263_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4383616)))]; + tensor var_263_cast_fp16 = conv(bias = var_263_bias_0_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = op_263_weight_0_to_fp16_quantized, x = input_29_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_263_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_277_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_277_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_277_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_280_cast_fp16 = mul(x = x_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_280_cast_fp16")]; + tensor var_282_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_280_cast_fp16, y = var_282_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("replicate")]; + fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)]; + tensor input_35_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = input_35_mode_0, pad = input_35_pad_0, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([4])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(512)]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4384704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4387328))))[name = string("main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4388416)))]; + tensor h_15_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor var_300_axes_0 = const()[name = string("op_300_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4389504)))]; + tensor main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4390592)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_19_cast_fp16)[name = string("transpose_113")]; + tensor var_300_cast_fp16 = layer_norm(axes = var_300_axes_0, beta = main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16, x = input_37_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor([0, 2, 1])]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4391680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5440320))))[name = string("main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5444480)))]; + tensor input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_300_cast_fp16)[name = string("transpose_112")]; + tensor h_17_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("h_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = h_17_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)]; + tensor op_317_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5448640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6497280))))[name = string("op_317_weight_0_to_fp16_quantized")]; + tensor var_317_bias_0_to_fp16 = const()[name = string("op_317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6498368)))]; + tensor var_317_cast_fp16 = conv(bias = var_317_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = op_317_weight_0_to_fp16_quantized, x = input_41_cast_fp16)[name = string("op_317_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_317_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor var_331_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_331_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_331_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = x_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor var_336_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_336_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_334_cast_fp16, y = var_336_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")]; + fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([8])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(512)]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6499456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6502080))))[name = string("main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6503168)))]; + tensor h_21_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6504256)))]; + tensor main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6505344)))]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_25_cast_fp16)[name = string("transpose_111")]; + tensor var_354_cast_fp16 = layer_norm(axes = var_354_axes_0, beta = main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_354_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6506432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7555072))))[name = string("main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7559232)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_354_cast_fp16)[name = string("transpose_110")]; + tensor h_23_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("h_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_23_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)]; + tensor op_371_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7563392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8612032))))[name = string("op_371_weight_0_to_fp16_quantized")]; + tensor var_371_bias_0_to_fp16 = const()[name = string("op_371_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8613120)))]; + tensor var_371_cast_fp16 = conv(bias = var_371_bias_0_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = op_371_weight_0_to_fp16_quantized, x = input_53_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_371_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor input_55_cast_fp16 = squeeze(axes = input_55_axes_0, x = time_emb_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor main_blocks_0_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8614208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8647040))))[name = string("main_blocks_0_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8648128)))]; + tensor linear_2_cast_fp16 = linear(bias = main_blocks_0_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_0_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor t_5_axes_0 = const()[name = string("t_5_axes_0"), val = tensor([-1])]; + tensor t_5_cast_fp16 = expand_dims(axes = t_5_axes_0, x = linear_2_cast_fp16)[name = string("t_5_cast_fp16")]; + tensor var_381_cast_fp16 = add(x = x_27_cast_fp16, y = t_5_cast_fp16)[name = string("op_381_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_381_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_394_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_394_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_394_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_397_cast_fp16 = mul(x = x_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = var_397_cast_fp16, y = var_399_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("replicate")]; + fp16 const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = fp16(0x0p+0)]; + tensor input_61_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = input_61_mode_0, pad = input_61_pad_0, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(512)]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8649216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8651840))))[name = string("main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8652928)))]; + tensor h_27_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_61_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417_axes_0 = const()[name = string("op_417_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8654016)))]; + tensor main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8655104)))]; + tensor input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_33_cast_fp16)[name = string("transpose_109")]; + tensor var_417_cast_fp16 = layer_norm(axes = var_417_axes_0, beta = main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16, x = input_63_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8656192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9704832))))[name = string("main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9708992)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = var_417_cast_fp16)[name = string("transpose_108")]; + tensor h_29_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("h_29_cast_fp16")]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("EXACT")]; + tensor input_67_cast_fp16 = gelu(mode = input_67_mode_0, x = h_29_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)]; + tensor op_434_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9713152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10761792))))[name = string("op_434_weight_0_to_fp16_quantized")]; + tensor var_434_bias_0_to_fp16 = const()[name = string("op_434_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10762880)))]; + tensor var_434_cast_fp16 = conv(bias = var_434_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = op_434_weight_0_to_fp16_quantized, x = input_67_cast_fp16)[name = string("op_434_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_434_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_m_1_cast_fp16 = mul(x = x_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_1_cast_fp16")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10763968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11026176))))[name = string("main_blocks_0_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11027264)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_35_cast_fp16)[name = string("transpose_107")]; + tensor linear_3_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([2, 128, 8, 64])]; + tensor var_453_cast_fp16 = reshape(shape = var_452, x = linear_3_cast_fp16)[name = string("op_453_cast_fp16")]; + tensor x_37_perm_0 = const()[name = string("x_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11028352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11159488))))[name = string("main_blocks_0_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11160576)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = text_emb_cast_fp16)[name = string("transpose_106")]; + tensor linear_4_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([2, 128, 8, 64])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = linear_4_cast_fp16)[name = string("op_461_cast_fp16")]; + tensor x_39_perm_0 = const()[name = string("x_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11161664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11292800))))[name = string("main_blocks_0_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11293888)))]; + tensor linear_5_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_468 = const()[name = string("op_468"), val = tensor([2, 128, 8, 64])]; + tensor var_469_cast_fp16 = reshape(shape = var_468, x = linear_5_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor divisor_1_axes_0 = const()[name = string("divisor_1_axes_0"), val = tensor([1, 2])]; + bool divisor_1_keep_dims_0 = const()[name = string("divisor_1_keep_dims_0"), val = bool(false)]; + tensor divisor_1_cast_fp16 = reduce_sum(axes = divisor_1_axes_0, keep_dims = divisor_1_keep_dims_0, x = latent_mask_b_cast_fp16)[name = string("divisor_1_cast_fp16")]; + tensor divisor_3_axes_0 = const()[name = string("divisor_3_axes_0"), val = tensor([1, 2])]; + bool divisor_3_keep_dims_0 = const()[name = string("divisor_3_keep_dims_0"), val = bool(false)]; + tensor divisor_3_cast_fp16 = reduce_sum(axes = divisor_3_axes_0, keep_dims = divisor_3_keep_dims_0, x = text_mask_cast_fp16)[name = string("divisor_3_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([-1, 1, 1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = divisor_1_cast_fp16)[name = string("op_488_cast_fp16")]; + tensor const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11294976)))]; + tensor scaled_1_cast_fp16 = real_div(x = const_16_to_fp16, y = var_488_cast_fp16)[name = string("scaled_1_cast_fp16")]; + tensor main_blocks_0_text_attn_theta_to_fp16 = const()[name = string("main_blocks_0_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11295552)))]; + tensor angles_1_cast_fp16 = mul(x = scaled_1_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_1_cast_fp16")]; + tensor var_491_cast_fp16 = cos(x = angles_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = var_491_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor var_493_cast_fp16 = sin(x = angles_1_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = var_493_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor x_a_1_begin_0 = const()[name = string("x_a_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_1_end_0 = const()[name = string("x_a_1_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_1_end_mask_0 = const()[name = string("x_a_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_37_cast_fp16 = transpose(perm = x_37_perm_0, x = var_453_cast_fp16)[name = string("transpose_105")]; + tensor x_a_1_cast_fp16 = slice_by_index(begin = x_a_1_begin_0, end = x_a_1_end_0, end_mask = x_a_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_a_1_cast_fp16")]; + tensor x_b_1_begin_0 = const()[name = string("x_b_1_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_1_end_0 = const()[name = string("x_b_1_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_1_end_mask_0 = const()[name = string("x_b_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_1_cast_fp16 = slice_by_index(begin = x_b_1_begin_0, end = x_b_1_end_0, end_mask = x_b_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_b_1_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = x_a_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor var_498_cast_fp16 = mul(x = x_b_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_498_cast_fp16")]; + tensor rot_a_1_cast_fp16 = sub(x = var_497_cast_fp16, y = var_498_cast_fp16)[name = string("rot_a_1_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = x_a_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = x_b_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor rot_b_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("rot_b_1_cast_fp16")]; + bool q_1_interleave_0 = const()[name = string("q_1_interleave_0"), val = bool(false)]; + tensor q_1_cast_fp16 = concat(axis = var_134, interleave = q_1_interleave_0, values = (rot_a_1_cast_fp16, rot_b_1_cast_fp16))[name = string("q_1_cast_fp16")]; + tensor var_505 = const()[name = string("op_505"), val = tensor([-1, 1, 1])]; + tensor var_506_cast_fp16 = reshape(shape = var_505, x = divisor_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor scaled_3_cast_fp16 = real_div(x = const_16_to_fp16, y = var_506_cast_fp16)[name = string("scaled_3_cast_fp16")]; + tensor angles_3_cast_fp16 = mul(x = scaled_3_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_3_cast_fp16")]; + tensor var_509_cast_fp16 = cos(x = angles_3_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor cos_3_axes_0 = const()[name = string("cos_3_axes_0"), val = tensor([1])]; + tensor cos_3_cast_fp16 = expand_dims(axes = cos_3_axes_0, x = var_509_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor var_511_cast_fp16 = sin(x = angles_3_cast_fp16)[name = string("op_511_cast_fp16")]; + tensor sin_3_axes_0 = const()[name = string("sin_3_axes_0"), val = tensor([1])]; + tensor sin_3_cast_fp16 = expand_dims(axes = sin_3_axes_0, x = var_511_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor x_a_3_begin_0 = const()[name = string("x_a_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_3_end_0 = const()[name = string("x_a_3_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_3_end_mask_0 = const()[name = string("x_a_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_39_cast_fp16 = transpose(perm = x_39_perm_0, x = var_461_cast_fp16)[name = string("transpose_104")]; + tensor x_a_3_cast_fp16 = slice_by_index(begin = x_a_3_begin_0, end = x_a_3_end_0, end_mask = x_a_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_a_3_cast_fp16")]; + tensor x_b_3_begin_0 = const()[name = string("x_b_3_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_3_end_0 = const()[name = string("x_b_3_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_3_end_mask_0 = const()[name = string("x_b_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_3_cast_fp16 = slice_by_index(begin = x_b_3_begin_0, end = x_b_3_end_0, end_mask = x_b_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_b_3_cast_fp16")]; + tensor var_515_cast_fp16 = mul(x = x_a_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_515_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = x_b_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor rot_a_3_cast_fp16 = sub(x = var_515_cast_fp16, y = var_516_cast_fp16)[name = string("rot_a_3_cast_fp16")]; + tensor var_518_cast_fp16 = mul(x = x_a_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = mul(x = x_b_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor rot_b_3_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = string("rot_b_3_cast_fp16")]; + bool k_1_interleave_0 = const()[name = string("k_1_interleave_0"), val = bool(false)]; + tensor k_1_cast_fp16 = concat(axis = var_134, interleave = k_1_interleave_0, values = (rot_a_3_cast_fp16, rot_b_3_cast_fp16))[name = string("k_1_cast_fp16")]; + bool var_524_transpose_x_1 = const()[name = string("op_524_transpose_x_1"), val = bool(false)]; + bool var_524_transpose_y_1 = const()[name = string("op_524_transpose_y_1"), val = bool(true)]; + tensor var_524_cast_fp16 = matmul(transpose_x = var_524_transpose_x_1, transpose_y = var_524_transpose_y_1, x = q_1_cast_fp16, y = k_1_cast_fp16)[name = string("op_524_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_524_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([2])]; + tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = text_mask_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528_cast_fp16 = sub(x = var_133_to_fp16, y = var_527_cast_fp16)[name = string("op_528_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_530_cast_fp16 = mul(x = var_528_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_75_cast_fp16 = sub(x = _inversed_scores_1_cast_fp16, y = var_530_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_134, x = input_75_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool var_533_transpose_x_0 = const()[name = string("op_533_transpose_x_0"), val = bool(false)]; + bool var_533_transpose_y_0 = const()[name = string("op_533_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_469_cast_fp16)[name = string("transpose_103")]; + tensor var_533_cast_fp16 = matmul(transpose_x = var_533_transpose_x_0, transpose_y = var_533_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("op_533_cast_fp16")]; + tensor var_534_perm_0 = const()[name = string("op_534_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_535 = const()[name = string("op_535"), val = tensor([2, 128, 512])]; + tensor var_534_cast_fp16 = transpose(perm = var_534_perm_0, x = var_533_cast_fp16)[name = string("transpose_102")]; + tensor input_77_cast_fp16 = reshape(shape = var_535, x = var_534_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11295680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11557888))))[name = string("main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11558976)))]; + tensor linear_6_cast_fp16 = linear(bias = main_blocks_0_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor out_11_perm_0 = const()[name = string("out_11_perm_0"), val = tensor([0, 2, 1])]; + tensor out_11_cast_fp16 = transpose(perm = out_11_perm_0, x = linear_6_cast_fp16)[name = string("transpose_101")]; + tensor var_542_cast_fp16 = mul(x = out_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_542_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = x_m_1_cast_fp16, y = var_542_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor var_549_axes_0 = const()[name = string("op_549_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11560064)))]; + tensor main_blocks_0_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11561152)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_41_cast_fp16)[name = string("transpose_100")]; + tensor var_549_cast_fp16 = layer_norm(axes = var_549_axes_0, beta = main_blocks_0_text_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_text_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor var_550_perm_0 = const()[name = string("op_550_perm_0"), val = tensor([0, 2, 1])]; + tensor var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = var_549_cast_fp16)[name = string("transpose_99")]; + tensor x_43_cast_fp16 = mul(x = var_550_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = x_45_cast_fp16, y = last_1_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor x_last_11_axes_0 = const()[name = string("x_last_11_axes_0"), val = tensor([-1])]; + bool x_last_11_keep_dims_0 = const()[name = string("x_last_11_keep_dims_0"), val = bool(true)]; + tensor x_last_11_cast_fp16 = reduce_sum(axes = x_last_11_axes_0, keep_dims = x_last_11_keep_dims_0, x = var_563_cast_fp16)[name = string("x_last_11_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = x_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor var_568_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_11_cast_fp16)[name = string("op_568_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = var_566_cast_fp16, y = var_568_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_85_mode_0 = const()[name = string("input_85_mode_0"), val = string("replicate")]; + fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)]; + tensor input_85_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(512)]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11562240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11564864))))[name = string("main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11565952)))]; + tensor h_33_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("h_33_cast_fp16")]; + tensor x_47_cast_fp16 = mul(x = h_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11567040)))]; + tensor main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11568128)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_47_cast_fp16)[name = string("transpose_98")]; + tensor var_586_cast_fp16 = layer_norm(axes = var_586_axes_0, beta = main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_586_cast_fp16")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11569216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12617856))))[name = string("main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12622016)))]; + tensor input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = var_586_cast_fp16)[name = string("transpose_97")]; + tensor h_35_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("h_35_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = h_35_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")]; + tensor h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor([1])]; + tensor h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor([0, 0])]; + tensor h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor([1])]; + int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)]; + tensor op_603_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12626176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13674816))))[name = string("op_603_weight_0_to_fp16_quantized")]; + tensor var_603_bias_0_to_fp16 = const()[name = string("op_603_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13675904)))]; + tensor var_603_cast_fp16 = conv(bias = var_603_bias_0_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = op_603_weight_0_to_fp16_quantized, x = input_91_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor out_13_cast_fp16 = add(x = x_45_cast_fp16, y = var_603_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_49_cast_fp16 = mul(x = out_13_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_m_3_cast_fp16 = mul(x = x_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_3_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13676992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13808128))))[name = string("main_blocks_0_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13808704)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_49_cast_fp16)[name = string("transpose_96")]; + tensor linear_7_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([2, 128, 2, 128])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = linear_7_cast_fp16)[name = string("op_620_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_0_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13809280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13874880))))[name = string("main_blocks_0_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13875456)))]; + tensor linear_9_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_633 = const()[name = string("op_633"), val = tensor([2, 50, 2, 128])]; + tensor var_634_cast_fp16 = reshape(shape = var_633, x = linear_9_cast_fp16)[name = string("op_634_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_638_transpose_x_0 = const()[name = string("op_638_transpose_x_0"), val = bool(false)]; + bool var_638_transpose_y_0 = const()[name = string("op_638_transpose_y_0"), val = bool(false)]; + tensor op_637_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13876032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13901696))))[name = string("op_637_to_fp16_quantized")]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_620_cast_fp16)[name = string("transpose_95")]; + tensor var_638_cast_fp16 = matmul(transpose_x = var_638_transpose_x_0, transpose_y = var_638_transpose_y_0, x = q_3_cast_fp16, y = op_637_to_fp16_quantized)[name = string("op_638_cast_fp16")]; + fp16 _inversed_input_97_y_0_to_fp16 = const()[name = string("_inversed_input_97_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_97_cast_fp16 = mul(x = var_638_cast_fp16, y = _inversed_input_97_y_0_to_fp16)[name = string("_inversed_input_97_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_134, x = _inversed_input_97_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_642_perm_0 = const()[name = string("op_642_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_1_axes_0 = const()[name = string("mask_1_axes_0"), val = tensor([1])]; + tensor var_642_cast_fp16 = transpose(perm = var_642_perm_0, x = latent_mask_b_cast_fp16)[name = string("transpose_94")]; + tensor mask_1_cast_fp16 = expand_dims(axes = mask_1_axes_0, x = var_642_cast_fp16)[name = string("mask_1_cast_fp16")]; + tensor attn_5_cast_fp16 = mul(x = attn_3_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool var_645_transpose_x_0 = const()[name = string("op_645_transpose_x_0"), val = bool(false)]; + bool var_645_transpose_y_0 = const()[name = string("op_645_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_634_cast_fp16)[name = string("transpose_93")]; + tensor var_645_cast_fp16 = matmul(transpose_x = var_645_transpose_x_0, transpose_y = var_645_transpose_y_0, x = attn_5_cast_fp16, y = v_3_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_646_perm_0 = const()[name = string("op_646_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_647 = const()[name = string("op_647"), val = tensor([2, 128, 256])]; + tensor var_646_cast_fp16 = transpose(perm = var_646_perm_0, x = var_645_cast_fp16)[name = string("transpose_92")]; + tensor input_99_cast_fp16 = reshape(shape = var_647, x = var_646_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13901888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14033024))))[name = string("main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14034112)))]; + tensor linear_10_cast_fp16 = linear(bias = main_blocks_0_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor out_15_perm_0 = const()[name = string("out_15_perm_0"), val = tensor([0, 2, 1])]; + tensor out_15_cast_fp16 = transpose(perm = out_15_perm_0, x = linear_10_cast_fp16)[name = string("transpose_91")]; + tensor var_654_cast_fp16 = mul(x = out_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor x_51_cast_fp16 = add(x = x_m_3_cast_fp16, y = var_654_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14035200)))]; + tensor main_blocks_0_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14036288)))]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_51_cast_fp16)[name = string("transpose_90")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = main_blocks_0_style_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_style_norm_norm_weight_to_fp16, x = input_101_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_662_perm_0 = const()[name = string("op_662_perm_0"), val = tensor([0, 2, 1])]; + tensor var_662_cast_fp16 = transpose(perm = var_662_perm_0, x = var_661_cast_fp16)[name = string("transpose_89")]; + tensor x_53_cast_fp16 = mul(x = var_662_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_53_cast_fp16")]; + int32 var_681 = const()[name = string("op_681"), val = int32(-1)]; + tensor x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = x_55_cast_fp16, y = last_1_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor x_last_13_axes_0 = const()[name = string("x_last_13_axes_0"), val = tensor([-1])]; + bool x_last_13_keep_dims_0 = const()[name = string("x_last_13_keep_dims_0"), val = bool(true)]; + tensor x_last_13_cast_fp16 = reduce_sum(axes = x_last_13_axes_0, keep_dims = x_last_13_keep_dims_0, x = var_716_cast_fp16)[name = string("x_last_13_cast_fp16")]; + tensor var_719_cast_fp16 = mul(x = x_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_721_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_13_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_719_cast_fp16, y = var_721_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("replicate")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(512)]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14037376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14040000))))[name = string("main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14041088)))]; + tensor h_39_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor x_57_cast_fp16 = mul(x = h_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_739_axes_0 = const()[name = string("op_739_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14042176)))]; + tensor main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14043264)))]; + fp16 var_686_to_fp16 = const()[name = string("op_686_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_57_cast_fp16)[name = string("transpose_88")]; + tensor var_739_cast_fp16 = layer_norm(axes = var_739_axes_0, beta = main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_739_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + string h_41_pad_type_0 = const()[name = string("h_41_pad_type_0"), val = string("valid")]; + tensor h_41_strides_0 = const()[name = string("h_41_strides_0"), val = tensor([1])]; + tensor h_41_pad_0 = const()[name = string("h_41_pad_0"), val = tensor([0, 0])]; + tensor h_41_dilations_0 = const()[name = string("h_41_dilations_0"), val = tensor([1])]; + int32 h_41_groups_0 = const()[name = string("h_41_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14044352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15092992))))[name = string("main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15097152)))]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = var_739_cast_fp16)[name = string("transpose_87")]; + tensor h_41_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_41_dilations_0, groups = h_41_groups_0, pad = h_41_pad_0, pad_type = h_41_pad_type_0, strides = h_41_strides_0, weight = main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = h_41_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor op_756_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15101312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16149952))))[name = string("op_756_weight_0_to_fp16_quantized")]; + tensor var_756_bias_0_to_fp16 = const()[name = string("op_756_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16151040)))]; + tensor var_756_cast_fp16 = conv(bias = var_756_bias_0_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = op_756_weight_0_to_fp16_quantized, x = input_113_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor out_17_cast_fp16 = add(x = x_55_cast_fp16, y = var_756_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_59_cast_fp16 = mul(x = out_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor x_61_cast_fp16 = mul(x = x_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_770_cast_fp16 = mul(x = x_61_cast_fp16, y = last_1_cast_fp16)[name = string("op_770_cast_fp16")]; + tensor x_last_15_axes_0 = const()[name = string("x_last_15_axes_0"), val = tensor([-1])]; + bool x_last_15_keep_dims_0 = const()[name = string("x_last_15_keep_dims_0"), val = bool(true)]; + tensor x_last_15_cast_fp16 = reduce_sum(axes = x_last_15_axes_0, keep_dims = x_last_15_keep_dims_0, x = var_770_cast_fp16)[name = string("x_last_15_cast_fp16")]; + tensor var_773_cast_fp16 = mul(x = x_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor var_775_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_15_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = var_773_cast_fp16, y = var_775_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("replicate")]; + fp16 const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = fp16(0x0p+0)]; + tensor input_119_cast_fp16 = pad(constant_val = const_26_to_fp16, mode = input_119_mode_0, pad = input_119_pad_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")]; + string h_45_pad_type_0 = const()[name = string("h_45_pad_type_0"), val = string("valid")]; + tensor h_45_dilations_0 = const()[name = string("h_45_dilations_0"), val = tensor([2])]; + int32 h_45_groups_0 = const()[name = string("h_45_groups_0"), val = int32(512)]; + tensor h_45_strides_0 = const()[name = string("h_45_strides_0"), val = tensor([1])]; + tensor h_45_pad_0 = const()[name = string("h_45_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16152128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16154752))))[name = string("main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16155840)))]; + tensor h_45_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_45_dilations_0, groups = h_45_groups_0, pad = h_45_pad_0, pad_type = h_45_pad_type_0, strides = h_45_strides_0, weight = main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = h_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16156928)))]; + tensor main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16158016)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_63_cast_fp16)[name = string("transpose_86")]; + tensor var_793_cast_fp16 = layer_norm(axes = var_793_axes_0, beta = main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor input_123_perm_0 = const()[name = string("input_123_perm_0"), val = tensor([0, 2, 1])]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16159104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17207744))))[name = string("main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17211904)))]; + tensor input_123_cast_fp16 = transpose(perm = input_123_perm_0, x = var_793_cast_fp16)[name = string("transpose_85")]; + tensor h_47_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_123_cast_fp16)[name = string("h_47_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = h_47_cast_fp16)[name = string("input_125_cast_fp16")]; + string h_49_pad_type_0 = const()[name = string("h_49_pad_type_0"), val = string("valid")]; + tensor h_49_strides_0 = const()[name = string("h_49_strides_0"), val = tensor([1])]; + tensor h_49_pad_0 = const()[name = string("h_49_pad_0"), val = tensor([0, 0])]; + tensor h_49_dilations_0 = const()[name = string("h_49_dilations_0"), val = tensor([1])]; + int32 h_49_groups_0 = const()[name = string("h_49_groups_0"), val = int32(1)]; + tensor op_810_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17216064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18264704))))[name = string("op_810_weight_0_to_fp16_quantized")]; + tensor var_810_bias_0_to_fp16 = const()[name = string("op_810_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18265792)))]; + tensor var_810_cast_fp16 = conv(bias = var_810_bias_0_to_fp16, dilations = h_49_dilations_0, groups = h_49_groups_0, pad = h_49_pad_0, pad_type = h_49_pad_type_0, strides = h_49_strides_0, weight = op_810_weight_0_to_fp16_quantized, x = input_125_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = x_61_cast_fp16, y = var_810_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor x_65_cast_fp16 = mul(x = out_19_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = x_67_cast_fp16, y = last_1_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_last_17_axes_0 = const()[name = string("x_last_17_axes_0"), val = tensor([-1])]; + bool x_last_17_keep_dims_0 = const()[name = string("x_last_17_keep_dims_0"), val = bool(true)]; + tensor x_last_17_cast_fp16 = reduce_sum(axes = x_last_17_axes_0, keep_dims = x_last_17_keep_dims_0, x = var_824_cast_fp16)[name = string("x_last_17_cast_fp16")]; + tensor var_827_cast_fp16 = mul(x = x_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_829_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_17_cast_fp16)[name = string("op_829_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = var_827_cast_fp16, y = var_829_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("replicate")]; + fp16 const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = fp16(0x0p+0)]; + tensor input_131_cast_fp16 = pad(constant_val = const_28_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + string h_51_pad_type_0 = const()[name = string("h_51_pad_type_0"), val = string("valid")]; + tensor h_51_dilations_0 = const()[name = string("h_51_dilations_0"), val = tensor([4])]; + int32 h_51_groups_0 = const()[name = string("h_51_groups_0"), val = int32(512)]; + tensor h_51_strides_0 = const()[name = string("h_51_strides_0"), val = tensor([1])]; + tensor h_51_pad_0 = const()[name = string("h_51_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18266880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18269504))))[name = string("main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18270592)))]; + tensor h_51_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_51_dilations_0, groups = h_51_groups_0, pad = h_51_pad_0, pad_type = h_51_pad_type_0, strides = h_51_strides_0, weight = main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor x_69_cast_fp16 = mul(x = h_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_847_axes_0 = const()[name = string("op_847_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18271680)))]; + tensor main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18272768)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_69_cast_fp16)[name = string("transpose_84")]; + tensor var_847_cast_fp16 = layer_norm(axes = var_847_axes_0, beta = main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_847_cast_fp16")]; + tensor input_135_perm_0 = const()[name = string("input_135_perm_0"), val = tensor([0, 2, 1])]; + string h_53_pad_type_0 = const()[name = string("h_53_pad_type_0"), val = string("valid")]; + tensor h_53_strides_0 = const()[name = string("h_53_strides_0"), val = tensor([1])]; + tensor h_53_pad_0 = const()[name = string("h_53_pad_0"), val = tensor([0, 0])]; + tensor h_53_dilations_0 = const()[name = string("h_53_dilations_0"), val = tensor([1])]; + int32 h_53_groups_0 = const()[name = string("h_53_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18273856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19322496))))[name = string("main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19326656)))]; + tensor input_135_cast_fp16 = transpose(perm = input_135_perm_0, x = var_847_cast_fp16)[name = string("transpose_83")]; + tensor h_53_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_53_dilations_0, groups = h_53_groups_0, pad = h_53_pad_0, pad_type = h_53_pad_type_0, strides = h_53_strides_0, weight = main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("h_53_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = h_53_cast_fp16)[name = string("input_137_cast_fp16")]; + string h_55_pad_type_0 = const()[name = string("h_55_pad_type_0"), val = string("valid")]; + tensor h_55_strides_0 = const()[name = string("h_55_strides_0"), val = tensor([1])]; + tensor h_55_pad_0 = const()[name = string("h_55_pad_0"), val = tensor([0, 0])]; + tensor h_55_dilations_0 = const()[name = string("h_55_dilations_0"), val = tensor([1])]; + int32 h_55_groups_0 = const()[name = string("h_55_groups_0"), val = int32(1)]; + tensor op_864_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19330816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20379456))))[name = string("op_864_weight_0_to_fp16_quantized")]; + tensor var_864_bias_0_to_fp16 = const()[name = string("op_864_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20380544)))]; + tensor var_864_cast_fp16 = conv(bias = var_864_bias_0_to_fp16, dilations = h_55_dilations_0, groups = h_55_groups_0, pad = h_55_pad_0, pad_type = h_55_pad_type_0, strides = h_55_strides_0, weight = op_864_weight_0_to_fp16_quantized, x = input_137_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor out_21_cast_fp16 = add(x = x_67_cast_fp16, y = var_864_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = out_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor x_73_cast_fp16 = mul(x = x_71_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_878_cast_fp16 = mul(x = x_73_cast_fp16, y = last_1_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor x_last_19_axes_0 = const()[name = string("x_last_19_axes_0"), val = tensor([-1])]; + bool x_last_19_keep_dims_0 = const()[name = string("x_last_19_keep_dims_0"), val = bool(true)]; + tensor x_last_19_cast_fp16 = reduce_sum(axes = x_last_19_axes_0, keep_dims = x_last_19_keep_dims_0, x = var_878_cast_fp16)[name = string("x_last_19_cast_fp16")]; + tensor var_881_cast_fp16 = mul(x = x_73_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_881_cast_fp16")]; + tensor var_883_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_19_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = var_881_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("replicate")]; + fp16 const_30_to_fp16 = const()[name = string("const_30_to_fp16"), val = fp16(0x0p+0)]; + tensor input_143_cast_fp16 = pad(constant_val = const_30_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + string h_57_pad_type_0 = const()[name = string("h_57_pad_type_0"), val = string("valid")]; + tensor h_57_dilations_0 = const()[name = string("h_57_dilations_0"), val = tensor([8])]; + int32 h_57_groups_0 = const()[name = string("h_57_groups_0"), val = int32(512)]; + tensor h_57_strides_0 = const()[name = string("h_57_strides_0"), val = tensor([1])]; + tensor h_57_pad_0 = const()[name = string("h_57_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20381632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20384256))))[name = string("main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20385344)))]; + tensor h_57_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_57_dilations_0, groups = h_57_groups_0, pad = h_57_pad_0, pad_type = h_57_pad_type_0, strides = h_57_strides_0, weight = main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_143_cast_fp16)[name = string("h_57_cast_fp16")]; + tensor x_75_cast_fp16 = mul(x = h_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_901_axes_0 = const()[name = string("op_901_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20386432)))]; + tensor main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20387520)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_75_cast_fp16)[name = string("transpose_82")]; + tensor var_901_cast_fp16 = layer_norm(axes = var_901_axes_0, beta = main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor input_147_perm_0 = const()[name = string("input_147_perm_0"), val = tensor([0, 2, 1])]; + string h_59_pad_type_0 = const()[name = string("h_59_pad_type_0"), val = string("valid")]; + tensor h_59_strides_0 = const()[name = string("h_59_strides_0"), val = tensor([1])]; + tensor h_59_pad_0 = const()[name = string("h_59_pad_0"), val = tensor([0, 0])]; + tensor h_59_dilations_0 = const()[name = string("h_59_dilations_0"), val = tensor([1])]; + int32 h_59_groups_0 = const()[name = string("h_59_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20388608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21437248))))[name = string("main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21441408)))]; + tensor input_147_cast_fp16 = transpose(perm = input_147_perm_0, x = var_901_cast_fp16)[name = string("transpose_81")]; + tensor h_59_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_59_dilations_0, groups = h_59_groups_0, pad = h_59_pad_0, pad_type = h_59_pad_type_0, strides = h_59_strides_0, weight = main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_147_cast_fp16)[name = string("h_59_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = h_59_cast_fp16)[name = string("input_149_cast_fp16")]; + string h_61_pad_type_0 = const()[name = string("h_61_pad_type_0"), val = string("valid")]; + tensor h_61_strides_0 = const()[name = string("h_61_strides_0"), val = tensor([1])]; + tensor h_61_pad_0 = const()[name = string("h_61_pad_0"), val = tensor([0, 0])]; + tensor h_61_dilations_0 = const()[name = string("h_61_dilations_0"), val = tensor([1])]; + int32 h_61_groups_0 = const()[name = string("h_61_groups_0"), val = int32(1)]; + tensor op_918_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21445568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22494208))))[name = string("op_918_weight_0_to_fp16_quantized")]; + tensor var_918_bias_0_to_fp16 = const()[name = string("op_918_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22495296)))]; + tensor var_918_cast_fp16 = conv(bias = var_918_bias_0_to_fp16, dilations = h_61_dilations_0, groups = h_61_groups_0, pad = h_61_pad_0, pad_type = h_61_pad_type_0, strides = h_61_strides_0, weight = op_918_weight_0_to_fp16_quantized, x = input_149_cast_fp16)[name = string("op_918_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = x_73_cast_fp16, y = var_918_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor x_77_cast_fp16 = mul(x = out_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor main_blocks_1_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22496384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22529216))))[name = string("main_blocks_1_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22530304)))]; + tensor linear_11_cast_fp16 = linear(bias = main_blocks_1_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_1_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor t_7_axes_0 = const()[name = string("t_7_axes_0"), val = tensor([-1])]; + tensor t_7_cast_fp16 = expand_dims(axes = t_7_axes_0, x = linear_11_cast_fp16)[name = string("t_7_cast_fp16")]; + tensor var_928_cast_fp16 = add(x = x_77_cast_fp16, y = t_7_cast_fp16)[name = string("op_928_cast_fp16")]; + tensor x_79_cast_fp16 = mul(x = var_928_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor x_81_cast_fp16 = mul(x = x_79_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = x_81_cast_fp16, y = last_1_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor x_last_21_axes_0 = const()[name = string("x_last_21_axes_0"), val = tensor([-1])]; + bool x_last_21_keep_dims_0 = const()[name = string("x_last_21_keep_dims_0"), val = bool(true)]; + tensor x_last_21_cast_fp16 = reduce_sum(axes = x_last_21_axes_0, keep_dims = x_last_21_keep_dims_0, x = var_941_cast_fp16)[name = string("x_last_21_cast_fp16")]; + tensor var_944_cast_fp16 = mul(x = x_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor var_946_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_21_cast_fp16)[name = string("op_946_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = var_944_cast_fp16, y = var_946_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("replicate")]; + fp16 const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = fp16(0x0p+0)]; + tensor input_157_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string h_63_pad_type_0 = const()[name = string("h_63_pad_type_0"), val = string("valid")]; + int32 h_63_groups_0 = const()[name = string("h_63_groups_0"), val = int32(512)]; + tensor h_63_strides_0 = const()[name = string("h_63_strides_0"), val = tensor([1])]; + tensor h_63_pad_0 = const()[name = string("h_63_pad_0"), val = tensor([0, 0])]; + tensor h_63_dilations_0 = const()[name = string("h_63_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22531392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22534016))))[name = string("main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22535104)))]; + tensor h_63_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_63_dilations_0, groups = h_63_groups_0, pad = h_63_pad_0, pad_type = h_63_pad_type_0, strides = h_63_strides_0, weight = main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_157_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor x_83_cast_fp16 = mul(x = h_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor var_964_axes_0 = const()[name = string("op_964_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22536192)))]; + tensor main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22537280)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_83_cast_fp16)[name = string("transpose_80")]; + tensor var_964_cast_fp16 = layer_norm(axes = var_964_axes_0, beta = main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + string h_65_pad_type_0 = const()[name = string("h_65_pad_type_0"), val = string("valid")]; + tensor h_65_strides_0 = const()[name = string("h_65_strides_0"), val = tensor([1])]; + tensor h_65_pad_0 = const()[name = string("h_65_pad_0"), val = tensor([0, 0])]; + tensor h_65_dilations_0 = const()[name = string("h_65_dilations_0"), val = tensor([1])]; + int32 h_65_groups_0 = const()[name = string("h_65_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22538368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23587008))))[name = string("main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23591168)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = var_964_cast_fp16)[name = string("transpose_79")]; + tensor h_65_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_65_dilations_0, groups = h_65_groups_0, pad = h_65_pad_0, pad_type = h_65_pad_type_0, strides = h_65_strides_0, weight = main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_161_cast_fp16)[name = string("h_65_cast_fp16")]; + string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("EXACT")]; + tensor input_163_cast_fp16 = gelu(mode = input_163_mode_0, x = h_65_cast_fp16)[name = string("input_163_cast_fp16")]; + string h_67_pad_type_0 = const()[name = string("h_67_pad_type_0"), val = string("valid")]; + tensor h_67_strides_0 = const()[name = string("h_67_strides_0"), val = tensor([1])]; + tensor h_67_pad_0 = const()[name = string("h_67_pad_0"), val = tensor([0, 0])]; + tensor h_67_dilations_0 = const()[name = string("h_67_dilations_0"), val = tensor([1])]; + int32 h_67_groups_0 = const()[name = string("h_67_groups_0"), val = int32(1)]; + tensor op_981_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23595328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24643968))))[name = string("op_981_weight_0_to_fp16_quantized")]; + tensor var_981_bias_0_to_fp16 = const()[name = string("op_981_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24645056)))]; + tensor var_981_cast_fp16 = conv(bias = var_981_bias_0_to_fp16, dilations = h_67_dilations_0, groups = h_67_groups_0, pad = h_67_pad_0, pad_type = h_67_pad_type_0, strides = h_67_strides_0, weight = op_981_weight_0_to_fp16_quantized, x = input_163_cast_fp16)[name = string("op_981_cast_fp16")]; + tensor out_25_cast_fp16 = add(x = x_81_cast_fp16, y = var_981_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = out_25_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor x_m_5_cast_fp16 = mul(x = x_85_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_5_cast_fp16")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24646144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24908352))))[name = string("main_blocks_1_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24909440)))]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_85_cast_fp16)[name = string("transpose_78")]; + tensor linear_12_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_999 = const()[name = string("op_999"), val = tensor([2, 128, 8, 64])]; + tensor var_1000_cast_fp16 = reshape(shape = var_999, x = linear_12_cast_fp16)[name = string("op_1000_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24910528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25041664))))[name = string("main_blocks_1_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25042752)))]; + tensor linear_13_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_1007 = const()[name = string("op_1007"), val = tensor([2, 128, 8, 64])]; + tensor var_1008_cast_fp16 = reshape(shape = var_1007, x = linear_13_cast_fp16)[name = string("op_1008_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25043840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25174976))))[name = string("main_blocks_1_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25176064)))]; + tensor linear_14_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_1015 = const()[name = string("op_1015"), val = tensor([2, 128, 8, 64])]; + tensor var_1016_cast_fp16 = reshape(shape = var_1015, x = linear_14_cast_fp16)[name = string("op_1016_cast_fp16")]; + tensor v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_5_begin_0 = const()[name = string("x_a_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_5_end_0 = const()[name = string("x_a_5_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_5_end_mask_0 = const()[name = string("x_a_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_1000_cast_fp16)[name = string("transpose_77")]; + tensor x_a_5_cast_fp16 = slice_by_index(begin = x_a_5_begin_0, end = x_a_5_end_0, end_mask = x_a_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_a_5_cast_fp16")]; + tensor x_b_5_begin_0 = const()[name = string("x_b_5_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_5_end_0 = const()[name = string("x_b_5_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_5_end_mask_0 = const()[name = string("x_b_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_5_cast_fp16 = slice_by_index(begin = x_b_5_begin_0, end = x_b_5_end_0, end_mask = x_b_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_b_5_cast_fp16")]; + tensor var_1044_cast_fp16 = mul(x = x_a_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1044_cast_fp16")]; + tensor var_1045_cast_fp16 = mul(x = x_b_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor rot_a_5_cast_fp16 = sub(x = var_1044_cast_fp16, y = var_1045_cast_fp16)[name = string("rot_a_5_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_a_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_cast_fp16 = mul(x = x_b_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1048_cast_fp16")]; + tensor rot_b_5_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("rot_b_5_cast_fp16")]; + bool q_5_interleave_0 = const()[name = string("q_5_interleave_0"), val = bool(false)]; + tensor q_5_cast_fp16 = concat(axis = var_681, interleave = q_5_interleave_0, values = (rot_a_5_cast_fp16, rot_b_5_cast_fp16))[name = string("q_5_cast_fp16")]; + tensor x_a_7_begin_0 = const()[name = string("x_a_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_7_end_0 = const()[name = string("x_a_7_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_7_end_mask_0 = const()[name = string("x_a_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_1008_cast_fp16)[name = string("transpose_76")]; + tensor x_a_7_cast_fp16 = slice_by_index(begin = x_a_7_begin_0, end = x_a_7_end_0, end_mask = x_a_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_a_7_cast_fp16")]; + tensor x_b_7_begin_0 = const()[name = string("x_b_7_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_7_end_0 = const()[name = string("x_b_7_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_7_end_mask_0 = const()[name = string("x_b_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_7_cast_fp16 = slice_by_index(begin = x_b_7_begin_0, end = x_b_7_end_0, end_mask = x_b_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_b_7_cast_fp16")]; + tensor var_1062_cast_fp16 = mul(x = x_a_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = x_b_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor rot_a_7_cast_fp16 = sub(x = var_1062_cast_fp16, y = var_1063_cast_fp16)[name = string("rot_a_7_cast_fp16")]; + tensor var_1065_cast_fp16 = mul(x = x_a_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1065_cast_fp16")]; + tensor var_1066_cast_fp16 = mul(x = x_b_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1066_cast_fp16")]; + tensor rot_b_7_cast_fp16 = add(x = var_1065_cast_fp16, y = var_1066_cast_fp16)[name = string("rot_b_7_cast_fp16")]; + bool k_5_interleave_0 = const()[name = string("k_5_interleave_0"), val = bool(false)]; + tensor k_5_cast_fp16 = concat(axis = var_681, interleave = k_5_interleave_0, values = (rot_a_7_cast_fp16, rot_b_7_cast_fp16))[name = string("k_5_cast_fp16")]; + bool var_1071_transpose_x_1 = const()[name = string("op_1071_transpose_x_1"), val = bool(false)]; + bool var_1071_transpose_y_1 = const()[name = string("op_1071_transpose_y_1"), val = bool(true)]; + tensor var_1071_cast_fp16 = matmul(transpose_x = var_1071_transpose_x_1, transpose_y = var_1071_transpose_y_1, x = q_5_cast_fp16, y = k_5_cast_fp16)[name = string("op_1071_cast_fp16")]; + fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_3_cast_fp16 = mul(x = var_1071_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")]; + tensor input_171_cast_fp16 = sub(x = _inversed_scores_3_cast_fp16, y = var_530_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_681, x = input_171_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool var_1080_transpose_x_0 = const()[name = string("op_1080_transpose_x_0"), val = bool(false)]; + bool var_1080_transpose_y_0 = const()[name = string("op_1080_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1016_cast_fp16)[name = string("transpose_75")]; + tensor var_1080_cast_fp16 = matmul(transpose_x = var_1080_transpose_x_0, transpose_y = var_1080_transpose_y_0, x = attn_7_cast_fp16, y = v_5_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_perm_0 = const()[name = string("op_1081_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1082 = const()[name = string("op_1082"), val = tensor([2, 128, 512])]; + tensor var_1081_cast_fp16 = transpose(perm = var_1081_perm_0, x = var_1080_cast_fp16)[name = string("transpose_74")]; + tensor input_173_cast_fp16 = reshape(shape = var_1082, x = var_1081_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25177152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25439360))))[name = string("main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25440448)))]; + tensor linear_15_cast_fp16 = linear(bias = main_blocks_1_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_173_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor out_27_perm_0 = const()[name = string("out_27_perm_0"), val = tensor([0, 2, 1])]; + tensor out_27_cast_fp16 = transpose(perm = out_27_perm_0, x = linear_15_cast_fp16)[name = string("transpose_73")]; + tensor var_1089_cast_fp16 = mul(x = out_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor x_91_cast_fp16 = add(x = x_m_5_cast_fp16, y = var_1089_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_175_perm_0 = const()[name = string("input_175_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1096_axes_0 = const()[name = string("op_1096_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25441536)))]; + tensor main_blocks_1_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25442624)))]; + tensor input_175_cast_fp16 = transpose(perm = input_175_perm_0, x = x_91_cast_fp16)[name = string("transpose_72")]; + tensor var_1096_cast_fp16 = layer_norm(axes = var_1096_axes_0, beta = main_blocks_1_text_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_text_norm_norm_weight_to_fp16, x = input_175_cast_fp16)[name = string("op_1096_cast_fp16")]; + tensor var_1097_perm_0 = const()[name = string("op_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1097_cast_fp16 = transpose(perm = var_1097_perm_0, x = var_1096_cast_fp16)[name = string("transpose_71")]; + tensor x_93_cast_fp16 = mul(x = var_1097_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor x_95_cast_fp16 = mul(x = x_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_1110_cast_fp16 = mul(x = x_95_cast_fp16, y = last_1_cast_fp16)[name = string("op_1110_cast_fp16")]; + tensor x_last_23_axes_0 = const()[name = string("x_last_23_axes_0"), val = tensor([-1])]; + bool x_last_23_keep_dims_0 = const()[name = string("x_last_23_keep_dims_0"), val = bool(true)]; + tensor x_last_23_cast_fp16 = reduce_sum(axes = x_last_23_axes_0, keep_dims = x_last_23_keep_dims_0, x = var_1110_cast_fp16)[name = string("x_last_23_cast_fp16")]; + tensor var_1113_cast_fp16 = mul(x = x_95_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1115_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_23_cast_fp16)[name = string("op_1115_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = var_1113_cast_fp16, y = var_1115_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("replicate")]; + fp16 const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = fp16(0x0p+0)]; + tensor input_181_cast_fp16 = pad(constant_val = const_39_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + string h_69_pad_type_0 = const()[name = string("h_69_pad_type_0"), val = string("valid")]; + int32 h_69_groups_0 = const()[name = string("h_69_groups_0"), val = int32(512)]; + tensor h_69_strides_0 = const()[name = string("h_69_strides_0"), val = tensor([1])]; + tensor h_69_pad_0 = const()[name = string("h_69_pad_0"), val = tensor([0, 0])]; + tensor h_69_dilations_0 = const()[name = string("h_69_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25443712))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25446336))))[name = string("main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25447424)))]; + tensor h_69_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_69_dilations_0, groups = h_69_groups_0, pad = h_69_pad_0, pad_type = h_69_pad_type_0, strides = h_69_strides_0, weight = main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_181_cast_fp16)[name = string("h_69_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = h_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_183_perm_0 = const()[name = string("input_183_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1133_axes_0 = const()[name = string("op_1133_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25448512)))]; + tensor main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25449600)))]; + tensor input_183_cast_fp16 = transpose(perm = input_183_perm_0, x = x_97_cast_fp16)[name = string("transpose_70")]; + tensor var_1133_cast_fp16 = layer_norm(axes = var_1133_axes_0, beta = main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16, x = input_183_cast_fp16)[name = string("op_1133_cast_fp16")]; + tensor input_185_perm_0 = const()[name = string("input_185_perm_0"), val = tensor([0, 2, 1])]; + string h_71_pad_type_0 = const()[name = string("h_71_pad_type_0"), val = string("valid")]; + tensor h_71_strides_0 = const()[name = string("h_71_strides_0"), val = tensor([1])]; + tensor h_71_pad_0 = const()[name = string("h_71_pad_0"), val = tensor([0, 0])]; + tensor h_71_dilations_0 = const()[name = string("h_71_dilations_0"), val = tensor([1])]; + int32 h_71_groups_0 = const()[name = string("h_71_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25450688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26499328))))[name = string("main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26503488)))]; + tensor input_185_cast_fp16 = transpose(perm = input_185_perm_0, x = var_1133_cast_fp16)[name = string("transpose_69")]; + tensor h_71_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_71_dilations_0, groups = h_71_groups_0, pad = h_71_pad_0, pad_type = h_71_pad_type_0, strides = h_71_strides_0, weight = main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("h_71_cast_fp16")]; + string input_187_mode_0 = const()[name = string("input_187_mode_0"), val = string("EXACT")]; + tensor input_187_cast_fp16 = gelu(mode = input_187_mode_0, x = h_71_cast_fp16)[name = string("input_187_cast_fp16")]; + string h_73_pad_type_0 = const()[name = string("h_73_pad_type_0"), val = string("valid")]; + tensor h_73_strides_0 = const()[name = string("h_73_strides_0"), val = tensor([1])]; + tensor h_73_pad_0 = const()[name = string("h_73_pad_0"), val = tensor([0, 0])]; + tensor h_73_dilations_0 = const()[name = string("h_73_dilations_0"), val = tensor([1])]; + int32 h_73_groups_0 = const()[name = string("h_73_groups_0"), val = int32(1)]; + tensor op_1150_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26507648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27556288))))[name = string("op_1150_weight_0_to_fp16_quantized")]; + tensor var_1150_bias_0_to_fp16 = const()[name = string("op_1150_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27557376)))]; + tensor var_1150_cast_fp16 = conv(bias = var_1150_bias_0_to_fp16, dilations = h_73_dilations_0, groups = h_73_groups_0, pad = h_73_pad_0, pad_type = h_73_pad_type_0, strides = h_73_strides_0, weight = op_1150_weight_0_to_fp16_quantized, x = input_187_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor out_29_cast_fp16 = add(x = x_95_cast_fp16, y = var_1150_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor x_99_cast_fp16 = mul(x = out_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor x_m_7_cast_fp16 = mul(x = x_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_7_cast_fp16")]; + tensor input_189_perm_0 = const()[name = string("input_189_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27558464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27689600))))[name = string("main_blocks_1_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27690176)))]; + tensor input_189_cast_fp16 = transpose(perm = input_189_perm_0, x = x_99_cast_fp16)[name = string("transpose_68")]; + tensor linear_16_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_1166 = const()[name = string("op_1166"), val = tensor([2, 128, 2, 128])]; + tensor var_1167_cast_fp16 = reshape(shape = var_1166, x = linear_16_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_1_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27690752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27756352))))[name = string("main_blocks_1_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27756928)))]; + tensor linear_18_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_1180 = const()[name = string("op_1180"), val = tensor([2, 50, 2, 128])]; + tensor var_1181_cast_fp16 = reshape(shape = var_1180, x = linear_18_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1185_transpose_x_0 = const()[name = string("op_1185_transpose_x_0"), val = bool(false)]; + bool var_1185_transpose_y_0 = const()[name = string("op_1185_transpose_y_0"), val = bool(false)]; + tensor op_1184_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27757504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27783168))))[name = string("op_1184_to_fp16_quantized")]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1167_cast_fp16)[name = string("transpose_67")]; + tensor var_1185_cast_fp16 = matmul(transpose_x = var_1185_transpose_x_0, transpose_y = var_1185_transpose_y_0, x = q_7_cast_fp16, y = op_1184_to_fp16_quantized)[name = string("op_1185_cast_fp16")]; + fp16 _inversed_input_191_y_0_to_fp16 = const()[name = string("_inversed_input_191_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_191_cast_fp16 = mul(x = var_1185_cast_fp16, y = _inversed_input_191_y_0_to_fp16)[name = string("_inversed_input_191_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_681, x = _inversed_input_191_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor attn_11_cast_fp16 = mul(x = attn_9_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool var_1192_transpose_x_0 = const()[name = string("op_1192_transpose_x_0"), val = bool(false)]; + bool var_1192_transpose_y_0 = const()[name = string("op_1192_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1181_cast_fp16)[name = string("transpose_66")]; + tensor var_1192_cast_fp16 = matmul(transpose_x = var_1192_transpose_x_0, transpose_y = var_1192_transpose_y_0, x = attn_11_cast_fp16, y = v_7_cast_fp16)[name = string("op_1192_cast_fp16")]; + tensor var_1193_perm_0 = const()[name = string("op_1193_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1194 = const()[name = string("op_1194"), val = tensor([2, 128, 256])]; + tensor var_1193_cast_fp16 = transpose(perm = var_1193_perm_0, x = var_1192_cast_fp16)[name = string("transpose_65")]; + tensor input_193_cast_fp16 = reshape(shape = var_1194, x = var_1193_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27783360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27914496))))[name = string("main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27915584)))]; + tensor linear_19_cast_fp16 = linear(bias = main_blocks_1_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_193_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor out_31_perm_0 = const()[name = string("out_31_perm_0"), val = tensor([0, 2, 1])]; + tensor out_31_cast_fp16 = transpose(perm = out_31_perm_0, x = linear_19_cast_fp16)[name = string("transpose_64")]; + tensor var_1201_cast_fp16 = mul(x = out_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1201_cast_fp16")]; + tensor x_101_cast_fp16 = add(x = x_m_7_cast_fp16, y = var_1201_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_195_perm_0 = const()[name = string("input_195_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1208_axes_0 = const()[name = string("op_1208_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27916672)))]; + tensor main_blocks_1_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27917760)))]; + tensor input_195_cast_fp16 = transpose(perm = input_195_perm_0, x = x_101_cast_fp16)[name = string("transpose_63")]; + tensor var_1208_cast_fp16 = layer_norm(axes = var_1208_axes_0, beta = main_blocks_1_style_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_style_norm_norm_weight_to_fp16, x = input_195_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_perm_0 = const()[name = string("op_1209_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1209_cast_fp16 = transpose(perm = var_1209_perm_0, x = var_1208_cast_fp16)[name = string("transpose_62")]; + tensor x_103_cast_fp16 = mul(x = var_1209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_103_cast_fp16")]; + int32 var_1228 = const()[name = string("op_1228"), val = int32(-1)]; + tensor x_105_cast_fp16 = mul(x = x_103_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_1263_cast_fp16 = mul(x = x_105_cast_fp16, y = last_1_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor x_last_25_axes_0 = const()[name = string("x_last_25_axes_0"), val = tensor([-1])]; + bool x_last_25_keep_dims_0 = const()[name = string("x_last_25_keep_dims_0"), val = bool(true)]; + tensor x_last_25_cast_fp16 = reduce_sum(axes = x_last_25_axes_0, keep_dims = x_last_25_keep_dims_0, x = var_1263_cast_fp16)[name = string("x_last_25_cast_fp16")]; + tensor var_1266_cast_fp16 = mul(x = x_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1266_cast_fp16")]; + tensor var_1268_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_25_cast_fp16)[name = string("op_1268_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1266_cast_fp16, y = var_1268_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_201_mode_0 = const()[name = string("input_201_mode_0"), val = string("replicate")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_201_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")]; + string h_75_pad_type_0 = const()[name = string("h_75_pad_type_0"), val = string("valid")]; + int32 h_75_groups_0 = const()[name = string("h_75_groups_0"), val = int32(512)]; + tensor h_75_strides_0 = const()[name = string("h_75_strides_0"), val = tensor([1])]; + tensor h_75_pad_0 = const()[name = string("h_75_pad_0"), val = tensor([0, 0])]; + tensor h_75_dilations_0 = const()[name = string("h_75_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27918848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27921472))))[name = string("main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27922560)))]; + tensor h_75_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_75_dilations_0, groups = h_75_groups_0, pad = h_75_pad_0, pad_type = h_75_pad_type_0, strides = h_75_strides_0, weight = main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_201_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = h_75_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_203_perm_0 = const()[name = string("input_203_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1286_axes_0 = const()[name = string("op_1286_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27923648)))]; + tensor main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27924736)))]; + fp16 var_1233_to_fp16 = const()[name = string("op_1233_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_203_cast_fp16 = transpose(perm = input_203_perm_0, x = x_107_cast_fp16)[name = string("transpose_61")]; + tensor var_1286_cast_fp16 = layer_norm(axes = var_1286_axes_0, beta = main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16, x = input_203_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string h_77_pad_type_0 = const()[name = string("h_77_pad_type_0"), val = string("valid")]; + tensor h_77_strides_0 = const()[name = string("h_77_strides_0"), val = tensor([1])]; + tensor h_77_pad_0 = const()[name = string("h_77_pad_0"), val = tensor([0, 0])]; + tensor h_77_dilations_0 = const()[name = string("h_77_dilations_0"), val = tensor([1])]; + int32 h_77_groups_0 = const()[name = string("h_77_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27925824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28974464))))[name = string("main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28978624)))]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = var_1286_cast_fp16)[name = string("transpose_60")]; + tensor h_77_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_77_dilations_0, groups = h_77_groups_0, pad = h_77_pad_0, pad_type = h_77_pad_type_0, strides = h_77_strides_0, weight = main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("h_77_cast_fp16")]; + string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")]; + tensor input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = h_77_cast_fp16)[name = string("input_207_cast_fp16")]; + string h_79_pad_type_0 = const()[name = string("h_79_pad_type_0"), val = string("valid")]; + tensor h_79_strides_0 = const()[name = string("h_79_strides_0"), val = tensor([1])]; + tensor h_79_pad_0 = const()[name = string("h_79_pad_0"), val = tensor([0, 0])]; + tensor h_79_dilations_0 = const()[name = string("h_79_dilations_0"), val = tensor([1])]; + int32 h_79_groups_0 = const()[name = string("h_79_groups_0"), val = int32(1)]; + tensor op_1303_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28982784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30031424))))[name = string("op_1303_weight_0_to_fp16_quantized")]; + tensor var_1303_bias_0_to_fp16 = const()[name = string("op_1303_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30032512)))]; + tensor var_1303_cast_fp16 = conv(bias = var_1303_bias_0_to_fp16, dilations = h_79_dilations_0, groups = h_79_groups_0, pad = h_79_pad_0, pad_type = h_79_pad_type_0, strides = h_79_strides_0, weight = op_1303_weight_0_to_fp16_quantized, x = input_207_cast_fp16)[name = string("op_1303_cast_fp16")]; + tensor out_33_cast_fp16 = add(x = x_105_cast_fp16, y = var_1303_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor x_109_cast_fp16 = mul(x = out_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor x_111_cast_fp16 = mul(x = x_109_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_1317_cast_fp16 = mul(x = x_111_cast_fp16, y = last_1_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor x_last_27_axes_0 = const()[name = string("x_last_27_axes_0"), val = tensor([-1])]; + bool x_last_27_keep_dims_0 = const()[name = string("x_last_27_keep_dims_0"), val = bool(true)]; + tensor x_last_27_cast_fp16 = reduce_sum(axes = x_last_27_axes_0, keep_dims = x_last_27_keep_dims_0, x = var_1317_cast_fp16)[name = string("x_last_27_cast_fp16")]; + tensor var_1320_cast_fp16 = mul(x = x_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1322_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_27_cast_fp16)[name = string("op_1322_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1320_cast_fp16, y = var_1322_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("replicate")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor input_213_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = input_213_mode_0, pad = input_213_pad_0, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + string h_81_pad_type_0 = const()[name = string("h_81_pad_type_0"), val = string("valid")]; + tensor h_81_dilations_0 = const()[name = string("h_81_dilations_0"), val = tensor([2])]; + int32 h_81_groups_0 = const()[name = string("h_81_groups_0"), val = int32(512)]; + tensor h_81_strides_0 = const()[name = string("h_81_strides_0"), val = tensor([1])]; + tensor h_81_pad_0 = const()[name = string("h_81_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30033600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30036224))))[name = string("main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30037312)))]; + tensor h_81_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_81_dilations_0, groups = h_81_groups_0, pad = h_81_pad_0, pad_type = h_81_pad_type_0, strides = h_81_strides_0, weight = main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_213_cast_fp16)[name = string("h_81_cast_fp16")]; + tensor x_113_cast_fp16 = mul(x = h_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor input_215_perm_0 = const()[name = string("input_215_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1340_axes_0 = const()[name = string("op_1340_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30038400)))]; + tensor main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30039488)))]; + tensor input_215_cast_fp16 = transpose(perm = input_215_perm_0, x = x_113_cast_fp16)[name = string("transpose_59")]; + tensor var_1340_cast_fp16 = layer_norm(axes = var_1340_axes_0, beta = main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16, x = input_215_cast_fp16)[name = string("op_1340_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + string h_83_pad_type_0 = const()[name = string("h_83_pad_type_0"), val = string("valid")]; + tensor h_83_strides_0 = const()[name = string("h_83_strides_0"), val = tensor([1])]; + tensor h_83_pad_0 = const()[name = string("h_83_pad_0"), val = tensor([0, 0])]; + tensor h_83_dilations_0 = const()[name = string("h_83_dilations_0"), val = tensor([1])]; + int32 h_83_groups_0 = const()[name = string("h_83_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30040576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31089216))))[name = string("main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31093376)))]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = var_1340_cast_fp16)[name = string("transpose_58")]; + tensor h_83_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_83_dilations_0, groups = h_83_groups_0, pad = h_83_pad_0, pad_type = h_83_pad_type_0, strides = h_83_strides_0, weight = main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_217_cast_fp16)[name = string("h_83_cast_fp16")]; + string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")]; + tensor input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = h_83_cast_fp16)[name = string("input_219_cast_fp16")]; + string h_85_pad_type_0 = const()[name = string("h_85_pad_type_0"), val = string("valid")]; + tensor h_85_strides_0 = const()[name = string("h_85_strides_0"), val = tensor([1])]; + tensor h_85_pad_0 = const()[name = string("h_85_pad_0"), val = tensor([0, 0])]; + tensor h_85_dilations_0 = const()[name = string("h_85_dilations_0"), val = tensor([1])]; + int32 h_85_groups_0 = const()[name = string("h_85_groups_0"), val = int32(1)]; + tensor op_1357_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31097536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32146176))))[name = string("op_1357_weight_0_to_fp16_quantized")]; + tensor var_1357_bias_0_to_fp16 = const()[name = string("op_1357_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32147264)))]; + tensor var_1357_cast_fp16 = conv(bias = var_1357_bias_0_to_fp16, dilations = h_85_dilations_0, groups = h_85_groups_0, pad = h_85_pad_0, pad_type = h_85_pad_type_0, strides = h_85_strides_0, weight = op_1357_weight_0_to_fp16_quantized, x = input_219_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor out_35_cast_fp16 = add(x = x_111_cast_fp16, y = var_1357_cast_fp16)[name = string("out_35_cast_fp16")]; + tensor x_115_cast_fp16 = mul(x = out_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_cast_fp16 = mul(x = x_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1371_cast_fp16 = mul(x = x_117_cast_fp16, y = last_1_cast_fp16)[name = string("op_1371_cast_fp16")]; + tensor x_last_29_axes_0 = const()[name = string("x_last_29_axes_0"), val = tensor([-1])]; + bool x_last_29_keep_dims_0 = const()[name = string("x_last_29_keep_dims_0"), val = bool(true)]; + tensor x_last_29_cast_fp16 = reduce_sum(axes = x_last_29_axes_0, keep_dims = x_last_29_keep_dims_0, x = var_1371_cast_fp16)[name = string("x_last_29_cast_fp16")]; + tensor var_1374_cast_fp16 = mul(x = x_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_29_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1376_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_225_mode_0 = const()[name = string("input_225_mode_0"), val = string("replicate")]; + fp16 const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = fp16(0x0p+0)]; + tensor input_225_cast_fp16 = pad(constant_val = const_48_to_fp16, mode = input_225_mode_0, pad = input_225_pad_0, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")]; + string h_87_pad_type_0 = const()[name = string("h_87_pad_type_0"), val = string("valid")]; + tensor h_87_dilations_0 = const()[name = string("h_87_dilations_0"), val = tensor([4])]; + int32 h_87_groups_0 = const()[name = string("h_87_groups_0"), val = int32(512)]; + tensor h_87_strides_0 = const()[name = string("h_87_strides_0"), val = tensor([1])]; + tensor h_87_pad_0 = const()[name = string("h_87_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32148352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32150976))))[name = string("main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32152064)))]; + tensor h_87_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_87_dilations_0, groups = h_87_groups_0, pad = h_87_pad_0, pad_type = h_87_pad_type_0, strides = h_87_strides_0, weight = main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor x_119_cast_fp16 = mul(x = h_87_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_227_perm_0 = const()[name = string("input_227_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1394_axes_0 = const()[name = string("op_1394_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32153152)))]; + tensor main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32154240)))]; + tensor input_227_cast_fp16 = transpose(perm = input_227_perm_0, x = x_119_cast_fp16)[name = string("transpose_57")]; + tensor var_1394_cast_fp16 = layer_norm(axes = var_1394_axes_0, beta = main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16, x = input_227_cast_fp16)[name = string("op_1394_cast_fp16")]; + tensor input_229_perm_0 = const()[name = string("input_229_perm_0"), val = tensor([0, 2, 1])]; + string h_89_pad_type_0 = const()[name = string("h_89_pad_type_0"), val = string("valid")]; + tensor h_89_strides_0 = const()[name = string("h_89_strides_0"), val = tensor([1])]; + tensor h_89_pad_0 = const()[name = string("h_89_pad_0"), val = tensor([0, 0])]; + tensor h_89_dilations_0 = const()[name = string("h_89_dilations_0"), val = tensor([1])]; + int32 h_89_groups_0 = const()[name = string("h_89_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32155328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33203968))))[name = string("main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33208128)))]; + tensor input_229_cast_fp16 = transpose(perm = input_229_perm_0, x = var_1394_cast_fp16)[name = string("transpose_56")]; + tensor h_89_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_89_dilations_0, groups = h_89_groups_0, pad = h_89_pad_0, pad_type = h_89_pad_type_0, strides = h_89_strides_0, weight = main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_229_cast_fp16)[name = string("h_89_cast_fp16")]; + string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")]; + tensor input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = h_89_cast_fp16)[name = string("input_231_cast_fp16")]; + string h_91_pad_type_0 = const()[name = string("h_91_pad_type_0"), val = string("valid")]; + tensor h_91_strides_0 = const()[name = string("h_91_strides_0"), val = tensor([1])]; + tensor h_91_pad_0 = const()[name = string("h_91_pad_0"), val = tensor([0, 0])]; + tensor h_91_dilations_0 = const()[name = string("h_91_dilations_0"), val = tensor([1])]; + int32 h_91_groups_0 = const()[name = string("h_91_groups_0"), val = int32(1)]; + tensor op_1411_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33212288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34260928))))[name = string("op_1411_weight_0_to_fp16_quantized")]; + tensor var_1411_bias_0_to_fp16 = const()[name = string("op_1411_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34262016)))]; + tensor var_1411_cast_fp16 = conv(bias = var_1411_bias_0_to_fp16, dilations = h_91_dilations_0, groups = h_91_groups_0, pad = h_91_pad_0, pad_type = h_91_pad_type_0, strides = h_91_strides_0, weight = op_1411_weight_0_to_fp16_quantized, x = input_231_cast_fp16)[name = string("op_1411_cast_fp16")]; + tensor out_37_cast_fp16 = add(x = x_117_cast_fp16, y = var_1411_cast_fp16)[name = string("out_37_cast_fp16")]; + tensor x_121_cast_fp16 = mul(x = out_37_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_121_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1425_cast_fp16 = mul(x = x_123_cast_fp16, y = last_1_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor x_last_31_axes_0 = const()[name = string("x_last_31_axes_0"), val = tensor([-1])]; + bool x_last_31_keep_dims_0 = const()[name = string("x_last_31_keep_dims_0"), val = bool(true)]; + tensor x_last_31_cast_fp16 = reduce_sum(axes = x_last_31_axes_0, keep_dims = x_last_31_keep_dims_0, x = var_1425_cast_fp16)[name = string("x_last_31_cast_fp16")]; + tensor var_1428_cast_fp16 = mul(x = x_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_31_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = var_1428_cast_fp16, y = var_1430_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("replicate")]; + fp16 const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = fp16(0x0p+0)]; + tensor input_237_cast_fp16 = pad(constant_val = const_50_to_fp16, mode = input_237_mode_0, pad = input_237_pad_0, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + string h_93_pad_type_0 = const()[name = string("h_93_pad_type_0"), val = string("valid")]; + tensor h_93_dilations_0 = const()[name = string("h_93_dilations_0"), val = tensor([8])]; + int32 h_93_groups_0 = const()[name = string("h_93_groups_0"), val = int32(512)]; + tensor h_93_strides_0 = const()[name = string("h_93_strides_0"), val = tensor([1])]; + tensor h_93_pad_0 = const()[name = string("h_93_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34263104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34265728))))[name = string("main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34266816)))]; + tensor h_93_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_93_dilations_0, groups = h_93_groups_0, pad = h_93_pad_0, pad_type = h_93_pad_type_0, strides = h_93_strides_0, weight = main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_237_cast_fp16)[name = string("h_93_cast_fp16")]; + tensor x_125_cast_fp16 = mul(x = h_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_239_perm_0 = const()[name = string("input_239_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1448_axes_0 = const()[name = string("op_1448_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34267904)))]; + tensor main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34268992)))]; + tensor input_239_cast_fp16 = transpose(perm = input_239_perm_0, x = x_125_cast_fp16)[name = string("transpose_55")]; + tensor var_1448_cast_fp16 = layer_norm(axes = var_1448_axes_0, beta = main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16, x = input_239_cast_fp16)[name = string("op_1448_cast_fp16")]; + tensor input_241_perm_0 = const()[name = string("input_241_perm_0"), val = tensor([0, 2, 1])]; + string h_95_pad_type_0 = const()[name = string("h_95_pad_type_0"), val = string("valid")]; + tensor h_95_strides_0 = const()[name = string("h_95_strides_0"), val = tensor([1])]; + tensor h_95_pad_0 = const()[name = string("h_95_pad_0"), val = tensor([0, 0])]; + tensor h_95_dilations_0 = const()[name = string("h_95_dilations_0"), val = tensor([1])]; + int32 h_95_groups_0 = const()[name = string("h_95_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34270080))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35318720))))[name = string("main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35322880)))]; + tensor input_241_cast_fp16 = transpose(perm = input_241_perm_0, x = var_1448_cast_fp16)[name = string("transpose_54")]; + tensor h_95_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_95_dilations_0, groups = h_95_groups_0, pad = h_95_pad_0, pad_type = h_95_pad_type_0, strides = h_95_strides_0, weight = main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_241_cast_fp16)[name = string("h_95_cast_fp16")]; + string input_243_mode_0 = const()[name = string("input_243_mode_0"), val = string("EXACT")]; + tensor input_243_cast_fp16 = gelu(mode = input_243_mode_0, x = h_95_cast_fp16)[name = string("input_243_cast_fp16")]; + string h_97_pad_type_0 = const()[name = string("h_97_pad_type_0"), val = string("valid")]; + tensor h_97_strides_0 = const()[name = string("h_97_strides_0"), val = tensor([1])]; + tensor h_97_pad_0 = const()[name = string("h_97_pad_0"), val = tensor([0, 0])]; + tensor h_97_dilations_0 = const()[name = string("h_97_dilations_0"), val = tensor([1])]; + int32 h_97_groups_0 = const()[name = string("h_97_groups_0"), val = int32(1)]; + tensor op_1465_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35327040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36375680))))[name = string("op_1465_weight_0_to_fp16_quantized")]; + tensor var_1465_bias_0_to_fp16 = const()[name = string("op_1465_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36376768)))]; + tensor var_1465_cast_fp16 = conv(bias = var_1465_bias_0_to_fp16, dilations = h_97_dilations_0, groups = h_97_groups_0, pad = h_97_pad_0, pad_type = h_97_pad_type_0, strides = h_97_strides_0, weight = op_1465_weight_0_to_fp16_quantized, x = input_243_cast_fp16)[name = string("op_1465_cast_fp16")]; + tensor out_39_cast_fp16 = add(x = x_123_cast_fp16, y = var_1465_cast_fp16)[name = string("out_39_cast_fp16")]; + tensor x_127_cast_fp16 = mul(x = out_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor main_blocks_2_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36377856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36410688))))[name = string("main_blocks_2_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36411776)))]; + tensor linear_20_cast_fp16 = linear(bias = main_blocks_2_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_2_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor t_9_axes_0 = const()[name = string("t_9_axes_0"), val = tensor([-1])]; + tensor t_9_cast_fp16 = expand_dims(axes = t_9_axes_0, x = linear_20_cast_fp16)[name = string("t_9_cast_fp16")]; + tensor var_1475_cast_fp16 = add(x = x_127_cast_fp16, y = t_9_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = var_1475_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor x_131_cast_fp16 = mul(x = x_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor var_1488_cast_fp16 = mul(x = x_131_cast_fp16, y = last_1_cast_fp16)[name = string("op_1488_cast_fp16")]; + tensor x_last_33_axes_0 = const()[name = string("x_last_33_axes_0"), val = tensor([-1])]; + bool x_last_33_keep_dims_0 = const()[name = string("x_last_33_keep_dims_0"), val = bool(true)]; + tensor x_last_33_cast_fp16 = reduce_sum(axes = x_last_33_axes_0, keep_dims = x_last_33_keep_dims_0, x = var_1488_cast_fp16)[name = string("x_last_33_cast_fp16")]; + tensor var_1491_cast_fp16 = mul(x = x_131_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1491_cast_fp16")]; + tensor var_1493_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_33_cast_fp16)[name = string("op_1493_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = var_1491_cast_fp16, y = var_1493_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_251_mode_0 = const()[name = string("input_251_mode_0"), val = string("replicate")]; + fp16 const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = fp16(0x0p+0)]; + tensor input_251_cast_fp16 = pad(constant_val = const_52_to_fp16, mode = input_251_mode_0, pad = input_251_pad_0, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + string h_99_pad_type_0 = const()[name = string("h_99_pad_type_0"), val = string("valid")]; + int32 h_99_groups_0 = const()[name = string("h_99_groups_0"), val = int32(512)]; + tensor h_99_strides_0 = const()[name = string("h_99_strides_0"), val = tensor([1])]; + tensor h_99_pad_0 = const()[name = string("h_99_pad_0"), val = tensor([0, 0])]; + tensor h_99_dilations_0 = const()[name = string("h_99_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36412864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36415488))))[name = string("main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36416576)))]; + tensor h_99_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_99_dilations_0, groups = h_99_groups_0, pad = h_99_pad_0, pad_type = h_99_pad_type_0, strides = h_99_strides_0, weight = main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor x_133_cast_fp16 = mul(x = h_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_253_perm_0 = const()[name = string("input_253_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1511_axes_0 = const()[name = string("op_1511_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36417664)))]; + tensor main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36418752)))]; + tensor input_253_cast_fp16 = transpose(perm = input_253_perm_0, x = x_133_cast_fp16)[name = string("transpose_53")]; + tensor var_1511_cast_fp16 = layer_norm(axes = var_1511_axes_0, beta = main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16, x = input_253_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor input_255_perm_0 = const()[name = string("input_255_perm_0"), val = tensor([0, 2, 1])]; + string h_101_pad_type_0 = const()[name = string("h_101_pad_type_0"), val = string("valid")]; + tensor h_101_strides_0 = const()[name = string("h_101_strides_0"), val = tensor([1])]; + tensor h_101_pad_0 = const()[name = string("h_101_pad_0"), val = tensor([0, 0])]; + tensor h_101_dilations_0 = const()[name = string("h_101_dilations_0"), val = tensor([1])]; + int32 h_101_groups_0 = const()[name = string("h_101_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36419840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37468480))))[name = string("main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37472640)))]; + tensor input_255_cast_fp16 = transpose(perm = input_255_perm_0, x = var_1511_cast_fp16)[name = string("transpose_52")]; + tensor h_101_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_101_dilations_0, groups = h_101_groups_0, pad = h_101_pad_0, pad_type = h_101_pad_type_0, strides = h_101_strides_0, weight = main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("h_101_cast_fp16")]; + string input_257_mode_0 = const()[name = string("input_257_mode_0"), val = string("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = h_101_cast_fp16)[name = string("input_257_cast_fp16")]; + string h_103_pad_type_0 = const()[name = string("h_103_pad_type_0"), val = string("valid")]; + tensor h_103_strides_0 = const()[name = string("h_103_strides_0"), val = tensor([1])]; + tensor h_103_pad_0 = const()[name = string("h_103_pad_0"), val = tensor([0, 0])]; + tensor h_103_dilations_0 = const()[name = string("h_103_dilations_0"), val = tensor([1])]; + int32 h_103_groups_0 = const()[name = string("h_103_groups_0"), val = int32(1)]; + tensor op_1528_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37476800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38525440))))[name = string("op_1528_weight_0_to_fp16_quantized")]; + tensor var_1528_bias_0_to_fp16 = const()[name = string("op_1528_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38526528)))]; + tensor var_1528_cast_fp16 = conv(bias = var_1528_bias_0_to_fp16, dilations = h_103_dilations_0, groups = h_103_groups_0, pad = h_103_pad_0, pad_type = h_103_pad_type_0, strides = h_103_strides_0, weight = op_1528_weight_0_to_fp16_quantized, x = input_257_cast_fp16)[name = string("op_1528_cast_fp16")]; + tensor out_41_cast_fp16 = add(x = x_131_cast_fp16, y = var_1528_cast_fp16)[name = string("out_41_cast_fp16")]; + tensor x_135_cast_fp16 = mul(x = out_41_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor x_m_9_cast_fp16 = mul(x = x_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_9_cast_fp16")]; + tensor input_259_perm_0 = const()[name = string("input_259_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38527616))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38789824))))[name = string("main_blocks_2_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38790912)))]; + tensor input_259_cast_fp16 = transpose(perm = input_259_perm_0, x = x_135_cast_fp16)[name = string("transpose_51")]; + tensor linear_21_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_259_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_1546 = const()[name = string("op_1546"), val = tensor([2, 128, 8, 64])]; + tensor var_1547_cast_fp16 = reshape(shape = var_1546, x = linear_21_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor x_137_perm_0 = const()[name = string("x_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38792000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38923136))))[name = string("main_blocks_2_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38924224)))]; + tensor linear_22_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_1554 = const()[name = string("op_1554"), val = tensor([2, 128, 8, 64])]; + tensor var_1555_cast_fp16 = reshape(shape = var_1554, x = linear_22_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_139_perm_0 = const()[name = string("x_139_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38925312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39056448))))[name = string("main_blocks_2_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39057536)))]; + tensor linear_23_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_1562 = const()[name = string("op_1562"), val = tensor([2, 128, 8, 64])]; + tensor var_1563_cast_fp16 = reshape(shape = var_1562, x = linear_23_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_9_begin_0 = const()[name = string("x_a_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_9_end_0 = const()[name = string("x_a_9_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_9_end_mask_0 = const()[name = string("x_a_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_137_cast_fp16 = transpose(perm = x_137_perm_0, x = var_1547_cast_fp16)[name = string("transpose_50")]; + tensor x_a_9_cast_fp16 = slice_by_index(begin = x_a_9_begin_0, end = x_a_9_end_0, end_mask = x_a_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_a_9_cast_fp16")]; + tensor x_b_9_begin_0 = const()[name = string("x_b_9_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_9_end_0 = const()[name = string("x_b_9_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_9_end_mask_0 = const()[name = string("x_b_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_9_cast_fp16 = slice_by_index(begin = x_b_9_begin_0, end = x_b_9_end_0, end_mask = x_b_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_b_9_cast_fp16")]; + tensor var_1591_cast_fp16 = mul(x = x_a_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1591_cast_fp16")]; + tensor var_1592_cast_fp16 = mul(x = x_b_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor rot_a_9_cast_fp16 = sub(x = var_1591_cast_fp16, y = var_1592_cast_fp16)[name = string("rot_a_9_cast_fp16")]; + tensor var_1594_cast_fp16 = mul(x = x_a_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595_cast_fp16 = mul(x = x_b_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1595_cast_fp16")]; + tensor rot_b_9_cast_fp16 = add(x = var_1594_cast_fp16, y = var_1595_cast_fp16)[name = string("rot_b_9_cast_fp16")]; + bool q_9_interleave_0 = const()[name = string("q_9_interleave_0"), val = bool(false)]; + tensor q_9_cast_fp16 = concat(axis = var_1228, interleave = q_9_interleave_0, values = (rot_a_9_cast_fp16, rot_b_9_cast_fp16))[name = string("q_9_cast_fp16")]; + tensor x_a_11_begin_0 = const()[name = string("x_a_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_11_end_0 = const()[name = string("x_a_11_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_11_end_mask_0 = const()[name = string("x_a_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_139_cast_fp16 = transpose(perm = x_139_perm_0, x = var_1555_cast_fp16)[name = string("transpose_49")]; + tensor x_a_11_cast_fp16 = slice_by_index(begin = x_a_11_begin_0, end = x_a_11_end_0, end_mask = x_a_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_a_11_cast_fp16")]; + tensor x_b_11_begin_0 = const()[name = string("x_b_11_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_11_end_0 = const()[name = string("x_b_11_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_11_end_mask_0 = const()[name = string("x_b_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_11_cast_fp16 = slice_by_index(begin = x_b_11_begin_0, end = x_b_11_end_0, end_mask = x_b_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_b_11_cast_fp16")]; + tensor var_1609_cast_fp16 = mul(x = x_a_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor var_1610_cast_fp16 = mul(x = x_b_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor rot_a_11_cast_fp16 = sub(x = var_1609_cast_fp16, y = var_1610_cast_fp16)[name = string("rot_a_11_cast_fp16")]; + tensor var_1612_cast_fp16 = mul(x = x_a_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1612_cast_fp16")]; + tensor var_1613_cast_fp16 = mul(x = x_b_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1613_cast_fp16")]; + tensor rot_b_11_cast_fp16 = add(x = var_1612_cast_fp16, y = var_1613_cast_fp16)[name = string("rot_b_11_cast_fp16")]; + bool k_9_interleave_0 = const()[name = string("k_9_interleave_0"), val = bool(false)]; + tensor k_9_cast_fp16 = concat(axis = var_1228, interleave = k_9_interleave_0, values = (rot_a_11_cast_fp16, rot_b_11_cast_fp16))[name = string("k_9_cast_fp16")]; + bool var_1618_transpose_x_1 = const()[name = string("op_1618_transpose_x_1"), val = bool(false)]; + bool var_1618_transpose_y_1 = const()[name = string("op_1618_transpose_y_1"), val = bool(true)]; + tensor var_1618_cast_fp16 = matmul(transpose_x = var_1618_transpose_x_1, transpose_y = var_1618_transpose_y_1, x = q_9_cast_fp16, y = k_9_cast_fp16)[name = string("op_1618_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_1618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor input_265_cast_fp16 = sub(x = _inversed_scores_5_cast_fp16, y = var_530_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1228, x = input_265_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool var_1627_transpose_x_0 = const()[name = string("op_1627_transpose_x_0"), val = bool(false)]; + bool var_1627_transpose_y_0 = const()[name = string("op_1627_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1563_cast_fp16)[name = string("transpose_48")]; + tensor var_1627_cast_fp16 = matmul(transpose_x = var_1627_transpose_x_0, transpose_y = var_1627_transpose_y_0, x = attn_13_cast_fp16, y = v_9_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor var_1628_perm_0 = const()[name = string("op_1628_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1629 = const()[name = string("op_1629"), val = tensor([2, 128, 512])]; + tensor var_1628_cast_fp16 = transpose(perm = var_1628_perm_0, x = var_1627_cast_fp16)[name = string("transpose_47")]; + tensor input_267_cast_fp16 = reshape(shape = var_1629, x = var_1628_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39058624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39320832))))[name = string("main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39321920)))]; + tensor linear_24_cast_fp16 = linear(bias = main_blocks_2_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_267_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor out_43_perm_0 = const()[name = string("out_43_perm_0"), val = tensor([0, 2, 1])]; + tensor out_43_cast_fp16 = transpose(perm = out_43_perm_0, x = linear_24_cast_fp16)[name = string("transpose_46")]; + tensor var_1636_cast_fp16 = mul(x = out_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1636_cast_fp16")]; + tensor x_141_cast_fp16 = add(x = x_m_9_cast_fp16, y = var_1636_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1643_axes_0 = const()[name = string("op_1643_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39323008)))]; + tensor main_blocks_2_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39324096)))]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_141_cast_fp16)[name = string("transpose_45")]; + tensor var_1643_cast_fp16 = layer_norm(axes = var_1643_axes_0, beta = main_blocks_2_text_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_text_norm_norm_weight_to_fp16, x = input_269_cast_fp16)[name = string("op_1643_cast_fp16")]; + tensor var_1644_perm_0 = const()[name = string("op_1644_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1644_cast_fp16 = transpose(perm = var_1644_perm_0, x = var_1643_cast_fp16)[name = string("transpose_44")]; + tensor x_143_cast_fp16 = mul(x = var_1644_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor x_145_cast_fp16 = mul(x = x_143_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1657_cast_fp16 = mul(x = x_145_cast_fp16, y = last_1_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor x_last_35_axes_0 = const()[name = string("x_last_35_axes_0"), val = tensor([-1])]; + bool x_last_35_keep_dims_0 = const()[name = string("x_last_35_keep_dims_0"), val = bool(true)]; + tensor x_last_35_cast_fp16 = reduce_sum(axes = x_last_35_axes_0, keep_dims = x_last_35_keep_dims_0, x = var_1657_cast_fp16)[name = string("x_last_35_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = x_145_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1662_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_35_cast_fp16)[name = string("op_1662_cast_fp16")]; + tensor input_273_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1662_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("replicate")]; + fp16 const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = fp16(0x0p+0)]; + tensor input_275_cast_fp16 = pad(constant_val = const_59_to_fp16, mode = input_275_mode_0, pad = input_275_pad_0, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")]; + string h_105_pad_type_0 = const()[name = string("h_105_pad_type_0"), val = string("valid")]; + int32 h_105_groups_0 = const()[name = string("h_105_groups_0"), val = int32(512)]; + tensor h_105_strides_0 = const()[name = string("h_105_strides_0"), val = tensor([1])]; + tensor h_105_pad_0 = const()[name = string("h_105_pad_0"), val = tensor([0, 0])]; + tensor h_105_dilations_0 = const()[name = string("h_105_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39325184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39327808))))[name = string("main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39328896)))]; + tensor h_105_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_105_dilations_0, groups = h_105_groups_0, pad = h_105_pad_0, pad_type = h_105_pad_type_0, strides = h_105_strides_0, weight = main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("h_105_cast_fp16")]; + tensor x_147_cast_fp16 = mul(x = h_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_277_perm_0 = const()[name = string("input_277_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1680_axes_0 = const()[name = string("op_1680_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39329984)))]; + tensor main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39331072)))]; + tensor input_277_cast_fp16 = transpose(perm = input_277_perm_0, x = x_147_cast_fp16)[name = string("transpose_43")]; + tensor var_1680_cast_fp16 = layer_norm(axes = var_1680_axes_0, beta = main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16, x = input_277_cast_fp16)[name = string("op_1680_cast_fp16")]; + tensor input_279_perm_0 = const()[name = string("input_279_perm_0"), val = tensor([0, 2, 1])]; + string h_107_pad_type_0 = const()[name = string("h_107_pad_type_0"), val = string("valid")]; + tensor h_107_strides_0 = const()[name = string("h_107_strides_0"), val = tensor([1])]; + tensor h_107_pad_0 = const()[name = string("h_107_pad_0"), val = tensor([0, 0])]; + tensor h_107_dilations_0 = const()[name = string("h_107_dilations_0"), val = tensor([1])]; + int32 h_107_groups_0 = const()[name = string("h_107_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39332160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40380800))))[name = string("main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40384960)))]; + tensor input_279_cast_fp16 = transpose(perm = input_279_perm_0, x = var_1680_cast_fp16)[name = string("transpose_42")]; + tensor h_107_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_107_dilations_0, groups = h_107_groups_0, pad = h_107_pad_0, pad_type = h_107_pad_type_0, strides = h_107_strides_0, weight = main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_279_cast_fp16)[name = string("h_107_cast_fp16")]; + string input_281_mode_0 = const()[name = string("input_281_mode_0"), val = string("EXACT")]; + tensor input_281_cast_fp16 = gelu(mode = input_281_mode_0, x = h_107_cast_fp16)[name = string("input_281_cast_fp16")]; + string h_109_pad_type_0 = const()[name = string("h_109_pad_type_0"), val = string("valid")]; + tensor h_109_strides_0 = const()[name = string("h_109_strides_0"), val = tensor([1])]; + tensor h_109_pad_0 = const()[name = string("h_109_pad_0"), val = tensor([0, 0])]; + tensor h_109_dilations_0 = const()[name = string("h_109_dilations_0"), val = tensor([1])]; + int32 h_109_groups_0 = const()[name = string("h_109_groups_0"), val = int32(1)]; + tensor op_1697_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40389120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41437760))))[name = string("op_1697_weight_0_to_fp16_quantized")]; + tensor var_1697_bias_0_to_fp16 = const()[name = string("op_1697_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41438848)))]; + tensor var_1697_cast_fp16 = conv(bias = var_1697_bias_0_to_fp16, dilations = h_109_dilations_0, groups = h_109_groups_0, pad = h_109_pad_0, pad_type = h_109_pad_type_0, strides = h_109_strides_0, weight = op_1697_weight_0_to_fp16_quantized, x = input_281_cast_fp16)[name = string("op_1697_cast_fp16")]; + tensor out_45_cast_fp16 = add(x = x_145_cast_fp16, y = var_1697_cast_fp16)[name = string("out_45_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = out_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor x_m_11_cast_fp16 = mul(x = x_149_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_11_cast_fp16")]; + tensor input_283_perm_0 = const()[name = string("input_283_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41439936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41571072))))[name = string("main_blocks_2_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41571648)))]; + tensor input_283_cast_fp16 = transpose(perm = input_283_perm_0, x = x_149_cast_fp16)[name = string("transpose_41")]; + tensor linear_25_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_283_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_1713 = const()[name = string("op_1713"), val = tensor([2, 128, 2, 128])]; + tensor var_1714_cast_fp16 = reshape(shape = var_1713, x = linear_25_cast_fp16)[name = string("op_1714_cast_fp16")]; + tensor q_11_perm_0 = const()[name = string("q_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_2_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41572224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41637824))))[name = string("main_blocks_2_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41638400)))]; + tensor linear_27_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1727 = const()[name = string("op_1727"), val = tensor([2, 50, 2, 128])]; + tensor var_1728_cast_fp16 = reshape(shape = var_1727, x = linear_27_cast_fp16)[name = string("op_1728_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1732_transpose_x_0 = const()[name = string("op_1732_transpose_x_0"), val = bool(false)]; + bool var_1732_transpose_y_0 = const()[name = string("op_1732_transpose_y_0"), val = bool(false)]; + tensor op_1731_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41638976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41664640))))[name = string("op_1731_to_fp16_quantized")]; + tensor q_11_cast_fp16 = transpose(perm = q_11_perm_0, x = var_1714_cast_fp16)[name = string("transpose_40")]; + tensor var_1732_cast_fp16 = matmul(transpose_x = var_1732_transpose_x_0, transpose_y = var_1732_transpose_y_0, x = q_11_cast_fp16, y = op_1731_to_fp16_quantized)[name = string("op_1732_cast_fp16")]; + fp16 _inversed_input_285_y_0_to_fp16 = const()[name = string("_inversed_input_285_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_285_cast_fp16 = mul(x = var_1732_cast_fp16, y = _inversed_input_285_y_0_to_fp16)[name = string("_inversed_input_285_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1228, x = _inversed_input_285_cast_fp16)[name = string("attn_15_cast_fp16")]; + tensor attn_17_cast_fp16 = mul(x = attn_15_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool var_1739_transpose_x_0 = const()[name = string("op_1739_transpose_x_0"), val = bool(false)]; + bool var_1739_transpose_y_0 = const()[name = string("op_1739_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_1728_cast_fp16)[name = string("transpose_39")]; + tensor var_1739_cast_fp16 = matmul(transpose_x = var_1739_transpose_x_0, transpose_y = var_1739_transpose_y_0, x = attn_17_cast_fp16, y = v_11_cast_fp16)[name = string("op_1739_cast_fp16")]; + tensor var_1740_perm_0 = const()[name = string("op_1740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1741 = const()[name = string("op_1741"), val = tensor([2, 128, 256])]; + tensor var_1740_cast_fp16 = transpose(perm = var_1740_perm_0, x = var_1739_cast_fp16)[name = string("transpose_38")]; + tensor input_287_cast_fp16 = reshape(shape = var_1741, x = var_1740_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41664832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41795968))))[name = string("main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41797056)))]; + tensor linear_28_cast_fp16 = linear(bias = main_blocks_2_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_287_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor out_47_perm_0 = const()[name = string("out_47_perm_0"), val = tensor([0, 2, 1])]; + tensor out_47_cast_fp16 = transpose(perm = out_47_perm_0, x = linear_28_cast_fp16)[name = string("transpose_37")]; + tensor var_1748_cast_fp16 = mul(x = out_47_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1748_cast_fp16")]; + tensor x_151_cast_fp16 = add(x = x_m_11_cast_fp16, y = var_1748_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_289_perm_0 = const()[name = string("input_289_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1755_axes_0 = const()[name = string("op_1755_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41798144)))]; + tensor main_blocks_2_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41799232)))]; + tensor input_289_cast_fp16 = transpose(perm = input_289_perm_0, x = x_151_cast_fp16)[name = string("transpose_36")]; + tensor var_1755_cast_fp16 = layer_norm(axes = var_1755_axes_0, beta = main_blocks_2_style_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_style_norm_norm_weight_to_fp16, x = input_289_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor var_1756_perm_0 = const()[name = string("op_1756_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1756_cast_fp16 = transpose(perm = var_1756_perm_0, x = var_1755_cast_fp16)[name = string("transpose_35")]; + tensor x_153_cast_fp16 = mul(x = var_1756_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_153_cast_fp16")]; + int32 var_1775 = const()[name = string("op_1775"), val = int32(-1)]; + tensor x_155_cast_fp16 = mul(x = x_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor var_1810_cast_fp16 = mul(x = x_155_cast_fp16, y = last_1_cast_fp16)[name = string("op_1810_cast_fp16")]; + tensor x_last_37_axes_0 = const()[name = string("x_last_37_axes_0"), val = tensor([-1])]; + bool x_last_37_keep_dims_0 = const()[name = string("x_last_37_keep_dims_0"), val = bool(true)]; + tensor x_last_37_cast_fp16 = reduce_sum(axes = x_last_37_axes_0, keep_dims = x_last_37_keep_dims_0, x = var_1810_cast_fp16)[name = string("x_last_37_cast_fp16")]; + tensor var_1813_cast_fp16 = mul(x = x_155_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1815_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_37_cast_fp16)[name = string("op_1815_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = var_1813_cast_fp16, y = var_1815_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_295_mode_0 = const()[name = string("input_295_mode_0"), val = string("replicate")]; + fp16 const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = fp16(0x0p+0)]; + tensor input_295_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")]; + string h_111_pad_type_0 = const()[name = string("h_111_pad_type_0"), val = string("valid")]; + int32 h_111_groups_0 = const()[name = string("h_111_groups_0"), val = int32(512)]; + tensor h_111_strides_0 = const()[name = string("h_111_strides_0"), val = tensor([1])]; + tensor h_111_pad_0 = const()[name = string("h_111_pad_0"), val = tensor([0, 0])]; + tensor h_111_dilations_0 = const()[name = string("h_111_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41800320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41802944))))[name = string("main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41804032)))]; + tensor h_111_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_111_dilations_0, groups = h_111_groups_0, pad = h_111_pad_0, pad_type = h_111_pad_type_0, strides = h_111_strides_0, weight = main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_295_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor x_157_cast_fp16 = mul(x = h_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor input_297_perm_0 = const()[name = string("input_297_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1833_axes_0 = const()[name = string("op_1833_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41805120)))]; + tensor main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41806208)))]; + fp16 var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_297_cast_fp16 = transpose(perm = input_297_perm_0, x = x_157_cast_fp16)[name = string("transpose_34")]; + tensor var_1833_cast_fp16 = layer_norm(axes = var_1833_axes_0, beta = main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16, x = input_297_cast_fp16)[name = string("op_1833_cast_fp16")]; + tensor input_299_perm_0 = const()[name = string("input_299_perm_0"), val = tensor([0, 2, 1])]; + string h_113_pad_type_0 = const()[name = string("h_113_pad_type_0"), val = string("valid")]; + tensor h_113_strides_0 = const()[name = string("h_113_strides_0"), val = tensor([1])]; + tensor h_113_pad_0 = const()[name = string("h_113_pad_0"), val = tensor([0, 0])]; + tensor h_113_dilations_0 = const()[name = string("h_113_dilations_0"), val = tensor([1])]; + int32 h_113_groups_0 = const()[name = string("h_113_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41807296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42855936))))[name = string("main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42860096)))]; + tensor input_299_cast_fp16 = transpose(perm = input_299_perm_0, x = var_1833_cast_fp16)[name = string("transpose_33")]; + tensor h_113_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_113_dilations_0, groups = h_113_groups_0, pad = h_113_pad_0, pad_type = h_113_pad_type_0, strides = h_113_strides_0, weight = main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_299_cast_fp16)[name = string("h_113_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = h_113_cast_fp16)[name = string("input_301_cast_fp16")]; + string h_115_pad_type_0 = const()[name = string("h_115_pad_type_0"), val = string("valid")]; + tensor h_115_strides_0 = const()[name = string("h_115_strides_0"), val = tensor([1])]; + tensor h_115_pad_0 = const()[name = string("h_115_pad_0"), val = tensor([0, 0])]; + tensor h_115_dilations_0 = const()[name = string("h_115_dilations_0"), val = tensor([1])]; + int32 h_115_groups_0 = const()[name = string("h_115_groups_0"), val = int32(1)]; + tensor op_1850_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42864256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43912896))))[name = string("op_1850_weight_0_to_fp16_quantized")]; + tensor var_1850_bias_0_to_fp16 = const()[name = string("op_1850_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43913984)))]; + tensor var_1850_cast_fp16 = conv(bias = var_1850_bias_0_to_fp16, dilations = h_115_dilations_0, groups = h_115_groups_0, pad = h_115_pad_0, pad_type = h_115_pad_type_0, strides = h_115_strides_0, weight = op_1850_weight_0_to_fp16_quantized, x = input_301_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor out_49_cast_fp16 = add(x = x_155_cast_fp16, y = var_1850_cast_fp16)[name = string("out_49_cast_fp16")]; + tensor x_159_cast_fp16 = mul(x = out_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor x_161_cast_fp16 = mul(x = x_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor var_1864_cast_fp16 = mul(x = x_161_cast_fp16, y = last_1_cast_fp16)[name = string("op_1864_cast_fp16")]; + tensor x_last_39_axes_0 = const()[name = string("x_last_39_axes_0"), val = tensor([-1])]; + bool x_last_39_keep_dims_0 = const()[name = string("x_last_39_keep_dims_0"), val = bool(true)]; + tensor x_last_39_cast_fp16 = reduce_sum(axes = x_last_39_axes_0, keep_dims = x_last_39_keep_dims_0, x = var_1864_cast_fp16)[name = string("x_last_39_cast_fp16")]; + tensor var_1867_cast_fp16 = mul(x = x_161_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_39_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor input_305_cast_fp16 = add(x = var_1867_cast_fp16, y = var_1869_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("replicate")]; + fp16 const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_66_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string h_117_pad_type_0 = const()[name = string("h_117_pad_type_0"), val = string("valid")]; + tensor h_117_dilations_0 = const()[name = string("h_117_dilations_0"), val = tensor([2])]; + int32 h_117_groups_0 = const()[name = string("h_117_groups_0"), val = int32(512)]; + tensor h_117_strides_0 = const()[name = string("h_117_strides_0"), val = tensor([1])]; + tensor h_117_pad_0 = const()[name = string("h_117_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43915072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43917696))))[name = string("main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43918784)))]; + tensor h_117_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_117_dilations_0, groups = h_117_groups_0, pad = h_117_pad_0, pad_type = h_117_pad_type_0, strides = h_117_strides_0, weight = main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_307_cast_fp16)[name = string("h_117_cast_fp16")]; + tensor x_163_cast_fp16 = mul(x = h_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1887_axes_0 = const()[name = string("op_1887_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43919872)))]; + tensor main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43920960)))]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_163_cast_fp16)[name = string("transpose_32")]; + tensor var_1887_cast_fp16 = layer_norm(axes = var_1887_axes_0, beta = main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16, x = input_309_cast_fp16)[name = string("op_1887_cast_fp16")]; + tensor input_311_perm_0 = const()[name = string("input_311_perm_0"), val = tensor([0, 2, 1])]; + string h_119_pad_type_0 = const()[name = string("h_119_pad_type_0"), val = string("valid")]; + tensor h_119_strides_0 = const()[name = string("h_119_strides_0"), val = tensor([1])]; + tensor h_119_pad_0 = const()[name = string("h_119_pad_0"), val = tensor([0, 0])]; + tensor h_119_dilations_0 = const()[name = string("h_119_dilations_0"), val = tensor([1])]; + int32 h_119_groups_0 = const()[name = string("h_119_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43922048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44970688))))[name = string("main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44974848)))]; + tensor input_311_cast_fp16 = transpose(perm = input_311_perm_0, x = var_1887_cast_fp16)[name = string("transpose_31")]; + tensor h_119_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_119_dilations_0, groups = h_119_groups_0, pad = h_119_pad_0, pad_type = h_119_pad_type_0, strides = h_119_strides_0, weight = main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_311_cast_fp16)[name = string("h_119_cast_fp16")]; + string input_313_mode_0 = const()[name = string("input_313_mode_0"), val = string("EXACT")]; + tensor input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = h_119_cast_fp16)[name = string("input_313_cast_fp16")]; + string h_121_pad_type_0 = const()[name = string("h_121_pad_type_0"), val = string("valid")]; + tensor h_121_strides_0 = const()[name = string("h_121_strides_0"), val = tensor([1])]; + tensor h_121_pad_0 = const()[name = string("h_121_pad_0"), val = tensor([0, 0])]; + tensor h_121_dilations_0 = const()[name = string("h_121_dilations_0"), val = tensor([1])]; + int32 h_121_groups_0 = const()[name = string("h_121_groups_0"), val = int32(1)]; + tensor op_1904_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44979008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46027648))))[name = string("op_1904_weight_0_to_fp16_quantized")]; + tensor var_1904_bias_0_to_fp16 = const()[name = string("op_1904_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46028736)))]; + tensor var_1904_cast_fp16 = conv(bias = var_1904_bias_0_to_fp16, dilations = h_121_dilations_0, groups = h_121_groups_0, pad = h_121_pad_0, pad_type = h_121_pad_type_0, strides = h_121_strides_0, weight = op_1904_weight_0_to_fp16_quantized, x = input_313_cast_fp16)[name = string("op_1904_cast_fp16")]; + tensor out_51_cast_fp16 = add(x = x_161_cast_fp16, y = var_1904_cast_fp16)[name = string("out_51_cast_fp16")]; + tensor x_165_cast_fp16 = mul(x = out_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor x_167_cast_fp16 = mul(x = x_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1918_cast_fp16 = mul(x = x_167_cast_fp16, y = last_1_cast_fp16)[name = string("op_1918_cast_fp16")]; + tensor x_last_41_axes_0 = const()[name = string("x_last_41_axes_0"), val = tensor([-1])]; + bool x_last_41_keep_dims_0 = const()[name = string("x_last_41_keep_dims_0"), val = bool(true)]; + tensor x_last_41_cast_fp16 = reduce_sum(axes = x_last_41_axes_0, keep_dims = x_last_41_keep_dims_0, x = var_1918_cast_fp16)[name = string("x_last_41_cast_fp16")]; + tensor var_1921_cast_fp16 = mul(x = x_167_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1921_cast_fp16")]; + tensor var_1923_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_41_cast_fp16)[name = string("op_1923_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = var_1921_cast_fp16, y = var_1923_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("replicate")]; + fp16 const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = fp16(0x0p+0)]; + tensor input_319_cast_fp16 = pad(constant_val = const_68_to_fp16, mode = input_319_mode_0, pad = input_319_pad_0, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string h_123_pad_type_0 = const()[name = string("h_123_pad_type_0"), val = string("valid")]; + tensor h_123_dilations_0 = const()[name = string("h_123_dilations_0"), val = tensor([4])]; + int32 h_123_groups_0 = const()[name = string("h_123_groups_0"), val = int32(512)]; + tensor h_123_strides_0 = const()[name = string("h_123_strides_0"), val = tensor([1])]; + tensor h_123_pad_0 = const()[name = string("h_123_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46029824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46032448))))[name = string("main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46033536)))]; + tensor h_123_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_123_dilations_0, groups = h_123_groups_0, pad = h_123_pad_0, pad_type = h_123_pad_type_0, strides = h_123_strides_0, weight = main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_319_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor x_169_cast_fp16 = mul(x = h_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1941_axes_0 = const()[name = string("op_1941_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46034624)))]; + tensor main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46035712)))]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_169_cast_fp16)[name = string("transpose_30")]; + tensor var_1941_cast_fp16 = layer_norm(axes = var_1941_axes_0, beta = main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16, x = input_321_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor([0, 2, 1])]; + string h_125_pad_type_0 = const()[name = string("h_125_pad_type_0"), val = string("valid")]; + tensor h_125_strides_0 = const()[name = string("h_125_strides_0"), val = tensor([1])]; + tensor h_125_pad_0 = const()[name = string("h_125_pad_0"), val = tensor([0, 0])]; + tensor h_125_dilations_0 = const()[name = string("h_125_dilations_0"), val = tensor([1])]; + int32 h_125_groups_0 = const()[name = string("h_125_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46036800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47085440))))[name = string("main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47089600)))]; + tensor input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = var_1941_cast_fp16)[name = string("transpose_29")]; + tensor h_125_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_125_dilations_0, groups = h_125_groups_0, pad = h_125_pad_0, pad_type = h_125_pad_type_0, strides = h_125_strides_0, weight = main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_323_cast_fp16)[name = string("h_125_cast_fp16")]; + string input_325_mode_0 = const()[name = string("input_325_mode_0"), val = string("EXACT")]; + tensor input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = h_125_cast_fp16)[name = string("input_325_cast_fp16")]; + string h_127_pad_type_0 = const()[name = string("h_127_pad_type_0"), val = string("valid")]; + tensor h_127_strides_0 = const()[name = string("h_127_strides_0"), val = tensor([1])]; + tensor h_127_pad_0 = const()[name = string("h_127_pad_0"), val = tensor([0, 0])]; + tensor h_127_dilations_0 = const()[name = string("h_127_dilations_0"), val = tensor([1])]; + int32 h_127_groups_0 = const()[name = string("h_127_groups_0"), val = int32(1)]; + tensor op_1958_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47093760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48142400))))[name = string("op_1958_weight_0_to_fp16_quantized")]; + tensor var_1958_bias_0_to_fp16 = const()[name = string("op_1958_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48143488)))]; + tensor var_1958_cast_fp16 = conv(bias = var_1958_bias_0_to_fp16, dilations = h_127_dilations_0, groups = h_127_groups_0, pad = h_127_pad_0, pad_type = h_127_pad_type_0, strides = h_127_strides_0, weight = op_1958_weight_0_to_fp16_quantized, x = input_325_cast_fp16)[name = string("op_1958_cast_fp16")]; + tensor out_53_cast_fp16 = add(x = x_167_cast_fp16, y = var_1958_cast_fp16)[name = string("out_53_cast_fp16")]; + tensor x_171_cast_fp16 = mul(x = out_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_171_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor var_1972_cast_fp16 = mul(x = x_173_cast_fp16, y = last_1_cast_fp16)[name = string("op_1972_cast_fp16")]; + tensor x_last_43_axes_0 = const()[name = string("x_last_43_axes_0"), val = tensor([-1])]; + bool x_last_43_keep_dims_0 = const()[name = string("x_last_43_keep_dims_0"), val = bool(true)]; + tensor x_last_43_cast_fp16 = reduce_sum(axes = x_last_43_axes_0, keep_dims = x_last_43_keep_dims_0, x = var_1972_cast_fp16)[name = string("x_last_43_cast_fp16")]; + tensor var_1975_cast_fp16 = mul(x = x_173_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1975_cast_fp16")]; + tensor var_1977_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_43_cast_fp16)[name = string("op_1977_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1977_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("replicate")]; + fp16 const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = fp16(0x0p+0)]; + tensor input_331_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = input_331_mode_0, pad = input_331_pad_0, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + string h_129_pad_type_0 = const()[name = string("h_129_pad_type_0"), val = string("valid")]; + tensor h_129_dilations_0 = const()[name = string("h_129_dilations_0"), val = tensor([8])]; + int32 h_129_groups_0 = const()[name = string("h_129_groups_0"), val = int32(512)]; + tensor h_129_strides_0 = const()[name = string("h_129_strides_0"), val = tensor([1])]; + tensor h_129_pad_0 = const()[name = string("h_129_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48144576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48147200))))[name = string("main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48148288)))]; + tensor h_129_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_129_dilations_0, groups = h_129_groups_0, pad = h_129_pad_0, pad_type = h_129_pad_type_0, strides = h_129_strides_0, weight = main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_331_cast_fp16)[name = string("h_129_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = h_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_333_perm_0 = const()[name = string("input_333_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1995_axes_0 = const()[name = string("op_1995_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48149376)))]; + tensor main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48150464)))]; + tensor input_333_cast_fp16 = transpose(perm = input_333_perm_0, x = x_175_cast_fp16)[name = string("transpose_28")]; + tensor var_1995_cast_fp16 = layer_norm(axes = var_1995_axes_0, beta = main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16, x = input_333_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor input_335_perm_0 = const()[name = string("input_335_perm_0"), val = tensor([0, 2, 1])]; + string h_131_pad_type_0 = const()[name = string("h_131_pad_type_0"), val = string("valid")]; + tensor h_131_strides_0 = const()[name = string("h_131_strides_0"), val = tensor([1])]; + tensor h_131_pad_0 = const()[name = string("h_131_pad_0"), val = tensor([0, 0])]; + tensor h_131_dilations_0 = const()[name = string("h_131_dilations_0"), val = tensor([1])]; + int32 h_131_groups_0 = const()[name = string("h_131_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48151552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49200192))))[name = string("main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49204352)))]; + tensor input_335_cast_fp16 = transpose(perm = input_335_perm_0, x = var_1995_cast_fp16)[name = string("transpose_27")]; + tensor h_131_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_131_dilations_0, groups = h_131_groups_0, pad = h_131_pad_0, pad_type = h_131_pad_type_0, strides = h_131_strides_0, weight = main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_335_cast_fp16)[name = string("h_131_cast_fp16")]; + string input_337_mode_0 = const()[name = string("input_337_mode_0"), val = string("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = h_131_cast_fp16)[name = string("input_337_cast_fp16")]; + string h_133_pad_type_0 = const()[name = string("h_133_pad_type_0"), val = string("valid")]; + tensor h_133_strides_0 = const()[name = string("h_133_strides_0"), val = tensor([1])]; + tensor h_133_pad_0 = const()[name = string("h_133_pad_0"), val = tensor([0, 0])]; + tensor h_133_dilations_0 = const()[name = string("h_133_dilations_0"), val = tensor([1])]; + int32 h_133_groups_0 = const()[name = string("h_133_groups_0"), val = int32(1)]; + tensor op_2012_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49208512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50257152))))[name = string("op_2012_weight_0_to_fp16_quantized")]; + tensor var_2012_bias_0_to_fp16 = const()[name = string("op_2012_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50258240)))]; + tensor var_2012_cast_fp16 = conv(bias = var_2012_bias_0_to_fp16, dilations = h_133_dilations_0, groups = h_133_groups_0, pad = h_133_pad_0, pad_type = h_133_pad_type_0, strides = h_133_strides_0, weight = op_2012_weight_0_to_fp16_quantized, x = input_337_cast_fp16)[name = string("op_2012_cast_fp16")]; + tensor out_55_cast_fp16 = add(x = x_173_cast_fp16, y = var_2012_cast_fp16)[name = string("out_55_cast_fp16")]; + tensor x_177_cast_fp16 = mul(x = out_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor main_blocks_3_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50259328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50292160))))[name = string("main_blocks_3_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50293248)))]; + tensor linear_29_cast_fp16 = linear(bias = main_blocks_3_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_3_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor t_axes_0 = const()[name = string("t_axes_0"), val = tensor([-1])]; + tensor t_cast_fp16 = expand_dims(axes = t_axes_0, x = linear_29_cast_fp16)[name = string("t_cast_fp16")]; + tensor var_2022_cast_fp16 = add(x = x_177_cast_fp16, y = t_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor x_179_cast_fp16 = mul(x = var_2022_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor x_181_cast_fp16 = mul(x = x_179_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor var_2035_cast_fp16 = mul(x = x_181_cast_fp16, y = last_1_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor x_last_45_axes_0 = const()[name = string("x_last_45_axes_0"), val = tensor([-1])]; + bool x_last_45_keep_dims_0 = const()[name = string("x_last_45_keep_dims_0"), val = bool(true)]; + tensor x_last_45_cast_fp16 = reduce_sum(axes = x_last_45_axes_0, keep_dims = x_last_45_keep_dims_0, x = var_2035_cast_fp16)[name = string("x_last_45_cast_fp16")]; + tensor var_2038_cast_fp16 = mul(x = x_181_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2038_cast_fp16")]; + tensor var_2040_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_45_cast_fp16)[name = string("op_2040_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = var_2038_cast_fp16, y = var_2040_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_345_mode_0 = const()[name = string("input_345_mode_0"), val = string("replicate")]; + fp16 const_72_to_fp16 = const()[name = string("const_72_to_fp16"), val = fp16(0x0p+0)]; + tensor input_345_cast_fp16 = pad(constant_val = const_72_to_fp16, mode = input_345_mode_0, pad = input_345_pad_0, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + string h_135_pad_type_0 = const()[name = string("h_135_pad_type_0"), val = string("valid")]; + int32 h_135_groups_0 = const()[name = string("h_135_groups_0"), val = int32(512)]; + tensor h_135_strides_0 = const()[name = string("h_135_strides_0"), val = tensor([1])]; + tensor h_135_pad_0 = const()[name = string("h_135_pad_0"), val = tensor([0, 0])]; + tensor h_135_dilations_0 = const()[name = string("h_135_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50294336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50296960))))[name = string("main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50298048)))]; + tensor h_135_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_135_dilations_0, groups = h_135_groups_0, pad = h_135_pad_0, pad_type = h_135_pad_type_0, strides = h_135_strides_0, weight = main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_345_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor x_183_cast_fp16 = mul(x = h_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_183_cast_fp16")]; + tensor input_347_perm_0 = const()[name = string("input_347_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2058_axes_0 = const()[name = string("op_2058_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50299136)))]; + tensor main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50300224)))]; + tensor input_347_cast_fp16 = transpose(perm = input_347_perm_0, x = x_183_cast_fp16)[name = string("transpose_26")]; + tensor var_2058_cast_fp16 = layer_norm(axes = var_2058_axes_0, beta = main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16, x = input_347_cast_fp16)[name = string("op_2058_cast_fp16")]; + tensor input_349_perm_0 = const()[name = string("input_349_perm_0"), val = tensor([0, 2, 1])]; + string h_137_pad_type_0 = const()[name = string("h_137_pad_type_0"), val = string("valid")]; + tensor h_137_strides_0 = const()[name = string("h_137_strides_0"), val = tensor([1])]; + tensor h_137_pad_0 = const()[name = string("h_137_pad_0"), val = tensor([0, 0])]; + tensor h_137_dilations_0 = const()[name = string("h_137_dilations_0"), val = tensor([1])]; + int32 h_137_groups_0 = const()[name = string("h_137_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50301312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51349952))))[name = string("main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51354112)))]; + tensor input_349_cast_fp16 = transpose(perm = input_349_perm_0, x = var_2058_cast_fp16)[name = string("transpose_25")]; + tensor h_137_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_137_dilations_0, groups = h_137_groups_0, pad = h_137_pad_0, pad_type = h_137_pad_type_0, strides = h_137_strides_0, weight = main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_349_cast_fp16)[name = string("h_137_cast_fp16")]; + string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")]; + tensor input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = h_137_cast_fp16)[name = string("input_351_cast_fp16")]; + string h_139_pad_type_0 = const()[name = string("h_139_pad_type_0"), val = string("valid")]; + tensor h_139_strides_0 = const()[name = string("h_139_strides_0"), val = tensor([1])]; + tensor h_139_pad_0 = const()[name = string("h_139_pad_0"), val = tensor([0, 0])]; + tensor h_139_dilations_0 = const()[name = string("h_139_dilations_0"), val = tensor([1])]; + int32 h_139_groups_0 = const()[name = string("h_139_groups_0"), val = int32(1)]; + tensor op_2075_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51358272))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52406912))))[name = string("op_2075_weight_0_to_fp16_quantized")]; + tensor var_2075_bias_0_to_fp16 = const()[name = string("op_2075_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52408000)))]; + tensor var_2075_cast_fp16 = conv(bias = var_2075_bias_0_to_fp16, dilations = h_139_dilations_0, groups = h_139_groups_0, pad = h_139_pad_0, pad_type = h_139_pad_type_0, strides = h_139_strides_0, weight = op_2075_weight_0_to_fp16_quantized, x = input_351_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor out_57_cast_fp16 = add(x = x_181_cast_fp16, y = var_2075_cast_fp16)[name = string("out_57_cast_fp16")]; + tensor x_185_cast_fp16 = mul(x = out_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor x_m_13_cast_fp16 = mul(x = x_185_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_13_cast_fp16")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52409088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52671296))))[name = string("main_blocks_3_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52672384)))]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_185_cast_fp16)[name = string("transpose_24")]; + tensor linear_30_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_353_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_2093 = const()[name = string("op_2093"), val = tensor([2, 128, 8, 64])]; + tensor var_2094_cast_fp16 = reshape(shape = var_2093, x = linear_30_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor x_187_perm_0 = const()[name = string("x_187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52673472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52804608))))[name = string("main_blocks_3_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52805696)))]; + tensor linear_31_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_2101 = const()[name = string("op_2101"), val = tensor([2, 128, 8, 64])]; + tensor var_2102_cast_fp16 = reshape(shape = var_2101, x = linear_31_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor x_189_perm_0 = const()[name = string("x_189_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52806784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52937920))))[name = string("main_blocks_3_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52939008)))]; + tensor linear_32_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_2109 = const()[name = string("op_2109"), val = tensor([2, 128, 8, 64])]; + tensor var_2110_cast_fp16 = reshape(shape = var_2109, x = linear_32_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_13_begin_0 = const()[name = string("x_a_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_13_end_0 = const()[name = string("x_a_13_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_13_end_mask_0 = const()[name = string("x_a_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_187_cast_fp16 = transpose(perm = x_187_perm_0, x = var_2094_cast_fp16)[name = string("transpose_23")]; + tensor x_a_13_cast_fp16 = slice_by_index(begin = x_a_13_begin_0, end = x_a_13_end_0, end_mask = x_a_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_a_13_cast_fp16")]; + tensor x_b_13_begin_0 = const()[name = string("x_b_13_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_13_end_0 = const()[name = string("x_b_13_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_13_end_mask_0 = const()[name = string("x_b_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_13_cast_fp16 = slice_by_index(begin = x_b_13_begin_0, end = x_b_13_end_0, end_mask = x_b_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_b_13_cast_fp16")]; + tensor var_2138_cast_fp16 = mul(x = x_a_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2138_cast_fp16")]; + tensor var_2139_cast_fp16 = mul(x = x_b_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2139_cast_fp16")]; + tensor rot_a_13_cast_fp16 = sub(x = var_2138_cast_fp16, y = var_2139_cast_fp16)[name = string("rot_a_13_cast_fp16")]; + tensor var_2141_cast_fp16 = mul(x = x_a_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2141_cast_fp16")]; + tensor var_2142_cast_fp16 = mul(x = x_b_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2142_cast_fp16")]; + tensor rot_b_13_cast_fp16 = add(x = var_2141_cast_fp16, y = var_2142_cast_fp16)[name = string("rot_b_13_cast_fp16")]; + bool q_13_interleave_0 = const()[name = string("q_13_interleave_0"), val = bool(false)]; + tensor q_13_cast_fp16 = concat(axis = var_1775, interleave = q_13_interleave_0, values = (rot_a_13_cast_fp16, rot_b_13_cast_fp16))[name = string("q_13_cast_fp16")]; + tensor x_a_begin_0 = const()[name = string("x_a_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_end_0 = const()[name = string("x_a_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_end_mask_0 = const()[name = string("x_a_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_189_cast_fp16 = transpose(perm = x_189_perm_0, x = var_2102_cast_fp16)[name = string("transpose_22")]; + tensor x_a_cast_fp16 = slice_by_index(begin = x_a_begin_0, end = x_a_end_0, end_mask = x_a_end_mask_0, x = x_189_cast_fp16)[name = string("x_a_cast_fp16")]; + tensor x_b_begin_0 = const()[name = string("x_b_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_end_0 = const()[name = string("x_b_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_end_mask_0 = const()[name = string("x_b_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_cast_fp16 = slice_by_index(begin = x_b_begin_0, end = x_b_end_0, end_mask = x_b_end_mask_0, x = x_189_cast_fp16)[name = string("x_b_cast_fp16")]; + tensor var_2156_cast_fp16 = mul(x = x_a_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2156_cast_fp16")]; + tensor var_2157_cast_fp16 = mul(x = x_b_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2157_cast_fp16")]; + tensor rot_a_cast_fp16 = sub(x = var_2156_cast_fp16, y = var_2157_cast_fp16)[name = string("rot_a_cast_fp16")]; + tensor var_2159_cast_fp16 = mul(x = x_a_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2159_cast_fp16")]; + tensor var_2160_cast_fp16 = mul(x = x_b_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2160_cast_fp16")]; + tensor rot_b_cast_fp16 = add(x = var_2159_cast_fp16, y = var_2160_cast_fp16)[name = string("rot_b_cast_fp16")]; + bool k_13_interleave_0 = const()[name = string("k_13_interleave_0"), val = bool(false)]; + tensor k_13_cast_fp16 = concat(axis = var_1775, interleave = k_13_interleave_0, values = (rot_a_cast_fp16, rot_b_cast_fp16))[name = string("k_13_cast_fp16")]; + bool var_2165_transpose_x_1 = const()[name = string("op_2165_transpose_x_1"), val = bool(false)]; + bool var_2165_transpose_y_1 = const()[name = string("op_2165_transpose_y_1"), val = bool(true)]; + tensor var_2165_cast_fp16 = matmul(transpose_x = var_2165_transpose_x_1, transpose_y = var_2165_transpose_y_1, x = q_13_cast_fp16, y = k_13_cast_fp16)[name = string("op_2165_cast_fp16")]; + fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_cast_fp16 = mul(x = var_2165_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")]; + tensor input_359_cast_fp16 = sub(x = _inversed_scores_cast_fp16, y = var_530_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_1775, x = input_359_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool var_2174_transpose_x_0 = const()[name = string("op_2174_transpose_x_0"), val = bool(false)]; + bool var_2174_transpose_y_0 = const()[name = string("op_2174_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2110_cast_fp16)[name = string("transpose_21")]; + tensor var_2174_cast_fp16 = matmul(transpose_x = var_2174_transpose_x_0, transpose_y = var_2174_transpose_y_0, x = attn_19_cast_fp16, y = v_13_cast_fp16)[name = string("op_2174_cast_fp16")]; + tensor var_2175_perm_0 = const()[name = string("op_2175_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2176 = const()[name = string("op_2176"), val = tensor([2, 128, 512])]; + tensor var_2175_cast_fp16 = transpose(perm = var_2175_perm_0, x = var_2174_cast_fp16)[name = string("transpose_20")]; + tensor input_361_cast_fp16 = reshape(shape = var_2176, x = var_2175_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52940096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53202304))))[name = string("main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53203392)))]; + tensor linear_33_cast_fp16 = linear(bias = main_blocks_3_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_361_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor out_59_perm_0 = const()[name = string("out_59_perm_0"), val = tensor([0, 2, 1])]; + tensor out_59_cast_fp16 = transpose(perm = out_59_perm_0, x = linear_33_cast_fp16)[name = string("transpose_19")]; + tensor var_2183_cast_fp16 = mul(x = out_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_m_13_cast_fp16, y = var_2183_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor input_363_perm_0 = const()[name = string("input_363_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2190_axes_0 = const()[name = string("op_2190_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53204480)))]; + tensor main_blocks_3_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53205568)))]; + tensor input_363_cast_fp16 = transpose(perm = input_363_perm_0, x = x_191_cast_fp16)[name = string("transpose_18")]; + tensor var_2190_cast_fp16 = layer_norm(axes = var_2190_axes_0, beta = main_blocks_3_text_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_text_norm_norm_weight_to_fp16, x = input_363_cast_fp16)[name = string("op_2190_cast_fp16")]; + tensor var_2191_perm_0 = const()[name = string("op_2191_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2191_perm_0, x = var_2190_cast_fp16)[name = string("transpose_17")]; + tensor x_193_cast_fp16 = mul(x = var_2191_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_193_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2204_cast_fp16 = mul(x = x_195_cast_fp16, y = last_1_cast_fp16)[name = string("op_2204_cast_fp16")]; + tensor x_last_47_axes_0 = const()[name = string("x_last_47_axes_0"), val = tensor([-1])]; + bool x_last_47_keep_dims_0 = const()[name = string("x_last_47_keep_dims_0"), val = bool(true)]; + tensor x_last_47_cast_fp16 = reduce_sum(axes = x_last_47_axes_0, keep_dims = x_last_47_keep_dims_0, x = var_2204_cast_fp16)[name = string("x_last_47_cast_fp16")]; + tensor var_2207_cast_fp16 = mul(x = x_195_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2207_cast_fp16")]; + tensor var_2209_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_47_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor input_367_cast_fp16 = add(x = var_2207_cast_fp16, y = var_2209_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_369_mode_0 = const()[name = string("input_369_mode_0"), val = string("replicate")]; + fp16 const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = fp16(0x0p+0)]; + tensor input_369_cast_fp16 = pad(constant_val = const_79_to_fp16, mode = input_369_mode_0, pad = input_369_pad_0, x = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + string h_141_pad_type_0 = const()[name = string("h_141_pad_type_0"), val = string("valid")]; + int32 h_141_groups_0 = const()[name = string("h_141_groups_0"), val = int32(512)]; + tensor h_141_strides_0 = const()[name = string("h_141_strides_0"), val = tensor([1])]; + tensor h_141_pad_0 = const()[name = string("h_141_pad_0"), val = tensor([0, 0])]; + tensor h_141_dilations_0 = const()[name = string("h_141_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53206656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53209280))))[name = string("main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53210368)))]; + tensor h_141_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_141_dilations_0, groups = h_141_groups_0, pad = h_141_pad_0, pad_type = h_141_pad_type_0, strides = h_141_strides_0, weight = main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_369_cast_fp16)[name = string("h_141_cast_fp16")]; + tensor x_197_cast_fp16 = mul(x = h_141_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_371_perm_0 = const()[name = string("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2227_axes_0 = const()[name = string("op_2227_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53211456)))]; + tensor main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53212544)))]; + tensor input_371_cast_fp16 = transpose(perm = input_371_perm_0, x = x_197_cast_fp16)[name = string("transpose_16")]; + tensor var_2227_cast_fp16 = layer_norm(axes = var_2227_axes_0, beta = main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16, x = input_371_cast_fp16)[name = string("op_2227_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + string h_143_pad_type_0 = const()[name = string("h_143_pad_type_0"), val = string("valid")]; + tensor h_143_strides_0 = const()[name = string("h_143_strides_0"), val = tensor([1])]; + tensor h_143_pad_0 = const()[name = string("h_143_pad_0"), val = tensor([0, 0])]; + tensor h_143_dilations_0 = const()[name = string("h_143_dilations_0"), val = tensor([1])]; + int32 h_143_groups_0 = const()[name = string("h_143_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53213632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54262272))))[name = string("main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54266432)))]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = var_2227_cast_fp16)[name = string("transpose_15")]; + tensor h_143_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_143_dilations_0, groups = h_143_groups_0, pad = h_143_pad_0, pad_type = h_143_pad_type_0, strides = h_143_strides_0, weight = main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_373_cast_fp16)[name = string("h_143_cast_fp16")]; + string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")]; + tensor input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = h_143_cast_fp16)[name = string("input_375_cast_fp16")]; + string h_145_pad_type_0 = const()[name = string("h_145_pad_type_0"), val = string("valid")]; + tensor h_145_strides_0 = const()[name = string("h_145_strides_0"), val = tensor([1])]; + tensor h_145_pad_0 = const()[name = string("h_145_pad_0"), val = tensor([0, 0])]; + tensor h_145_dilations_0 = const()[name = string("h_145_dilations_0"), val = tensor([1])]; + int32 h_145_groups_0 = const()[name = string("h_145_groups_0"), val = int32(1)]; + tensor op_2244_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54270592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55319232))))[name = string("op_2244_weight_0_to_fp16_quantized")]; + tensor var_2244_bias_0_to_fp16 = const()[name = string("op_2244_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55320320)))]; + tensor var_2244_cast_fp16 = conv(bias = var_2244_bias_0_to_fp16, dilations = h_145_dilations_0, groups = h_145_groups_0, pad = h_145_pad_0, pad_type = h_145_pad_type_0, strides = h_145_strides_0, weight = op_2244_weight_0_to_fp16_quantized, x = input_375_cast_fp16)[name = string("op_2244_cast_fp16")]; + tensor out_61_cast_fp16 = add(x = x_195_cast_fp16, y = var_2244_cast_fp16)[name = string("out_61_cast_fp16")]; + tensor x_199_cast_fp16 = mul(x = out_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor x_m_cast_fp16 = mul(x = x_199_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_cast_fp16")]; + tensor input_377_perm_0 = const()[name = string("input_377_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55321408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55452544))))[name = string("main_blocks_3_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55453120)))]; + tensor input_377_cast_fp16 = transpose(perm = input_377_perm_0, x = x_199_cast_fp16)[name = string("transpose_14")]; + tensor linear_34_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_377_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_2260 = const()[name = string("op_2260"), val = tensor([2, 128, 2, 128])]; + tensor var_2261_cast_fp16 = reshape(shape = var_2260, x = linear_34_cast_fp16)[name = string("op_2261_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_3_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55453696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55519296))))[name = string("main_blocks_3_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55519872)))]; + tensor linear_36_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_2274 = const()[name = string("op_2274"), val = tensor([2, 50, 2, 128])]; + tensor var_2275_cast_fp16 = reshape(shape = var_2274, x = linear_36_cast_fp16)[name = string("op_2275_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_2279_transpose_x_0 = const()[name = string("op_2279_transpose_x_0"), val = bool(false)]; + bool var_2279_transpose_y_0 = const()[name = string("op_2279_transpose_y_0"), val = bool(false)]; + tensor op_2278_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55520448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55546112))))[name = string("op_2278_to_fp16_quantized")]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_2261_cast_fp16)[name = string("transpose_13")]; + tensor var_2279_cast_fp16 = matmul(transpose_x = var_2279_transpose_x_0, transpose_y = var_2279_transpose_y_0, x = q_cast_fp16, y = op_2278_to_fp16_quantized)[name = string("op_2279_cast_fp16")]; + fp16 _inversed_input_379_y_0_to_fp16 = const()[name = string("_inversed_input_379_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_379_cast_fp16 = mul(x = var_2279_cast_fp16, y = _inversed_input_379_y_0_to_fp16)[name = string("_inversed_input_379_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_1775, x = _inversed_input_379_cast_fp16)[name = string("attn_21_cast_fp16")]; + tensor attn_cast_fp16 = mul(x = attn_21_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_cast_fp16")]; + bool var_2286_transpose_x_0 = const()[name = string("op_2286_transpose_x_0"), val = bool(false)]; + bool var_2286_transpose_y_0 = const()[name = string("op_2286_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_2275_cast_fp16)[name = string("transpose_12")]; + tensor var_2286_cast_fp16 = matmul(transpose_x = var_2286_transpose_x_0, transpose_y = var_2286_transpose_y_0, x = attn_cast_fp16, y = v_15_cast_fp16)[name = string("op_2286_cast_fp16")]; + tensor var_2287_perm_0 = const()[name = string("op_2287_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2288 = const()[name = string("op_2288"), val = tensor([2, 128, 256])]; + tensor var_2287_cast_fp16 = transpose(perm = var_2287_perm_0, x = var_2286_cast_fp16)[name = string("transpose_11")]; + tensor input_381_cast_fp16 = reshape(shape = var_2288, x = var_2287_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55546304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55677440))))[name = string("main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55678528)))]; + tensor linear_37_cast_fp16 = linear(bias = main_blocks_3_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_381_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor out_63_perm_0 = const()[name = string("out_63_perm_0"), val = tensor([0, 2, 1])]; + tensor out_63_cast_fp16 = transpose(perm = out_63_perm_0, x = linear_37_cast_fp16)[name = string("transpose_10")]; + tensor var_2295_cast_fp16 = mul(x = out_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2295_cast_fp16")]; + tensor x_201_cast_fp16 = add(x = x_m_cast_fp16, y = var_2295_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_383_perm_0 = const()[name = string("input_383_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2302_axes_0 = const()[name = string("op_2302_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55679616)))]; + tensor main_blocks_3_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55680704)))]; + tensor input_383_cast_fp16 = transpose(perm = input_383_perm_0, x = x_201_cast_fp16)[name = string("transpose_9")]; + tensor var_2302_cast_fp16 = layer_norm(axes = var_2302_axes_0, beta = main_blocks_3_style_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_style_norm_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_perm_0 = const()[name = string("op_2303_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2303_cast_fp16 = transpose(perm = var_2303_perm_0, x = var_2302_cast_fp16)[name = string("transpose_8")]; + tensor x_203_cast_fp16 = mul(x = var_2303_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor x_205_cast_fp16 = mul(x = x_203_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor var_2330_cast_fp16 = mul(x = x_205_cast_fp16, y = last_1_cast_fp16)[name = string("op_2330_cast_fp16")]; + tensor x_last_49_axes_0 = const()[name = string("x_last_49_axes_0"), val = tensor([-1])]; + bool x_last_49_keep_dims_0 = const()[name = string("x_last_49_keep_dims_0"), val = bool(true)]; + tensor x_last_49_cast_fp16 = reduce_sum(axes = x_last_49_axes_0, keep_dims = x_last_49_keep_dims_0, x = var_2330_cast_fp16)[name = string("x_last_49_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = x_205_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor var_2335_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_49_cast_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2335_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_pad_0 = const()[name = string("input_389_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("replicate")]; + fp16 const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = fp16(0x0p+0)]; + tensor input_389_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = input_389_mode_0, pad = input_389_pad_0, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + string h_147_pad_type_0 = const()[name = string("h_147_pad_type_0"), val = string("valid")]; + int32 h_147_groups_0 = const()[name = string("h_147_groups_0"), val = int32(512)]; + tensor h_147_strides_0 = const()[name = string("h_147_strides_0"), val = tensor([1])]; + tensor h_147_pad_0 = const()[name = string("h_147_pad_0"), val = tensor([0, 0])]; + tensor h_147_dilations_0 = const()[name = string("h_147_dilations_0"), val = tensor([1])]; + tensor last_convnext_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55681792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55684416))))[name = string("last_convnext_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55685504)))]; + tensor h_147_cast_fp16 = conv(bias = last_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_147_dilations_0, groups = h_147_groups_0, pad = h_147_pad_0, pad_type = h_147_pad_type_0, strides = h_147_strides_0, weight = last_convnext_0_dwconv__conv_weight_to_fp16_quantized, x = input_389_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor x_207_cast_fp16 = mul(x = h_147_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_391_perm_0 = const()[name = string("input_391_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2353_axes_0 = const()[name = string("op_2353_axes_0"), val = tensor([-1])]; + tensor last_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55686592)))]; + tensor last_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55687680)))]; + fp16 var_2307_to_fp16 = const()[name = string("op_2307_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_391_cast_fp16 = transpose(perm = input_391_perm_0, x = x_207_cast_fp16)[name = string("transpose_7")]; + tensor var_2353_cast_fp16 = layer_norm(axes = var_2353_axes_0, beta = last_convnext_0_norm_norm_bias_to_fp16, epsilon = var_2307_to_fp16, gamma = last_convnext_0_norm_norm_weight_to_fp16, x = input_391_cast_fp16)[name = string("op_2353_cast_fp16")]; + tensor input_393_perm_0 = const()[name = string("input_393_perm_0"), val = tensor([0, 2, 1])]; + string h_149_pad_type_0 = const()[name = string("h_149_pad_type_0"), val = string("valid")]; + tensor h_149_strides_0 = const()[name = string("h_149_strides_0"), val = tensor([1])]; + tensor h_149_pad_0 = const()[name = string("h_149_pad_0"), val = tensor([0, 0])]; + tensor h_149_dilations_0 = const()[name = string("h_149_dilations_0"), val = tensor([1])]; + int32 h_149_groups_0 = const()[name = string("h_149_groups_0"), val = int32(1)]; + tensor last_convnext_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55688768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56737408))))[name = string("last_convnext_0_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56741568)))]; + tensor input_393_cast_fp16 = transpose(perm = input_393_perm_0, x = var_2353_cast_fp16)[name = string("transpose_6")]; + tensor h_149_cast_fp16 = conv(bias = last_convnext_0_pwconv1_bias_to_fp16, dilations = h_149_dilations_0, groups = h_149_groups_0, pad = h_149_pad_0, pad_type = h_149_pad_type_0, strides = h_149_strides_0, weight = last_convnext_0_pwconv1_weight_to_fp16_quantized, x = input_393_cast_fp16)[name = string("h_149_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = h_149_cast_fp16)[name = string("input_395_cast_fp16")]; + string h_151_pad_type_0 = const()[name = string("h_151_pad_type_0"), val = string("valid")]; + tensor h_151_strides_0 = const()[name = string("h_151_strides_0"), val = tensor([1])]; + tensor h_151_pad_0 = const()[name = string("h_151_pad_0"), val = tensor([0, 0])]; + tensor h_151_dilations_0 = const()[name = string("h_151_dilations_0"), val = tensor([1])]; + int32 h_151_groups_0 = const()[name = string("h_151_groups_0"), val = int32(1)]; + tensor op_2370_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56745728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57794368))))[name = string("op_2370_weight_0_to_fp16_quantized")]; + tensor var_2370_bias_0_to_fp16 = const()[name = string("op_2370_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57795456)))]; + tensor var_2370_cast_fp16 = conv(bias = var_2370_bias_0_to_fp16, dilations = h_151_dilations_0, groups = h_151_groups_0, pad = h_151_pad_0, pad_type = h_151_pad_type_0, strides = h_151_strides_0, weight = op_2370_weight_0_to_fp16_quantized, x = input_395_cast_fp16)[name = string("op_2370_cast_fp16")]; + tensor out_65_cast_fp16 = add(x = x_205_cast_fp16, y = var_2370_cast_fp16)[name = string("out_65_cast_fp16")]; + tensor x_209_cast_fp16 = mul(x = out_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor x_211_cast_fp16 = mul(x = x_209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_2398_cast_fp16 = mul(x = x_211_cast_fp16, y = last_1_cast_fp16)[name = string("op_2398_cast_fp16")]; + tensor x_last_51_axes_0 = const()[name = string("x_last_51_axes_0"), val = tensor([-1])]; + bool x_last_51_keep_dims_0 = const()[name = string("x_last_51_keep_dims_0"), val = bool(true)]; + tensor x_last_51_cast_fp16 = reduce_sum(axes = x_last_51_axes_0, keep_dims = x_last_51_keep_dims_0, x = var_2398_cast_fp16)[name = string("x_last_51_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = x_211_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_51_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor input_399_cast_fp16 = add(x = var_2401_cast_fp16, y = var_2403_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor input_401_pad_0 = const()[name = string("input_401_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_401_mode_0 = const()[name = string("input_401_mode_0"), val = string("replicate")]; + fp16 const_86_to_fp16 = const()[name = string("const_86_to_fp16"), val = fp16(0x0p+0)]; + tensor input_401_cast_fp16 = pad(constant_val = const_86_to_fp16, mode = input_401_mode_0, pad = input_401_pad_0, x = input_399_cast_fp16)[name = string("input_401_cast_fp16")]; + string h_153_pad_type_0 = const()[name = string("h_153_pad_type_0"), val = string("valid")]; + int32 h_153_groups_0 = const()[name = string("h_153_groups_0"), val = int32(512)]; + tensor h_153_strides_0 = const()[name = string("h_153_strides_0"), val = tensor([1])]; + tensor h_153_pad_0 = const()[name = string("h_153_pad_0"), val = tensor([0, 0])]; + tensor h_153_dilations_0 = const()[name = string("h_153_dilations_0"), val = tensor([1])]; + tensor last_convnext_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57796544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57799168))))[name = string("last_convnext_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57800256)))]; + tensor h_153_cast_fp16 = conv(bias = last_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_153_dilations_0, groups = h_153_groups_0, pad = h_153_pad_0, pad_type = h_153_pad_type_0, strides = h_153_strides_0, weight = last_convnext_1_dwconv__conv_weight_to_fp16_quantized, x = input_401_cast_fp16)[name = string("h_153_cast_fp16")]; + tensor x_213_cast_fp16 = mul(x = h_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_213_cast_fp16")]; + tensor input_403_perm_0 = const()[name = string("input_403_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2421_axes_0 = const()[name = string("op_2421_axes_0"), val = tensor([-1])]; + tensor last_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57801344)))]; + tensor last_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57802432)))]; + fp16 var_2375_to_fp16 = const()[name = string("op_2375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_403_cast_fp16 = transpose(perm = input_403_perm_0, x = x_213_cast_fp16)[name = string("transpose_5")]; + tensor var_2421_cast_fp16 = layer_norm(axes = var_2421_axes_0, beta = last_convnext_1_norm_norm_bias_to_fp16, epsilon = var_2375_to_fp16, gamma = last_convnext_1_norm_norm_weight_to_fp16, x = input_403_cast_fp16)[name = string("op_2421_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string h_155_pad_type_0 = const()[name = string("h_155_pad_type_0"), val = string("valid")]; + tensor h_155_strides_0 = const()[name = string("h_155_strides_0"), val = tensor([1])]; + tensor h_155_pad_0 = const()[name = string("h_155_pad_0"), val = tensor([0, 0])]; + tensor h_155_dilations_0 = const()[name = string("h_155_dilations_0"), val = tensor([1])]; + int32 h_155_groups_0 = const()[name = string("h_155_groups_0"), val = int32(1)]; + tensor last_convnext_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57803520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58852160))))[name = string("last_convnext_1_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58856320)))]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = var_2421_cast_fp16)[name = string("transpose_4")]; + tensor h_155_cast_fp16 = conv(bias = last_convnext_1_pwconv1_bias_to_fp16, dilations = h_155_dilations_0, groups = h_155_groups_0, pad = h_155_pad_0, pad_type = h_155_pad_type_0, strides = h_155_strides_0, weight = last_convnext_1_pwconv1_weight_to_fp16_quantized, x = input_405_cast_fp16)[name = string("h_155_cast_fp16")]; + string input_407_mode_0 = const()[name = string("input_407_mode_0"), val = string("EXACT")]; + tensor input_407_cast_fp16 = gelu(mode = input_407_mode_0, x = h_155_cast_fp16)[name = string("input_407_cast_fp16")]; + string h_157_pad_type_0 = const()[name = string("h_157_pad_type_0"), val = string("valid")]; + tensor h_157_strides_0 = const()[name = string("h_157_strides_0"), val = tensor([1])]; + tensor h_157_pad_0 = const()[name = string("h_157_pad_0"), val = tensor([0, 0])]; + tensor h_157_dilations_0 = const()[name = string("h_157_dilations_0"), val = tensor([1])]; + int32 h_157_groups_0 = const()[name = string("h_157_groups_0"), val = int32(1)]; + tensor op_2438_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58860480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59909120))))[name = string("op_2438_weight_0_to_fp16_quantized")]; + tensor var_2438_bias_0_to_fp16 = const()[name = string("op_2438_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59910208)))]; + tensor var_2438_cast_fp16 = conv(bias = var_2438_bias_0_to_fp16, dilations = h_157_dilations_0, groups = h_157_groups_0, pad = h_157_pad_0, pad_type = h_157_pad_type_0, strides = h_157_strides_0, weight = op_2438_weight_0_to_fp16_quantized, x = input_407_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor out_67_cast_fp16 = add(x = x_211_cast_fp16, y = var_2438_cast_fp16)[name = string("out_67_cast_fp16")]; + tensor x_215_cast_fp16 = mul(x = out_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_215_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2466_cast_fp16 = mul(x = x_217_cast_fp16, y = last_1_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor x_last_53_axes_0 = const()[name = string("x_last_53_axes_0"), val = tensor([-1])]; + bool x_last_53_keep_dims_0 = const()[name = string("x_last_53_keep_dims_0"), val = bool(true)]; + tensor x_last_53_cast_fp16 = reduce_sum(axes = x_last_53_axes_0, keep_dims = x_last_53_keep_dims_0, x = var_2466_cast_fp16)[name = string("x_last_53_cast_fp16")]; + tensor var_2469_cast_fp16 = mul(x = x_217_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor var_2471_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_53_cast_fp16)[name = string("op_2471_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = var_2469_cast_fp16, y = var_2471_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("replicate")]; + fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)]; + tensor input_413_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = input_413_mode_0, pad = input_413_pad_0, x = input_411_cast_fp16)[name = string("input_413_cast_fp16")]; + string h_159_pad_type_0 = const()[name = string("h_159_pad_type_0"), val = string("valid")]; + int32 h_159_groups_0 = const()[name = string("h_159_groups_0"), val = int32(512)]; + tensor h_159_strides_0 = const()[name = string("h_159_strides_0"), val = tensor([1])]; + tensor h_159_pad_0 = const()[name = string("h_159_pad_0"), val = tensor([0, 0])]; + tensor h_159_dilations_0 = const()[name = string("h_159_dilations_0"), val = tensor([1])]; + tensor last_convnext_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59911296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59913920))))[name = string("last_convnext_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59915008)))]; + tensor h_159_cast_fp16 = conv(bias = last_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_159_dilations_0, groups = h_159_groups_0, pad = h_159_pad_0, pad_type = h_159_pad_type_0, strides = h_159_strides_0, weight = last_convnext_2_dwconv__conv_weight_to_fp16_quantized, x = input_413_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor x_219_cast_fp16 = mul(x = h_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_415_perm_0 = const()[name = string("input_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2489_axes_0 = const()[name = string("op_2489_axes_0"), val = tensor([-1])]; + tensor last_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59916096)))]; + tensor last_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59917184)))]; + fp16 var_2443_to_fp16 = const()[name = string("op_2443_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_415_cast_fp16 = transpose(perm = input_415_perm_0, x = x_219_cast_fp16)[name = string("transpose_3")]; + tensor var_2489_cast_fp16 = layer_norm(axes = var_2489_axes_0, beta = last_convnext_2_norm_norm_bias_to_fp16, epsilon = var_2443_to_fp16, gamma = last_convnext_2_norm_norm_weight_to_fp16, x = input_415_cast_fp16)[name = string("op_2489_cast_fp16")]; + tensor input_417_perm_0 = const()[name = string("input_417_perm_0"), val = tensor([0, 2, 1])]; + string h_161_pad_type_0 = const()[name = string("h_161_pad_type_0"), val = string("valid")]; + tensor h_161_strides_0 = const()[name = string("h_161_strides_0"), val = tensor([1])]; + tensor h_161_pad_0 = const()[name = string("h_161_pad_0"), val = tensor([0, 0])]; + tensor h_161_dilations_0 = const()[name = string("h_161_dilations_0"), val = tensor([1])]; + int32 h_161_groups_0 = const()[name = string("h_161_groups_0"), val = int32(1)]; + tensor last_convnext_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59918272))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60966912))))[name = string("last_convnext_2_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60971072)))]; + tensor input_417_cast_fp16 = transpose(perm = input_417_perm_0, x = var_2489_cast_fp16)[name = string("transpose_2")]; + tensor h_161_cast_fp16 = conv(bias = last_convnext_2_pwconv1_bias_to_fp16, dilations = h_161_dilations_0, groups = h_161_groups_0, pad = h_161_pad_0, pad_type = h_161_pad_type_0, strides = h_161_strides_0, weight = last_convnext_2_pwconv1_weight_to_fp16_quantized, x = input_417_cast_fp16)[name = string("h_161_cast_fp16")]; + string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("EXACT")]; + tensor input_419_cast_fp16 = gelu(mode = input_419_mode_0, x = h_161_cast_fp16)[name = string("input_419_cast_fp16")]; + string h_163_pad_type_0 = const()[name = string("h_163_pad_type_0"), val = string("valid")]; + tensor h_163_strides_0 = const()[name = string("h_163_strides_0"), val = tensor([1])]; + tensor h_163_pad_0 = const()[name = string("h_163_pad_0"), val = tensor([0, 0])]; + tensor h_163_dilations_0 = const()[name = string("h_163_dilations_0"), val = tensor([1])]; + int32 h_163_groups_0 = const()[name = string("h_163_groups_0"), val = int32(1)]; + tensor op_2506_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60975232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62023872))))[name = string("op_2506_weight_0_to_fp16_quantized")]; + tensor var_2506_bias_0_to_fp16 = const()[name = string("op_2506_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62024960)))]; + tensor var_2506_cast_fp16 = conv(bias = var_2506_bias_0_to_fp16, dilations = h_163_dilations_0, groups = h_163_groups_0, pad = h_163_pad_0, pad_type = h_163_pad_type_0, strides = h_163_strides_0, weight = op_2506_weight_0_to_fp16_quantized, x = input_419_cast_fp16)[name = string("op_2506_cast_fp16")]; + tensor out_69_cast_fp16 = add(x = x_217_cast_fp16, y = var_2506_cast_fp16)[name = string("out_69_cast_fp16")]; + tensor x_221_cast_fp16 = mul(x = out_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor x_223_cast_fp16 = mul(x = x_221_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_2534_cast_fp16 = mul(x = x_223_cast_fp16, y = last_1_cast_fp16)[name = string("op_2534_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_2534_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_2537_cast_fp16 = mul(x = x_223_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2537_cast_fp16")]; + tensor var_2539_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_cast_fp16)[name = string("op_2539_cast_fp16")]; + tensor input_423_cast_fp16 = add(x = var_2537_cast_fp16, y = var_2539_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_425_mode_0 = const()[name = string("input_425_mode_0"), val = string("replicate")]; + fp16 const_90_to_fp16 = const()[name = string("const_90_to_fp16"), val = fp16(0x0p+0)]; + tensor input_425_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = input_425_mode_0, pad = input_425_pad_0, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")]; + string h_165_pad_type_0 = const()[name = string("h_165_pad_type_0"), val = string("valid")]; + int32 h_165_groups_0 = const()[name = string("h_165_groups_0"), val = int32(512)]; + tensor h_165_strides_0 = const()[name = string("h_165_strides_0"), val = tensor([1])]; + tensor h_165_pad_0 = const()[name = string("h_165_pad_0"), val = tensor([0, 0])]; + tensor h_165_dilations_0 = const()[name = string("h_165_dilations_0"), val = tensor([1])]; + tensor last_convnext_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62026048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62028672))))[name = string("last_convnext_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62029760)))]; + tensor h_165_cast_fp16 = conv(bias = last_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_165_dilations_0, groups = h_165_groups_0, pad = h_165_pad_0, pad_type = h_165_pad_type_0, strides = h_165_strides_0, weight = last_convnext_3_dwconv__conv_weight_to_fp16_quantized, x = input_425_cast_fp16)[name = string("h_165_cast_fp16")]; + tensor x_cast_fp16 = mul(x = h_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2557_axes_0 = const()[name = string("op_2557_axes_0"), val = tensor([-1])]; + tensor last_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62030848)))]; + tensor last_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62031936)))]; + fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_cast_fp16)[name = string("transpose_1")]; + tensor var_2557_cast_fp16 = layer_norm(axes = var_2557_axes_0, beta = last_convnext_3_norm_norm_bias_to_fp16, epsilon = var_2511_to_fp16, gamma = last_convnext_3_norm_norm_weight_to_fp16, x = input_427_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor input_429_perm_0 = const()[name = string("input_429_perm_0"), val = tensor([0, 2, 1])]; + string h_167_pad_type_0 = const()[name = string("h_167_pad_type_0"), val = string("valid")]; + tensor h_167_strides_0 = const()[name = string("h_167_strides_0"), val = tensor([1])]; + tensor h_167_pad_0 = const()[name = string("h_167_pad_0"), val = tensor([0, 0])]; + tensor h_167_dilations_0 = const()[name = string("h_167_dilations_0"), val = tensor([1])]; + int32 h_167_groups_0 = const()[name = string("h_167_groups_0"), val = int32(1)]; + tensor last_convnext_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62033024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63081664))))[name = string("last_convnext_3_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63085824)))]; + tensor input_429_cast_fp16 = transpose(perm = input_429_perm_0, x = var_2557_cast_fp16)[name = string("transpose_0")]; + tensor h_167_cast_fp16 = conv(bias = last_convnext_3_pwconv1_bias_to_fp16, dilations = h_167_dilations_0, groups = h_167_groups_0, pad = h_167_pad_0, pad_type = h_167_pad_type_0, strides = h_167_strides_0, weight = last_convnext_3_pwconv1_weight_to_fp16_quantized, x = input_429_cast_fp16)[name = string("h_167_cast_fp16")]; + string input_431_mode_0 = const()[name = string("input_431_mode_0"), val = string("EXACT")]; + tensor input_431_cast_fp16 = gelu(mode = input_431_mode_0, x = h_167_cast_fp16)[name = string("input_431_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor op_2574_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63089984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64138624))))[name = string("op_2574_weight_0_to_fp16_quantized")]; + tensor var_2574_bias_0_to_fp16 = const()[name = string("op_2574_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64139712)))]; + tensor var_2574_cast_fp16 = conv(bias = var_2574_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = op_2574_weight_0_to_fp16_quantized, x = input_431_cast_fp16)[name = string("op_2574_cast_fp16")]; + tensor out_cast_fp16 = add(x = x_223_cast_fp16, y = var_2574_cast_fp16)[name = string("out_cast_fp16")]; + tensor input_cast_fp16 = mul(x = out_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("input_cast_fp16")]; + string var_2587_pad_type_0 = const()[name = string("op_2587_pad_type_0"), val = string("valid")]; + tensor var_2587_strides_0 = const()[name = string("op_2587_strides_0"), val = tensor([1])]; + tensor var_2587_pad_0 = const()[name = string("op_2587_pad_0"), val = tensor([0, 0])]; + tensor var_2587_dilations_0 = const()[name = string("op_2587_dilations_0"), val = tensor([1])]; + int32 var_2587_groups_0 = const()[name = string("op_2587_groups_0"), val = int32(1)]; + tensor proj_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64140800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64214592))))[name = string("proj_out_weight_to_fp16_quantized")]; + tensor var_2587_cast_fp16 = conv(dilations = var_2587_dilations_0, groups = var_2587_groups_0, pad = var_2587_pad_0, pad_type = var_2587_pad_type_0, strides = var_2587_strides_0, weight = proj_out_weight_to_fp16_quantized, x = input_cast_fp16)[name = string("op_2587_cast_fp16")]; + tensor v_cast_fp16 = mul(x = var_2587_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("v_cast_fp16")]; + tensor cond_begin_0 = const()[name = string("cond_begin_0"), val = tensor([0, 0, 0])]; + tensor cond_end_0 = const()[name = string("cond_end_0"), val = tensor([1, 144, 128])]; + tensor cond_end_mask_0 = const()[name = string("cond_end_mask_0"), val = tensor([false, true, true])]; + tensor cond_cast_fp16 = slice_by_index(begin = cond_begin_0, end = cond_end_0, end_mask = cond_end_mask_0, x = v_cast_fp16)[name = string("cond_cast_fp16")]; + tensor uncond_begin_0 = const()[name = string("uncond_begin_0"), val = tensor([1, 0, 0])]; + tensor uncond_end_0 = const()[name = string("uncond_end_0"), val = tensor([2, 144, 128])]; + tensor uncond_end_mask_0 = const()[name = string("uncond_end_mask_0"), val = tensor([true, true, true])]; + tensor uncond_cast_fp16 = slice_by_index(begin = uncond_begin_0, end = uncond_end_0, end_mask = uncond_end_mask_0, x = v_cast_fp16)[name = string("uncond_cast_fp16")]; + fp32 var_2597_epsilon_0 = const()[name = string("op_2597_epsilon_0"), val = fp32(0x1.a36e2ep-14)]; + tensor var_2597_cast_fp16 = inverse(epsilon = var_2597_epsilon_0, x = total_step_to_fp16)[name = string("op_2597_cast_fp16")]; + tensor var_2603 = const()[name = string("op_2603"), val = tensor([-1, 1, 1])]; + tensor step_cast_fp16 = reshape(shape = var_2603, x = var_2597_cast_fp16)[name = string("step_cast_fp16")]; + fp16 var_2605_to_fp16 = const()[name = string("op_2605_to_fp16"), val = fp16(0x1p+2)]; + tensor var_2606_cast_fp16 = mul(x = cond_cast_fp16, y = var_2605_to_fp16)[name = string("op_2606_cast_fp16")]; + fp16 var_2607_to_fp16 = const()[name = string("op_2607_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_2608_cast_fp16 = mul(x = uncond_cast_fp16, y = var_2607_to_fp16)[name = string("op_2608_cast_fp16")]; + tensor var_2610_cast_fp16 = sub(x = var_2606_cast_fp16, y = var_2608_cast_fp16)[name = string("op_2610_cast_fp16")]; + tensor var_2611_cast_fp16 = mul(x = step_cast_fp16, y = var_2610_cast_fp16)[name = string("op_2611_cast_fp16")]; + tensor var_2613_cast_fp16 = add(x = noisy_latent_to_fp16, y = var_2611_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor var_2614_cast_fp16 = mul(x = var_2613_cast_fp16, y = latent_mask_to_fp16)[name = string("op_2614_cast_fp16")]; + string var_2614_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2614_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor denoised_latent = cast(dtype = var_2614_cast_fp16_to_fp32_dtype_0, x = var_2614_cast_fp16)[name = string("cast_0")]; + } -> (denoised_latent); +} \ No newline at end of file diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L128_int8.mlmodelc/weights/weight.bin b/VectorEstimatorVariants/VectorEstimator_v2_L128_int8.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..acb01e382d610ebc0a5362884fe4bacdb2f0ff34 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L128_int8.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/VectorEstimatorVariants/VectorEstimator_v2_L256_int4.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..bdeb27ff4c66ba26b1aae3b2140c662ad24ae39b --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L256_int4.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7cb6db0a28a0efa6e50248f9ecdb07b4ae8a7e078873eadb2a87f476a310d758 +size 553 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L256_int4.mlmodelc/model.mil b/VectorEstimatorVariants/VectorEstimator_v2_L256_int4.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..a636ec02d75b1cea9d92fed73b21607bf76a7f3f --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L256_int4.mlmodelc/model.mil @@ -0,0 +1,2102 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor current_step, tensor latent_mask, tensor noisy_latent, tensor style_ttl, tensor text_emb, tensor text_mask, tensor total_step) { + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + bool text_emb_interleave_0 = const()[name = string("text_emb_interleave_0"), val = bool(false)]; + string text_emb_to_fp16_dtype_0 = const()[name = string("text_emb_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_uncond_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16512))))[name = string("text_uncond_to_fp16_palettized")]; + tensor text_emb_to_fp16 = cast(dtype = text_emb_to_fp16_dtype_0, x = text_emb)[name = string("cast_7")]; + tensor text_emb_cast_fp16 = concat(axis = var_39, interleave = text_emb_interleave_0, values = (text_emb_to_fp16, text_uncond_to_fp16_palettized))[name = string("text_emb_cast_fp16")]; + bool input_95_interleave_0 = const()[name = string("input_95_interleave_0"), val = bool(false)]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor uncond_masker_style_value_special_token_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23104))))[name = string("uncond_masker_style_value_special_token_to_fp16_palettized")]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_6")]; + tensor input_95_cast_fp16 = concat(axis = var_39, interleave = input_95_interleave_0, values = (style_ttl_to_fp16, uncond_masker_style_value_special_token_to_fp16_palettized))[name = string("input_95_cast_fp16")]; + int32 var_64 = const()[name = string("op_64"), val = int32(0)]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + string noisy_latent_to_fp16_dtype_0 = const()[name = string("noisy_latent_to_fp16_dtype_0"), val = string("fp16")]; + tensor noisy_latent_to_fp16 = cast(dtype = noisy_latent_to_fp16_dtype_0, x = noisy_latent)[name = string("cast_5")]; + tensor input_5_cast_fp16 = concat(axis = var_64, interleave = input_5_interleave_0, values = (noisy_latent_to_fp16, noisy_latent_to_fp16))[name = string("input_5_cast_fp16")]; + int32 var_67 = const()[name = string("op_67"), val = int32(0)]; + bool latent_mask_b_interleave_0 = const()[name = string("latent_mask_b_interleave_0"), val = bool(false)]; + string latent_mask_to_fp16_dtype_0 = const()[name = string("latent_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor latent_mask_to_fp16 = cast(dtype = latent_mask_to_fp16_dtype_0, x = latent_mask)[name = string("cast_4")]; + tensor latent_mask_b_cast_fp16 = concat(axis = var_67, interleave = latent_mask_b_interleave_0, values = (latent_mask_to_fp16, latent_mask_to_fp16))[name = string("latent_mask_b_cast_fp16")]; + int32 var_70 = const()[name = string("op_70"), val = int32(0)]; + bool text_mask_interleave_0 = const()[name = string("text_mask_interleave_0"), val = bool(false)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_3")]; + tensor text_mask_cast_fp16 = concat(axis = var_70, interleave = text_mask_interleave_0, values = (text_mask_to_fp16, text_mask_to_fp16))[name = string("text_mask_cast_fp16")]; + string current_step_to_fp16_dtype_0 = const()[name = string("current_step_to_fp16_dtype_0"), val = string("fp16")]; + string total_step_to_fp16_dtype_0 = const()[name = string("total_step_to_fp16_dtype_0"), val = string("fp16")]; + tensor current_step_to_fp16 = cast(dtype = current_step_to_fp16_dtype_0, x = current_step)[name = string("cast_1")]; + tensor total_step_to_fp16 = cast(dtype = total_step_to_fp16_dtype_0, x = total_step)[name = string("cast_2")]; + tensor t_1_cast_fp16 = real_div(x = current_step_to_fp16, y = total_step_to_fp16)[name = string("t_1_cast_fp16")]; + int32 var_74 = const()[name = string("op_74"), val = int32(0)]; + bool t_3_interleave_0 = const()[name = string("t_3_interleave_0"), val = bool(false)]; + tensor t_3_cast_fp16 = concat(axis = var_74, interleave = t_3_interleave_0, values = (t_1_cast_fp16, t_1_cast_fp16))[name = string("t_3_cast_fp16")]; + int32 var_78 = const()[name = string("op_78"), val = int32(-1)]; + tensor var_85 = const()[name = string("op_85"), val = tensor([-1, 1])]; + tensor var_86_cast_fp16 = reshape(shape = var_85, x = t_3_cast_fp16)[name = string("op_86_cast_fp16")]; + fp16 time_encoder_time_scale_to_fp16 = const()[name = string("time_encoder_time_scale_to_fp16"), val = fp16(0x1.f4p+9)]; + tensor t_scaled_cast_fp16 = mul(x = var_86_cast_fp16, y = time_encoder_time_scale_to_fp16)[name = string("t_scaled_cast_fp16")]; + tensor time_encoder_omegas_to_fp16 = const()[name = string("time_encoder_omegas_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23232)))]; + tensor phase_cast_fp16 = mul(x = t_scaled_cast_fp16, y = time_encoder_omegas_to_fp16)[name = string("phase_cast_fp16")]; + tensor var_89_cast_fp16 = sin(x = phase_cast_fp16)[name = string("op_89_cast_fp16")]; + tensor var_90_cast_fp16 = cos(x = phase_cast_fp16)[name = string("op_90_cast_fp16")]; + bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)]; + tensor input_1_cast_fp16 = concat(axis = var_78, interleave = input_1_interleave_0, values = (var_89_cast_fp16, var_90_cast_fp16))[name = string("input_1_cast_fp16")]; + tensor time_encoder_mlp_0_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31616))))[name = string("time_encoder_mlp_0_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_0_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31744)))]; + tensor linear_0_cast_fp16 = linear(bias = time_encoder_mlp_0_linear_bias_to_fp16, weight = time_encoder_mlp_0_linear_weight_to_fp16_palettized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_97_cast_fp16 = softplus(x = linear_0_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = tanh(x = var_97_cast_fp16)[name = string("op_98_cast_fp16")]; + tensor input_3_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_98_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor time_encoder_mlp_2_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40576))))[name = string("time_encoder_mlp_2_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_2_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40704)))]; + tensor linear_1_cast_fp16 = linear(bias = time_encoder_mlp_2_linear_bias_to_fp16, weight = time_encoder_mlp_2_linear_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor time_emb_axes_0 = const()[name = string("time_emb_axes_0"), val = tensor([-1])]; + tensor time_emb_cast_fp16 = expand_dims(axes = time_emb_axes_0, x = linear_1_cast_fp16)[name = string("time_emb_cast_fp16")]; + string var_115_pad_type_0 = const()[name = string("op_115_pad_type_0"), val = string("valid")]; + tensor var_115_strides_0 = const()[name = string("op_115_strides_0"), val = tensor([1])]; + tensor var_115_pad_0 = const()[name = string("op_115_pad_0"), val = tensor([0, 0])]; + tensor var_115_dilations_0 = const()[name = string("op_115_dilations_0"), val = tensor([1])]; + int32 var_115_groups_0 = const()[name = string("op_115_groups_0"), val = int32(1)]; + tensor proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77824))))[name = string("proj_in_weight_to_fp16_palettized")]; + tensor var_115_cast_fp16 = conv(dilations = var_115_dilations_0, groups = var_115_groups_0, pad = var_115_pad_0, pad_type = var_115_pad_type_0, strides = var_115_strides_0, weight = proj_in_weight_to_fp16_palettized, x = input_5_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = var_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_3_cast_fp16")]; + int32 var_134 = const()[name = string("op_134"), val = int32(-1)]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([0, 0, 1])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 256])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([true, true, true])]; + tensor input_7_cast_fp16 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, x = latent_mask_b_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_166_pad_0 = const()[name = string("op_166_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_166_mode_0 = const()[name = string("op_166_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_166_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_166_mode_0, pad = var_166_pad_0, x = input_7_cast_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_133_to_fp16 = const()[name = string("op_133_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = sub(x = var_133_to_fp16, y = var_166_cast_fp16)[name = string("op_167_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = latent_mask_b_cast_fp16, y = var_167_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_169_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_172_cast_fp16 = mul(x = x_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor var_173_cast_fp16 = sub(x = var_133_to_fp16, y = latent_mask_b_cast_fp16)[name = string("op_173_cast_fp16")]; + tensor var_174_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_174_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_174_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("replicate")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor input_11_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_11_mode_0, pad = input_11_pad_0, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(512)]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79296))))[name = string("main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79424)))]; + tensor h_3_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, 2, 1])]; + tensor var_192_axes_0 = const()[name = string("op_192_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80512)))]; + tensor main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81600)))]; + fp16 var_139_to_fp16 = const()[name = string("op_139_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_7_cast_fp16)[name = string("transpose_117")]; + tensor var_192_cast_fp16 = layer_norm(axes = var_192_axes_0, beta = main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16, x = input_13_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, 2, 1])]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(607040))))[name = string("main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(607168)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = var_192_cast_fp16)[name = string("transpose_116")]; + tensor h_5_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = string("h_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = h_5_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)]; + tensor op_209_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(611328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135680))))[name = string("op_209_weight_0_to_fp16_palettized")]; + tensor var_209_bias_0_to_fp16 = const()[name = string("op_209_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135808)))]; + tensor var_209_cast_fp16 = conv(bias = var_209_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = op_209_weight_0_to_fp16_palettized, x = input_17_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_209_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_223_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_223_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_226_cast_fp16 = mul(x = x_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_226_cast_fp16")]; + tensor var_228_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_228_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_226_cast_fp16, y = var_228_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")]; + fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)]; + tensor input_23_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([2])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(512)]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1138240))))[name = string("main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1138368)))]; + tensor h_9_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor var_246_axes_0 = const()[name = string("op_246_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1139456)))]; + tensor main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1140544)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_13_cast_fp16)[name = string("transpose_115")]; + tensor var_246_cast_fp16 = layer_norm(axes = var_246_axes_0, beta = main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_246_cast_fp16")]; + tensor input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor([0, 2, 1])]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1141632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1665984))))[name = string("main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1666112)))]; + tensor input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_246_cast_fp16)[name = string("transpose_114")]; + tensor h_11_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_27_cast_fp16)[name = string("h_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_11_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)]; + tensor op_263_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1670272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2194624))))[name = string("op_263_weight_0_to_fp16_palettized")]; + tensor var_263_bias_0_to_fp16 = const()[name = string("op_263_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2194752)))]; + tensor var_263_cast_fp16 = conv(bias = var_263_bias_0_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = op_263_weight_0_to_fp16_palettized, x = input_29_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_263_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_277_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_277_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_277_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_280_cast_fp16 = mul(x = x_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_280_cast_fp16")]; + tensor var_282_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_280_cast_fp16, y = var_282_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("replicate")]; + fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)]; + tensor input_35_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = input_35_mode_0, pad = input_35_pad_0, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([4])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(512)]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2195840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2197184))))[name = string("main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2197312)))]; + tensor h_15_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor var_300_axes_0 = const()[name = string("op_300_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2198400)))]; + tensor main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2199488)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_19_cast_fp16)[name = string("transpose_113")]; + tensor var_300_cast_fp16 = layer_norm(axes = var_300_axes_0, beta = main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16, x = input_37_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor([0, 2, 1])]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2200576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2724928))))[name = string("main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2725056)))]; + tensor input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_300_cast_fp16)[name = string("transpose_112")]; + tensor h_17_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = string("h_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = h_17_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)]; + tensor op_317_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2729216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3253568))))[name = string("op_317_weight_0_to_fp16_palettized")]; + tensor var_317_bias_0_to_fp16 = const()[name = string("op_317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3253696)))]; + tensor var_317_cast_fp16 = conv(bias = var_317_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = op_317_weight_0_to_fp16_palettized, x = input_41_cast_fp16)[name = string("op_317_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_317_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor var_331_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_331_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_331_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = x_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor var_336_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_336_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_334_cast_fp16, y = var_336_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")]; + fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([8])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(512)]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3254784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3256128))))[name = string("main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3256256)))]; + tensor h_21_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3257344)))]; + tensor main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3258432)))]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_25_cast_fp16)[name = string("transpose_111")]; + tensor var_354_cast_fp16 = layer_norm(axes = var_354_axes_0, beta = main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_354_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3259520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3783872))))[name = string("main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3784000)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_354_cast_fp16)[name = string("transpose_110")]; + tensor h_23_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("h_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_23_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)]; + tensor op_371_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3788160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4312512))))[name = string("op_371_weight_0_to_fp16_palettized")]; + tensor var_371_bias_0_to_fp16 = const()[name = string("op_371_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4312640)))]; + tensor var_371_cast_fp16 = conv(bias = var_371_bias_0_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = op_371_weight_0_to_fp16_palettized, x = input_53_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_371_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor input_55_cast_fp16 = squeeze(axes = input_55_axes_0, x = time_emb_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4313728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4330176))))[name = string("main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4330304)))]; + tensor linear_2_cast_fp16 = linear(bias = main_blocks_0_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor t_5_axes_0 = const()[name = string("t_5_axes_0"), val = tensor([-1])]; + tensor t_5_cast_fp16 = expand_dims(axes = t_5_axes_0, x = linear_2_cast_fp16)[name = string("t_5_cast_fp16")]; + tensor var_381_cast_fp16 = add(x = x_27_cast_fp16, y = t_5_cast_fp16)[name = string("op_381_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_381_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_394_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_394_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_394_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_397_cast_fp16 = mul(x = x_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = var_397_cast_fp16, y = var_399_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("replicate")]; + fp16 const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = fp16(0x0p+0)]; + tensor input_61_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = input_61_mode_0, pad = input_61_pad_0, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(512)]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4331392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4332736))))[name = string("main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4332864)))]; + tensor h_27_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417_axes_0 = const()[name = string("op_417_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4333952)))]; + tensor main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4335040)))]; + tensor input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_33_cast_fp16)[name = string("transpose_109")]; + tensor var_417_cast_fp16 = layer_norm(axes = var_417_axes_0, beta = main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16, x = input_63_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4336128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4860480))))[name = string("main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4860608)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = var_417_cast_fp16)[name = string("transpose_108")]; + tensor h_29_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_65_cast_fp16)[name = string("h_29_cast_fp16")]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("EXACT")]; + tensor input_67_cast_fp16 = gelu(mode = input_67_mode_0, x = h_29_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)]; + tensor op_434_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4864768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5389120))))[name = string("op_434_weight_0_to_fp16_palettized")]; + tensor var_434_bias_0_to_fp16 = const()[name = string("op_434_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5389248)))]; + tensor var_434_cast_fp16 = conv(bias = var_434_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = op_434_weight_0_to_fp16_palettized, x = input_67_cast_fp16)[name = string("op_434_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_434_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_m_1_cast_fp16 = mul(x = x_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_1_cast_fp16")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5390336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5521472))))[name = string("main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5521600)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_35_cast_fp16)[name = string("transpose_107")]; + tensor linear_3_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([2, 256, 8, 64])]; + tensor var_453_cast_fp16 = reshape(shape = var_452, x = linear_3_cast_fp16)[name = string("op_453_cast_fp16")]; + tensor x_37_perm_0 = const()[name = string("x_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5522688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5588288))))[name = string("main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5588416)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = text_emb_cast_fp16)[name = string("transpose_106")]; + tensor linear_4_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([2, 128, 8, 64])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = linear_4_cast_fp16)[name = string("op_461_cast_fp16")]; + tensor x_39_perm_0 = const()[name = string("x_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5589504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5655104))))[name = string("main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5655232)))]; + tensor linear_5_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_468 = const()[name = string("op_468"), val = tensor([2, 128, 8, 64])]; + tensor var_469_cast_fp16 = reshape(shape = var_468, x = linear_5_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor divisor_1_axes_0 = const()[name = string("divisor_1_axes_0"), val = tensor([1, 2])]; + bool divisor_1_keep_dims_0 = const()[name = string("divisor_1_keep_dims_0"), val = bool(false)]; + tensor divisor_1_cast_fp16 = reduce_sum(axes = divisor_1_axes_0, keep_dims = divisor_1_keep_dims_0, x = latent_mask_b_cast_fp16)[name = string("divisor_1_cast_fp16")]; + tensor divisor_3_axes_0 = const()[name = string("divisor_3_axes_0"), val = tensor([1, 2])]; + bool divisor_3_keep_dims_0 = const()[name = string("divisor_3_keep_dims_0"), val = bool(false)]; + tensor divisor_3_cast_fp16 = reduce_sum(axes = divisor_3_axes_0, keep_dims = divisor_3_keep_dims_0, x = text_mask_cast_fp16)[name = string("divisor_3_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([-1, 1, 1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = divisor_1_cast_fp16)[name = string("op_488_cast_fp16")]; + tensor const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5656320)))]; + tensor scaled_1_cast_fp16 = real_div(x = const_16_to_fp16, y = var_488_cast_fp16)[name = string("scaled_1_cast_fp16")]; + tensor main_blocks_0_text_attn_theta_to_fp16 = const()[name = string("main_blocks_0_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5657408)))]; + tensor angles_1_cast_fp16 = mul(x = scaled_1_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_1_cast_fp16")]; + tensor var_491_cast_fp16 = cos(x = angles_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = var_491_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor var_493_cast_fp16 = sin(x = angles_1_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = var_493_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor x_a_1_begin_0 = const()[name = string("x_a_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_1_end_0 = const()[name = string("x_a_1_end_0"), val = tensor([2, 8, 256, 32])]; + tensor x_a_1_end_mask_0 = const()[name = string("x_a_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_37_cast_fp16 = transpose(perm = x_37_perm_0, x = var_453_cast_fp16)[name = string("transpose_105")]; + tensor x_a_1_cast_fp16 = slice_by_index(begin = x_a_1_begin_0, end = x_a_1_end_0, end_mask = x_a_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_a_1_cast_fp16")]; + tensor x_b_1_begin_0 = const()[name = string("x_b_1_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_1_end_0 = const()[name = string("x_b_1_end_0"), val = tensor([2, 8, 256, 64])]; + tensor x_b_1_end_mask_0 = const()[name = string("x_b_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_1_cast_fp16 = slice_by_index(begin = x_b_1_begin_0, end = x_b_1_end_0, end_mask = x_b_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_b_1_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = x_a_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor var_498_cast_fp16 = mul(x = x_b_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_498_cast_fp16")]; + tensor rot_a_1_cast_fp16 = sub(x = var_497_cast_fp16, y = var_498_cast_fp16)[name = string("rot_a_1_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = x_a_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = x_b_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor rot_b_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("rot_b_1_cast_fp16")]; + bool q_1_interleave_0 = const()[name = string("q_1_interleave_0"), val = bool(false)]; + tensor q_1_cast_fp16 = concat(axis = var_134, interleave = q_1_interleave_0, values = (rot_a_1_cast_fp16, rot_b_1_cast_fp16))[name = string("q_1_cast_fp16")]; + tensor var_505 = const()[name = string("op_505"), val = tensor([-1, 1, 1])]; + tensor var_506_cast_fp16 = reshape(shape = var_505, x = divisor_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5657536)))]; + tensor scaled_3_cast_fp16 = real_div(x = const_17_to_fp16, y = var_506_cast_fp16)[name = string("scaled_3_cast_fp16")]; + tensor angles_3_cast_fp16 = mul(x = scaled_3_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_3_cast_fp16")]; + tensor var_509_cast_fp16 = cos(x = angles_3_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor cos_3_axes_0 = const()[name = string("cos_3_axes_0"), val = tensor([1])]; + tensor cos_3_cast_fp16 = expand_dims(axes = cos_3_axes_0, x = var_509_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor var_511_cast_fp16 = sin(x = angles_3_cast_fp16)[name = string("op_511_cast_fp16")]; + tensor sin_3_axes_0 = const()[name = string("sin_3_axes_0"), val = tensor([1])]; + tensor sin_3_cast_fp16 = expand_dims(axes = sin_3_axes_0, x = var_511_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor x_a_3_begin_0 = const()[name = string("x_a_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_3_end_0 = const()[name = string("x_a_3_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_3_end_mask_0 = const()[name = string("x_a_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_39_cast_fp16 = transpose(perm = x_39_perm_0, x = var_461_cast_fp16)[name = string("transpose_104")]; + tensor x_a_3_cast_fp16 = slice_by_index(begin = x_a_3_begin_0, end = x_a_3_end_0, end_mask = x_a_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_a_3_cast_fp16")]; + tensor x_b_3_begin_0 = const()[name = string("x_b_3_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_3_end_0 = const()[name = string("x_b_3_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_3_end_mask_0 = const()[name = string("x_b_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_3_cast_fp16 = slice_by_index(begin = x_b_3_begin_0, end = x_b_3_end_0, end_mask = x_b_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_b_3_cast_fp16")]; + tensor var_515_cast_fp16 = mul(x = x_a_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_515_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = x_b_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor rot_a_3_cast_fp16 = sub(x = var_515_cast_fp16, y = var_516_cast_fp16)[name = string("rot_a_3_cast_fp16")]; + tensor var_518_cast_fp16 = mul(x = x_a_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = mul(x = x_b_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor rot_b_3_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = string("rot_b_3_cast_fp16")]; + bool k_1_interleave_0 = const()[name = string("k_1_interleave_0"), val = bool(false)]; + tensor k_1_cast_fp16 = concat(axis = var_134, interleave = k_1_interleave_0, values = (rot_a_3_cast_fp16, rot_b_3_cast_fp16))[name = string("k_1_cast_fp16")]; + bool var_524_transpose_x_1 = const()[name = string("op_524_transpose_x_1"), val = bool(false)]; + bool var_524_transpose_y_1 = const()[name = string("op_524_transpose_y_1"), val = bool(true)]; + tensor var_524_cast_fp16 = matmul(transpose_x = var_524_transpose_x_1, transpose_y = var_524_transpose_y_1, x = q_1_cast_fp16, y = k_1_cast_fp16)[name = string("op_524_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_524_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([2])]; + tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = text_mask_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528_cast_fp16 = sub(x = var_133_to_fp16, y = var_527_cast_fp16)[name = string("op_528_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_530_cast_fp16 = mul(x = var_528_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_75_cast_fp16 = sub(x = _inversed_scores_1_cast_fp16, y = var_530_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_134, x = input_75_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool var_533_transpose_x_0 = const()[name = string("op_533_transpose_x_0"), val = bool(false)]; + bool var_533_transpose_y_0 = const()[name = string("op_533_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_469_cast_fp16)[name = string("transpose_103")]; + tensor var_533_cast_fp16 = matmul(transpose_x = var_533_transpose_x_0, transpose_y = var_533_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("op_533_cast_fp16")]; + tensor var_534_perm_0 = const()[name = string("op_534_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_535 = const()[name = string("op_535"), val = tensor([2, 256, 512])]; + tensor var_534_cast_fp16 = transpose(perm = var_534_perm_0, x = var_533_cast_fp16)[name = string("transpose_102")]; + tensor input_77_cast_fp16 = reshape(shape = var_535, x = var_534_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5658112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5789248))))[name = string("main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5789376)))]; + tensor linear_6_cast_fp16 = linear(bias = main_blocks_0_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor out_11_perm_0 = const()[name = string("out_11_perm_0"), val = tensor([0, 2, 1])]; + tensor out_11_cast_fp16 = transpose(perm = out_11_perm_0, x = linear_6_cast_fp16)[name = string("transpose_101")]; + tensor var_542_cast_fp16 = mul(x = out_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_542_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = x_m_1_cast_fp16, y = var_542_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor var_549_axes_0 = const()[name = string("op_549_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5790464)))]; + tensor main_blocks_0_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5791552)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_41_cast_fp16)[name = string("transpose_100")]; + tensor var_549_cast_fp16 = layer_norm(axes = var_549_axes_0, beta = main_blocks_0_text_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_text_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor var_550_perm_0 = const()[name = string("op_550_perm_0"), val = tensor([0, 2, 1])]; + tensor var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = var_549_cast_fp16)[name = string("transpose_99")]; + tensor x_43_cast_fp16 = mul(x = var_550_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = x_45_cast_fp16, y = last_1_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor x_last_11_axes_0 = const()[name = string("x_last_11_axes_0"), val = tensor([-1])]; + bool x_last_11_keep_dims_0 = const()[name = string("x_last_11_keep_dims_0"), val = bool(true)]; + tensor x_last_11_cast_fp16 = reduce_sum(axes = x_last_11_axes_0, keep_dims = x_last_11_keep_dims_0, x = var_563_cast_fp16)[name = string("x_last_11_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = x_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor var_568_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_11_cast_fp16)[name = string("op_568_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = var_566_cast_fp16, y = var_568_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_85_mode_0 = const()[name = string("input_85_mode_0"), val = string("replicate")]; + fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)]; + tensor input_85_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(512)]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5792640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5793984))))[name = string("main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5794112)))]; + tensor h_33_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("h_33_cast_fp16")]; + tensor x_47_cast_fp16 = mul(x = h_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5795200)))]; + tensor main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5796288)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_47_cast_fp16)[name = string("transpose_98")]; + tensor var_586_cast_fp16 = layer_norm(axes = var_586_axes_0, beta = main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_586_cast_fp16")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5797376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6321728))))[name = string("main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6321856)))]; + tensor input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = var_586_cast_fp16)[name = string("transpose_97")]; + tensor h_35_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_89_cast_fp16)[name = string("h_35_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = h_35_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")]; + tensor h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor([1])]; + tensor h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor([0, 0])]; + tensor h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor([1])]; + int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)]; + tensor op_603_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6326016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6850368))))[name = string("op_603_weight_0_to_fp16_palettized")]; + tensor var_603_bias_0_to_fp16 = const()[name = string("op_603_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6850496)))]; + tensor var_603_cast_fp16 = conv(bias = var_603_bias_0_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = op_603_weight_0_to_fp16_palettized, x = input_91_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor out_13_cast_fp16 = add(x = x_45_cast_fp16, y = var_603_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_49_cast_fp16 = mul(x = out_13_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_m_3_cast_fp16 = mul(x = x_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_3_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6851584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6917184))))[name = string("main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6917312)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_49_cast_fp16)[name = string("transpose_96")]; + tensor linear_7_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([2, 256, 2, 128])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = linear_7_cast_fp16)[name = string("op_620_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6917888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6950720))))[name = string("main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6950848)))]; + tensor linear_9_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_633 = const()[name = string("op_633"), val = tensor([2, 50, 2, 128])]; + tensor var_634_cast_fp16 = reshape(shape = var_633, x = linear_9_cast_fp16)[name = string("op_634_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_638_transpose_x_0 = const()[name = string("op_638_transpose_x_0"), val = bool(false)]; + bool var_638_transpose_y_0 = const()[name = string("op_638_transpose_y_0"), val = bool(false)]; + tensor op_637_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6951424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6964288))))[name = string("op_637_to_fp16_palettized")]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_620_cast_fp16)[name = string("transpose_95")]; + tensor var_638_cast_fp16 = matmul(transpose_x = var_638_transpose_x_0, transpose_y = var_638_transpose_y_0, x = q_3_cast_fp16, y = op_637_to_fp16_palettized)[name = string("op_638_cast_fp16")]; + fp16 _inversed_input_97_y_0_to_fp16 = const()[name = string("_inversed_input_97_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_97_cast_fp16 = mul(x = var_638_cast_fp16, y = _inversed_input_97_y_0_to_fp16)[name = string("_inversed_input_97_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_134, x = _inversed_input_97_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_642_perm_0 = const()[name = string("op_642_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_1_axes_0 = const()[name = string("mask_1_axes_0"), val = tensor([1])]; + tensor var_642_cast_fp16 = transpose(perm = var_642_perm_0, x = latent_mask_b_cast_fp16)[name = string("transpose_94")]; + tensor mask_1_cast_fp16 = expand_dims(axes = mask_1_axes_0, x = var_642_cast_fp16)[name = string("mask_1_cast_fp16")]; + tensor attn_5_cast_fp16 = mul(x = attn_3_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool var_645_transpose_x_0 = const()[name = string("op_645_transpose_x_0"), val = bool(false)]; + bool var_645_transpose_y_0 = const()[name = string("op_645_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_634_cast_fp16)[name = string("transpose_93")]; + tensor var_645_cast_fp16 = matmul(transpose_x = var_645_transpose_x_0, transpose_y = var_645_transpose_y_0, x = attn_5_cast_fp16, y = v_3_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_646_perm_0 = const()[name = string("op_646_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_647 = const()[name = string("op_647"), val = tensor([2, 256, 256])]; + tensor var_646_cast_fp16 = transpose(perm = var_646_perm_0, x = var_645_cast_fp16)[name = string("transpose_92")]; + tensor input_99_cast_fp16 = reshape(shape = var_647, x = var_646_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6964416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7030016))))[name = string("main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7030144)))]; + tensor linear_10_cast_fp16 = linear(bias = main_blocks_0_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor out_15_perm_0 = const()[name = string("out_15_perm_0"), val = tensor([0, 2, 1])]; + tensor out_15_cast_fp16 = transpose(perm = out_15_perm_0, x = linear_10_cast_fp16)[name = string("transpose_91")]; + tensor var_654_cast_fp16 = mul(x = out_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor x_51_cast_fp16 = add(x = x_m_3_cast_fp16, y = var_654_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7031232)))]; + tensor main_blocks_0_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7032320)))]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_51_cast_fp16)[name = string("transpose_90")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = main_blocks_0_style_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_style_norm_norm_weight_to_fp16, x = input_101_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_662_perm_0 = const()[name = string("op_662_perm_0"), val = tensor([0, 2, 1])]; + tensor var_662_cast_fp16 = transpose(perm = var_662_perm_0, x = var_661_cast_fp16)[name = string("transpose_89")]; + tensor x_53_cast_fp16 = mul(x = var_662_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_53_cast_fp16")]; + int32 var_681 = const()[name = string("op_681"), val = int32(-1)]; + tensor x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = x_55_cast_fp16, y = last_1_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor x_last_13_axes_0 = const()[name = string("x_last_13_axes_0"), val = tensor([-1])]; + bool x_last_13_keep_dims_0 = const()[name = string("x_last_13_keep_dims_0"), val = bool(true)]; + tensor x_last_13_cast_fp16 = reduce_sum(axes = x_last_13_axes_0, keep_dims = x_last_13_keep_dims_0, x = var_716_cast_fp16)[name = string("x_last_13_cast_fp16")]; + tensor var_719_cast_fp16 = mul(x = x_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_721_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_13_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_719_cast_fp16, y = var_721_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("replicate")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(512)]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7033408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7034752))))[name = string("main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7034880)))]; + tensor h_39_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor x_57_cast_fp16 = mul(x = h_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_739_axes_0 = const()[name = string("op_739_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7035968)))]; + tensor main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7037056)))]; + fp16 var_686_to_fp16 = const()[name = string("op_686_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_57_cast_fp16)[name = string("transpose_88")]; + tensor var_739_cast_fp16 = layer_norm(axes = var_739_axes_0, beta = main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_739_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + string h_41_pad_type_0 = const()[name = string("h_41_pad_type_0"), val = string("valid")]; + tensor h_41_strides_0 = const()[name = string("h_41_strides_0"), val = tensor([1])]; + tensor h_41_pad_0 = const()[name = string("h_41_pad_0"), val = tensor([0, 0])]; + tensor h_41_dilations_0 = const()[name = string("h_41_dilations_0"), val = tensor([1])]; + int32 h_41_groups_0 = const()[name = string("h_41_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7038144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7562496))))[name = string("main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7562624)))]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = var_739_cast_fp16)[name = string("transpose_87")]; + tensor h_41_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_41_dilations_0, groups = h_41_groups_0, pad = h_41_pad_0, pad_type = h_41_pad_type_0, strides = h_41_strides_0, weight = main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = h_41_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor op_756_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7566784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8091136))))[name = string("op_756_weight_0_to_fp16_palettized")]; + tensor var_756_bias_0_to_fp16 = const()[name = string("op_756_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8091264)))]; + tensor var_756_cast_fp16 = conv(bias = var_756_bias_0_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = op_756_weight_0_to_fp16_palettized, x = input_113_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor out_17_cast_fp16 = add(x = x_55_cast_fp16, y = var_756_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_59_cast_fp16 = mul(x = out_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor x_61_cast_fp16 = mul(x = x_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_770_cast_fp16 = mul(x = x_61_cast_fp16, y = last_1_cast_fp16)[name = string("op_770_cast_fp16")]; + tensor x_last_15_axes_0 = const()[name = string("x_last_15_axes_0"), val = tensor([-1])]; + bool x_last_15_keep_dims_0 = const()[name = string("x_last_15_keep_dims_0"), val = bool(true)]; + tensor x_last_15_cast_fp16 = reduce_sum(axes = x_last_15_axes_0, keep_dims = x_last_15_keep_dims_0, x = var_770_cast_fp16)[name = string("x_last_15_cast_fp16")]; + tensor var_773_cast_fp16 = mul(x = x_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor var_775_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_15_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = var_773_cast_fp16, y = var_775_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("replicate")]; + fp16 const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = fp16(0x0p+0)]; + tensor input_119_cast_fp16 = pad(constant_val = const_26_to_fp16, mode = input_119_mode_0, pad = input_119_pad_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")]; + string h_45_pad_type_0 = const()[name = string("h_45_pad_type_0"), val = string("valid")]; + tensor h_45_dilations_0 = const()[name = string("h_45_dilations_0"), val = tensor([2])]; + int32 h_45_groups_0 = const()[name = string("h_45_groups_0"), val = int32(512)]; + tensor h_45_strides_0 = const()[name = string("h_45_strides_0"), val = tensor([1])]; + tensor h_45_pad_0 = const()[name = string("h_45_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8092352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8093696))))[name = string("main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8093824)))]; + tensor h_45_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_45_dilations_0, groups = h_45_groups_0, pad = h_45_pad_0, pad_type = h_45_pad_type_0, strides = h_45_strides_0, weight = main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = h_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8094912)))]; + tensor main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8096000)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_63_cast_fp16)[name = string("transpose_86")]; + tensor var_793_cast_fp16 = layer_norm(axes = var_793_axes_0, beta = main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor input_123_perm_0 = const()[name = string("input_123_perm_0"), val = tensor([0, 2, 1])]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8097088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8621440))))[name = string("main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8621568)))]; + tensor input_123_cast_fp16 = transpose(perm = input_123_perm_0, x = var_793_cast_fp16)[name = string("transpose_85")]; + tensor h_47_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = string("h_47_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = h_47_cast_fp16)[name = string("input_125_cast_fp16")]; + string h_49_pad_type_0 = const()[name = string("h_49_pad_type_0"), val = string("valid")]; + tensor h_49_strides_0 = const()[name = string("h_49_strides_0"), val = tensor([1])]; + tensor h_49_pad_0 = const()[name = string("h_49_pad_0"), val = tensor([0, 0])]; + tensor h_49_dilations_0 = const()[name = string("h_49_dilations_0"), val = tensor([1])]; + int32 h_49_groups_0 = const()[name = string("h_49_groups_0"), val = int32(1)]; + tensor op_810_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8625728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9150080))))[name = string("op_810_weight_0_to_fp16_palettized")]; + tensor var_810_bias_0_to_fp16 = const()[name = string("op_810_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9150208)))]; + tensor var_810_cast_fp16 = conv(bias = var_810_bias_0_to_fp16, dilations = h_49_dilations_0, groups = h_49_groups_0, pad = h_49_pad_0, pad_type = h_49_pad_type_0, strides = h_49_strides_0, weight = op_810_weight_0_to_fp16_palettized, x = input_125_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = x_61_cast_fp16, y = var_810_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor x_65_cast_fp16 = mul(x = out_19_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = x_67_cast_fp16, y = last_1_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_last_17_axes_0 = const()[name = string("x_last_17_axes_0"), val = tensor([-1])]; + bool x_last_17_keep_dims_0 = const()[name = string("x_last_17_keep_dims_0"), val = bool(true)]; + tensor x_last_17_cast_fp16 = reduce_sum(axes = x_last_17_axes_0, keep_dims = x_last_17_keep_dims_0, x = var_824_cast_fp16)[name = string("x_last_17_cast_fp16")]; + tensor var_827_cast_fp16 = mul(x = x_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_829_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_17_cast_fp16)[name = string("op_829_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = var_827_cast_fp16, y = var_829_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("replicate")]; + fp16 const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = fp16(0x0p+0)]; + tensor input_131_cast_fp16 = pad(constant_val = const_28_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + string h_51_pad_type_0 = const()[name = string("h_51_pad_type_0"), val = string("valid")]; + tensor h_51_dilations_0 = const()[name = string("h_51_dilations_0"), val = tensor([4])]; + int32 h_51_groups_0 = const()[name = string("h_51_groups_0"), val = int32(512)]; + tensor h_51_strides_0 = const()[name = string("h_51_strides_0"), val = tensor([1])]; + tensor h_51_pad_0 = const()[name = string("h_51_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9151296))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9152640))))[name = string("main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9152768)))]; + tensor h_51_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_51_dilations_0, groups = h_51_groups_0, pad = h_51_pad_0, pad_type = h_51_pad_type_0, strides = h_51_strides_0, weight = main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor x_69_cast_fp16 = mul(x = h_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_847_axes_0 = const()[name = string("op_847_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9153856)))]; + tensor main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9154944)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_69_cast_fp16)[name = string("transpose_84")]; + tensor var_847_cast_fp16 = layer_norm(axes = var_847_axes_0, beta = main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_847_cast_fp16")]; + tensor input_135_perm_0 = const()[name = string("input_135_perm_0"), val = tensor([0, 2, 1])]; + string h_53_pad_type_0 = const()[name = string("h_53_pad_type_0"), val = string("valid")]; + tensor h_53_strides_0 = const()[name = string("h_53_strides_0"), val = tensor([1])]; + tensor h_53_pad_0 = const()[name = string("h_53_pad_0"), val = tensor([0, 0])]; + tensor h_53_dilations_0 = const()[name = string("h_53_dilations_0"), val = tensor([1])]; + int32 h_53_groups_0 = const()[name = string("h_53_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9156032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9680384))))[name = string("main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9680512)))]; + tensor input_135_cast_fp16 = transpose(perm = input_135_perm_0, x = var_847_cast_fp16)[name = string("transpose_83")]; + tensor h_53_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_53_dilations_0, groups = h_53_groups_0, pad = h_53_pad_0, pad_type = h_53_pad_type_0, strides = h_53_strides_0, weight = main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = string("h_53_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = h_53_cast_fp16)[name = string("input_137_cast_fp16")]; + string h_55_pad_type_0 = const()[name = string("h_55_pad_type_0"), val = string("valid")]; + tensor h_55_strides_0 = const()[name = string("h_55_strides_0"), val = tensor([1])]; + tensor h_55_pad_0 = const()[name = string("h_55_pad_0"), val = tensor([0, 0])]; + tensor h_55_dilations_0 = const()[name = string("h_55_dilations_0"), val = tensor([1])]; + int32 h_55_groups_0 = const()[name = string("h_55_groups_0"), val = int32(1)]; + tensor op_864_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10209024))))[name = string("op_864_weight_0_to_fp16_palettized")]; + tensor var_864_bias_0_to_fp16 = const()[name = string("op_864_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10209152)))]; + tensor var_864_cast_fp16 = conv(bias = var_864_bias_0_to_fp16, dilations = h_55_dilations_0, groups = h_55_groups_0, pad = h_55_pad_0, pad_type = h_55_pad_type_0, strides = h_55_strides_0, weight = op_864_weight_0_to_fp16_palettized, x = input_137_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor out_21_cast_fp16 = add(x = x_67_cast_fp16, y = var_864_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = out_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor x_73_cast_fp16 = mul(x = x_71_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_878_cast_fp16 = mul(x = x_73_cast_fp16, y = last_1_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor x_last_19_axes_0 = const()[name = string("x_last_19_axes_0"), val = tensor([-1])]; + bool x_last_19_keep_dims_0 = const()[name = string("x_last_19_keep_dims_0"), val = bool(true)]; + tensor x_last_19_cast_fp16 = reduce_sum(axes = x_last_19_axes_0, keep_dims = x_last_19_keep_dims_0, x = var_878_cast_fp16)[name = string("x_last_19_cast_fp16")]; + tensor var_881_cast_fp16 = mul(x = x_73_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_881_cast_fp16")]; + tensor var_883_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_19_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = var_881_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("replicate")]; + fp16 const_30_to_fp16 = const()[name = string("const_30_to_fp16"), val = fp16(0x0p+0)]; + tensor input_143_cast_fp16 = pad(constant_val = const_30_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + string h_57_pad_type_0 = const()[name = string("h_57_pad_type_0"), val = string("valid")]; + tensor h_57_dilations_0 = const()[name = string("h_57_dilations_0"), val = tensor([8])]; + int32 h_57_groups_0 = const()[name = string("h_57_groups_0"), val = int32(512)]; + tensor h_57_strides_0 = const()[name = string("h_57_strides_0"), val = tensor([1])]; + tensor h_57_pad_0 = const()[name = string("h_57_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10210240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10211584))))[name = string("main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10211712)))]; + tensor h_57_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_57_dilations_0, groups = h_57_groups_0, pad = h_57_pad_0, pad_type = h_57_pad_type_0, strides = h_57_strides_0, weight = main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = string("h_57_cast_fp16")]; + tensor x_75_cast_fp16 = mul(x = h_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_901_axes_0 = const()[name = string("op_901_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10212800)))]; + tensor main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10213888)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_75_cast_fp16)[name = string("transpose_82")]; + tensor var_901_cast_fp16 = layer_norm(axes = var_901_axes_0, beta = main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor input_147_perm_0 = const()[name = string("input_147_perm_0"), val = tensor([0, 2, 1])]; + string h_59_pad_type_0 = const()[name = string("h_59_pad_type_0"), val = string("valid")]; + tensor h_59_strides_0 = const()[name = string("h_59_strides_0"), val = tensor([1])]; + tensor h_59_pad_0 = const()[name = string("h_59_pad_0"), val = tensor([0, 0])]; + tensor h_59_dilations_0 = const()[name = string("h_59_dilations_0"), val = tensor([1])]; + int32 h_59_groups_0 = const()[name = string("h_59_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10214976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10739328))))[name = string("main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10739456)))]; + tensor input_147_cast_fp16 = transpose(perm = input_147_perm_0, x = var_901_cast_fp16)[name = string("transpose_81")]; + tensor h_59_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_59_dilations_0, groups = h_59_groups_0, pad = h_59_pad_0, pad_type = h_59_pad_type_0, strides = h_59_strides_0, weight = main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("h_59_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = h_59_cast_fp16)[name = string("input_149_cast_fp16")]; + string h_61_pad_type_0 = const()[name = string("h_61_pad_type_0"), val = string("valid")]; + tensor h_61_strides_0 = const()[name = string("h_61_strides_0"), val = tensor([1])]; + tensor h_61_pad_0 = const()[name = string("h_61_pad_0"), val = tensor([0, 0])]; + tensor h_61_dilations_0 = const()[name = string("h_61_dilations_0"), val = tensor([1])]; + int32 h_61_groups_0 = const()[name = string("h_61_groups_0"), val = int32(1)]; + tensor op_918_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10743616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11267968))))[name = string("op_918_weight_0_to_fp16_palettized")]; + tensor var_918_bias_0_to_fp16 = const()[name = string("op_918_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11268096)))]; + tensor var_918_cast_fp16 = conv(bias = var_918_bias_0_to_fp16, dilations = h_61_dilations_0, groups = h_61_groups_0, pad = h_61_pad_0, pad_type = h_61_pad_type_0, strides = h_61_strides_0, weight = op_918_weight_0_to_fp16_palettized, x = input_149_cast_fp16)[name = string("op_918_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = x_73_cast_fp16, y = var_918_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor x_77_cast_fp16 = mul(x = out_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11269184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11285632))))[name = string("main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11285760)))]; + tensor linear_11_cast_fp16 = linear(bias = main_blocks_1_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor t_7_axes_0 = const()[name = string("t_7_axes_0"), val = tensor([-1])]; + tensor t_7_cast_fp16 = expand_dims(axes = t_7_axes_0, x = linear_11_cast_fp16)[name = string("t_7_cast_fp16")]; + tensor var_928_cast_fp16 = add(x = x_77_cast_fp16, y = t_7_cast_fp16)[name = string("op_928_cast_fp16")]; + tensor x_79_cast_fp16 = mul(x = var_928_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor x_81_cast_fp16 = mul(x = x_79_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = x_81_cast_fp16, y = last_1_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor x_last_21_axes_0 = const()[name = string("x_last_21_axes_0"), val = tensor([-1])]; + bool x_last_21_keep_dims_0 = const()[name = string("x_last_21_keep_dims_0"), val = bool(true)]; + tensor x_last_21_cast_fp16 = reduce_sum(axes = x_last_21_axes_0, keep_dims = x_last_21_keep_dims_0, x = var_941_cast_fp16)[name = string("x_last_21_cast_fp16")]; + tensor var_944_cast_fp16 = mul(x = x_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor var_946_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_21_cast_fp16)[name = string("op_946_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = var_944_cast_fp16, y = var_946_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("replicate")]; + fp16 const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = fp16(0x0p+0)]; + tensor input_157_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string h_63_pad_type_0 = const()[name = string("h_63_pad_type_0"), val = string("valid")]; + int32 h_63_groups_0 = const()[name = string("h_63_groups_0"), val = int32(512)]; + tensor h_63_strides_0 = const()[name = string("h_63_strides_0"), val = tensor([1])]; + tensor h_63_pad_0 = const()[name = string("h_63_pad_0"), val = tensor([0, 0])]; + tensor h_63_dilations_0 = const()[name = string("h_63_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11286848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11288192))))[name = string("main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11288320)))]; + tensor h_63_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_63_dilations_0, groups = h_63_groups_0, pad = h_63_pad_0, pad_type = h_63_pad_type_0, strides = h_63_strides_0, weight = main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor x_83_cast_fp16 = mul(x = h_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor var_964_axes_0 = const()[name = string("op_964_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11289408)))]; + tensor main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11290496)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_83_cast_fp16)[name = string("transpose_80")]; + tensor var_964_cast_fp16 = layer_norm(axes = var_964_axes_0, beta = main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + string h_65_pad_type_0 = const()[name = string("h_65_pad_type_0"), val = string("valid")]; + tensor h_65_strides_0 = const()[name = string("h_65_strides_0"), val = tensor([1])]; + tensor h_65_pad_0 = const()[name = string("h_65_pad_0"), val = tensor([0, 0])]; + tensor h_65_dilations_0 = const()[name = string("h_65_dilations_0"), val = tensor([1])]; + int32 h_65_groups_0 = const()[name = string("h_65_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11291584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11815936))))[name = string("main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11816064)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = var_964_cast_fp16)[name = string("transpose_79")]; + tensor h_65_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_65_dilations_0, groups = h_65_groups_0, pad = h_65_pad_0, pad_type = h_65_pad_type_0, strides = h_65_strides_0, weight = main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = string("h_65_cast_fp16")]; + string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("EXACT")]; + tensor input_163_cast_fp16 = gelu(mode = input_163_mode_0, x = h_65_cast_fp16)[name = string("input_163_cast_fp16")]; + string h_67_pad_type_0 = const()[name = string("h_67_pad_type_0"), val = string("valid")]; + tensor h_67_strides_0 = const()[name = string("h_67_strides_0"), val = tensor([1])]; + tensor h_67_pad_0 = const()[name = string("h_67_pad_0"), val = tensor([0, 0])]; + tensor h_67_dilations_0 = const()[name = string("h_67_dilations_0"), val = tensor([1])]; + int32 h_67_groups_0 = const()[name = string("h_67_groups_0"), val = int32(1)]; + tensor op_981_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11820224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12344576))))[name = string("op_981_weight_0_to_fp16_palettized")]; + tensor var_981_bias_0_to_fp16 = const()[name = string("op_981_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12344704)))]; + tensor var_981_cast_fp16 = conv(bias = var_981_bias_0_to_fp16, dilations = h_67_dilations_0, groups = h_67_groups_0, pad = h_67_pad_0, pad_type = h_67_pad_type_0, strides = h_67_strides_0, weight = op_981_weight_0_to_fp16_palettized, x = input_163_cast_fp16)[name = string("op_981_cast_fp16")]; + tensor out_25_cast_fp16 = add(x = x_81_cast_fp16, y = var_981_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = out_25_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor x_m_5_cast_fp16 = mul(x = x_85_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_5_cast_fp16")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12345792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12476928))))[name = string("main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12477056)))]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_85_cast_fp16)[name = string("transpose_78")]; + tensor linear_12_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_999 = const()[name = string("op_999"), val = tensor([2, 256, 8, 64])]; + tensor var_1000_cast_fp16 = reshape(shape = var_999, x = linear_12_cast_fp16)[name = string("op_1000_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12478144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12543744))))[name = string("main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12543872)))]; + tensor linear_13_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_1007 = const()[name = string("op_1007"), val = tensor([2, 128, 8, 64])]; + tensor var_1008_cast_fp16 = reshape(shape = var_1007, x = linear_13_cast_fp16)[name = string("op_1008_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12544960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12610560))))[name = string("main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12610688)))]; + tensor linear_14_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_1015 = const()[name = string("op_1015"), val = tensor([2, 128, 8, 64])]; + tensor var_1016_cast_fp16 = reshape(shape = var_1015, x = linear_14_cast_fp16)[name = string("op_1016_cast_fp16")]; + tensor v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_5_begin_0 = const()[name = string("x_a_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_5_end_0 = const()[name = string("x_a_5_end_0"), val = tensor([2, 8, 256, 32])]; + tensor x_a_5_end_mask_0 = const()[name = string("x_a_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_1000_cast_fp16)[name = string("transpose_77")]; + tensor x_a_5_cast_fp16 = slice_by_index(begin = x_a_5_begin_0, end = x_a_5_end_0, end_mask = x_a_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_a_5_cast_fp16")]; + tensor x_b_5_begin_0 = const()[name = string("x_b_5_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_5_end_0 = const()[name = string("x_b_5_end_0"), val = tensor([2, 8, 256, 64])]; + tensor x_b_5_end_mask_0 = const()[name = string("x_b_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_5_cast_fp16 = slice_by_index(begin = x_b_5_begin_0, end = x_b_5_end_0, end_mask = x_b_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_b_5_cast_fp16")]; + tensor var_1044_cast_fp16 = mul(x = x_a_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1044_cast_fp16")]; + tensor var_1045_cast_fp16 = mul(x = x_b_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor rot_a_5_cast_fp16 = sub(x = var_1044_cast_fp16, y = var_1045_cast_fp16)[name = string("rot_a_5_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_a_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_cast_fp16 = mul(x = x_b_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1048_cast_fp16")]; + tensor rot_b_5_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("rot_b_5_cast_fp16")]; + bool q_5_interleave_0 = const()[name = string("q_5_interleave_0"), val = bool(false)]; + tensor q_5_cast_fp16 = concat(axis = var_681, interleave = q_5_interleave_0, values = (rot_a_5_cast_fp16, rot_b_5_cast_fp16))[name = string("q_5_cast_fp16")]; + tensor x_a_7_begin_0 = const()[name = string("x_a_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_7_end_0 = const()[name = string("x_a_7_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_7_end_mask_0 = const()[name = string("x_a_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_1008_cast_fp16)[name = string("transpose_76")]; + tensor x_a_7_cast_fp16 = slice_by_index(begin = x_a_7_begin_0, end = x_a_7_end_0, end_mask = x_a_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_a_7_cast_fp16")]; + tensor x_b_7_begin_0 = const()[name = string("x_b_7_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_7_end_0 = const()[name = string("x_b_7_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_7_end_mask_0 = const()[name = string("x_b_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_7_cast_fp16 = slice_by_index(begin = x_b_7_begin_0, end = x_b_7_end_0, end_mask = x_b_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_b_7_cast_fp16")]; + tensor var_1062_cast_fp16 = mul(x = x_a_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = x_b_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor rot_a_7_cast_fp16 = sub(x = var_1062_cast_fp16, y = var_1063_cast_fp16)[name = string("rot_a_7_cast_fp16")]; + tensor var_1065_cast_fp16 = mul(x = x_a_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1065_cast_fp16")]; + tensor var_1066_cast_fp16 = mul(x = x_b_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1066_cast_fp16")]; + tensor rot_b_7_cast_fp16 = add(x = var_1065_cast_fp16, y = var_1066_cast_fp16)[name = string("rot_b_7_cast_fp16")]; + bool k_5_interleave_0 = const()[name = string("k_5_interleave_0"), val = bool(false)]; + tensor k_5_cast_fp16 = concat(axis = var_681, interleave = k_5_interleave_0, values = (rot_a_7_cast_fp16, rot_b_7_cast_fp16))[name = string("k_5_cast_fp16")]; + bool var_1071_transpose_x_1 = const()[name = string("op_1071_transpose_x_1"), val = bool(false)]; + bool var_1071_transpose_y_1 = const()[name = string("op_1071_transpose_y_1"), val = bool(true)]; + tensor var_1071_cast_fp16 = matmul(transpose_x = var_1071_transpose_x_1, transpose_y = var_1071_transpose_y_1, x = q_5_cast_fp16, y = k_5_cast_fp16)[name = string("op_1071_cast_fp16")]; + fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_3_cast_fp16 = mul(x = var_1071_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")]; + tensor input_171_cast_fp16 = sub(x = _inversed_scores_3_cast_fp16, y = var_530_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_681, x = input_171_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool var_1080_transpose_x_0 = const()[name = string("op_1080_transpose_x_0"), val = bool(false)]; + bool var_1080_transpose_y_0 = const()[name = string("op_1080_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1016_cast_fp16)[name = string("transpose_75")]; + tensor var_1080_cast_fp16 = matmul(transpose_x = var_1080_transpose_x_0, transpose_y = var_1080_transpose_y_0, x = attn_7_cast_fp16, y = v_5_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_perm_0 = const()[name = string("op_1081_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1082 = const()[name = string("op_1082"), val = tensor([2, 256, 512])]; + tensor var_1081_cast_fp16 = transpose(perm = var_1081_perm_0, x = var_1080_cast_fp16)[name = string("transpose_74")]; + tensor input_173_cast_fp16 = reshape(shape = var_1082, x = var_1081_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12611776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12742912))))[name = string("main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12743040)))]; + tensor linear_15_cast_fp16 = linear(bias = main_blocks_1_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor out_27_perm_0 = const()[name = string("out_27_perm_0"), val = tensor([0, 2, 1])]; + tensor out_27_cast_fp16 = transpose(perm = out_27_perm_0, x = linear_15_cast_fp16)[name = string("transpose_73")]; + tensor var_1089_cast_fp16 = mul(x = out_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor x_91_cast_fp16 = add(x = x_m_5_cast_fp16, y = var_1089_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_175_perm_0 = const()[name = string("input_175_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1096_axes_0 = const()[name = string("op_1096_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12744128)))]; + tensor main_blocks_1_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12745216)))]; + tensor input_175_cast_fp16 = transpose(perm = input_175_perm_0, x = x_91_cast_fp16)[name = string("transpose_72")]; + tensor var_1096_cast_fp16 = layer_norm(axes = var_1096_axes_0, beta = main_blocks_1_text_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_text_norm_norm_weight_to_fp16, x = input_175_cast_fp16)[name = string("op_1096_cast_fp16")]; + tensor var_1097_perm_0 = const()[name = string("op_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1097_cast_fp16 = transpose(perm = var_1097_perm_0, x = var_1096_cast_fp16)[name = string("transpose_71")]; + tensor x_93_cast_fp16 = mul(x = var_1097_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor x_95_cast_fp16 = mul(x = x_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_1110_cast_fp16 = mul(x = x_95_cast_fp16, y = last_1_cast_fp16)[name = string("op_1110_cast_fp16")]; + tensor x_last_23_axes_0 = const()[name = string("x_last_23_axes_0"), val = tensor([-1])]; + bool x_last_23_keep_dims_0 = const()[name = string("x_last_23_keep_dims_0"), val = bool(true)]; + tensor x_last_23_cast_fp16 = reduce_sum(axes = x_last_23_axes_0, keep_dims = x_last_23_keep_dims_0, x = var_1110_cast_fp16)[name = string("x_last_23_cast_fp16")]; + tensor var_1113_cast_fp16 = mul(x = x_95_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1115_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_23_cast_fp16)[name = string("op_1115_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = var_1113_cast_fp16, y = var_1115_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("replicate")]; + fp16 const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = fp16(0x0p+0)]; + tensor input_181_cast_fp16 = pad(constant_val = const_39_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + string h_69_pad_type_0 = const()[name = string("h_69_pad_type_0"), val = string("valid")]; + int32 h_69_groups_0 = const()[name = string("h_69_groups_0"), val = int32(512)]; + tensor h_69_strides_0 = const()[name = string("h_69_strides_0"), val = tensor([1])]; + tensor h_69_pad_0 = const()[name = string("h_69_pad_0"), val = tensor([0, 0])]; + tensor h_69_dilations_0 = const()[name = string("h_69_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12746304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12747648))))[name = string("main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12747776)))]; + tensor h_69_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_69_dilations_0, groups = h_69_groups_0, pad = h_69_pad_0, pad_type = h_69_pad_type_0, strides = h_69_strides_0, weight = main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = string("h_69_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = h_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_183_perm_0 = const()[name = string("input_183_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1133_axes_0 = const()[name = string("op_1133_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12748864)))]; + tensor main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12749952)))]; + tensor input_183_cast_fp16 = transpose(perm = input_183_perm_0, x = x_97_cast_fp16)[name = string("transpose_70")]; + tensor var_1133_cast_fp16 = layer_norm(axes = var_1133_axes_0, beta = main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16, x = input_183_cast_fp16)[name = string("op_1133_cast_fp16")]; + tensor input_185_perm_0 = const()[name = string("input_185_perm_0"), val = tensor([0, 2, 1])]; + string h_71_pad_type_0 = const()[name = string("h_71_pad_type_0"), val = string("valid")]; + tensor h_71_strides_0 = const()[name = string("h_71_strides_0"), val = tensor([1])]; + tensor h_71_pad_0 = const()[name = string("h_71_pad_0"), val = tensor([0, 0])]; + tensor h_71_dilations_0 = const()[name = string("h_71_dilations_0"), val = tensor([1])]; + int32 h_71_groups_0 = const()[name = string("h_71_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12751040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13275392))))[name = string("main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13275520)))]; + tensor input_185_cast_fp16 = transpose(perm = input_185_perm_0, x = var_1133_cast_fp16)[name = string("transpose_69")]; + tensor h_71_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_71_dilations_0, groups = h_71_groups_0, pad = h_71_pad_0, pad_type = h_71_pad_type_0, strides = h_71_strides_0, weight = main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = string("h_71_cast_fp16")]; + string input_187_mode_0 = const()[name = string("input_187_mode_0"), val = string("EXACT")]; + tensor input_187_cast_fp16 = gelu(mode = input_187_mode_0, x = h_71_cast_fp16)[name = string("input_187_cast_fp16")]; + string h_73_pad_type_0 = const()[name = string("h_73_pad_type_0"), val = string("valid")]; + tensor h_73_strides_0 = const()[name = string("h_73_strides_0"), val = tensor([1])]; + tensor h_73_pad_0 = const()[name = string("h_73_pad_0"), val = tensor([0, 0])]; + tensor h_73_dilations_0 = const()[name = string("h_73_dilations_0"), val = tensor([1])]; + int32 h_73_groups_0 = const()[name = string("h_73_groups_0"), val = int32(1)]; + tensor op_1150_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13279680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13804032))))[name = string("op_1150_weight_0_to_fp16_palettized")]; + tensor var_1150_bias_0_to_fp16 = const()[name = string("op_1150_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13804160)))]; + tensor var_1150_cast_fp16 = conv(bias = var_1150_bias_0_to_fp16, dilations = h_73_dilations_0, groups = h_73_groups_0, pad = h_73_pad_0, pad_type = h_73_pad_type_0, strides = h_73_strides_0, weight = op_1150_weight_0_to_fp16_palettized, x = input_187_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor out_29_cast_fp16 = add(x = x_95_cast_fp16, y = var_1150_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor x_99_cast_fp16 = mul(x = out_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor x_m_7_cast_fp16 = mul(x = x_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_7_cast_fp16")]; + tensor input_189_perm_0 = const()[name = string("input_189_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13805248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13870848))))[name = string("main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13870976)))]; + tensor input_189_cast_fp16 = transpose(perm = input_189_perm_0, x = x_99_cast_fp16)[name = string("transpose_68")]; + tensor linear_16_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_1166 = const()[name = string("op_1166"), val = tensor([2, 256, 2, 128])]; + tensor var_1167_cast_fp16 = reshape(shape = var_1166, x = linear_16_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13871552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13904384))))[name = string("main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13904512)))]; + tensor linear_18_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_1180 = const()[name = string("op_1180"), val = tensor([2, 50, 2, 128])]; + tensor var_1181_cast_fp16 = reshape(shape = var_1180, x = linear_18_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1185_transpose_x_0 = const()[name = string("op_1185_transpose_x_0"), val = bool(false)]; + bool var_1185_transpose_y_0 = const()[name = string("op_1185_transpose_y_0"), val = bool(false)]; + tensor op_1184_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13905088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13917952))))[name = string("op_1184_to_fp16_palettized")]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1167_cast_fp16)[name = string("transpose_67")]; + tensor var_1185_cast_fp16 = matmul(transpose_x = var_1185_transpose_x_0, transpose_y = var_1185_transpose_y_0, x = q_7_cast_fp16, y = op_1184_to_fp16_palettized)[name = string("op_1185_cast_fp16")]; + fp16 _inversed_input_191_y_0_to_fp16 = const()[name = string("_inversed_input_191_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_191_cast_fp16 = mul(x = var_1185_cast_fp16, y = _inversed_input_191_y_0_to_fp16)[name = string("_inversed_input_191_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_681, x = _inversed_input_191_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor attn_11_cast_fp16 = mul(x = attn_9_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool var_1192_transpose_x_0 = const()[name = string("op_1192_transpose_x_0"), val = bool(false)]; + bool var_1192_transpose_y_0 = const()[name = string("op_1192_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1181_cast_fp16)[name = string("transpose_66")]; + tensor var_1192_cast_fp16 = matmul(transpose_x = var_1192_transpose_x_0, transpose_y = var_1192_transpose_y_0, x = attn_11_cast_fp16, y = v_7_cast_fp16)[name = string("op_1192_cast_fp16")]; + tensor var_1193_perm_0 = const()[name = string("op_1193_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1194 = const()[name = string("op_1194"), val = tensor([2, 256, 256])]; + tensor var_1193_cast_fp16 = transpose(perm = var_1193_perm_0, x = var_1192_cast_fp16)[name = string("transpose_65")]; + tensor input_193_cast_fp16 = reshape(shape = var_1194, x = var_1193_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13918080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13983680))))[name = string("main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13983808)))]; + tensor linear_19_cast_fp16 = linear(bias = main_blocks_1_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_193_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor out_31_perm_0 = const()[name = string("out_31_perm_0"), val = tensor([0, 2, 1])]; + tensor out_31_cast_fp16 = transpose(perm = out_31_perm_0, x = linear_19_cast_fp16)[name = string("transpose_64")]; + tensor var_1201_cast_fp16 = mul(x = out_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1201_cast_fp16")]; + tensor x_101_cast_fp16 = add(x = x_m_7_cast_fp16, y = var_1201_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_195_perm_0 = const()[name = string("input_195_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1208_axes_0 = const()[name = string("op_1208_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13984896)))]; + tensor main_blocks_1_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13985984)))]; + tensor input_195_cast_fp16 = transpose(perm = input_195_perm_0, x = x_101_cast_fp16)[name = string("transpose_63")]; + tensor var_1208_cast_fp16 = layer_norm(axes = var_1208_axes_0, beta = main_blocks_1_style_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_style_norm_norm_weight_to_fp16, x = input_195_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_perm_0 = const()[name = string("op_1209_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1209_cast_fp16 = transpose(perm = var_1209_perm_0, x = var_1208_cast_fp16)[name = string("transpose_62")]; + tensor x_103_cast_fp16 = mul(x = var_1209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_103_cast_fp16")]; + int32 var_1228 = const()[name = string("op_1228"), val = int32(-1)]; + tensor x_105_cast_fp16 = mul(x = x_103_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_1263_cast_fp16 = mul(x = x_105_cast_fp16, y = last_1_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor x_last_25_axes_0 = const()[name = string("x_last_25_axes_0"), val = tensor([-1])]; + bool x_last_25_keep_dims_0 = const()[name = string("x_last_25_keep_dims_0"), val = bool(true)]; + tensor x_last_25_cast_fp16 = reduce_sum(axes = x_last_25_axes_0, keep_dims = x_last_25_keep_dims_0, x = var_1263_cast_fp16)[name = string("x_last_25_cast_fp16")]; + tensor var_1266_cast_fp16 = mul(x = x_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1266_cast_fp16")]; + tensor var_1268_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_25_cast_fp16)[name = string("op_1268_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1266_cast_fp16, y = var_1268_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_201_mode_0 = const()[name = string("input_201_mode_0"), val = string("replicate")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_201_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")]; + string h_75_pad_type_0 = const()[name = string("h_75_pad_type_0"), val = string("valid")]; + int32 h_75_groups_0 = const()[name = string("h_75_groups_0"), val = int32(512)]; + tensor h_75_strides_0 = const()[name = string("h_75_strides_0"), val = tensor([1])]; + tensor h_75_pad_0 = const()[name = string("h_75_pad_0"), val = tensor([0, 0])]; + tensor h_75_dilations_0 = const()[name = string("h_75_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13987072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13988416))))[name = string("main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13988544)))]; + tensor h_75_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_75_dilations_0, groups = h_75_groups_0, pad = h_75_pad_0, pad_type = h_75_pad_type_0, strides = h_75_strides_0, weight = main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_201_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = h_75_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_203_perm_0 = const()[name = string("input_203_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1286_axes_0 = const()[name = string("op_1286_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13989632)))]; + tensor main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13990720)))]; + fp16 var_1233_to_fp16 = const()[name = string("op_1233_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_203_cast_fp16 = transpose(perm = input_203_perm_0, x = x_107_cast_fp16)[name = string("transpose_61")]; + tensor var_1286_cast_fp16 = layer_norm(axes = var_1286_axes_0, beta = main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16, x = input_203_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string h_77_pad_type_0 = const()[name = string("h_77_pad_type_0"), val = string("valid")]; + tensor h_77_strides_0 = const()[name = string("h_77_strides_0"), val = tensor([1])]; + tensor h_77_pad_0 = const()[name = string("h_77_pad_0"), val = tensor([0, 0])]; + tensor h_77_dilations_0 = const()[name = string("h_77_dilations_0"), val = tensor([1])]; + int32 h_77_groups_0 = const()[name = string("h_77_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13991808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14516160))))[name = string("main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14516288)))]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = var_1286_cast_fp16)[name = string("transpose_60")]; + tensor h_77_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_77_dilations_0, groups = h_77_groups_0, pad = h_77_pad_0, pad_type = h_77_pad_type_0, strides = h_77_strides_0, weight = main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_205_cast_fp16)[name = string("h_77_cast_fp16")]; + string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")]; + tensor input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = h_77_cast_fp16)[name = string("input_207_cast_fp16")]; + string h_79_pad_type_0 = const()[name = string("h_79_pad_type_0"), val = string("valid")]; + tensor h_79_strides_0 = const()[name = string("h_79_strides_0"), val = tensor([1])]; + tensor h_79_pad_0 = const()[name = string("h_79_pad_0"), val = tensor([0, 0])]; + tensor h_79_dilations_0 = const()[name = string("h_79_dilations_0"), val = tensor([1])]; + int32 h_79_groups_0 = const()[name = string("h_79_groups_0"), val = int32(1)]; + tensor op_1303_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14520448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15044800))))[name = string("op_1303_weight_0_to_fp16_palettized")]; + tensor var_1303_bias_0_to_fp16 = const()[name = string("op_1303_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15044928)))]; + tensor var_1303_cast_fp16 = conv(bias = var_1303_bias_0_to_fp16, dilations = h_79_dilations_0, groups = h_79_groups_0, pad = h_79_pad_0, pad_type = h_79_pad_type_0, strides = h_79_strides_0, weight = op_1303_weight_0_to_fp16_palettized, x = input_207_cast_fp16)[name = string("op_1303_cast_fp16")]; + tensor out_33_cast_fp16 = add(x = x_105_cast_fp16, y = var_1303_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor x_109_cast_fp16 = mul(x = out_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor x_111_cast_fp16 = mul(x = x_109_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_1317_cast_fp16 = mul(x = x_111_cast_fp16, y = last_1_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor x_last_27_axes_0 = const()[name = string("x_last_27_axes_0"), val = tensor([-1])]; + bool x_last_27_keep_dims_0 = const()[name = string("x_last_27_keep_dims_0"), val = bool(true)]; + tensor x_last_27_cast_fp16 = reduce_sum(axes = x_last_27_axes_0, keep_dims = x_last_27_keep_dims_0, x = var_1317_cast_fp16)[name = string("x_last_27_cast_fp16")]; + tensor var_1320_cast_fp16 = mul(x = x_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1322_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_27_cast_fp16)[name = string("op_1322_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1320_cast_fp16, y = var_1322_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("replicate")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor input_213_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = input_213_mode_0, pad = input_213_pad_0, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + string h_81_pad_type_0 = const()[name = string("h_81_pad_type_0"), val = string("valid")]; + tensor h_81_dilations_0 = const()[name = string("h_81_dilations_0"), val = tensor([2])]; + int32 h_81_groups_0 = const()[name = string("h_81_groups_0"), val = int32(512)]; + tensor h_81_strides_0 = const()[name = string("h_81_strides_0"), val = tensor([1])]; + tensor h_81_pad_0 = const()[name = string("h_81_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15046016))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15047360))))[name = string("main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15047488)))]; + tensor h_81_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_81_dilations_0, groups = h_81_groups_0, pad = h_81_pad_0, pad_type = h_81_pad_type_0, strides = h_81_strides_0, weight = main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_213_cast_fp16)[name = string("h_81_cast_fp16")]; + tensor x_113_cast_fp16 = mul(x = h_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor input_215_perm_0 = const()[name = string("input_215_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1340_axes_0 = const()[name = string("op_1340_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15048576)))]; + tensor main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15049664)))]; + tensor input_215_cast_fp16 = transpose(perm = input_215_perm_0, x = x_113_cast_fp16)[name = string("transpose_59")]; + tensor var_1340_cast_fp16 = layer_norm(axes = var_1340_axes_0, beta = main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16, x = input_215_cast_fp16)[name = string("op_1340_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + string h_83_pad_type_0 = const()[name = string("h_83_pad_type_0"), val = string("valid")]; + tensor h_83_strides_0 = const()[name = string("h_83_strides_0"), val = tensor([1])]; + tensor h_83_pad_0 = const()[name = string("h_83_pad_0"), val = tensor([0, 0])]; + tensor h_83_dilations_0 = const()[name = string("h_83_dilations_0"), val = tensor([1])]; + int32 h_83_groups_0 = const()[name = string("h_83_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15050752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15575104))))[name = string("main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15575232)))]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = var_1340_cast_fp16)[name = string("transpose_58")]; + tensor h_83_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_83_dilations_0, groups = h_83_groups_0, pad = h_83_pad_0, pad_type = h_83_pad_type_0, strides = h_83_strides_0, weight = main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = string("h_83_cast_fp16")]; + string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")]; + tensor input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = h_83_cast_fp16)[name = string("input_219_cast_fp16")]; + string h_85_pad_type_0 = const()[name = string("h_85_pad_type_0"), val = string("valid")]; + tensor h_85_strides_0 = const()[name = string("h_85_strides_0"), val = tensor([1])]; + tensor h_85_pad_0 = const()[name = string("h_85_pad_0"), val = tensor([0, 0])]; + tensor h_85_dilations_0 = const()[name = string("h_85_dilations_0"), val = tensor([1])]; + int32 h_85_groups_0 = const()[name = string("h_85_groups_0"), val = int32(1)]; + tensor op_1357_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15579392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16103744))))[name = string("op_1357_weight_0_to_fp16_palettized")]; + tensor var_1357_bias_0_to_fp16 = const()[name = string("op_1357_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16103872)))]; + tensor var_1357_cast_fp16 = conv(bias = var_1357_bias_0_to_fp16, dilations = h_85_dilations_0, groups = h_85_groups_0, pad = h_85_pad_0, pad_type = h_85_pad_type_0, strides = h_85_strides_0, weight = op_1357_weight_0_to_fp16_palettized, x = input_219_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor out_35_cast_fp16 = add(x = x_111_cast_fp16, y = var_1357_cast_fp16)[name = string("out_35_cast_fp16")]; + tensor x_115_cast_fp16 = mul(x = out_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_cast_fp16 = mul(x = x_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1371_cast_fp16 = mul(x = x_117_cast_fp16, y = last_1_cast_fp16)[name = string("op_1371_cast_fp16")]; + tensor x_last_29_axes_0 = const()[name = string("x_last_29_axes_0"), val = tensor([-1])]; + bool x_last_29_keep_dims_0 = const()[name = string("x_last_29_keep_dims_0"), val = bool(true)]; + tensor x_last_29_cast_fp16 = reduce_sum(axes = x_last_29_axes_0, keep_dims = x_last_29_keep_dims_0, x = var_1371_cast_fp16)[name = string("x_last_29_cast_fp16")]; + tensor var_1374_cast_fp16 = mul(x = x_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_29_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1376_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_225_mode_0 = const()[name = string("input_225_mode_0"), val = string("replicate")]; + fp16 const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = fp16(0x0p+0)]; + tensor input_225_cast_fp16 = pad(constant_val = const_48_to_fp16, mode = input_225_mode_0, pad = input_225_pad_0, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")]; + string h_87_pad_type_0 = const()[name = string("h_87_pad_type_0"), val = string("valid")]; + tensor h_87_dilations_0 = const()[name = string("h_87_dilations_0"), val = tensor([4])]; + int32 h_87_groups_0 = const()[name = string("h_87_groups_0"), val = int32(512)]; + tensor h_87_strides_0 = const()[name = string("h_87_strides_0"), val = tensor([1])]; + tensor h_87_pad_0 = const()[name = string("h_87_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16104960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16106304))))[name = string("main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16106432)))]; + tensor h_87_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_87_dilations_0, groups = h_87_groups_0, pad = h_87_pad_0, pad_type = h_87_pad_type_0, strides = h_87_strides_0, weight = main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor x_119_cast_fp16 = mul(x = h_87_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_227_perm_0 = const()[name = string("input_227_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1394_axes_0 = const()[name = string("op_1394_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16107520)))]; + tensor main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16108608)))]; + tensor input_227_cast_fp16 = transpose(perm = input_227_perm_0, x = x_119_cast_fp16)[name = string("transpose_57")]; + tensor var_1394_cast_fp16 = layer_norm(axes = var_1394_axes_0, beta = main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16, x = input_227_cast_fp16)[name = string("op_1394_cast_fp16")]; + tensor input_229_perm_0 = const()[name = string("input_229_perm_0"), val = tensor([0, 2, 1])]; + string h_89_pad_type_0 = const()[name = string("h_89_pad_type_0"), val = string("valid")]; + tensor h_89_strides_0 = const()[name = string("h_89_strides_0"), val = tensor([1])]; + tensor h_89_pad_0 = const()[name = string("h_89_pad_0"), val = tensor([0, 0])]; + tensor h_89_dilations_0 = const()[name = string("h_89_dilations_0"), val = tensor([1])]; + int32 h_89_groups_0 = const()[name = string("h_89_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16109696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16634048))))[name = string("main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16634176)))]; + tensor input_229_cast_fp16 = transpose(perm = input_229_perm_0, x = var_1394_cast_fp16)[name = string("transpose_56")]; + tensor h_89_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_89_dilations_0, groups = h_89_groups_0, pad = h_89_pad_0, pad_type = h_89_pad_type_0, strides = h_89_strides_0, weight = main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = string("h_89_cast_fp16")]; + string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")]; + tensor input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = h_89_cast_fp16)[name = string("input_231_cast_fp16")]; + string h_91_pad_type_0 = const()[name = string("h_91_pad_type_0"), val = string("valid")]; + tensor h_91_strides_0 = const()[name = string("h_91_strides_0"), val = tensor([1])]; + tensor h_91_pad_0 = const()[name = string("h_91_pad_0"), val = tensor([0, 0])]; + tensor h_91_dilations_0 = const()[name = string("h_91_dilations_0"), val = tensor([1])]; + int32 h_91_groups_0 = const()[name = string("h_91_groups_0"), val = int32(1)]; + tensor op_1411_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16638336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17162688))))[name = string("op_1411_weight_0_to_fp16_palettized")]; + tensor var_1411_bias_0_to_fp16 = const()[name = string("op_1411_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17162816)))]; + tensor var_1411_cast_fp16 = conv(bias = var_1411_bias_0_to_fp16, dilations = h_91_dilations_0, groups = h_91_groups_0, pad = h_91_pad_0, pad_type = h_91_pad_type_0, strides = h_91_strides_0, weight = op_1411_weight_0_to_fp16_palettized, x = input_231_cast_fp16)[name = string("op_1411_cast_fp16")]; + tensor out_37_cast_fp16 = add(x = x_117_cast_fp16, y = var_1411_cast_fp16)[name = string("out_37_cast_fp16")]; + tensor x_121_cast_fp16 = mul(x = out_37_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_121_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1425_cast_fp16 = mul(x = x_123_cast_fp16, y = last_1_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor x_last_31_axes_0 = const()[name = string("x_last_31_axes_0"), val = tensor([-1])]; + bool x_last_31_keep_dims_0 = const()[name = string("x_last_31_keep_dims_0"), val = bool(true)]; + tensor x_last_31_cast_fp16 = reduce_sum(axes = x_last_31_axes_0, keep_dims = x_last_31_keep_dims_0, x = var_1425_cast_fp16)[name = string("x_last_31_cast_fp16")]; + tensor var_1428_cast_fp16 = mul(x = x_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_31_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = var_1428_cast_fp16, y = var_1430_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("replicate")]; + fp16 const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = fp16(0x0p+0)]; + tensor input_237_cast_fp16 = pad(constant_val = const_50_to_fp16, mode = input_237_mode_0, pad = input_237_pad_0, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + string h_93_pad_type_0 = const()[name = string("h_93_pad_type_0"), val = string("valid")]; + tensor h_93_dilations_0 = const()[name = string("h_93_dilations_0"), val = tensor([8])]; + int32 h_93_groups_0 = const()[name = string("h_93_groups_0"), val = int32(512)]; + tensor h_93_strides_0 = const()[name = string("h_93_strides_0"), val = tensor([1])]; + tensor h_93_pad_0 = const()[name = string("h_93_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17163904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17165248))))[name = string("main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17165376)))]; + tensor h_93_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_93_dilations_0, groups = h_93_groups_0, pad = h_93_pad_0, pad_type = h_93_pad_type_0, strides = h_93_strides_0, weight = main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = string("h_93_cast_fp16")]; + tensor x_125_cast_fp16 = mul(x = h_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_239_perm_0 = const()[name = string("input_239_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1448_axes_0 = const()[name = string("op_1448_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17166464)))]; + tensor main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17167552)))]; + tensor input_239_cast_fp16 = transpose(perm = input_239_perm_0, x = x_125_cast_fp16)[name = string("transpose_55")]; + tensor var_1448_cast_fp16 = layer_norm(axes = var_1448_axes_0, beta = main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16, x = input_239_cast_fp16)[name = string("op_1448_cast_fp16")]; + tensor input_241_perm_0 = const()[name = string("input_241_perm_0"), val = tensor([0, 2, 1])]; + string h_95_pad_type_0 = const()[name = string("h_95_pad_type_0"), val = string("valid")]; + tensor h_95_strides_0 = const()[name = string("h_95_strides_0"), val = tensor([1])]; + tensor h_95_pad_0 = const()[name = string("h_95_pad_0"), val = tensor([0, 0])]; + tensor h_95_dilations_0 = const()[name = string("h_95_dilations_0"), val = tensor([1])]; + int32 h_95_groups_0 = const()[name = string("h_95_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17168640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17692992))))[name = string("main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17693120)))]; + tensor input_241_cast_fp16 = transpose(perm = input_241_perm_0, x = var_1448_cast_fp16)[name = string("transpose_54")]; + tensor h_95_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_95_dilations_0, groups = h_95_groups_0, pad = h_95_pad_0, pad_type = h_95_pad_type_0, strides = h_95_strides_0, weight = main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_241_cast_fp16)[name = string("h_95_cast_fp16")]; + string input_243_mode_0 = const()[name = string("input_243_mode_0"), val = string("EXACT")]; + tensor input_243_cast_fp16 = gelu(mode = input_243_mode_0, x = h_95_cast_fp16)[name = string("input_243_cast_fp16")]; + string h_97_pad_type_0 = const()[name = string("h_97_pad_type_0"), val = string("valid")]; + tensor h_97_strides_0 = const()[name = string("h_97_strides_0"), val = tensor([1])]; + tensor h_97_pad_0 = const()[name = string("h_97_pad_0"), val = tensor([0, 0])]; + tensor h_97_dilations_0 = const()[name = string("h_97_dilations_0"), val = tensor([1])]; + int32 h_97_groups_0 = const()[name = string("h_97_groups_0"), val = int32(1)]; + tensor op_1465_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17697280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18221632))))[name = string("op_1465_weight_0_to_fp16_palettized")]; + tensor var_1465_bias_0_to_fp16 = const()[name = string("op_1465_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18221760)))]; + tensor var_1465_cast_fp16 = conv(bias = var_1465_bias_0_to_fp16, dilations = h_97_dilations_0, groups = h_97_groups_0, pad = h_97_pad_0, pad_type = h_97_pad_type_0, strides = h_97_strides_0, weight = op_1465_weight_0_to_fp16_palettized, x = input_243_cast_fp16)[name = string("op_1465_cast_fp16")]; + tensor out_39_cast_fp16 = add(x = x_123_cast_fp16, y = var_1465_cast_fp16)[name = string("out_39_cast_fp16")]; + tensor x_127_cast_fp16 = mul(x = out_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18222848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18239296))))[name = string("main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18239424)))]; + tensor linear_20_cast_fp16 = linear(bias = main_blocks_2_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor t_9_axes_0 = const()[name = string("t_9_axes_0"), val = tensor([-1])]; + tensor t_9_cast_fp16 = expand_dims(axes = t_9_axes_0, x = linear_20_cast_fp16)[name = string("t_9_cast_fp16")]; + tensor var_1475_cast_fp16 = add(x = x_127_cast_fp16, y = t_9_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = var_1475_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor x_131_cast_fp16 = mul(x = x_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor var_1488_cast_fp16 = mul(x = x_131_cast_fp16, y = last_1_cast_fp16)[name = string("op_1488_cast_fp16")]; + tensor x_last_33_axes_0 = const()[name = string("x_last_33_axes_0"), val = tensor([-1])]; + bool x_last_33_keep_dims_0 = const()[name = string("x_last_33_keep_dims_0"), val = bool(true)]; + tensor x_last_33_cast_fp16 = reduce_sum(axes = x_last_33_axes_0, keep_dims = x_last_33_keep_dims_0, x = var_1488_cast_fp16)[name = string("x_last_33_cast_fp16")]; + tensor var_1491_cast_fp16 = mul(x = x_131_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1491_cast_fp16")]; + tensor var_1493_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_33_cast_fp16)[name = string("op_1493_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = var_1491_cast_fp16, y = var_1493_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_251_mode_0 = const()[name = string("input_251_mode_0"), val = string("replicate")]; + fp16 const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = fp16(0x0p+0)]; + tensor input_251_cast_fp16 = pad(constant_val = const_52_to_fp16, mode = input_251_mode_0, pad = input_251_pad_0, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + string h_99_pad_type_0 = const()[name = string("h_99_pad_type_0"), val = string("valid")]; + int32 h_99_groups_0 = const()[name = string("h_99_groups_0"), val = int32(512)]; + tensor h_99_strides_0 = const()[name = string("h_99_strides_0"), val = tensor([1])]; + tensor h_99_pad_0 = const()[name = string("h_99_pad_0"), val = tensor([0, 0])]; + tensor h_99_dilations_0 = const()[name = string("h_99_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18240512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18241856))))[name = string("main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18241984)))]; + tensor h_99_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_99_dilations_0, groups = h_99_groups_0, pad = h_99_pad_0, pad_type = h_99_pad_type_0, strides = h_99_strides_0, weight = main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor x_133_cast_fp16 = mul(x = h_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_253_perm_0 = const()[name = string("input_253_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1511_axes_0 = const()[name = string("op_1511_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18243072)))]; + tensor main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18244160)))]; + tensor input_253_cast_fp16 = transpose(perm = input_253_perm_0, x = x_133_cast_fp16)[name = string("transpose_53")]; + tensor var_1511_cast_fp16 = layer_norm(axes = var_1511_axes_0, beta = main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16, x = input_253_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor input_255_perm_0 = const()[name = string("input_255_perm_0"), val = tensor([0, 2, 1])]; + string h_101_pad_type_0 = const()[name = string("h_101_pad_type_0"), val = string("valid")]; + tensor h_101_strides_0 = const()[name = string("h_101_strides_0"), val = tensor([1])]; + tensor h_101_pad_0 = const()[name = string("h_101_pad_0"), val = tensor([0, 0])]; + tensor h_101_dilations_0 = const()[name = string("h_101_dilations_0"), val = tensor([1])]; + int32 h_101_groups_0 = const()[name = string("h_101_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18245248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18769600))))[name = string("main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18769728)))]; + tensor input_255_cast_fp16 = transpose(perm = input_255_perm_0, x = var_1511_cast_fp16)[name = string("transpose_52")]; + tensor h_101_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_101_dilations_0, groups = h_101_groups_0, pad = h_101_pad_0, pad_type = h_101_pad_type_0, strides = h_101_strides_0, weight = main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_255_cast_fp16)[name = string("h_101_cast_fp16")]; + string input_257_mode_0 = const()[name = string("input_257_mode_0"), val = string("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = h_101_cast_fp16)[name = string("input_257_cast_fp16")]; + string h_103_pad_type_0 = const()[name = string("h_103_pad_type_0"), val = string("valid")]; + tensor h_103_strides_0 = const()[name = string("h_103_strides_0"), val = tensor([1])]; + tensor h_103_pad_0 = const()[name = string("h_103_pad_0"), val = tensor([0, 0])]; + tensor h_103_dilations_0 = const()[name = string("h_103_dilations_0"), val = tensor([1])]; + int32 h_103_groups_0 = const()[name = string("h_103_groups_0"), val = int32(1)]; + tensor op_1528_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18773888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19298240))))[name = string("op_1528_weight_0_to_fp16_palettized")]; + tensor var_1528_bias_0_to_fp16 = const()[name = string("op_1528_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19298368)))]; + tensor var_1528_cast_fp16 = conv(bias = var_1528_bias_0_to_fp16, dilations = h_103_dilations_0, groups = h_103_groups_0, pad = h_103_pad_0, pad_type = h_103_pad_type_0, strides = h_103_strides_0, weight = op_1528_weight_0_to_fp16_palettized, x = input_257_cast_fp16)[name = string("op_1528_cast_fp16")]; + tensor out_41_cast_fp16 = add(x = x_131_cast_fp16, y = var_1528_cast_fp16)[name = string("out_41_cast_fp16")]; + tensor x_135_cast_fp16 = mul(x = out_41_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor x_m_9_cast_fp16 = mul(x = x_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_9_cast_fp16")]; + tensor input_259_perm_0 = const()[name = string("input_259_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19299456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19430592))))[name = string("main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19430720)))]; + tensor input_259_cast_fp16 = transpose(perm = input_259_perm_0, x = x_135_cast_fp16)[name = string("transpose_51")]; + tensor linear_21_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_259_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_1546 = const()[name = string("op_1546"), val = tensor([2, 256, 8, 64])]; + tensor var_1547_cast_fp16 = reshape(shape = var_1546, x = linear_21_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor x_137_perm_0 = const()[name = string("x_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19431808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19497408))))[name = string("main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19497536)))]; + tensor linear_22_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_1554 = const()[name = string("op_1554"), val = tensor([2, 128, 8, 64])]; + tensor var_1555_cast_fp16 = reshape(shape = var_1554, x = linear_22_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_139_perm_0 = const()[name = string("x_139_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19498624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19564224))))[name = string("main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19564352)))]; + tensor linear_23_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_1562 = const()[name = string("op_1562"), val = tensor([2, 128, 8, 64])]; + tensor var_1563_cast_fp16 = reshape(shape = var_1562, x = linear_23_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_9_begin_0 = const()[name = string("x_a_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_9_end_0 = const()[name = string("x_a_9_end_0"), val = tensor([2, 8, 256, 32])]; + tensor x_a_9_end_mask_0 = const()[name = string("x_a_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_137_cast_fp16 = transpose(perm = x_137_perm_0, x = var_1547_cast_fp16)[name = string("transpose_50")]; + tensor x_a_9_cast_fp16 = slice_by_index(begin = x_a_9_begin_0, end = x_a_9_end_0, end_mask = x_a_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_a_9_cast_fp16")]; + tensor x_b_9_begin_0 = const()[name = string("x_b_9_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_9_end_0 = const()[name = string("x_b_9_end_0"), val = tensor([2, 8, 256, 64])]; + tensor x_b_9_end_mask_0 = const()[name = string("x_b_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_9_cast_fp16 = slice_by_index(begin = x_b_9_begin_0, end = x_b_9_end_0, end_mask = x_b_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_b_9_cast_fp16")]; + tensor var_1591_cast_fp16 = mul(x = x_a_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1591_cast_fp16")]; + tensor var_1592_cast_fp16 = mul(x = x_b_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor rot_a_9_cast_fp16 = sub(x = var_1591_cast_fp16, y = var_1592_cast_fp16)[name = string("rot_a_9_cast_fp16")]; + tensor var_1594_cast_fp16 = mul(x = x_a_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595_cast_fp16 = mul(x = x_b_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1595_cast_fp16")]; + tensor rot_b_9_cast_fp16 = add(x = var_1594_cast_fp16, y = var_1595_cast_fp16)[name = string("rot_b_9_cast_fp16")]; + bool q_9_interleave_0 = const()[name = string("q_9_interleave_0"), val = bool(false)]; + tensor q_9_cast_fp16 = concat(axis = var_1228, interleave = q_9_interleave_0, values = (rot_a_9_cast_fp16, rot_b_9_cast_fp16))[name = string("q_9_cast_fp16")]; + tensor x_a_11_begin_0 = const()[name = string("x_a_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_11_end_0 = const()[name = string("x_a_11_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_11_end_mask_0 = const()[name = string("x_a_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_139_cast_fp16 = transpose(perm = x_139_perm_0, x = var_1555_cast_fp16)[name = string("transpose_49")]; + tensor x_a_11_cast_fp16 = slice_by_index(begin = x_a_11_begin_0, end = x_a_11_end_0, end_mask = x_a_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_a_11_cast_fp16")]; + tensor x_b_11_begin_0 = const()[name = string("x_b_11_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_11_end_0 = const()[name = string("x_b_11_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_11_end_mask_0 = const()[name = string("x_b_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_11_cast_fp16 = slice_by_index(begin = x_b_11_begin_0, end = x_b_11_end_0, end_mask = x_b_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_b_11_cast_fp16")]; + tensor var_1609_cast_fp16 = mul(x = x_a_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor var_1610_cast_fp16 = mul(x = x_b_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor rot_a_11_cast_fp16 = sub(x = var_1609_cast_fp16, y = var_1610_cast_fp16)[name = string("rot_a_11_cast_fp16")]; + tensor var_1612_cast_fp16 = mul(x = x_a_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1612_cast_fp16")]; + tensor var_1613_cast_fp16 = mul(x = x_b_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1613_cast_fp16")]; + tensor rot_b_11_cast_fp16 = add(x = var_1612_cast_fp16, y = var_1613_cast_fp16)[name = string("rot_b_11_cast_fp16")]; + bool k_9_interleave_0 = const()[name = string("k_9_interleave_0"), val = bool(false)]; + tensor k_9_cast_fp16 = concat(axis = var_1228, interleave = k_9_interleave_0, values = (rot_a_11_cast_fp16, rot_b_11_cast_fp16))[name = string("k_9_cast_fp16")]; + bool var_1618_transpose_x_1 = const()[name = string("op_1618_transpose_x_1"), val = bool(false)]; + bool var_1618_transpose_y_1 = const()[name = string("op_1618_transpose_y_1"), val = bool(true)]; + tensor var_1618_cast_fp16 = matmul(transpose_x = var_1618_transpose_x_1, transpose_y = var_1618_transpose_y_1, x = q_9_cast_fp16, y = k_9_cast_fp16)[name = string("op_1618_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_1618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor input_265_cast_fp16 = sub(x = _inversed_scores_5_cast_fp16, y = var_530_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1228, x = input_265_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool var_1627_transpose_x_0 = const()[name = string("op_1627_transpose_x_0"), val = bool(false)]; + bool var_1627_transpose_y_0 = const()[name = string("op_1627_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1563_cast_fp16)[name = string("transpose_48")]; + tensor var_1627_cast_fp16 = matmul(transpose_x = var_1627_transpose_x_0, transpose_y = var_1627_transpose_y_0, x = attn_13_cast_fp16, y = v_9_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor var_1628_perm_0 = const()[name = string("op_1628_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1629 = const()[name = string("op_1629"), val = tensor([2, 256, 512])]; + tensor var_1628_cast_fp16 = transpose(perm = var_1628_perm_0, x = var_1627_cast_fp16)[name = string("transpose_47")]; + tensor input_267_cast_fp16 = reshape(shape = var_1629, x = var_1628_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19565440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19696576))))[name = string("main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19696704)))]; + tensor linear_24_cast_fp16 = linear(bias = main_blocks_2_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor out_43_perm_0 = const()[name = string("out_43_perm_0"), val = tensor([0, 2, 1])]; + tensor out_43_cast_fp16 = transpose(perm = out_43_perm_0, x = linear_24_cast_fp16)[name = string("transpose_46")]; + tensor var_1636_cast_fp16 = mul(x = out_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1636_cast_fp16")]; + tensor x_141_cast_fp16 = add(x = x_m_9_cast_fp16, y = var_1636_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1643_axes_0 = const()[name = string("op_1643_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19697792)))]; + tensor main_blocks_2_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19698880)))]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_141_cast_fp16)[name = string("transpose_45")]; + tensor var_1643_cast_fp16 = layer_norm(axes = var_1643_axes_0, beta = main_blocks_2_text_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_text_norm_norm_weight_to_fp16, x = input_269_cast_fp16)[name = string("op_1643_cast_fp16")]; + tensor var_1644_perm_0 = const()[name = string("op_1644_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1644_cast_fp16 = transpose(perm = var_1644_perm_0, x = var_1643_cast_fp16)[name = string("transpose_44")]; + tensor x_143_cast_fp16 = mul(x = var_1644_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor x_145_cast_fp16 = mul(x = x_143_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1657_cast_fp16 = mul(x = x_145_cast_fp16, y = last_1_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor x_last_35_axes_0 = const()[name = string("x_last_35_axes_0"), val = tensor([-1])]; + bool x_last_35_keep_dims_0 = const()[name = string("x_last_35_keep_dims_0"), val = bool(true)]; + tensor x_last_35_cast_fp16 = reduce_sum(axes = x_last_35_axes_0, keep_dims = x_last_35_keep_dims_0, x = var_1657_cast_fp16)[name = string("x_last_35_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = x_145_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1662_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_35_cast_fp16)[name = string("op_1662_cast_fp16")]; + tensor input_273_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1662_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("replicate")]; + fp16 const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = fp16(0x0p+0)]; + tensor input_275_cast_fp16 = pad(constant_val = const_59_to_fp16, mode = input_275_mode_0, pad = input_275_pad_0, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")]; + string h_105_pad_type_0 = const()[name = string("h_105_pad_type_0"), val = string("valid")]; + int32 h_105_groups_0 = const()[name = string("h_105_groups_0"), val = int32(512)]; + tensor h_105_strides_0 = const()[name = string("h_105_strides_0"), val = tensor([1])]; + tensor h_105_pad_0 = const()[name = string("h_105_pad_0"), val = tensor([0, 0])]; + tensor h_105_dilations_0 = const()[name = string("h_105_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19699968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19701312))))[name = string("main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19701440)))]; + tensor h_105_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_105_dilations_0, groups = h_105_groups_0, pad = h_105_pad_0, pad_type = h_105_pad_type_0, strides = h_105_strides_0, weight = main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_275_cast_fp16)[name = string("h_105_cast_fp16")]; + tensor x_147_cast_fp16 = mul(x = h_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_277_perm_0 = const()[name = string("input_277_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1680_axes_0 = const()[name = string("op_1680_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19702528)))]; + tensor main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19703616)))]; + tensor input_277_cast_fp16 = transpose(perm = input_277_perm_0, x = x_147_cast_fp16)[name = string("transpose_43")]; + tensor var_1680_cast_fp16 = layer_norm(axes = var_1680_axes_0, beta = main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16, x = input_277_cast_fp16)[name = string("op_1680_cast_fp16")]; + tensor input_279_perm_0 = const()[name = string("input_279_perm_0"), val = tensor([0, 2, 1])]; + string h_107_pad_type_0 = const()[name = string("h_107_pad_type_0"), val = string("valid")]; + tensor h_107_strides_0 = const()[name = string("h_107_strides_0"), val = tensor([1])]; + tensor h_107_pad_0 = const()[name = string("h_107_pad_0"), val = tensor([0, 0])]; + tensor h_107_dilations_0 = const()[name = string("h_107_dilations_0"), val = tensor([1])]; + int32 h_107_groups_0 = const()[name = string("h_107_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19704704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20229056))))[name = string("main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20229184)))]; + tensor input_279_cast_fp16 = transpose(perm = input_279_perm_0, x = var_1680_cast_fp16)[name = string("transpose_42")]; + tensor h_107_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_107_dilations_0, groups = h_107_groups_0, pad = h_107_pad_0, pad_type = h_107_pad_type_0, strides = h_107_strides_0, weight = main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_279_cast_fp16)[name = string("h_107_cast_fp16")]; + string input_281_mode_0 = const()[name = string("input_281_mode_0"), val = string("EXACT")]; + tensor input_281_cast_fp16 = gelu(mode = input_281_mode_0, x = h_107_cast_fp16)[name = string("input_281_cast_fp16")]; + string h_109_pad_type_0 = const()[name = string("h_109_pad_type_0"), val = string("valid")]; + tensor h_109_strides_0 = const()[name = string("h_109_strides_0"), val = tensor([1])]; + tensor h_109_pad_0 = const()[name = string("h_109_pad_0"), val = tensor([0, 0])]; + tensor h_109_dilations_0 = const()[name = string("h_109_dilations_0"), val = tensor([1])]; + int32 h_109_groups_0 = const()[name = string("h_109_groups_0"), val = int32(1)]; + tensor op_1697_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20233344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20757696))))[name = string("op_1697_weight_0_to_fp16_palettized")]; + tensor var_1697_bias_0_to_fp16 = const()[name = string("op_1697_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20757824)))]; + tensor var_1697_cast_fp16 = conv(bias = var_1697_bias_0_to_fp16, dilations = h_109_dilations_0, groups = h_109_groups_0, pad = h_109_pad_0, pad_type = h_109_pad_type_0, strides = h_109_strides_0, weight = op_1697_weight_0_to_fp16_palettized, x = input_281_cast_fp16)[name = string("op_1697_cast_fp16")]; + tensor out_45_cast_fp16 = add(x = x_145_cast_fp16, y = var_1697_cast_fp16)[name = string("out_45_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = out_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor x_m_11_cast_fp16 = mul(x = x_149_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_11_cast_fp16")]; + tensor input_283_perm_0 = const()[name = string("input_283_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20758912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20824512))))[name = string("main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20824640)))]; + tensor input_283_cast_fp16 = transpose(perm = input_283_perm_0, x = x_149_cast_fp16)[name = string("transpose_41")]; + tensor linear_25_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_283_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_1713 = const()[name = string("op_1713"), val = tensor([2, 256, 2, 128])]; + tensor var_1714_cast_fp16 = reshape(shape = var_1713, x = linear_25_cast_fp16)[name = string("op_1714_cast_fp16")]; + tensor q_11_perm_0 = const()[name = string("q_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20825216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20858048))))[name = string("main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20858176)))]; + tensor linear_27_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1727 = const()[name = string("op_1727"), val = tensor([2, 50, 2, 128])]; + tensor var_1728_cast_fp16 = reshape(shape = var_1727, x = linear_27_cast_fp16)[name = string("op_1728_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1732_transpose_x_0 = const()[name = string("op_1732_transpose_x_0"), val = bool(false)]; + bool var_1732_transpose_y_0 = const()[name = string("op_1732_transpose_y_0"), val = bool(false)]; + tensor op_1731_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20858752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20871616))))[name = string("op_1731_to_fp16_palettized")]; + tensor q_11_cast_fp16 = transpose(perm = q_11_perm_0, x = var_1714_cast_fp16)[name = string("transpose_40")]; + tensor var_1732_cast_fp16 = matmul(transpose_x = var_1732_transpose_x_0, transpose_y = var_1732_transpose_y_0, x = q_11_cast_fp16, y = op_1731_to_fp16_palettized)[name = string("op_1732_cast_fp16")]; + fp16 _inversed_input_285_y_0_to_fp16 = const()[name = string("_inversed_input_285_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_285_cast_fp16 = mul(x = var_1732_cast_fp16, y = _inversed_input_285_y_0_to_fp16)[name = string("_inversed_input_285_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1228, x = _inversed_input_285_cast_fp16)[name = string("attn_15_cast_fp16")]; + tensor attn_17_cast_fp16 = mul(x = attn_15_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool var_1739_transpose_x_0 = const()[name = string("op_1739_transpose_x_0"), val = bool(false)]; + bool var_1739_transpose_y_0 = const()[name = string("op_1739_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_1728_cast_fp16)[name = string("transpose_39")]; + tensor var_1739_cast_fp16 = matmul(transpose_x = var_1739_transpose_x_0, transpose_y = var_1739_transpose_y_0, x = attn_17_cast_fp16, y = v_11_cast_fp16)[name = string("op_1739_cast_fp16")]; + tensor var_1740_perm_0 = const()[name = string("op_1740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1741 = const()[name = string("op_1741"), val = tensor([2, 256, 256])]; + tensor var_1740_cast_fp16 = transpose(perm = var_1740_perm_0, x = var_1739_cast_fp16)[name = string("transpose_38")]; + tensor input_287_cast_fp16 = reshape(shape = var_1741, x = var_1740_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20871744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20937344))))[name = string("main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20937472)))]; + tensor linear_28_cast_fp16 = linear(bias = main_blocks_2_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor out_47_perm_0 = const()[name = string("out_47_perm_0"), val = tensor([0, 2, 1])]; + tensor out_47_cast_fp16 = transpose(perm = out_47_perm_0, x = linear_28_cast_fp16)[name = string("transpose_37")]; + tensor var_1748_cast_fp16 = mul(x = out_47_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1748_cast_fp16")]; + tensor x_151_cast_fp16 = add(x = x_m_11_cast_fp16, y = var_1748_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_289_perm_0 = const()[name = string("input_289_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1755_axes_0 = const()[name = string("op_1755_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20938560)))]; + tensor main_blocks_2_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20939648)))]; + tensor input_289_cast_fp16 = transpose(perm = input_289_perm_0, x = x_151_cast_fp16)[name = string("transpose_36")]; + tensor var_1755_cast_fp16 = layer_norm(axes = var_1755_axes_0, beta = main_blocks_2_style_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_style_norm_norm_weight_to_fp16, x = input_289_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor var_1756_perm_0 = const()[name = string("op_1756_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1756_cast_fp16 = transpose(perm = var_1756_perm_0, x = var_1755_cast_fp16)[name = string("transpose_35")]; + tensor x_153_cast_fp16 = mul(x = var_1756_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_153_cast_fp16")]; + int32 var_1775 = const()[name = string("op_1775"), val = int32(-1)]; + tensor x_155_cast_fp16 = mul(x = x_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor var_1810_cast_fp16 = mul(x = x_155_cast_fp16, y = last_1_cast_fp16)[name = string("op_1810_cast_fp16")]; + tensor x_last_37_axes_0 = const()[name = string("x_last_37_axes_0"), val = tensor([-1])]; + bool x_last_37_keep_dims_0 = const()[name = string("x_last_37_keep_dims_0"), val = bool(true)]; + tensor x_last_37_cast_fp16 = reduce_sum(axes = x_last_37_axes_0, keep_dims = x_last_37_keep_dims_0, x = var_1810_cast_fp16)[name = string("x_last_37_cast_fp16")]; + tensor var_1813_cast_fp16 = mul(x = x_155_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1815_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_37_cast_fp16)[name = string("op_1815_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = var_1813_cast_fp16, y = var_1815_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_295_mode_0 = const()[name = string("input_295_mode_0"), val = string("replicate")]; + fp16 const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = fp16(0x0p+0)]; + tensor input_295_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")]; + string h_111_pad_type_0 = const()[name = string("h_111_pad_type_0"), val = string("valid")]; + int32 h_111_groups_0 = const()[name = string("h_111_groups_0"), val = int32(512)]; + tensor h_111_strides_0 = const()[name = string("h_111_strides_0"), val = tensor([1])]; + tensor h_111_pad_0 = const()[name = string("h_111_pad_0"), val = tensor([0, 0])]; + tensor h_111_dilations_0 = const()[name = string("h_111_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20940736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20942080))))[name = string("main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20942208)))]; + tensor h_111_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_111_dilations_0, groups = h_111_groups_0, pad = h_111_pad_0, pad_type = h_111_pad_type_0, strides = h_111_strides_0, weight = main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_295_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor x_157_cast_fp16 = mul(x = h_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor input_297_perm_0 = const()[name = string("input_297_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1833_axes_0 = const()[name = string("op_1833_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20943296)))]; + tensor main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20944384)))]; + fp16 var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_297_cast_fp16 = transpose(perm = input_297_perm_0, x = x_157_cast_fp16)[name = string("transpose_34")]; + tensor var_1833_cast_fp16 = layer_norm(axes = var_1833_axes_0, beta = main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16, x = input_297_cast_fp16)[name = string("op_1833_cast_fp16")]; + tensor input_299_perm_0 = const()[name = string("input_299_perm_0"), val = tensor([0, 2, 1])]; + string h_113_pad_type_0 = const()[name = string("h_113_pad_type_0"), val = string("valid")]; + tensor h_113_strides_0 = const()[name = string("h_113_strides_0"), val = tensor([1])]; + tensor h_113_pad_0 = const()[name = string("h_113_pad_0"), val = tensor([0, 0])]; + tensor h_113_dilations_0 = const()[name = string("h_113_dilations_0"), val = tensor([1])]; + int32 h_113_groups_0 = const()[name = string("h_113_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20945472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21469824))))[name = string("main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21469952)))]; + tensor input_299_cast_fp16 = transpose(perm = input_299_perm_0, x = var_1833_cast_fp16)[name = string("transpose_33")]; + tensor h_113_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_113_dilations_0, groups = h_113_groups_0, pad = h_113_pad_0, pad_type = h_113_pad_type_0, strides = h_113_strides_0, weight = main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("h_113_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = h_113_cast_fp16)[name = string("input_301_cast_fp16")]; + string h_115_pad_type_0 = const()[name = string("h_115_pad_type_0"), val = string("valid")]; + tensor h_115_strides_0 = const()[name = string("h_115_strides_0"), val = tensor([1])]; + tensor h_115_pad_0 = const()[name = string("h_115_pad_0"), val = tensor([0, 0])]; + tensor h_115_dilations_0 = const()[name = string("h_115_dilations_0"), val = tensor([1])]; + int32 h_115_groups_0 = const()[name = string("h_115_groups_0"), val = int32(1)]; + tensor op_1850_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21474112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21998464))))[name = string("op_1850_weight_0_to_fp16_palettized")]; + tensor var_1850_bias_0_to_fp16 = const()[name = string("op_1850_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21998592)))]; + tensor var_1850_cast_fp16 = conv(bias = var_1850_bias_0_to_fp16, dilations = h_115_dilations_0, groups = h_115_groups_0, pad = h_115_pad_0, pad_type = h_115_pad_type_0, strides = h_115_strides_0, weight = op_1850_weight_0_to_fp16_palettized, x = input_301_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor out_49_cast_fp16 = add(x = x_155_cast_fp16, y = var_1850_cast_fp16)[name = string("out_49_cast_fp16")]; + tensor x_159_cast_fp16 = mul(x = out_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor x_161_cast_fp16 = mul(x = x_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor var_1864_cast_fp16 = mul(x = x_161_cast_fp16, y = last_1_cast_fp16)[name = string("op_1864_cast_fp16")]; + tensor x_last_39_axes_0 = const()[name = string("x_last_39_axes_0"), val = tensor([-1])]; + bool x_last_39_keep_dims_0 = const()[name = string("x_last_39_keep_dims_0"), val = bool(true)]; + tensor x_last_39_cast_fp16 = reduce_sum(axes = x_last_39_axes_0, keep_dims = x_last_39_keep_dims_0, x = var_1864_cast_fp16)[name = string("x_last_39_cast_fp16")]; + tensor var_1867_cast_fp16 = mul(x = x_161_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_39_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor input_305_cast_fp16 = add(x = var_1867_cast_fp16, y = var_1869_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("replicate")]; + fp16 const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_66_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string h_117_pad_type_0 = const()[name = string("h_117_pad_type_0"), val = string("valid")]; + tensor h_117_dilations_0 = const()[name = string("h_117_dilations_0"), val = tensor([2])]; + int32 h_117_groups_0 = const()[name = string("h_117_groups_0"), val = int32(512)]; + tensor h_117_strides_0 = const()[name = string("h_117_strides_0"), val = tensor([1])]; + tensor h_117_pad_0 = const()[name = string("h_117_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21999680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22001024))))[name = string("main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22001152)))]; + tensor h_117_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_117_dilations_0, groups = h_117_groups_0, pad = h_117_pad_0, pad_type = h_117_pad_type_0, strides = h_117_strides_0, weight = main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_307_cast_fp16)[name = string("h_117_cast_fp16")]; + tensor x_163_cast_fp16 = mul(x = h_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1887_axes_0 = const()[name = string("op_1887_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22002240)))]; + tensor main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22003328)))]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_163_cast_fp16)[name = string("transpose_32")]; + tensor var_1887_cast_fp16 = layer_norm(axes = var_1887_axes_0, beta = main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16, x = input_309_cast_fp16)[name = string("op_1887_cast_fp16")]; + tensor input_311_perm_0 = const()[name = string("input_311_perm_0"), val = tensor([0, 2, 1])]; + string h_119_pad_type_0 = const()[name = string("h_119_pad_type_0"), val = string("valid")]; + tensor h_119_strides_0 = const()[name = string("h_119_strides_0"), val = tensor([1])]; + tensor h_119_pad_0 = const()[name = string("h_119_pad_0"), val = tensor([0, 0])]; + tensor h_119_dilations_0 = const()[name = string("h_119_dilations_0"), val = tensor([1])]; + int32 h_119_groups_0 = const()[name = string("h_119_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22004416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22528768))))[name = string("main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22528896)))]; + tensor input_311_cast_fp16 = transpose(perm = input_311_perm_0, x = var_1887_cast_fp16)[name = string("transpose_31")]; + tensor h_119_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_119_dilations_0, groups = h_119_groups_0, pad = h_119_pad_0, pad_type = h_119_pad_type_0, strides = h_119_strides_0, weight = main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_311_cast_fp16)[name = string("h_119_cast_fp16")]; + string input_313_mode_0 = const()[name = string("input_313_mode_0"), val = string("EXACT")]; + tensor input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = h_119_cast_fp16)[name = string("input_313_cast_fp16")]; + string h_121_pad_type_0 = const()[name = string("h_121_pad_type_0"), val = string("valid")]; + tensor h_121_strides_0 = const()[name = string("h_121_strides_0"), val = tensor([1])]; + tensor h_121_pad_0 = const()[name = string("h_121_pad_0"), val = tensor([0, 0])]; + tensor h_121_dilations_0 = const()[name = string("h_121_dilations_0"), val = tensor([1])]; + int32 h_121_groups_0 = const()[name = string("h_121_groups_0"), val = int32(1)]; + tensor op_1904_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22533056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23057408))))[name = string("op_1904_weight_0_to_fp16_palettized")]; + tensor var_1904_bias_0_to_fp16 = const()[name = string("op_1904_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23057536)))]; + tensor var_1904_cast_fp16 = conv(bias = var_1904_bias_0_to_fp16, dilations = h_121_dilations_0, groups = h_121_groups_0, pad = h_121_pad_0, pad_type = h_121_pad_type_0, strides = h_121_strides_0, weight = op_1904_weight_0_to_fp16_palettized, x = input_313_cast_fp16)[name = string("op_1904_cast_fp16")]; + tensor out_51_cast_fp16 = add(x = x_161_cast_fp16, y = var_1904_cast_fp16)[name = string("out_51_cast_fp16")]; + tensor x_165_cast_fp16 = mul(x = out_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor x_167_cast_fp16 = mul(x = x_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1918_cast_fp16 = mul(x = x_167_cast_fp16, y = last_1_cast_fp16)[name = string("op_1918_cast_fp16")]; + tensor x_last_41_axes_0 = const()[name = string("x_last_41_axes_0"), val = tensor([-1])]; + bool x_last_41_keep_dims_0 = const()[name = string("x_last_41_keep_dims_0"), val = bool(true)]; + tensor x_last_41_cast_fp16 = reduce_sum(axes = x_last_41_axes_0, keep_dims = x_last_41_keep_dims_0, x = var_1918_cast_fp16)[name = string("x_last_41_cast_fp16")]; + tensor var_1921_cast_fp16 = mul(x = x_167_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1921_cast_fp16")]; + tensor var_1923_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_41_cast_fp16)[name = string("op_1923_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = var_1921_cast_fp16, y = var_1923_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("replicate")]; + fp16 const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = fp16(0x0p+0)]; + tensor input_319_cast_fp16 = pad(constant_val = const_68_to_fp16, mode = input_319_mode_0, pad = input_319_pad_0, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string h_123_pad_type_0 = const()[name = string("h_123_pad_type_0"), val = string("valid")]; + tensor h_123_dilations_0 = const()[name = string("h_123_dilations_0"), val = tensor([4])]; + int32 h_123_groups_0 = const()[name = string("h_123_groups_0"), val = int32(512)]; + tensor h_123_strides_0 = const()[name = string("h_123_strides_0"), val = tensor([1])]; + tensor h_123_pad_0 = const()[name = string("h_123_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23058624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23059968))))[name = string("main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23060096)))]; + tensor h_123_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_123_dilations_0, groups = h_123_groups_0, pad = h_123_pad_0, pad_type = h_123_pad_type_0, strides = h_123_strides_0, weight = main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_319_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor x_169_cast_fp16 = mul(x = h_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1941_axes_0 = const()[name = string("op_1941_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23061184)))]; + tensor main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23062272)))]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_169_cast_fp16)[name = string("transpose_30")]; + tensor var_1941_cast_fp16 = layer_norm(axes = var_1941_axes_0, beta = main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16, x = input_321_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor([0, 2, 1])]; + string h_125_pad_type_0 = const()[name = string("h_125_pad_type_0"), val = string("valid")]; + tensor h_125_strides_0 = const()[name = string("h_125_strides_0"), val = tensor([1])]; + tensor h_125_pad_0 = const()[name = string("h_125_pad_0"), val = tensor([0, 0])]; + tensor h_125_dilations_0 = const()[name = string("h_125_dilations_0"), val = tensor([1])]; + int32 h_125_groups_0 = const()[name = string("h_125_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23063360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23587712))))[name = string("main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23587840)))]; + tensor input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = var_1941_cast_fp16)[name = string("transpose_29")]; + tensor h_125_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_125_dilations_0, groups = h_125_groups_0, pad = h_125_pad_0, pad_type = h_125_pad_type_0, strides = h_125_strides_0, weight = main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = string("h_125_cast_fp16")]; + string input_325_mode_0 = const()[name = string("input_325_mode_0"), val = string("EXACT")]; + tensor input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = h_125_cast_fp16)[name = string("input_325_cast_fp16")]; + string h_127_pad_type_0 = const()[name = string("h_127_pad_type_0"), val = string("valid")]; + tensor h_127_strides_0 = const()[name = string("h_127_strides_0"), val = tensor([1])]; + tensor h_127_pad_0 = const()[name = string("h_127_pad_0"), val = tensor([0, 0])]; + tensor h_127_dilations_0 = const()[name = string("h_127_dilations_0"), val = tensor([1])]; + int32 h_127_groups_0 = const()[name = string("h_127_groups_0"), val = int32(1)]; + tensor op_1958_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23592000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24116352))))[name = string("op_1958_weight_0_to_fp16_palettized")]; + tensor var_1958_bias_0_to_fp16 = const()[name = string("op_1958_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24116480)))]; + tensor var_1958_cast_fp16 = conv(bias = var_1958_bias_0_to_fp16, dilations = h_127_dilations_0, groups = h_127_groups_0, pad = h_127_pad_0, pad_type = h_127_pad_type_0, strides = h_127_strides_0, weight = op_1958_weight_0_to_fp16_palettized, x = input_325_cast_fp16)[name = string("op_1958_cast_fp16")]; + tensor out_53_cast_fp16 = add(x = x_167_cast_fp16, y = var_1958_cast_fp16)[name = string("out_53_cast_fp16")]; + tensor x_171_cast_fp16 = mul(x = out_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_171_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor var_1972_cast_fp16 = mul(x = x_173_cast_fp16, y = last_1_cast_fp16)[name = string("op_1972_cast_fp16")]; + tensor x_last_43_axes_0 = const()[name = string("x_last_43_axes_0"), val = tensor([-1])]; + bool x_last_43_keep_dims_0 = const()[name = string("x_last_43_keep_dims_0"), val = bool(true)]; + tensor x_last_43_cast_fp16 = reduce_sum(axes = x_last_43_axes_0, keep_dims = x_last_43_keep_dims_0, x = var_1972_cast_fp16)[name = string("x_last_43_cast_fp16")]; + tensor var_1975_cast_fp16 = mul(x = x_173_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1975_cast_fp16")]; + tensor var_1977_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_43_cast_fp16)[name = string("op_1977_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1977_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("replicate")]; + fp16 const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = fp16(0x0p+0)]; + tensor input_331_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = input_331_mode_0, pad = input_331_pad_0, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + string h_129_pad_type_0 = const()[name = string("h_129_pad_type_0"), val = string("valid")]; + tensor h_129_dilations_0 = const()[name = string("h_129_dilations_0"), val = tensor([8])]; + int32 h_129_groups_0 = const()[name = string("h_129_groups_0"), val = int32(512)]; + tensor h_129_strides_0 = const()[name = string("h_129_strides_0"), val = tensor([1])]; + tensor h_129_pad_0 = const()[name = string("h_129_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24117568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24118912))))[name = string("main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24119040)))]; + tensor h_129_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_129_dilations_0, groups = h_129_groups_0, pad = h_129_pad_0, pad_type = h_129_pad_type_0, strides = h_129_strides_0, weight = main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = string("h_129_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = h_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_333_perm_0 = const()[name = string("input_333_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1995_axes_0 = const()[name = string("op_1995_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24120128)))]; + tensor main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24121216)))]; + tensor input_333_cast_fp16 = transpose(perm = input_333_perm_0, x = x_175_cast_fp16)[name = string("transpose_28")]; + tensor var_1995_cast_fp16 = layer_norm(axes = var_1995_axes_0, beta = main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16, x = input_333_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor input_335_perm_0 = const()[name = string("input_335_perm_0"), val = tensor([0, 2, 1])]; + string h_131_pad_type_0 = const()[name = string("h_131_pad_type_0"), val = string("valid")]; + tensor h_131_strides_0 = const()[name = string("h_131_strides_0"), val = tensor([1])]; + tensor h_131_pad_0 = const()[name = string("h_131_pad_0"), val = tensor([0, 0])]; + tensor h_131_dilations_0 = const()[name = string("h_131_dilations_0"), val = tensor([1])]; + int32 h_131_groups_0 = const()[name = string("h_131_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24122304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24646656))))[name = string("main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24646784)))]; + tensor input_335_cast_fp16 = transpose(perm = input_335_perm_0, x = var_1995_cast_fp16)[name = string("transpose_27")]; + tensor h_131_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_131_dilations_0, groups = h_131_groups_0, pad = h_131_pad_0, pad_type = h_131_pad_type_0, strides = h_131_strides_0, weight = main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_335_cast_fp16)[name = string("h_131_cast_fp16")]; + string input_337_mode_0 = const()[name = string("input_337_mode_0"), val = string("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = h_131_cast_fp16)[name = string("input_337_cast_fp16")]; + string h_133_pad_type_0 = const()[name = string("h_133_pad_type_0"), val = string("valid")]; + tensor h_133_strides_0 = const()[name = string("h_133_strides_0"), val = tensor([1])]; + tensor h_133_pad_0 = const()[name = string("h_133_pad_0"), val = tensor([0, 0])]; + tensor h_133_dilations_0 = const()[name = string("h_133_dilations_0"), val = tensor([1])]; + int32 h_133_groups_0 = const()[name = string("h_133_groups_0"), val = int32(1)]; + tensor op_2012_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24650944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25175296))))[name = string("op_2012_weight_0_to_fp16_palettized")]; + tensor var_2012_bias_0_to_fp16 = const()[name = string("op_2012_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25175424)))]; + tensor var_2012_cast_fp16 = conv(bias = var_2012_bias_0_to_fp16, dilations = h_133_dilations_0, groups = h_133_groups_0, pad = h_133_pad_0, pad_type = h_133_pad_type_0, strides = h_133_strides_0, weight = op_2012_weight_0_to_fp16_palettized, x = input_337_cast_fp16)[name = string("op_2012_cast_fp16")]; + tensor out_55_cast_fp16 = add(x = x_173_cast_fp16, y = var_2012_cast_fp16)[name = string("out_55_cast_fp16")]; + tensor x_177_cast_fp16 = mul(x = out_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25176512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25192960))))[name = string("main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25193088)))]; + tensor linear_29_cast_fp16 = linear(bias = main_blocks_3_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor t_axes_0 = const()[name = string("t_axes_0"), val = tensor([-1])]; + tensor t_cast_fp16 = expand_dims(axes = t_axes_0, x = linear_29_cast_fp16)[name = string("t_cast_fp16")]; + tensor var_2022_cast_fp16 = add(x = x_177_cast_fp16, y = t_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor x_179_cast_fp16 = mul(x = var_2022_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor x_181_cast_fp16 = mul(x = x_179_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor var_2035_cast_fp16 = mul(x = x_181_cast_fp16, y = last_1_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor x_last_45_axes_0 = const()[name = string("x_last_45_axes_0"), val = tensor([-1])]; + bool x_last_45_keep_dims_0 = const()[name = string("x_last_45_keep_dims_0"), val = bool(true)]; + tensor x_last_45_cast_fp16 = reduce_sum(axes = x_last_45_axes_0, keep_dims = x_last_45_keep_dims_0, x = var_2035_cast_fp16)[name = string("x_last_45_cast_fp16")]; + tensor var_2038_cast_fp16 = mul(x = x_181_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2038_cast_fp16")]; + tensor var_2040_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_45_cast_fp16)[name = string("op_2040_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = var_2038_cast_fp16, y = var_2040_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_345_mode_0 = const()[name = string("input_345_mode_0"), val = string("replicate")]; + fp16 const_72_to_fp16 = const()[name = string("const_72_to_fp16"), val = fp16(0x0p+0)]; + tensor input_345_cast_fp16 = pad(constant_val = const_72_to_fp16, mode = input_345_mode_0, pad = input_345_pad_0, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + string h_135_pad_type_0 = const()[name = string("h_135_pad_type_0"), val = string("valid")]; + int32 h_135_groups_0 = const()[name = string("h_135_groups_0"), val = int32(512)]; + tensor h_135_strides_0 = const()[name = string("h_135_strides_0"), val = tensor([1])]; + tensor h_135_pad_0 = const()[name = string("h_135_pad_0"), val = tensor([0, 0])]; + tensor h_135_dilations_0 = const()[name = string("h_135_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25194176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25195520))))[name = string("main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25195648)))]; + tensor h_135_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_135_dilations_0, groups = h_135_groups_0, pad = h_135_pad_0, pad_type = h_135_pad_type_0, strides = h_135_strides_0, weight = main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_345_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor x_183_cast_fp16 = mul(x = h_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_183_cast_fp16")]; + tensor input_347_perm_0 = const()[name = string("input_347_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2058_axes_0 = const()[name = string("op_2058_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25196736)))]; + tensor main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25197824)))]; + tensor input_347_cast_fp16 = transpose(perm = input_347_perm_0, x = x_183_cast_fp16)[name = string("transpose_26")]; + tensor var_2058_cast_fp16 = layer_norm(axes = var_2058_axes_0, beta = main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16, x = input_347_cast_fp16)[name = string("op_2058_cast_fp16")]; + tensor input_349_perm_0 = const()[name = string("input_349_perm_0"), val = tensor([0, 2, 1])]; + string h_137_pad_type_0 = const()[name = string("h_137_pad_type_0"), val = string("valid")]; + tensor h_137_strides_0 = const()[name = string("h_137_strides_0"), val = tensor([1])]; + tensor h_137_pad_0 = const()[name = string("h_137_pad_0"), val = tensor([0, 0])]; + tensor h_137_dilations_0 = const()[name = string("h_137_dilations_0"), val = tensor([1])]; + int32 h_137_groups_0 = const()[name = string("h_137_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25198912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25723264))))[name = string("main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25723392)))]; + tensor input_349_cast_fp16 = transpose(perm = input_349_perm_0, x = var_2058_cast_fp16)[name = string("transpose_25")]; + tensor h_137_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_137_dilations_0, groups = h_137_groups_0, pad = h_137_pad_0, pad_type = h_137_pad_type_0, strides = h_137_strides_0, weight = main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_349_cast_fp16)[name = string("h_137_cast_fp16")]; + string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")]; + tensor input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = h_137_cast_fp16)[name = string("input_351_cast_fp16")]; + string h_139_pad_type_0 = const()[name = string("h_139_pad_type_0"), val = string("valid")]; + tensor h_139_strides_0 = const()[name = string("h_139_strides_0"), val = tensor([1])]; + tensor h_139_pad_0 = const()[name = string("h_139_pad_0"), val = tensor([0, 0])]; + tensor h_139_dilations_0 = const()[name = string("h_139_dilations_0"), val = tensor([1])]; + int32 h_139_groups_0 = const()[name = string("h_139_groups_0"), val = int32(1)]; + tensor op_2075_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25727552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26251904))))[name = string("op_2075_weight_0_to_fp16_palettized")]; + tensor var_2075_bias_0_to_fp16 = const()[name = string("op_2075_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26252032)))]; + tensor var_2075_cast_fp16 = conv(bias = var_2075_bias_0_to_fp16, dilations = h_139_dilations_0, groups = h_139_groups_0, pad = h_139_pad_0, pad_type = h_139_pad_type_0, strides = h_139_strides_0, weight = op_2075_weight_0_to_fp16_palettized, x = input_351_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor out_57_cast_fp16 = add(x = x_181_cast_fp16, y = var_2075_cast_fp16)[name = string("out_57_cast_fp16")]; + tensor x_185_cast_fp16 = mul(x = out_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor x_m_13_cast_fp16 = mul(x = x_185_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_13_cast_fp16")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26253120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26384256))))[name = string("main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26384384)))]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_185_cast_fp16)[name = string("transpose_24")]; + tensor linear_30_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_2093 = const()[name = string("op_2093"), val = tensor([2, 256, 8, 64])]; + tensor var_2094_cast_fp16 = reshape(shape = var_2093, x = linear_30_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor x_187_perm_0 = const()[name = string("x_187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26385472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26451072))))[name = string("main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26451200)))]; + tensor linear_31_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_2101 = const()[name = string("op_2101"), val = tensor([2, 128, 8, 64])]; + tensor var_2102_cast_fp16 = reshape(shape = var_2101, x = linear_31_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor x_189_perm_0 = const()[name = string("x_189_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26452288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26517888))))[name = string("main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26518016)))]; + tensor linear_32_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_2109 = const()[name = string("op_2109"), val = tensor([2, 128, 8, 64])]; + tensor var_2110_cast_fp16 = reshape(shape = var_2109, x = linear_32_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_13_begin_0 = const()[name = string("x_a_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_13_end_0 = const()[name = string("x_a_13_end_0"), val = tensor([2, 8, 256, 32])]; + tensor x_a_13_end_mask_0 = const()[name = string("x_a_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_187_cast_fp16 = transpose(perm = x_187_perm_0, x = var_2094_cast_fp16)[name = string("transpose_23")]; + tensor x_a_13_cast_fp16 = slice_by_index(begin = x_a_13_begin_0, end = x_a_13_end_0, end_mask = x_a_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_a_13_cast_fp16")]; + tensor x_b_13_begin_0 = const()[name = string("x_b_13_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_13_end_0 = const()[name = string("x_b_13_end_0"), val = tensor([2, 8, 256, 64])]; + tensor x_b_13_end_mask_0 = const()[name = string("x_b_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_13_cast_fp16 = slice_by_index(begin = x_b_13_begin_0, end = x_b_13_end_0, end_mask = x_b_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_b_13_cast_fp16")]; + tensor var_2138_cast_fp16 = mul(x = x_a_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2138_cast_fp16")]; + tensor var_2139_cast_fp16 = mul(x = x_b_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2139_cast_fp16")]; + tensor rot_a_13_cast_fp16 = sub(x = var_2138_cast_fp16, y = var_2139_cast_fp16)[name = string("rot_a_13_cast_fp16")]; + tensor var_2141_cast_fp16 = mul(x = x_a_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2141_cast_fp16")]; + tensor var_2142_cast_fp16 = mul(x = x_b_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2142_cast_fp16")]; + tensor rot_b_13_cast_fp16 = add(x = var_2141_cast_fp16, y = var_2142_cast_fp16)[name = string("rot_b_13_cast_fp16")]; + bool q_13_interleave_0 = const()[name = string("q_13_interleave_0"), val = bool(false)]; + tensor q_13_cast_fp16 = concat(axis = var_1775, interleave = q_13_interleave_0, values = (rot_a_13_cast_fp16, rot_b_13_cast_fp16))[name = string("q_13_cast_fp16")]; + tensor x_a_begin_0 = const()[name = string("x_a_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_end_0 = const()[name = string("x_a_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_end_mask_0 = const()[name = string("x_a_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_189_cast_fp16 = transpose(perm = x_189_perm_0, x = var_2102_cast_fp16)[name = string("transpose_22")]; + tensor x_a_cast_fp16 = slice_by_index(begin = x_a_begin_0, end = x_a_end_0, end_mask = x_a_end_mask_0, x = x_189_cast_fp16)[name = string("x_a_cast_fp16")]; + tensor x_b_begin_0 = const()[name = string("x_b_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_end_0 = const()[name = string("x_b_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_end_mask_0 = const()[name = string("x_b_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_cast_fp16 = slice_by_index(begin = x_b_begin_0, end = x_b_end_0, end_mask = x_b_end_mask_0, x = x_189_cast_fp16)[name = string("x_b_cast_fp16")]; + tensor var_2156_cast_fp16 = mul(x = x_a_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2156_cast_fp16")]; + tensor var_2157_cast_fp16 = mul(x = x_b_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2157_cast_fp16")]; + tensor rot_a_cast_fp16 = sub(x = var_2156_cast_fp16, y = var_2157_cast_fp16)[name = string("rot_a_cast_fp16")]; + tensor var_2159_cast_fp16 = mul(x = x_a_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2159_cast_fp16")]; + tensor var_2160_cast_fp16 = mul(x = x_b_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2160_cast_fp16")]; + tensor rot_b_cast_fp16 = add(x = var_2159_cast_fp16, y = var_2160_cast_fp16)[name = string("rot_b_cast_fp16")]; + bool k_13_interleave_0 = const()[name = string("k_13_interleave_0"), val = bool(false)]; + tensor k_13_cast_fp16 = concat(axis = var_1775, interleave = k_13_interleave_0, values = (rot_a_cast_fp16, rot_b_cast_fp16))[name = string("k_13_cast_fp16")]; + bool var_2165_transpose_x_1 = const()[name = string("op_2165_transpose_x_1"), val = bool(false)]; + bool var_2165_transpose_y_1 = const()[name = string("op_2165_transpose_y_1"), val = bool(true)]; + tensor var_2165_cast_fp16 = matmul(transpose_x = var_2165_transpose_x_1, transpose_y = var_2165_transpose_y_1, x = q_13_cast_fp16, y = k_13_cast_fp16)[name = string("op_2165_cast_fp16")]; + fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_cast_fp16 = mul(x = var_2165_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")]; + tensor input_359_cast_fp16 = sub(x = _inversed_scores_cast_fp16, y = var_530_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_1775, x = input_359_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool var_2174_transpose_x_0 = const()[name = string("op_2174_transpose_x_0"), val = bool(false)]; + bool var_2174_transpose_y_0 = const()[name = string("op_2174_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2110_cast_fp16)[name = string("transpose_21")]; + tensor var_2174_cast_fp16 = matmul(transpose_x = var_2174_transpose_x_0, transpose_y = var_2174_transpose_y_0, x = attn_19_cast_fp16, y = v_13_cast_fp16)[name = string("op_2174_cast_fp16")]; + tensor var_2175_perm_0 = const()[name = string("op_2175_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2176 = const()[name = string("op_2176"), val = tensor([2, 256, 512])]; + tensor var_2175_cast_fp16 = transpose(perm = var_2175_perm_0, x = var_2174_cast_fp16)[name = string("transpose_20")]; + tensor input_361_cast_fp16 = reshape(shape = var_2176, x = var_2175_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26519104))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26650240))))[name = string("main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26650368)))]; + tensor linear_33_cast_fp16 = linear(bias = main_blocks_3_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor out_59_perm_0 = const()[name = string("out_59_perm_0"), val = tensor([0, 2, 1])]; + tensor out_59_cast_fp16 = transpose(perm = out_59_perm_0, x = linear_33_cast_fp16)[name = string("transpose_19")]; + tensor var_2183_cast_fp16 = mul(x = out_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_m_13_cast_fp16, y = var_2183_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor input_363_perm_0 = const()[name = string("input_363_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2190_axes_0 = const()[name = string("op_2190_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26651456)))]; + tensor main_blocks_3_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26652544)))]; + tensor input_363_cast_fp16 = transpose(perm = input_363_perm_0, x = x_191_cast_fp16)[name = string("transpose_18")]; + tensor var_2190_cast_fp16 = layer_norm(axes = var_2190_axes_0, beta = main_blocks_3_text_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_text_norm_norm_weight_to_fp16, x = input_363_cast_fp16)[name = string("op_2190_cast_fp16")]; + tensor var_2191_perm_0 = const()[name = string("op_2191_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2191_perm_0, x = var_2190_cast_fp16)[name = string("transpose_17")]; + tensor x_193_cast_fp16 = mul(x = var_2191_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_193_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2204_cast_fp16 = mul(x = x_195_cast_fp16, y = last_1_cast_fp16)[name = string("op_2204_cast_fp16")]; + tensor x_last_47_axes_0 = const()[name = string("x_last_47_axes_0"), val = tensor([-1])]; + bool x_last_47_keep_dims_0 = const()[name = string("x_last_47_keep_dims_0"), val = bool(true)]; + tensor x_last_47_cast_fp16 = reduce_sum(axes = x_last_47_axes_0, keep_dims = x_last_47_keep_dims_0, x = var_2204_cast_fp16)[name = string("x_last_47_cast_fp16")]; + tensor var_2207_cast_fp16 = mul(x = x_195_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2207_cast_fp16")]; + tensor var_2209_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_47_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor input_367_cast_fp16 = add(x = var_2207_cast_fp16, y = var_2209_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_369_mode_0 = const()[name = string("input_369_mode_0"), val = string("replicate")]; + fp16 const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = fp16(0x0p+0)]; + tensor input_369_cast_fp16 = pad(constant_val = const_79_to_fp16, mode = input_369_mode_0, pad = input_369_pad_0, x = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + string h_141_pad_type_0 = const()[name = string("h_141_pad_type_0"), val = string("valid")]; + int32 h_141_groups_0 = const()[name = string("h_141_groups_0"), val = int32(512)]; + tensor h_141_strides_0 = const()[name = string("h_141_strides_0"), val = tensor([1])]; + tensor h_141_pad_0 = const()[name = string("h_141_pad_0"), val = tensor([0, 0])]; + tensor h_141_dilations_0 = const()[name = string("h_141_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26653632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26654976))))[name = string("main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26655104)))]; + tensor h_141_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_141_dilations_0, groups = h_141_groups_0, pad = h_141_pad_0, pad_type = h_141_pad_type_0, strides = h_141_strides_0, weight = main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_369_cast_fp16)[name = string("h_141_cast_fp16")]; + tensor x_197_cast_fp16 = mul(x = h_141_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_371_perm_0 = const()[name = string("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2227_axes_0 = const()[name = string("op_2227_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26656192)))]; + tensor main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26657280)))]; + tensor input_371_cast_fp16 = transpose(perm = input_371_perm_0, x = x_197_cast_fp16)[name = string("transpose_16")]; + tensor var_2227_cast_fp16 = layer_norm(axes = var_2227_axes_0, beta = main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16, x = input_371_cast_fp16)[name = string("op_2227_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + string h_143_pad_type_0 = const()[name = string("h_143_pad_type_0"), val = string("valid")]; + tensor h_143_strides_0 = const()[name = string("h_143_strides_0"), val = tensor([1])]; + tensor h_143_pad_0 = const()[name = string("h_143_pad_0"), val = tensor([0, 0])]; + tensor h_143_dilations_0 = const()[name = string("h_143_dilations_0"), val = tensor([1])]; + int32 h_143_groups_0 = const()[name = string("h_143_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26658368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27182720))))[name = string("main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27182848)))]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = var_2227_cast_fp16)[name = string("transpose_15")]; + tensor h_143_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_143_dilations_0, groups = h_143_groups_0, pad = h_143_pad_0, pad_type = h_143_pad_type_0, strides = h_143_strides_0, weight = main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = string("h_143_cast_fp16")]; + string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")]; + tensor input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = h_143_cast_fp16)[name = string("input_375_cast_fp16")]; + string h_145_pad_type_0 = const()[name = string("h_145_pad_type_0"), val = string("valid")]; + tensor h_145_strides_0 = const()[name = string("h_145_strides_0"), val = tensor([1])]; + tensor h_145_pad_0 = const()[name = string("h_145_pad_0"), val = tensor([0, 0])]; + tensor h_145_dilations_0 = const()[name = string("h_145_dilations_0"), val = tensor([1])]; + int32 h_145_groups_0 = const()[name = string("h_145_groups_0"), val = int32(1)]; + tensor op_2244_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27187008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27711360))))[name = string("op_2244_weight_0_to_fp16_palettized")]; + tensor var_2244_bias_0_to_fp16 = const()[name = string("op_2244_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27711488)))]; + tensor var_2244_cast_fp16 = conv(bias = var_2244_bias_0_to_fp16, dilations = h_145_dilations_0, groups = h_145_groups_0, pad = h_145_pad_0, pad_type = h_145_pad_type_0, strides = h_145_strides_0, weight = op_2244_weight_0_to_fp16_palettized, x = input_375_cast_fp16)[name = string("op_2244_cast_fp16")]; + tensor out_61_cast_fp16 = add(x = x_195_cast_fp16, y = var_2244_cast_fp16)[name = string("out_61_cast_fp16")]; + tensor x_199_cast_fp16 = mul(x = out_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor x_m_cast_fp16 = mul(x = x_199_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_cast_fp16")]; + tensor input_377_perm_0 = const()[name = string("input_377_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27712576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27778176))))[name = string("main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27778304)))]; + tensor input_377_cast_fp16 = transpose(perm = input_377_perm_0, x = x_199_cast_fp16)[name = string("transpose_14")]; + tensor linear_34_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_2260 = const()[name = string("op_2260"), val = tensor([2, 256, 2, 128])]; + tensor var_2261_cast_fp16 = reshape(shape = var_2260, x = linear_34_cast_fp16)[name = string("op_2261_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27778880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27811712))))[name = string("main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27811840)))]; + tensor linear_36_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_2274 = const()[name = string("op_2274"), val = tensor([2, 50, 2, 128])]; + tensor var_2275_cast_fp16 = reshape(shape = var_2274, x = linear_36_cast_fp16)[name = string("op_2275_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_2279_transpose_x_0 = const()[name = string("op_2279_transpose_x_0"), val = bool(false)]; + bool var_2279_transpose_y_0 = const()[name = string("op_2279_transpose_y_0"), val = bool(false)]; + tensor op_2278_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27812416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27825280))))[name = string("op_2278_to_fp16_palettized")]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_2261_cast_fp16)[name = string("transpose_13")]; + tensor var_2279_cast_fp16 = matmul(transpose_x = var_2279_transpose_x_0, transpose_y = var_2279_transpose_y_0, x = q_cast_fp16, y = op_2278_to_fp16_palettized)[name = string("op_2279_cast_fp16")]; + fp16 _inversed_input_379_y_0_to_fp16 = const()[name = string("_inversed_input_379_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_379_cast_fp16 = mul(x = var_2279_cast_fp16, y = _inversed_input_379_y_0_to_fp16)[name = string("_inversed_input_379_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_1775, x = _inversed_input_379_cast_fp16)[name = string("attn_21_cast_fp16")]; + tensor attn_cast_fp16 = mul(x = attn_21_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_cast_fp16")]; + bool var_2286_transpose_x_0 = const()[name = string("op_2286_transpose_x_0"), val = bool(false)]; + bool var_2286_transpose_y_0 = const()[name = string("op_2286_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_2275_cast_fp16)[name = string("transpose_12")]; + tensor var_2286_cast_fp16 = matmul(transpose_x = var_2286_transpose_x_0, transpose_y = var_2286_transpose_y_0, x = attn_cast_fp16, y = v_15_cast_fp16)[name = string("op_2286_cast_fp16")]; + tensor var_2287_perm_0 = const()[name = string("op_2287_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2288 = const()[name = string("op_2288"), val = tensor([2, 256, 256])]; + tensor var_2287_cast_fp16 = transpose(perm = var_2287_perm_0, x = var_2286_cast_fp16)[name = string("transpose_11")]; + tensor input_381_cast_fp16 = reshape(shape = var_2288, x = var_2287_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27825408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27891008))))[name = string("main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27891136)))]; + tensor linear_37_cast_fp16 = linear(bias = main_blocks_3_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor out_63_perm_0 = const()[name = string("out_63_perm_0"), val = tensor([0, 2, 1])]; + tensor out_63_cast_fp16 = transpose(perm = out_63_perm_0, x = linear_37_cast_fp16)[name = string("transpose_10")]; + tensor var_2295_cast_fp16 = mul(x = out_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2295_cast_fp16")]; + tensor x_201_cast_fp16 = add(x = x_m_cast_fp16, y = var_2295_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_383_perm_0 = const()[name = string("input_383_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2302_axes_0 = const()[name = string("op_2302_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27892224)))]; + tensor main_blocks_3_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27893312)))]; + tensor input_383_cast_fp16 = transpose(perm = input_383_perm_0, x = x_201_cast_fp16)[name = string("transpose_9")]; + tensor var_2302_cast_fp16 = layer_norm(axes = var_2302_axes_0, beta = main_blocks_3_style_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_style_norm_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_perm_0 = const()[name = string("op_2303_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2303_cast_fp16 = transpose(perm = var_2303_perm_0, x = var_2302_cast_fp16)[name = string("transpose_8")]; + tensor x_203_cast_fp16 = mul(x = var_2303_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor x_205_cast_fp16 = mul(x = x_203_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor var_2330_cast_fp16 = mul(x = x_205_cast_fp16, y = last_1_cast_fp16)[name = string("op_2330_cast_fp16")]; + tensor x_last_49_axes_0 = const()[name = string("x_last_49_axes_0"), val = tensor([-1])]; + bool x_last_49_keep_dims_0 = const()[name = string("x_last_49_keep_dims_0"), val = bool(true)]; + tensor x_last_49_cast_fp16 = reduce_sum(axes = x_last_49_axes_0, keep_dims = x_last_49_keep_dims_0, x = var_2330_cast_fp16)[name = string("x_last_49_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = x_205_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor var_2335_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_49_cast_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2335_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_pad_0 = const()[name = string("input_389_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("replicate")]; + fp16 const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = fp16(0x0p+0)]; + tensor input_389_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = input_389_mode_0, pad = input_389_pad_0, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + string h_147_pad_type_0 = const()[name = string("h_147_pad_type_0"), val = string("valid")]; + int32 h_147_groups_0 = const()[name = string("h_147_groups_0"), val = int32(512)]; + tensor h_147_strides_0 = const()[name = string("h_147_strides_0"), val = tensor([1])]; + tensor h_147_pad_0 = const()[name = string("h_147_pad_0"), val = tensor([0, 0])]; + tensor h_147_dilations_0 = const()[name = string("h_147_dilations_0"), val = tensor([1])]; + tensor last_convnext_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27895744))))[name = string("last_convnext_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27895872)))]; + tensor h_147_cast_fp16 = conv(bias = last_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_147_dilations_0, groups = h_147_groups_0, pad = h_147_pad_0, pad_type = h_147_pad_type_0, strides = h_147_strides_0, weight = last_convnext_0_dwconv__conv_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor x_207_cast_fp16 = mul(x = h_147_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_391_perm_0 = const()[name = string("input_391_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2353_axes_0 = const()[name = string("op_2353_axes_0"), val = tensor([-1])]; + tensor last_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896960)))]; + tensor last_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27898048)))]; + fp16 var_2307_to_fp16 = const()[name = string("op_2307_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_391_cast_fp16 = transpose(perm = input_391_perm_0, x = x_207_cast_fp16)[name = string("transpose_7")]; + tensor var_2353_cast_fp16 = layer_norm(axes = var_2353_axes_0, beta = last_convnext_0_norm_norm_bias_to_fp16, epsilon = var_2307_to_fp16, gamma = last_convnext_0_norm_norm_weight_to_fp16, x = input_391_cast_fp16)[name = string("op_2353_cast_fp16")]; + tensor input_393_perm_0 = const()[name = string("input_393_perm_0"), val = tensor([0, 2, 1])]; + string h_149_pad_type_0 = const()[name = string("h_149_pad_type_0"), val = string("valid")]; + tensor h_149_strides_0 = const()[name = string("h_149_strides_0"), val = tensor([1])]; + tensor h_149_pad_0 = const()[name = string("h_149_pad_0"), val = tensor([0, 0])]; + tensor h_149_dilations_0 = const()[name = string("h_149_dilations_0"), val = tensor([1])]; + int32 h_149_groups_0 = const()[name = string("h_149_groups_0"), val = int32(1)]; + tensor last_convnext_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27899136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28423488))))[name = string("last_convnext_0_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28423616)))]; + tensor input_393_cast_fp16 = transpose(perm = input_393_perm_0, x = var_2353_cast_fp16)[name = string("transpose_6")]; + tensor h_149_cast_fp16 = conv(bias = last_convnext_0_pwconv1_bias_to_fp16, dilations = h_149_dilations_0, groups = h_149_groups_0, pad = h_149_pad_0, pad_type = h_149_pad_type_0, strides = h_149_strides_0, weight = last_convnext_0_pwconv1_weight_to_fp16_palettized, x = input_393_cast_fp16)[name = string("h_149_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = h_149_cast_fp16)[name = string("input_395_cast_fp16")]; + string h_151_pad_type_0 = const()[name = string("h_151_pad_type_0"), val = string("valid")]; + tensor h_151_strides_0 = const()[name = string("h_151_strides_0"), val = tensor([1])]; + tensor h_151_pad_0 = const()[name = string("h_151_pad_0"), val = tensor([0, 0])]; + tensor h_151_dilations_0 = const()[name = string("h_151_dilations_0"), val = tensor([1])]; + int32 h_151_groups_0 = const()[name = string("h_151_groups_0"), val = int32(1)]; + tensor op_2370_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28427776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28952128))))[name = string("op_2370_weight_0_to_fp16_palettized")]; + tensor var_2370_bias_0_to_fp16 = const()[name = string("op_2370_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28952256)))]; + tensor var_2370_cast_fp16 = conv(bias = var_2370_bias_0_to_fp16, dilations = h_151_dilations_0, groups = h_151_groups_0, pad = h_151_pad_0, pad_type = h_151_pad_type_0, strides = h_151_strides_0, weight = op_2370_weight_0_to_fp16_palettized, x = input_395_cast_fp16)[name = string("op_2370_cast_fp16")]; + tensor out_65_cast_fp16 = add(x = x_205_cast_fp16, y = var_2370_cast_fp16)[name = string("out_65_cast_fp16")]; + tensor x_209_cast_fp16 = mul(x = out_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor x_211_cast_fp16 = mul(x = x_209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_2398_cast_fp16 = mul(x = x_211_cast_fp16, y = last_1_cast_fp16)[name = string("op_2398_cast_fp16")]; + tensor x_last_51_axes_0 = const()[name = string("x_last_51_axes_0"), val = tensor([-1])]; + bool x_last_51_keep_dims_0 = const()[name = string("x_last_51_keep_dims_0"), val = bool(true)]; + tensor x_last_51_cast_fp16 = reduce_sum(axes = x_last_51_axes_0, keep_dims = x_last_51_keep_dims_0, x = var_2398_cast_fp16)[name = string("x_last_51_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = x_211_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_51_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor input_399_cast_fp16 = add(x = var_2401_cast_fp16, y = var_2403_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor input_401_pad_0 = const()[name = string("input_401_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_401_mode_0 = const()[name = string("input_401_mode_0"), val = string("replicate")]; + fp16 const_86_to_fp16 = const()[name = string("const_86_to_fp16"), val = fp16(0x0p+0)]; + tensor input_401_cast_fp16 = pad(constant_val = const_86_to_fp16, mode = input_401_mode_0, pad = input_401_pad_0, x = input_399_cast_fp16)[name = string("input_401_cast_fp16")]; + string h_153_pad_type_0 = const()[name = string("h_153_pad_type_0"), val = string("valid")]; + int32 h_153_groups_0 = const()[name = string("h_153_groups_0"), val = int32(512)]; + tensor h_153_strides_0 = const()[name = string("h_153_strides_0"), val = tensor([1])]; + tensor h_153_pad_0 = const()[name = string("h_153_pad_0"), val = tensor([0, 0])]; + tensor h_153_dilations_0 = const()[name = string("h_153_dilations_0"), val = tensor([1])]; + tensor last_convnext_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28953344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28954688))))[name = string("last_convnext_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28954816)))]; + tensor h_153_cast_fp16 = conv(bias = last_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_153_dilations_0, groups = h_153_groups_0, pad = h_153_pad_0, pad_type = h_153_pad_type_0, strides = h_153_strides_0, weight = last_convnext_1_dwconv__conv_weight_to_fp16_palettized, x = input_401_cast_fp16)[name = string("h_153_cast_fp16")]; + tensor x_213_cast_fp16 = mul(x = h_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_213_cast_fp16")]; + tensor input_403_perm_0 = const()[name = string("input_403_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2421_axes_0 = const()[name = string("op_2421_axes_0"), val = tensor([-1])]; + tensor last_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28955904)))]; + tensor last_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28956992)))]; + fp16 var_2375_to_fp16 = const()[name = string("op_2375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_403_cast_fp16 = transpose(perm = input_403_perm_0, x = x_213_cast_fp16)[name = string("transpose_5")]; + tensor var_2421_cast_fp16 = layer_norm(axes = var_2421_axes_0, beta = last_convnext_1_norm_norm_bias_to_fp16, epsilon = var_2375_to_fp16, gamma = last_convnext_1_norm_norm_weight_to_fp16, x = input_403_cast_fp16)[name = string("op_2421_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string h_155_pad_type_0 = const()[name = string("h_155_pad_type_0"), val = string("valid")]; + tensor h_155_strides_0 = const()[name = string("h_155_strides_0"), val = tensor([1])]; + tensor h_155_pad_0 = const()[name = string("h_155_pad_0"), val = tensor([0, 0])]; + tensor h_155_dilations_0 = const()[name = string("h_155_dilations_0"), val = tensor([1])]; + int32 h_155_groups_0 = const()[name = string("h_155_groups_0"), val = int32(1)]; + tensor last_convnext_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28958080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29482432))))[name = string("last_convnext_1_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29482560)))]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = var_2421_cast_fp16)[name = string("transpose_4")]; + tensor h_155_cast_fp16 = conv(bias = last_convnext_1_pwconv1_bias_to_fp16, dilations = h_155_dilations_0, groups = h_155_groups_0, pad = h_155_pad_0, pad_type = h_155_pad_type_0, strides = h_155_strides_0, weight = last_convnext_1_pwconv1_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = string("h_155_cast_fp16")]; + string input_407_mode_0 = const()[name = string("input_407_mode_0"), val = string("EXACT")]; + tensor input_407_cast_fp16 = gelu(mode = input_407_mode_0, x = h_155_cast_fp16)[name = string("input_407_cast_fp16")]; + string h_157_pad_type_0 = const()[name = string("h_157_pad_type_0"), val = string("valid")]; + tensor h_157_strides_0 = const()[name = string("h_157_strides_0"), val = tensor([1])]; + tensor h_157_pad_0 = const()[name = string("h_157_pad_0"), val = tensor([0, 0])]; + tensor h_157_dilations_0 = const()[name = string("h_157_dilations_0"), val = tensor([1])]; + int32 h_157_groups_0 = const()[name = string("h_157_groups_0"), val = int32(1)]; + tensor op_2438_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29486720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30011072))))[name = string("op_2438_weight_0_to_fp16_palettized")]; + tensor var_2438_bias_0_to_fp16 = const()[name = string("op_2438_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30011200)))]; + tensor var_2438_cast_fp16 = conv(bias = var_2438_bias_0_to_fp16, dilations = h_157_dilations_0, groups = h_157_groups_0, pad = h_157_pad_0, pad_type = h_157_pad_type_0, strides = h_157_strides_0, weight = op_2438_weight_0_to_fp16_palettized, x = input_407_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor out_67_cast_fp16 = add(x = x_211_cast_fp16, y = var_2438_cast_fp16)[name = string("out_67_cast_fp16")]; + tensor x_215_cast_fp16 = mul(x = out_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_215_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2466_cast_fp16 = mul(x = x_217_cast_fp16, y = last_1_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor x_last_53_axes_0 = const()[name = string("x_last_53_axes_0"), val = tensor([-1])]; + bool x_last_53_keep_dims_0 = const()[name = string("x_last_53_keep_dims_0"), val = bool(true)]; + tensor x_last_53_cast_fp16 = reduce_sum(axes = x_last_53_axes_0, keep_dims = x_last_53_keep_dims_0, x = var_2466_cast_fp16)[name = string("x_last_53_cast_fp16")]; + tensor var_2469_cast_fp16 = mul(x = x_217_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor var_2471_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_53_cast_fp16)[name = string("op_2471_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = var_2469_cast_fp16, y = var_2471_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("replicate")]; + fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)]; + tensor input_413_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = input_413_mode_0, pad = input_413_pad_0, x = input_411_cast_fp16)[name = string("input_413_cast_fp16")]; + string h_159_pad_type_0 = const()[name = string("h_159_pad_type_0"), val = string("valid")]; + int32 h_159_groups_0 = const()[name = string("h_159_groups_0"), val = int32(512)]; + tensor h_159_strides_0 = const()[name = string("h_159_strides_0"), val = tensor([1])]; + tensor h_159_pad_0 = const()[name = string("h_159_pad_0"), val = tensor([0, 0])]; + tensor h_159_dilations_0 = const()[name = string("h_159_dilations_0"), val = tensor([1])]; + tensor last_convnext_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30012288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30013632))))[name = string("last_convnext_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30013760)))]; + tensor h_159_cast_fp16 = conv(bias = last_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_159_dilations_0, groups = h_159_groups_0, pad = h_159_pad_0, pad_type = h_159_pad_type_0, strides = h_159_strides_0, weight = last_convnext_2_dwconv__conv_weight_to_fp16_palettized, x = input_413_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor x_219_cast_fp16 = mul(x = h_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_415_perm_0 = const()[name = string("input_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2489_axes_0 = const()[name = string("op_2489_axes_0"), val = tensor([-1])]; + tensor last_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30014848)))]; + tensor last_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30015936)))]; + fp16 var_2443_to_fp16 = const()[name = string("op_2443_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_415_cast_fp16 = transpose(perm = input_415_perm_0, x = x_219_cast_fp16)[name = string("transpose_3")]; + tensor var_2489_cast_fp16 = layer_norm(axes = var_2489_axes_0, beta = last_convnext_2_norm_norm_bias_to_fp16, epsilon = var_2443_to_fp16, gamma = last_convnext_2_norm_norm_weight_to_fp16, x = input_415_cast_fp16)[name = string("op_2489_cast_fp16")]; + tensor input_417_perm_0 = const()[name = string("input_417_perm_0"), val = tensor([0, 2, 1])]; + string h_161_pad_type_0 = const()[name = string("h_161_pad_type_0"), val = string("valid")]; + tensor h_161_strides_0 = const()[name = string("h_161_strides_0"), val = tensor([1])]; + tensor h_161_pad_0 = const()[name = string("h_161_pad_0"), val = tensor([0, 0])]; + tensor h_161_dilations_0 = const()[name = string("h_161_dilations_0"), val = tensor([1])]; + int32 h_161_groups_0 = const()[name = string("h_161_groups_0"), val = int32(1)]; + tensor last_convnext_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30017024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30541376))))[name = string("last_convnext_2_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30541504)))]; + tensor input_417_cast_fp16 = transpose(perm = input_417_perm_0, x = var_2489_cast_fp16)[name = string("transpose_2")]; + tensor h_161_cast_fp16 = conv(bias = last_convnext_2_pwconv1_bias_to_fp16, dilations = h_161_dilations_0, groups = h_161_groups_0, pad = h_161_pad_0, pad_type = h_161_pad_type_0, strides = h_161_strides_0, weight = last_convnext_2_pwconv1_weight_to_fp16_palettized, x = input_417_cast_fp16)[name = string("h_161_cast_fp16")]; + string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("EXACT")]; + tensor input_419_cast_fp16 = gelu(mode = input_419_mode_0, x = h_161_cast_fp16)[name = string("input_419_cast_fp16")]; + string h_163_pad_type_0 = const()[name = string("h_163_pad_type_0"), val = string("valid")]; + tensor h_163_strides_0 = const()[name = string("h_163_strides_0"), val = tensor([1])]; + tensor h_163_pad_0 = const()[name = string("h_163_pad_0"), val = tensor([0, 0])]; + tensor h_163_dilations_0 = const()[name = string("h_163_dilations_0"), val = tensor([1])]; + int32 h_163_groups_0 = const()[name = string("h_163_groups_0"), val = int32(1)]; + tensor op_2506_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30545664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31070016))))[name = string("op_2506_weight_0_to_fp16_palettized")]; + tensor var_2506_bias_0_to_fp16 = const()[name = string("op_2506_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31070144)))]; + tensor var_2506_cast_fp16 = conv(bias = var_2506_bias_0_to_fp16, dilations = h_163_dilations_0, groups = h_163_groups_0, pad = h_163_pad_0, pad_type = h_163_pad_type_0, strides = h_163_strides_0, weight = op_2506_weight_0_to_fp16_palettized, x = input_419_cast_fp16)[name = string("op_2506_cast_fp16")]; + tensor out_69_cast_fp16 = add(x = x_217_cast_fp16, y = var_2506_cast_fp16)[name = string("out_69_cast_fp16")]; + tensor x_221_cast_fp16 = mul(x = out_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor x_223_cast_fp16 = mul(x = x_221_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_2534_cast_fp16 = mul(x = x_223_cast_fp16, y = last_1_cast_fp16)[name = string("op_2534_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_2534_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_2537_cast_fp16 = mul(x = x_223_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2537_cast_fp16")]; + tensor var_2539_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_cast_fp16)[name = string("op_2539_cast_fp16")]; + tensor input_423_cast_fp16 = add(x = var_2537_cast_fp16, y = var_2539_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_425_mode_0 = const()[name = string("input_425_mode_0"), val = string("replicate")]; + fp16 const_90_to_fp16 = const()[name = string("const_90_to_fp16"), val = fp16(0x0p+0)]; + tensor input_425_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = input_425_mode_0, pad = input_425_pad_0, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")]; + string h_165_pad_type_0 = const()[name = string("h_165_pad_type_0"), val = string("valid")]; + int32 h_165_groups_0 = const()[name = string("h_165_groups_0"), val = int32(512)]; + tensor h_165_strides_0 = const()[name = string("h_165_strides_0"), val = tensor([1])]; + tensor h_165_pad_0 = const()[name = string("h_165_pad_0"), val = tensor([0, 0])]; + tensor h_165_dilations_0 = const()[name = string("h_165_dilations_0"), val = tensor([1])]; + tensor last_convnext_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31071232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31072576))))[name = string("last_convnext_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31072704)))]; + tensor h_165_cast_fp16 = conv(bias = last_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_165_dilations_0, groups = h_165_groups_0, pad = h_165_pad_0, pad_type = h_165_pad_type_0, strides = h_165_strides_0, weight = last_convnext_3_dwconv__conv_weight_to_fp16_palettized, x = input_425_cast_fp16)[name = string("h_165_cast_fp16")]; + tensor x_cast_fp16 = mul(x = h_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2557_axes_0 = const()[name = string("op_2557_axes_0"), val = tensor([-1])]; + tensor last_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31073792)))]; + tensor last_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31074880)))]; + fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_cast_fp16)[name = string("transpose_1")]; + tensor var_2557_cast_fp16 = layer_norm(axes = var_2557_axes_0, beta = last_convnext_3_norm_norm_bias_to_fp16, epsilon = var_2511_to_fp16, gamma = last_convnext_3_norm_norm_weight_to_fp16, x = input_427_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor input_429_perm_0 = const()[name = string("input_429_perm_0"), val = tensor([0, 2, 1])]; + string h_167_pad_type_0 = const()[name = string("h_167_pad_type_0"), val = string("valid")]; + tensor h_167_strides_0 = const()[name = string("h_167_strides_0"), val = tensor([1])]; + tensor h_167_pad_0 = const()[name = string("h_167_pad_0"), val = tensor([0, 0])]; + tensor h_167_dilations_0 = const()[name = string("h_167_dilations_0"), val = tensor([1])]; + int32 h_167_groups_0 = const()[name = string("h_167_groups_0"), val = int32(1)]; + tensor last_convnext_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31075968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31600320))))[name = string("last_convnext_3_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31600448)))]; + tensor input_429_cast_fp16 = transpose(perm = input_429_perm_0, x = var_2557_cast_fp16)[name = string("transpose_0")]; + tensor h_167_cast_fp16 = conv(bias = last_convnext_3_pwconv1_bias_to_fp16, dilations = h_167_dilations_0, groups = h_167_groups_0, pad = h_167_pad_0, pad_type = h_167_pad_type_0, strides = h_167_strides_0, weight = last_convnext_3_pwconv1_weight_to_fp16_palettized, x = input_429_cast_fp16)[name = string("h_167_cast_fp16")]; + string input_431_mode_0 = const()[name = string("input_431_mode_0"), val = string("EXACT")]; + tensor input_431_cast_fp16 = gelu(mode = input_431_mode_0, x = h_167_cast_fp16)[name = string("input_431_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor op_2574_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31604608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32128960))))[name = string("op_2574_weight_0_to_fp16_palettized")]; + tensor var_2574_bias_0_to_fp16 = const()[name = string("op_2574_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32129088)))]; + tensor var_2574_cast_fp16 = conv(bias = var_2574_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = op_2574_weight_0_to_fp16_palettized, x = input_431_cast_fp16)[name = string("op_2574_cast_fp16")]; + tensor out_cast_fp16 = add(x = x_223_cast_fp16, y = var_2574_cast_fp16)[name = string("out_cast_fp16")]; + tensor input_cast_fp16 = mul(x = out_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("input_cast_fp16")]; + string var_2587_pad_type_0 = const()[name = string("op_2587_pad_type_0"), val = string("valid")]; + tensor var_2587_strides_0 = const()[name = string("op_2587_strides_0"), val = tensor([1])]; + tensor var_2587_pad_0 = const()[name = string("op_2587_pad_0"), val = tensor([0, 0])]; + tensor var_2587_dilations_0 = const()[name = string("op_2587_dilations_0"), val = tensor([1])]; + int32 var_2587_groups_0 = const()[name = string("op_2587_groups_0"), val = int32(1)]; + tensor proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32130176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32167104))))[name = string("proj_out_weight_to_fp16_palettized")]; + tensor var_2587_cast_fp16 = conv(dilations = var_2587_dilations_0, groups = var_2587_groups_0, pad = var_2587_pad_0, pad_type = var_2587_pad_type_0, strides = var_2587_strides_0, weight = proj_out_weight_to_fp16_palettized, x = input_cast_fp16)[name = string("op_2587_cast_fp16")]; + tensor v_cast_fp16 = mul(x = var_2587_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("v_cast_fp16")]; + tensor cond_begin_0 = const()[name = string("cond_begin_0"), val = tensor([0, 0, 0])]; + tensor cond_end_0 = const()[name = string("cond_end_0"), val = tensor([1, 144, 256])]; + tensor cond_end_mask_0 = const()[name = string("cond_end_mask_0"), val = tensor([false, true, true])]; + tensor cond_cast_fp16 = slice_by_index(begin = cond_begin_0, end = cond_end_0, end_mask = cond_end_mask_0, x = v_cast_fp16)[name = string("cond_cast_fp16")]; + tensor uncond_begin_0 = const()[name = string("uncond_begin_0"), val = tensor([1, 0, 0])]; + tensor uncond_end_0 = const()[name = string("uncond_end_0"), val = tensor([2, 144, 256])]; + tensor uncond_end_mask_0 = const()[name = string("uncond_end_mask_0"), val = tensor([true, true, true])]; + tensor uncond_cast_fp16 = slice_by_index(begin = uncond_begin_0, end = uncond_end_0, end_mask = uncond_end_mask_0, x = v_cast_fp16)[name = string("uncond_cast_fp16")]; + fp32 var_2597_epsilon_0 = const()[name = string("op_2597_epsilon_0"), val = fp32(0x1.a36e2ep-14)]; + tensor var_2597_cast_fp16 = inverse(epsilon = var_2597_epsilon_0, x = total_step_to_fp16)[name = string("op_2597_cast_fp16")]; + tensor var_2603 = const()[name = string("op_2603"), val = tensor([-1, 1, 1])]; + tensor step_cast_fp16 = reshape(shape = var_2603, x = var_2597_cast_fp16)[name = string("step_cast_fp16")]; + fp16 var_2605_to_fp16 = const()[name = string("op_2605_to_fp16"), val = fp16(0x1p+2)]; + tensor var_2606_cast_fp16 = mul(x = cond_cast_fp16, y = var_2605_to_fp16)[name = string("op_2606_cast_fp16")]; + fp16 var_2607_to_fp16 = const()[name = string("op_2607_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_2608_cast_fp16 = mul(x = uncond_cast_fp16, y = var_2607_to_fp16)[name = string("op_2608_cast_fp16")]; + tensor var_2610_cast_fp16 = sub(x = var_2606_cast_fp16, y = var_2608_cast_fp16)[name = string("op_2610_cast_fp16")]; + tensor var_2611_cast_fp16 = mul(x = step_cast_fp16, y = var_2610_cast_fp16)[name = string("op_2611_cast_fp16")]; + tensor var_2613_cast_fp16 = add(x = noisy_latent_to_fp16, y = var_2611_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor var_2614_cast_fp16 = mul(x = var_2613_cast_fp16, y = latent_mask_to_fp16)[name = string("op_2614_cast_fp16")]; + string var_2614_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2614_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor denoised_latent = cast(dtype = var_2614_cast_fp16_to_fp32_dtype_0, x = var_2614_cast_fp16)[name = string("cast_0")]; + } -> (denoised_latent); +} \ No newline at end of file diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L256_int4.mlmodelc/weights/weight.bin b/VectorEstimatorVariants/VectorEstimator_v2_L256_int4.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..5884957b65203e57c8fab9ce876ffb179268e697 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L256_int4.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e9574dcce25fd7d3f4a68b2257f74fea5227ccd9e6843d53f0e9f5476aa8dc87 +size 32167200 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L256_int4.mlpackage/Data/com.apple.CoreML/model.mlmodel 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--git a/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlmodelc/analytics/coremldata.bin b/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..4f4671b32661560cd6847d8da0aa4be565ad5faa --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c8457c157a24300793d4aec77935799b323576549630c285eb5427052e9d5eb6 +size 243 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlmodelc/coremldata.bin b/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2abd46591969da65c5d03ee77afac718f41accde --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c4d18f2b1f6404bae4cc39af4538ba9cd170c0e8c5ae32f86d29e45f614348ae +size 553 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlmodelc/model.mil b/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..e75671e648506d6d0333006775ba6c66153b1a90 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlmodelc/model.mil @@ -0,0 +1,2102 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor current_step, tensor latent_mask, tensor noisy_latent, tensor style_ttl, tensor text_emb, tensor text_mask, tensor total_step) { + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + bool text_emb_interleave_0 = const()[name = string("text_emb_interleave_0"), val = bool(false)]; + string text_emb_to_fp16_dtype_0 = const()[name = string("text_emb_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_uncond_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24704))))[name = string("text_uncond_to_fp16_palettized")]; + tensor text_emb_to_fp16 = cast(dtype = text_emb_to_fp16_dtype_0, x = text_emb)[name = string("cast_7")]; + tensor text_emb_cast_fp16 = concat(axis = var_39, interleave = text_emb_interleave_0, values = (text_emb_to_fp16, text_uncond_to_fp16_palettized))[name = string("text_emb_cast_fp16")]; + bool input_95_interleave_0 = const()[name = string("input_95_interleave_0"), val = bool(false)]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor uncond_masker_style_value_special_token_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34560))))[name = string("uncond_masker_style_value_special_token_to_fp16_palettized")]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_6")]; + tensor input_95_cast_fp16 = concat(axis = var_39, interleave = input_95_interleave_0, values = (style_ttl_to_fp16, uncond_masker_style_value_special_token_to_fp16_palettized))[name = string("input_95_cast_fp16")]; + int32 var_64 = const()[name = string("op_64"), val = int32(0)]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + string noisy_latent_to_fp16_dtype_0 = const()[name = string("noisy_latent_to_fp16_dtype_0"), val = string("fp16")]; + tensor noisy_latent_to_fp16 = cast(dtype = noisy_latent_to_fp16_dtype_0, x = noisy_latent)[name = string("cast_5")]; + tensor input_5_cast_fp16 = concat(axis = var_64, interleave = input_5_interleave_0, values = (noisy_latent_to_fp16, noisy_latent_to_fp16))[name = string("input_5_cast_fp16")]; + int32 var_67 = const()[name = string("op_67"), val = int32(0)]; + bool latent_mask_b_interleave_0 = const()[name = string("latent_mask_b_interleave_0"), val = bool(false)]; + string latent_mask_to_fp16_dtype_0 = const()[name = string("latent_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor latent_mask_to_fp16 = cast(dtype = latent_mask_to_fp16_dtype_0, x = latent_mask)[name = string("cast_4")]; + tensor latent_mask_b_cast_fp16 = concat(axis = var_67, interleave = latent_mask_b_interleave_0, values = (latent_mask_to_fp16, latent_mask_to_fp16))[name = string("latent_mask_b_cast_fp16")]; + int32 var_70 = const()[name = string("op_70"), val = int32(0)]; + bool text_mask_interleave_0 = const()[name = string("text_mask_interleave_0"), val = bool(false)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_3")]; + tensor text_mask_cast_fp16 = concat(axis = var_70, interleave = text_mask_interleave_0, values = (text_mask_to_fp16, text_mask_to_fp16))[name = string("text_mask_cast_fp16")]; + string current_step_to_fp16_dtype_0 = const()[name = string("current_step_to_fp16_dtype_0"), val = string("fp16")]; + string total_step_to_fp16_dtype_0 = const()[name = string("total_step_to_fp16_dtype_0"), val = string("fp16")]; + tensor current_step_to_fp16 = cast(dtype = current_step_to_fp16_dtype_0, x = current_step)[name = string("cast_1")]; + tensor total_step_to_fp16 = cast(dtype = total_step_to_fp16_dtype_0, x = total_step)[name = string("cast_2")]; + tensor t_1_cast_fp16 = real_div(x = current_step_to_fp16, y = total_step_to_fp16)[name = string("t_1_cast_fp16")]; + int32 var_74 = const()[name = string("op_74"), val = int32(0)]; + bool t_3_interleave_0 = const()[name = string("t_3_interleave_0"), val = bool(false)]; + tensor t_3_cast_fp16 = concat(axis = var_74, interleave = t_3_interleave_0, values = (t_1_cast_fp16, t_1_cast_fp16))[name = string("t_3_cast_fp16")]; + int32 var_78 = const()[name = string("op_78"), val = int32(-1)]; + tensor var_85 = const()[name = string("op_85"), val = tensor([-1, 1])]; + tensor var_86_cast_fp16 = reshape(shape = var_85, x = t_3_cast_fp16)[name = string("op_86_cast_fp16")]; + fp16 time_encoder_time_scale_to_fp16 = const()[name = string("time_encoder_time_scale_to_fp16"), val = fp16(0x1.f4p+9)]; + tensor t_scaled_cast_fp16 = mul(x = var_86_cast_fp16, y = time_encoder_time_scale_to_fp16)[name = string("t_scaled_cast_fp16")]; + tensor time_encoder_omegas_to_fp16 = const()[name = string("time_encoder_omegas_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34752)))]; + tensor phase_cast_fp16 = mul(x = t_scaled_cast_fp16, y = time_encoder_omegas_to_fp16)[name = string("phase_cast_fp16")]; + tensor var_89_cast_fp16 = sin(x = phase_cast_fp16)[name = string("op_89_cast_fp16")]; + tensor var_90_cast_fp16 = cos(x = phase_cast_fp16)[name = string("op_90_cast_fp16")]; + bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)]; + tensor input_1_cast_fp16 = concat(axis = var_78, interleave = input_1_interleave_0, values = (var_89_cast_fp16, var_90_cast_fp16))[name = string("input_1_cast_fp16")]; + tensor time_encoder_mlp_0_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47232))))[name = string("time_encoder_mlp_0_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_0_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47424)))]; + tensor linear_0_cast_fp16 = linear(bias = time_encoder_mlp_0_linear_bias_to_fp16, weight = time_encoder_mlp_0_linear_weight_to_fp16_palettized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_97_cast_fp16 = softplus(x = linear_0_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = tanh(x = var_97_cast_fp16)[name = string("op_98_cast_fp16")]; + tensor input_3_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_98_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor time_encoder_mlp_2_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60352))))[name = string("time_encoder_mlp_2_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_2_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60544)))]; + tensor linear_1_cast_fp16 = linear(bias = time_encoder_mlp_2_linear_bias_to_fp16, weight = time_encoder_mlp_2_linear_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor time_emb_axes_0 = const()[name = string("time_emb_axes_0"), val = tensor([-1])]; + tensor time_emb_cast_fp16 = expand_dims(axes = time_emb_axes_0, x = linear_1_cast_fp16)[name = string("time_emb_cast_fp16")]; + string var_115_pad_type_0 = const()[name = string("op_115_pad_type_0"), val = string("valid")]; + tensor var_115_strides_0 = const()[name = string("op_115_strides_0"), val = tensor([1])]; + tensor var_115_pad_0 = const()[name = string("op_115_pad_0"), val = tensor([0, 0])]; + tensor var_115_dilations_0 = const()[name = string("op_115_dilations_0"), val = tensor([1])]; + int32 var_115_groups_0 = const()[name = string("op_115_groups_0"), val = int32(1)]; + tensor proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116096))))[name = string("proj_in_weight_to_fp16_palettized")]; + tensor var_115_cast_fp16 = conv(dilations = var_115_dilations_0, groups = var_115_groups_0, pad = var_115_pad_0, pad_type = var_115_pad_type_0, strides = var_115_strides_0, weight = proj_in_weight_to_fp16_palettized, x = input_5_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = var_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_3_cast_fp16")]; + int32 var_134 = const()[name = string("op_134"), val = int32(-1)]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([0, 0, 1])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 256])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([true, true, true])]; + tensor input_7_cast_fp16 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, x = latent_mask_b_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_166_pad_0 = const()[name = string("op_166_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_166_mode_0 = const()[name = string("op_166_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_166_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_166_mode_0, pad = var_166_pad_0, x = input_7_cast_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_133_to_fp16 = const()[name = string("op_133_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = sub(x = var_133_to_fp16, y = var_166_cast_fp16)[name = string("op_167_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = latent_mask_b_cast_fp16, y = var_167_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_169_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_172_cast_fp16 = mul(x = x_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor var_173_cast_fp16 = sub(x = var_133_to_fp16, y = latent_mask_b_cast_fp16)[name = string("op_173_cast_fp16")]; + tensor var_174_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_174_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_174_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("replicate")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor input_11_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_11_mode_0, pad = input_11_pad_0, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(512)]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118272))))[name = string("main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118464)))]; + tensor h_3_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, 2, 1])]; + tensor var_192_axes_0 = const()[name = string("op_192_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119552)))]; + tensor main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120640)))]; + fp16 var_139_to_fp16 = const()[name = string("op_139_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_7_cast_fp16)[name = string("transpose_117")]; + tensor var_192_cast_fp16 = layer_norm(axes = var_192_axes_0, beta = main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16, x = input_13_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, 2, 1])]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908224))))[name = string("main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908416)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = var_192_cast_fp16)[name = string("transpose_116")]; + tensor h_5_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = string("h_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = h_5_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)]; + tensor op_209_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1699072))))[name = string("op_209_weight_0_to_fp16_palettized")]; + tensor var_209_bias_0_to_fp16 = const()[name = string("op_209_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1699264)))]; + tensor var_209_cast_fp16 = conv(bias = var_209_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = op_209_weight_0_to_fp16_palettized, x = input_17_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_209_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_223_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_223_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_226_cast_fp16 = mul(x = x_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_226_cast_fp16")]; + tensor var_228_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_228_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_226_cast_fp16, y = var_228_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")]; + fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)]; + tensor input_23_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([2])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(512)]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1700352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1702336))))[name = string("main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1702528)))]; + tensor h_9_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor var_246_axes_0 = const()[name = string("op_246_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1703616)))]; + tensor main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1704704)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_13_cast_fp16)[name = string("transpose_115")]; + tensor var_246_cast_fp16 = layer_norm(axes = var_246_axes_0, beta = main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_246_cast_fp16")]; + tensor input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor([0, 2, 1])]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1705792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2492288))))[name = string("main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2492480)))]; + tensor input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_246_cast_fp16)[name = string("transpose_114")]; + tensor h_11_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_27_cast_fp16)[name = string("h_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_11_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)]; + tensor op_263_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2496640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3283136))))[name = string("op_263_weight_0_to_fp16_palettized")]; + tensor var_263_bias_0_to_fp16 = const()[name = string("op_263_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3283328)))]; + tensor var_263_cast_fp16 = conv(bias = var_263_bias_0_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = op_263_weight_0_to_fp16_palettized, x = input_29_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_263_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_277_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_277_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_277_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_280_cast_fp16 = mul(x = x_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_280_cast_fp16")]; + tensor var_282_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_280_cast_fp16, y = var_282_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("replicate")]; + fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)]; + tensor input_35_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = input_35_mode_0, pad = input_35_pad_0, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([4])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(512)]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3284416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3286400))))[name = string("main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3286592)))]; + tensor h_15_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor var_300_axes_0 = const()[name = string("op_300_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3287680)))]; + tensor main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3288768)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_19_cast_fp16)[name = string("transpose_113")]; + tensor var_300_cast_fp16 = layer_norm(axes = var_300_axes_0, beta = main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16, x = input_37_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor([0, 2, 1])]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3289856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4076352))))[name = string("main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4076544)))]; + tensor input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_300_cast_fp16)[name = string("transpose_112")]; + tensor h_17_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = string("h_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = h_17_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)]; + tensor op_317_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4080704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4867200))))[name = string("op_317_weight_0_to_fp16_palettized")]; + tensor var_317_bias_0_to_fp16 = const()[name = string("op_317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4867392)))]; + tensor var_317_cast_fp16 = conv(bias = var_317_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = op_317_weight_0_to_fp16_palettized, x = input_41_cast_fp16)[name = string("op_317_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_317_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor var_331_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_331_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_331_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = x_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor var_336_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_336_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_334_cast_fp16, y = var_336_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")]; + fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([8])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(512)]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4868480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4870464))))[name = string("main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4870656)))]; + tensor h_21_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4871744)))]; + tensor main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4872832)))]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_25_cast_fp16)[name = string("transpose_111")]; + tensor var_354_cast_fp16 = layer_norm(axes = var_354_axes_0, beta = main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_354_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4873920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5660416))))[name = string("main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5660608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_354_cast_fp16)[name = string("transpose_110")]; + tensor h_23_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("h_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_23_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)]; + tensor op_371_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5664768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6451264))))[name = string("op_371_weight_0_to_fp16_palettized")]; + tensor var_371_bias_0_to_fp16 = const()[name = string("op_371_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6451456)))]; + tensor var_371_cast_fp16 = conv(bias = var_371_bias_0_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = op_371_weight_0_to_fp16_palettized, x = input_53_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_371_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor input_55_cast_fp16 = squeeze(axes = input_55_axes_0, x = time_emb_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6452544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6477184))))[name = string("main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6477376)))]; + tensor linear_2_cast_fp16 = linear(bias = main_blocks_0_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor t_5_axes_0 = const()[name = string("t_5_axes_0"), val = tensor([-1])]; + tensor t_5_cast_fp16 = expand_dims(axes = t_5_axes_0, x = linear_2_cast_fp16)[name = string("t_5_cast_fp16")]; + tensor var_381_cast_fp16 = add(x = x_27_cast_fp16, y = t_5_cast_fp16)[name = string("op_381_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_381_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_394_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_394_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_394_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_397_cast_fp16 = mul(x = x_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = var_397_cast_fp16, y = var_399_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("replicate")]; + fp16 const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = fp16(0x0p+0)]; + tensor input_61_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = input_61_mode_0, pad = input_61_pad_0, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(512)]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6478464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6480448))))[name = string("main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6480640)))]; + tensor h_27_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417_axes_0 = const()[name = string("op_417_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6481728)))]; + tensor main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6482816)))]; + tensor input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_33_cast_fp16)[name = string("transpose_109")]; + tensor var_417_cast_fp16 = layer_norm(axes = var_417_axes_0, beta = main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16, x = input_63_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6483904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7270400))))[name = string("main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7270592)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = var_417_cast_fp16)[name = string("transpose_108")]; + tensor h_29_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_65_cast_fp16)[name = string("h_29_cast_fp16")]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("EXACT")]; + tensor input_67_cast_fp16 = gelu(mode = input_67_mode_0, x = h_29_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)]; + tensor op_434_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7274752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8061248))))[name = string("op_434_weight_0_to_fp16_palettized")]; + tensor var_434_bias_0_to_fp16 = const()[name = string("op_434_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8061440)))]; + tensor var_434_cast_fp16 = conv(bias = var_434_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = op_434_weight_0_to_fp16_palettized, x = input_67_cast_fp16)[name = string("op_434_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_434_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_m_1_cast_fp16 = mul(x = x_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_1_cast_fp16")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8062528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8259200))))[name = string("main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8259392)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_35_cast_fp16)[name = string("transpose_107")]; + tensor linear_3_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([2, 256, 8, 64])]; + tensor var_453_cast_fp16 = reshape(shape = var_452, x = linear_3_cast_fp16)[name = string("op_453_cast_fp16")]; + tensor x_37_perm_0 = const()[name = string("x_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8260480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8358848))))[name = string("main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8359040)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = text_emb_cast_fp16)[name = string("transpose_106")]; + tensor linear_4_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([2, 128, 8, 64])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = linear_4_cast_fp16)[name = string("op_461_cast_fp16")]; + tensor x_39_perm_0 = const()[name = string("x_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8360128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8458496))))[name = string("main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8458688)))]; + tensor linear_5_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_468 = const()[name = string("op_468"), val = tensor([2, 128, 8, 64])]; + tensor var_469_cast_fp16 = reshape(shape = var_468, x = linear_5_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor divisor_1_axes_0 = const()[name = string("divisor_1_axes_0"), val = tensor([1, 2])]; + bool divisor_1_keep_dims_0 = const()[name = string("divisor_1_keep_dims_0"), val = bool(false)]; + tensor divisor_1_cast_fp16 = reduce_sum(axes = divisor_1_axes_0, keep_dims = divisor_1_keep_dims_0, x = latent_mask_b_cast_fp16)[name = string("divisor_1_cast_fp16")]; + tensor divisor_3_axes_0 = const()[name = string("divisor_3_axes_0"), val = tensor([1, 2])]; + bool divisor_3_keep_dims_0 = const()[name = string("divisor_3_keep_dims_0"), val = bool(false)]; + tensor divisor_3_cast_fp16 = reduce_sum(axes = divisor_3_axes_0, keep_dims = divisor_3_keep_dims_0, x = text_mask_cast_fp16)[name = string("divisor_3_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([-1, 1, 1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = divisor_1_cast_fp16)[name = string("op_488_cast_fp16")]; + tensor const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8459776)))]; + tensor scaled_1_cast_fp16 = real_div(x = const_16_to_fp16, y = var_488_cast_fp16)[name = string("scaled_1_cast_fp16")]; + tensor main_blocks_0_text_attn_theta_to_fp16 = const()[name = string("main_blocks_0_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8460864)))]; + tensor angles_1_cast_fp16 = mul(x = scaled_1_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_1_cast_fp16")]; + tensor var_491_cast_fp16 = cos(x = angles_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = var_491_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor var_493_cast_fp16 = sin(x = angles_1_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = var_493_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor x_a_1_begin_0 = const()[name = string("x_a_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_1_end_0 = const()[name = string("x_a_1_end_0"), val = tensor([2, 8, 256, 32])]; + tensor x_a_1_end_mask_0 = const()[name = string("x_a_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_37_cast_fp16 = transpose(perm = x_37_perm_0, x = var_453_cast_fp16)[name = string("transpose_105")]; + tensor x_a_1_cast_fp16 = slice_by_index(begin = x_a_1_begin_0, end = x_a_1_end_0, end_mask = x_a_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_a_1_cast_fp16")]; + tensor x_b_1_begin_0 = const()[name = string("x_b_1_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_1_end_0 = const()[name = string("x_b_1_end_0"), val = tensor([2, 8, 256, 64])]; + tensor x_b_1_end_mask_0 = const()[name = string("x_b_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_1_cast_fp16 = slice_by_index(begin = x_b_1_begin_0, end = x_b_1_end_0, end_mask = x_b_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_b_1_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = x_a_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor var_498_cast_fp16 = mul(x = x_b_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_498_cast_fp16")]; + tensor rot_a_1_cast_fp16 = sub(x = var_497_cast_fp16, y = var_498_cast_fp16)[name = string("rot_a_1_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = x_a_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = x_b_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor rot_b_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("rot_b_1_cast_fp16")]; + bool q_1_interleave_0 = const()[name = string("q_1_interleave_0"), val = bool(false)]; + tensor q_1_cast_fp16 = concat(axis = var_134, interleave = q_1_interleave_0, values = (rot_a_1_cast_fp16, rot_b_1_cast_fp16))[name = string("q_1_cast_fp16")]; + tensor var_505 = const()[name = string("op_505"), val = tensor([-1, 1, 1])]; + tensor var_506_cast_fp16 = reshape(shape = var_505, x = divisor_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8460992)))]; + tensor scaled_3_cast_fp16 = real_div(x = const_17_to_fp16, y = var_506_cast_fp16)[name = string("scaled_3_cast_fp16")]; + tensor angles_3_cast_fp16 = mul(x = scaled_3_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_3_cast_fp16")]; + tensor var_509_cast_fp16 = cos(x = angles_3_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor cos_3_axes_0 = const()[name = string("cos_3_axes_0"), val = tensor([1])]; + tensor cos_3_cast_fp16 = expand_dims(axes = cos_3_axes_0, x = var_509_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor var_511_cast_fp16 = sin(x = angles_3_cast_fp16)[name = string("op_511_cast_fp16")]; + tensor sin_3_axes_0 = const()[name = string("sin_3_axes_0"), val = tensor([1])]; + tensor sin_3_cast_fp16 = expand_dims(axes = sin_3_axes_0, x = var_511_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor x_a_3_begin_0 = const()[name = string("x_a_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_3_end_0 = const()[name = string("x_a_3_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_3_end_mask_0 = const()[name = string("x_a_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_39_cast_fp16 = transpose(perm = x_39_perm_0, x = var_461_cast_fp16)[name = string("transpose_104")]; + tensor x_a_3_cast_fp16 = slice_by_index(begin = x_a_3_begin_0, end = x_a_3_end_0, end_mask = x_a_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_a_3_cast_fp16")]; + tensor x_b_3_begin_0 = const()[name = string("x_b_3_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_3_end_0 = const()[name = string("x_b_3_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_3_end_mask_0 = const()[name = string("x_b_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_3_cast_fp16 = slice_by_index(begin = x_b_3_begin_0, end = x_b_3_end_0, end_mask = x_b_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_b_3_cast_fp16")]; + tensor var_515_cast_fp16 = mul(x = x_a_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_515_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = x_b_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor rot_a_3_cast_fp16 = sub(x = var_515_cast_fp16, y = var_516_cast_fp16)[name = string("rot_a_3_cast_fp16")]; + tensor var_518_cast_fp16 = mul(x = x_a_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = mul(x = x_b_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor rot_b_3_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = string("rot_b_3_cast_fp16")]; + bool k_1_interleave_0 = const()[name = string("k_1_interleave_0"), val = bool(false)]; + tensor k_1_cast_fp16 = concat(axis = var_134, interleave = k_1_interleave_0, values = (rot_a_3_cast_fp16, rot_b_3_cast_fp16))[name = string("k_1_cast_fp16")]; + bool var_524_transpose_x_1 = const()[name = string("op_524_transpose_x_1"), val = bool(false)]; + bool var_524_transpose_y_1 = const()[name = string("op_524_transpose_y_1"), val = bool(true)]; + tensor var_524_cast_fp16 = matmul(transpose_x = var_524_transpose_x_1, transpose_y = var_524_transpose_y_1, x = q_1_cast_fp16, y = k_1_cast_fp16)[name = string("op_524_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_524_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([2])]; + tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = text_mask_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528_cast_fp16 = sub(x = var_133_to_fp16, y = var_527_cast_fp16)[name = string("op_528_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_530_cast_fp16 = mul(x = var_528_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_75_cast_fp16 = sub(x = _inversed_scores_1_cast_fp16, y = var_530_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_134, x = input_75_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool var_533_transpose_x_0 = const()[name = string("op_533_transpose_x_0"), val = bool(false)]; + bool var_533_transpose_y_0 = const()[name = string("op_533_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_469_cast_fp16)[name = string("transpose_103")]; + tensor var_533_cast_fp16 = matmul(transpose_x = var_533_transpose_x_0, transpose_y = var_533_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("op_533_cast_fp16")]; + tensor var_534_perm_0 = const()[name = string("op_534_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_535 = const()[name = string("op_535"), val = tensor([2, 256, 512])]; + tensor var_534_cast_fp16 = transpose(perm = var_534_perm_0, x = var_533_cast_fp16)[name = string("transpose_102")]; + tensor input_77_cast_fp16 = reshape(shape = var_535, x = var_534_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8461568))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8658240))))[name = string("main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8658432)))]; + tensor linear_6_cast_fp16 = linear(bias = main_blocks_0_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor out_11_perm_0 = const()[name = string("out_11_perm_0"), val = tensor([0, 2, 1])]; + tensor out_11_cast_fp16 = transpose(perm = out_11_perm_0, x = linear_6_cast_fp16)[name = string("transpose_101")]; + tensor var_542_cast_fp16 = mul(x = out_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_542_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = x_m_1_cast_fp16, y = var_542_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor var_549_axes_0 = const()[name = string("op_549_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8659520)))]; + tensor main_blocks_0_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8660608)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_41_cast_fp16)[name = string("transpose_100")]; + tensor var_549_cast_fp16 = layer_norm(axes = var_549_axes_0, beta = main_blocks_0_text_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_text_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor var_550_perm_0 = const()[name = string("op_550_perm_0"), val = tensor([0, 2, 1])]; + tensor var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = var_549_cast_fp16)[name = string("transpose_99")]; + tensor x_43_cast_fp16 = mul(x = var_550_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = x_45_cast_fp16, y = last_1_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor x_last_11_axes_0 = const()[name = string("x_last_11_axes_0"), val = tensor([-1])]; + bool x_last_11_keep_dims_0 = const()[name = string("x_last_11_keep_dims_0"), val = bool(true)]; + tensor x_last_11_cast_fp16 = reduce_sum(axes = x_last_11_axes_0, keep_dims = x_last_11_keep_dims_0, x = var_563_cast_fp16)[name = string("x_last_11_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = x_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor var_568_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_11_cast_fp16)[name = string("op_568_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = var_566_cast_fp16, y = var_568_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_85_mode_0 = const()[name = string("input_85_mode_0"), val = string("replicate")]; + fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)]; + tensor input_85_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(512)]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8661696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8663680))))[name = string("main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8663872)))]; + tensor h_33_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("h_33_cast_fp16")]; + tensor x_47_cast_fp16 = mul(x = h_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8664960)))]; + tensor main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8666048)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_47_cast_fp16)[name = string("transpose_98")]; + tensor var_586_cast_fp16 = layer_norm(axes = var_586_axes_0, beta = main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_586_cast_fp16")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8667136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9453632))))[name = string("main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9453824)))]; + tensor input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = var_586_cast_fp16)[name = string("transpose_97")]; + tensor h_35_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_89_cast_fp16)[name = string("h_35_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = h_35_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")]; + tensor h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor([1])]; + tensor h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor([0, 0])]; + tensor h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor([1])]; + int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)]; + tensor op_603_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9457984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10244480))))[name = string("op_603_weight_0_to_fp16_palettized")]; + tensor var_603_bias_0_to_fp16 = const()[name = string("op_603_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10244672)))]; + tensor var_603_cast_fp16 = conv(bias = var_603_bias_0_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = op_603_weight_0_to_fp16_palettized, x = input_91_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor out_13_cast_fp16 = add(x = x_45_cast_fp16, y = var_603_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_49_cast_fp16 = mul(x = out_13_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_m_3_cast_fp16 = mul(x = x_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_3_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10245760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10344128))))[name = string("main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10344320)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_49_cast_fp16)[name = string("transpose_96")]; + tensor linear_7_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([2, 256, 2, 128])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = linear_7_cast_fp16)[name = string("op_620_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10344896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10394112))))[name = string("main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10394304)))]; + tensor linear_9_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_633 = const()[name = string("op_633"), val = tensor([2, 50, 2, 128])]; + tensor var_634_cast_fp16 = reshape(shape = var_633, x = linear_9_cast_fp16)[name = string("op_634_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_638_transpose_x_0 = const()[name = string("op_638_transpose_x_0"), val = bool(false)]; + bool var_638_transpose_y_0 = const()[name = string("op_638_transpose_y_0"), val = bool(false)]; + tensor op_637_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10394880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10414144))))[name = string("op_637_to_fp16_palettized")]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_620_cast_fp16)[name = string("transpose_95")]; + tensor var_638_cast_fp16 = matmul(transpose_x = var_638_transpose_x_0, transpose_y = var_638_transpose_y_0, x = q_3_cast_fp16, y = op_637_to_fp16_palettized)[name = string("op_638_cast_fp16")]; + fp16 _inversed_input_97_y_0_to_fp16 = const()[name = string("_inversed_input_97_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_97_cast_fp16 = mul(x = var_638_cast_fp16, y = _inversed_input_97_y_0_to_fp16)[name = string("_inversed_input_97_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_134, x = _inversed_input_97_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_642_perm_0 = const()[name = string("op_642_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_1_axes_0 = const()[name = string("mask_1_axes_0"), val = tensor([1])]; + tensor var_642_cast_fp16 = transpose(perm = var_642_perm_0, x = latent_mask_b_cast_fp16)[name = string("transpose_94")]; + tensor mask_1_cast_fp16 = expand_dims(axes = mask_1_axes_0, x = var_642_cast_fp16)[name = string("mask_1_cast_fp16")]; + tensor attn_5_cast_fp16 = mul(x = attn_3_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool var_645_transpose_x_0 = const()[name = string("op_645_transpose_x_0"), val = bool(false)]; + bool var_645_transpose_y_0 = const()[name = string("op_645_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_634_cast_fp16)[name = string("transpose_93")]; + tensor var_645_cast_fp16 = matmul(transpose_x = var_645_transpose_x_0, transpose_y = var_645_transpose_y_0, x = attn_5_cast_fp16, y = v_3_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_646_perm_0 = const()[name = string("op_646_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_647 = const()[name = string("op_647"), val = tensor([2, 256, 256])]; + tensor var_646_cast_fp16 = transpose(perm = var_646_perm_0, x = var_645_cast_fp16)[name = string("transpose_92")]; + tensor input_99_cast_fp16 = reshape(shape = var_647, x = var_646_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10414336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10512704))))[name = string("main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10512896)))]; + tensor linear_10_cast_fp16 = linear(bias = main_blocks_0_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor out_15_perm_0 = const()[name = string("out_15_perm_0"), val = tensor([0, 2, 1])]; + tensor out_15_cast_fp16 = transpose(perm = out_15_perm_0, x = linear_10_cast_fp16)[name = string("transpose_91")]; + tensor var_654_cast_fp16 = mul(x = out_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor x_51_cast_fp16 = add(x = x_m_3_cast_fp16, y = var_654_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10513984)))]; + tensor main_blocks_0_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10515072)))]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_51_cast_fp16)[name = string("transpose_90")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = main_blocks_0_style_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_style_norm_norm_weight_to_fp16, x = input_101_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_662_perm_0 = const()[name = string("op_662_perm_0"), val = tensor([0, 2, 1])]; + tensor var_662_cast_fp16 = transpose(perm = var_662_perm_0, x = var_661_cast_fp16)[name = string("transpose_89")]; + tensor x_53_cast_fp16 = mul(x = var_662_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_53_cast_fp16")]; + int32 var_681 = const()[name = string("op_681"), val = int32(-1)]; + tensor x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = x_55_cast_fp16, y = last_1_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor x_last_13_axes_0 = const()[name = string("x_last_13_axes_0"), val = tensor([-1])]; + bool x_last_13_keep_dims_0 = const()[name = string("x_last_13_keep_dims_0"), val = bool(true)]; + tensor x_last_13_cast_fp16 = reduce_sum(axes = x_last_13_axes_0, keep_dims = x_last_13_keep_dims_0, x = var_716_cast_fp16)[name = string("x_last_13_cast_fp16")]; + tensor var_719_cast_fp16 = mul(x = x_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_721_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_13_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_719_cast_fp16, y = var_721_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("replicate")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(512)]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10516160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10518144))))[name = string("main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10518336)))]; + tensor h_39_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor x_57_cast_fp16 = mul(x = h_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_739_axes_0 = const()[name = string("op_739_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10519424)))]; + tensor main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10520512)))]; + fp16 var_686_to_fp16 = const()[name = string("op_686_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_57_cast_fp16)[name = string("transpose_88")]; + tensor var_739_cast_fp16 = layer_norm(axes = var_739_axes_0, beta = main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_739_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + string h_41_pad_type_0 = const()[name = string("h_41_pad_type_0"), val = string("valid")]; + tensor h_41_strides_0 = const()[name = string("h_41_strides_0"), val = tensor([1])]; + tensor h_41_pad_0 = const()[name = string("h_41_pad_0"), val = tensor([0, 0])]; + tensor h_41_dilations_0 = const()[name = string("h_41_dilations_0"), val = tensor([1])]; + int32 h_41_groups_0 = const()[name = string("h_41_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10521600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11308096))))[name = string("main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11308288)))]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = var_739_cast_fp16)[name = string("transpose_87")]; + tensor h_41_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_41_dilations_0, groups = h_41_groups_0, pad = h_41_pad_0, pad_type = h_41_pad_type_0, strides = h_41_strides_0, weight = main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = h_41_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor op_756_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11312448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12098944))))[name = string("op_756_weight_0_to_fp16_palettized")]; + tensor var_756_bias_0_to_fp16 = const()[name = string("op_756_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12099136)))]; + tensor var_756_cast_fp16 = conv(bias = var_756_bias_0_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = op_756_weight_0_to_fp16_palettized, x = input_113_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor out_17_cast_fp16 = add(x = x_55_cast_fp16, y = var_756_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_59_cast_fp16 = mul(x = out_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor x_61_cast_fp16 = mul(x = x_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_770_cast_fp16 = mul(x = x_61_cast_fp16, y = last_1_cast_fp16)[name = string("op_770_cast_fp16")]; + tensor x_last_15_axes_0 = const()[name = string("x_last_15_axes_0"), val = tensor([-1])]; + bool x_last_15_keep_dims_0 = const()[name = string("x_last_15_keep_dims_0"), val = bool(true)]; + tensor x_last_15_cast_fp16 = reduce_sum(axes = x_last_15_axes_0, keep_dims = x_last_15_keep_dims_0, x = var_770_cast_fp16)[name = string("x_last_15_cast_fp16")]; + tensor var_773_cast_fp16 = mul(x = x_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor var_775_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_15_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = var_773_cast_fp16, y = var_775_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("replicate")]; + fp16 const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = fp16(0x0p+0)]; + tensor input_119_cast_fp16 = pad(constant_val = const_26_to_fp16, mode = input_119_mode_0, pad = input_119_pad_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")]; + string h_45_pad_type_0 = const()[name = string("h_45_pad_type_0"), val = string("valid")]; + tensor h_45_dilations_0 = const()[name = string("h_45_dilations_0"), val = tensor([2])]; + int32 h_45_groups_0 = const()[name = string("h_45_groups_0"), val = int32(512)]; + tensor h_45_strides_0 = const()[name = string("h_45_strides_0"), val = tensor([1])]; + tensor h_45_pad_0 = const()[name = string("h_45_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12100224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12102208))))[name = string("main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12102400)))]; + tensor h_45_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_45_dilations_0, groups = h_45_groups_0, pad = h_45_pad_0, pad_type = h_45_pad_type_0, strides = h_45_strides_0, weight = main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = h_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12103488)))]; + tensor main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12104576)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_63_cast_fp16)[name = string("transpose_86")]; + tensor var_793_cast_fp16 = layer_norm(axes = var_793_axes_0, beta = main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor input_123_perm_0 = const()[name = string("input_123_perm_0"), val = tensor([0, 2, 1])]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12105664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12892160))))[name = string("main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12892352)))]; + tensor input_123_cast_fp16 = transpose(perm = input_123_perm_0, x = var_793_cast_fp16)[name = string("transpose_85")]; + tensor h_47_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = string("h_47_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = h_47_cast_fp16)[name = string("input_125_cast_fp16")]; + string h_49_pad_type_0 = const()[name = string("h_49_pad_type_0"), val = string("valid")]; + tensor h_49_strides_0 = const()[name = string("h_49_strides_0"), val = tensor([1])]; + tensor h_49_pad_0 = const()[name = string("h_49_pad_0"), val = tensor([0, 0])]; + tensor h_49_dilations_0 = const()[name = string("h_49_dilations_0"), val = tensor([1])]; + int32 h_49_groups_0 = const()[name = string("h_49_groups_0"), val = int32(1)]; + tensor op_810_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12896512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13683008))))[name = string("op_810_weight_0_to_fp16_palettized")]; + tensor var_810_bias_0_to_fp16 = const()[name = string("op_810_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13683200)))]; + tensor var_810_cast_fp16 = conv(bias = var_810_bias_0_to_fp16, dilations = h_49_dilations_0, groups = h_49_groups_0, pad = h_49_pad_0, pad_type = h_49_pad_type_0, strides = h_49_strides_0, weight = op_810_weight_0_to_fp16_palettized, x = input_125_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = x_61_cast_fp16, y = var_810_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor x_65_cast_fp16 = mul(x = out_19_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = x_67_cast_fp16, y = last_1_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_last_17_axes_0 = const()[name = string("x_last_17_axes_0"), val = tensor([-1])]; + bool x_last_17_keep_dims_0 = const()[name = string("x_last_17_keep_dims_0"), val = bool(true)]; + tensor x_last_17_cast_fp16 = reduce_sum(axes = x_last_17_axes_0, keep_dims = x_last_17_keep_dims_0, x = var_824_cast_fp16)[name = string("x_last_17_cast_fp16")]; + tensor var_827_cast_fp16 = mul(x = x_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_829_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_17_cast_fp16)[name = string("op_829_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = var_827_cast_fp16, y = var_829_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("replicate")]; + fp16 const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = fp16(0x0p+0)]; + tensor input_131_cast_fp16 = pad(constant_val = const_28_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + string h_51_pad_type_0 = const()[name = string("h_51_pad_type_0"), val = string("valid")]; + tensor h_51_dilations_0 = const()[name = string("h_51_dilations_0"), val = tensor([4])]; + int32 h_51_groups_0 = const()[name = string("h_51_groups_0"), val = int32(512)]; + tensor h_51_strides_0 = const()[name = string("h_51_strides_0"), val = tensor([1])]; + tensor h_51_pad_0 = const()[name = string("h_51_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13684288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13686272))))[name = string("main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13686464)))]; + tensor h_51_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_51_dilations_0, groups = h_51_groups_0, pad = h_51_pad_0, pad_type = h_51_pad_type_0, strides = h_51_strides_0, weight = main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor x_69_cast_fp16 = mul(x = h_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_847_axes_0 = const()[name = string("op_847_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13687552)))]; + tensor main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13688640)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_69_cast_fp16)[name = string("transpose_84")]; + tensor var_847_cast_fp16 = layer_norm(axes = var_847_axes_0, beta = main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_847_cast_fp16")]; + tensor input_135_perm_0 = const()[name = string("input_135_perm_0"), val = tensor([0, 2, 1])]; + string h_53_pad_type_0 = const()[name = string("h_53_pad_type_0"), val = string("valid")]; + tensor h_53_strides_0 = const()[name = string("h_53_strides_0"), val = tensor([1])]; + tensor h_53_pad_0 = const()[name = string("h_53_pad_0"), val = tensor([0, 0])]; + tensor h_53_dilations_0 = const()[name = string("h_53_dilations_0"), val = tensor([1])]; + int32 h_53_groups_0 = const()[name = string("h_53_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13689728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14476224))))[name = string("main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14476416)))]; + tensor input_135_cast_fp16 = transpose(perm = input_135_perm_0, x = var_847_cast_fp16)[name = string("transpose_83")]; + tensor h_53_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_53_dilations_0, groups = h_53_groups_0, pad = h_53_pad_0, pad_type = h_53_pad_type_0, strides = h_53_strides_0, weight = main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = string("h_53_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = h_53_cast_fp16)[name = string("input_137_cast_fp16")]; + string h_55_pad_type_0 = const()[name = string("h_55_pad_type_0"), val = string("valid")]; + tensor h_55_strides_0 = const()[name = string("h_55_strides_0"), val = tensor([1])]; + tensor h_55_pad_0 = const()[name = string("h_55_pad_0"), val = tensor([0, 0])]; + tensor h_55_dilations_0 = const()[name = string("h_55_dilations_0"), val = tensor([1])]; + int32 h_55_groups_0 = const()[name = string("h_55_groups_0"), val = int32(1)]; + tensor op_864_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14480576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15267072))))[name = string("op_864_weight_0_to_fp16_palettized")]; + tensor var_864_bias_0_to_fp16 = const()[name = string("op_864_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15267264)))]; + tensor var_864_cast_fp16 = conv(bias = var_864_bias_0_to_fp16, dilations = h_55_dilations_0, groups = h_55_groups_0, pad = h_55_pad_0, pad_type = h_55_pad_type_0, strides = h_55_strides_0, weight = op_864_weight_0_to_fp16_palettized, x = input_137_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor out_21_cast_fp16 = add(x = x_67_cast_fp16, y = var_864_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = out_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor x_73_cast_fp16 = mul(x = x_71_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_878_cast_fp16 = mul(x = x_73_cast_fp16, y = last_1_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor x_last_19_axes_0 = const()[name = string("x_last_19_axes_0"), val = tensor([-1])]; + bool x_last_19_keep_dims_0 = const()[name = string("x_last_19_keep_dims_0"), val = bool(true)]; + tensor x_last_19_cast_fp16 = reduce_sum(axes = x_last_19_axes_0, keep_dims = x_last_19_keep_dims_0, x = var_878_cast_fp16)[name = string("x_last_19_cast_fp16")]; + tensor var_881_cast_fp16 = mul(x = x_73_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_881_cast_fp16")]; + tensor var_883_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_19_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = var_881_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("replicate")]; + fp16 const_30_to_fp16 = const()[name = string("const_30_to_fp16"), val = fp16(0x0p+0)]; + tensor input_143_cast_fp16 = pad(constant_val = const_30_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + string h_57_pad_type_0 = const()[name = string("h_57_pad_type_0"), val = string("valid")]; + tensor h_57_dilations_0 = const()[name = string("h_57_dilations_0"), val = tensor([8])]; + int32 h_57_groups_0 = const()[name = string("h_57_groups_0"), val = int32(512)]; + tensor h_57_strides_0 = const()[name = string("h_57_strides_0"), val = tensor([1])]; + tensor h_57_pad_0 = const()[name = string("h_57_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15268352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15270336))))[name = string("main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15270528)))]; + tensor h_57_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_57_dilations_0, groups = h_57_groups_0, pad = h_57_pad_0, pad_type = h_57_pad_type_0, strides = h_57_strides_0, weight = main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = string("h_57_cast_fp16")]; + tensor x_75_cast_fp16 = mul(x = h_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_901_axes_0 = const()[name = string("op_901_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15271616)))]; + tensor main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15272704)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_75_cast_fp16)[name = string("transpose_82")]; + tensor var_901_cast_fp16 = layer_norm(axes = var_901_axes_0, beta = main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor input_147_perm_0 = const()[name = string("input_147_perm_0"), val = tensor([0, 2, 1])]; + string h_59_pad_type_0 = const()[name = string("h_59_pad_type_0"), val = string("valid")]; + tensor h_59_strides_0 = const()[name = string("h_59_strides_0"), val = tensor([1])]; + tensor h_59_pad_0 = const()[name = string("h_59_pad_0"), val = tensor([0, 0])]; + tensor h_59_dilations_0 = const()[name = string("h_59_dilations_0"), val = tensor([1])]; + int32 h_59_groups_0 = const()[name = string("h_59_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15273792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16060288))))[name = string("main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16060480)))]; + tensor input_147_cast_fp16 = transpose(perm = input_147_perm_0, x = var_901_cast_fp16)[name = string("transpose_81")]; + tensor h_59_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_59_dilations_0, groups = h_59_groups_0, pad = h_59_pad_0, pad_type = h_59_pad_type_0, strides = h_59_strides_0, weight = main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("h_59_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = h_59_cast_fp16)[name = string("input_149_cast_fp16")]; + string h_61_pad_type_0 = const()[name = string("h_61_pad_type_0"), val = string("valid")]; + tensor h_61_strides_0 = const()[name = string("h_61_strides_0"), val = tensor([1])]; + tensor h_61_pad_0 = const()[name = string("h_61_pad_0"), val = tensor([0, 0])]; + tensor h_61_dilations_0 = const()[name = string("h_61_dilations_0"), val = tensor([1])]; + int32 h_61_groups_0 = const()[name = string("h_61_groups_0"), val = int32(1)]; + tensor op_918_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16064640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16851136))))[name = string("op_918_weight_0_to_fp16_palettized")]; + tensor var_918_bias_0_to_fp16 = const()[name = string("op_918_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16851328)))]; + tensor var_918_cast_fp16 = conv(bias = var_918_bias_0_to_fp16, dilations = h_61_dilations_0, groups = h_61_groups_0, pad = h_61_pad_0, pad_type = h_61_pad_type_0, strides = h_61_strides_0, weight = op_918_weight_0_to_fp16_palettized, x = input_149_cast_fp16)[name = string("op_918_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = x_73_cast_fp16, y = var_918_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor x_77_cast_fp16 = mul(x = out_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16852416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16877056))))[name = string("main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16877248)))]; + tensor linear_11_cast_fp16 = linear(bias = main_blocks_1_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor t_7_axes_0 = const()[name = string("t_7_axes_0"), val = tensor([-1])]; + tensor t_7_cast_fp16 = expand_dims(axes = t_7_axes_0, x = linear_11_cast_fp16)[name = string("t_7_cast_fp16")]; + tensor var_928_cast_fp16 = add(x = x_77_cast_fp16, y = t_7_cast_fp16)[name = string("op_928_cast_fp16")]; + tensor x_79_cast_fp16 = mul(x = var_928_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor x_81_cast_fp16 = mul(x = x_79_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = x_81_cast_fp16, y = last_1_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor x_last_21_axes_0 = const()[name = string("x_last_21_axes_0"), val = tensor([-1])]; + bool x_last_21_keep_dims_0 = const()[name = string("x_last_21_keep_dims_0"), val = bool(true)]; + tensor x_last_21_cast_fp16 = reduce_sum(axes = x_last_21_axes_0, keep_dims = x_last_21_keep_dims_0, x = var_941_cast_fp16)[name = string("x_last_21_cast_fp16")]; + tensor var_944_cast_fp16 = mul(x = x_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor var_946_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_21_cast_fp16)[name = string("op_946_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = var_944_cast_fp16, y = var_946_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("replicate")]; + fp16 const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = fp16(0x0p+0)]; + tensor input_157_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string h_63_pad_type_0 = const()[name = string("h_63_pad_type_0"), val = string("valid")]; + int32 h_63_groups_0 = const()[name = string("h_63_groups_0"), val = int32(512)]; + tensor h_63_strides_0 = const()[name = string("h_63_strides_0"), val = tensor([1])]; + tensor h_63_pad_0 = const()[name = string("h_63_pad_0"), val = tensor([0, 0])]; + tensor h_63_dilations_0 = const()[name = string("h_63_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16878336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16880320))))[name = string("main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16880512)))]; + tensor h_63_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_63_dilations_0, groups = h_63_groups_0, pad = h_63_pad_0, pad_type = h_63_pad_type_0, strides = h_63_strides_0, weight = main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor x_83_cast_fp16 = mul(x = h_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor var_964_axes_0 = const()[name = string("op_964_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16881600)))]; + tensor main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16882688)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_83_cast_fp16)[name = string("transpose_80")]; + tensor var_964_cast_fp16 = layer_norm(axes = var_964_axes_0, beta = main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + string h_65_pad_type_0 = const()[name = string("h_65_pad_type_0"), val = string("valid")]; + tensor h_65_strides_0 = const()[name = string("h_65_strides_0"), val = tensor([1])]; + tensor h_65_pad_0 = const()[name = string("h_65_pad_0"), val = tensor([0, 0])]; + tensor h_65_dilations_0 = const()[name = string("h_65_dilations_0"), val = tensor([1])]; + int32 h_65_groups_0 = const()[name = string("h_65_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16883776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17670272))))[name = string("main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17670464)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = var_964_cast_fp16)[name = string("transpose_79")]; + tensor h_65_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_65_dilations_0, groups = h_65_groups_0, pad = h_65_pad_0, pad_type = h_65_pad_type_0, strides = h_65_strides_0, weight = main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = string("h_65_cast_fp16")]; + string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("EXACT")]; + tensor input_163_cast_fp16 = gelu(mode = input_163_mode_0, x = h_65_cast_fp16)[name = string("input_163_cast_fp16")]; + string h_67_pad_type_0 = const()[name = string("h_67_pad_type_0"), val = string("valid")]; + tensor h_67_strides_0 = const()[name = string("h_67_strides_0"), val = tensor([1])]; + tensor h_67_pad_0 = const()[name = string("h_67_pad_0"), val = tensor([0, 0])]; + tensor h_67_dilations_0 = const()[name = string("h_67_dilations_0"), val = tensor([1])]; + int32 h_67_groups_0 = const()[name = string("h_67_groups_0"), val = int32(1)]; + tensor op_981_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17674624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18461120))))[name = string("op_981_weight_0_to_fp16_palettized")]; + tensor var_981_bias_0_to_fp16 = const()[name = string("op_981_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18461312)))]; + tensor var_981_cast_fp16 = conv(bias = var_981_bias_0_to_fp16, dilations = h_67_dilations_0, groups = h_67_groups_0, pad = h_67_pad_0, pad_type = h_67_pad_type_0, strides = h_67_strides_0, weight = op_981_weight_0_to_fp16_palettized, x = input_163_cast_fp16)[name = string("op_981_cast_fp16")]; + tensor out_25_cast_fp16 = add(x = x_81_cast_fp16, y = var_981_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = out_25_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor x_m_5_cast_fp16 = mul(x = x_85_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_5_cast_fp16")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18462400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18659072))))[name = string("main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18659264)))]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_85_cast_fp16)[name = string("transpose_78")]; + tensor linear_12_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_999 = const()[name = string("op_999"), val = tensor([2, 256, 8, 64])]; + tensor var_1000_cast_fp16 = reshape(shape = var_999, x = linear_12_cast_fp16)[name = string("op_1000_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18660352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18758720))))[name = string("main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18758912)))]; + tensor linear_13_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_1007 = const()[name = string("op_1007"), val = tensor([2, 128, 8, 64])]; + tensor var_1008_cast_fp16 = reshape(shape = var_1007, x = linear_13_cast_fp16)[name = string("op_1008_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18760000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18858368))))[name = string("main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18858560)))]; + tensor linear_14_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_1015 = const()[name = string("op_1015"), val = tensor([2, 128, 8, 64])]; + tensor var_1016_cast_fp16 = reshape(shape = var_1015, x = linear_14_cast_fp16)[name = string("op_1016_cast_fp16")]; + tensor v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_5_begin_0 = const()[name = string("x_a_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_5_end_0 = const()[name = string("x_a_5_end_0"), val = tensor([2, 8, 256, 32])]; + tensor x_a_5_end_mask_0 = const()[name = string("x_a_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_1000_cast_fp16)[name = string("transpose_77")]; + tensor x_a_5_cast_fp16 = slice_by_index(begin = x_a_5_begin_0, end = x_a_5_end_0, end_mask = x_a_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_a_5_cast_fp16")]; + tensor x_b_5_begin_0 = const()[name = string("x_b_5_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_5_end_0 = const()[name = string("x_b_5_end_0"), val = tensor([2, 8, 256, 64])]; + tensor x_b_5_end_mask_0 = const()[name = string("x_b_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_5_cast_fp16 = slice_by_index(begin = x_b_5_begin_0, end = x_b_5_end_0, end_mask = x_b_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_b_5_cast_fp16")]; + tensor var_1044_cast_fp16 = mul(x = x_a_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1044_cast_fp16")]; + tensor var_1045_cast_fp16 = mul(x = x_b_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor rot_a_5_cast_fp16 = sub(x = var_1044_cast_fp16, y = var_1045_cast_fp16)[name = string("rot_a_5_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_a_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_cast_fp16 = mul(x = x_b_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1048_cast_fp16")]; + tensor rot_b_5_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("rot_b_5_cast_fp16")]; + bool q_5_interleave_0 = const()[name = string("q_5_interleave_0"), val = bool(false)]; + tensor q_5_cast_fp16 = concat(axis = var_681, interleave = q_5_interleave_0, values = (rot_a_5_cast_fp16, rot_b_5_cast_fp16))[name = string("q_5_cast_fp16")]; + tensor x_a_7_begin_0 = const()[name = string("x_a_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_7_end_0 = const()[name = string("x_a_7_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_7_end_mask_0 = const()[name = string("x_a_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_1008_cast_fp16)[name = string("transpose_76")]; + tensor x_a_7_cast_fp16 = slice_by_index(begin = x_a_7_begin_0, end = x_a_7_end_0, end_mask = x_a_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_a_7_cast_fp16")]; + tensor x_b_7_begin_0 = const()[name = string("x_b_7_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_7_end_0 = const()[name = string("x_b_7_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_7_end_mask_0 = const()[name = string("x_b_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_7_cast_fp16 = slice_by_index(begin = x_b_7_begin_0, end = x_b_7_end_0, end_mask = x_b_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_b_7_cast_fp16")]; + tensor var_1062_cast_fp16 = mul(x = x_a_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = x_b_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor rot_a_7_cast_fp16 = sub(x = var_1062_cast_fp16, y = var_1063_cast_fp16)[name = string("rot_a_7_cast_fp16")]; + tensor var_1065_cast_fp16 = mul(x = x_a_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1065_cast_fp16")]; + tensor var_1066_cast_fp16 = mul(x = x_b_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1066_cast_fp16")]; + tensor rot_b_7_cast_fp16 = add(x = var_1065_cast_fp16, y = var_1066_cast_fp16)[name = string("rot_b_7_cast_fp16")]; + bool k_5_interleave_0 = const()[name = string("k_5_interleave_0"), val = bool(false)]; + tensor k_5_cast_fp16 = concat(axis = var_681, interleave = k_5_interleave_0, values = (rot_a_7_cast_fp16, rot_b_7_cast_fp16))[name = string("k_5_cast_fp16")]; + bool var_1071_transpose_x_1 = const()[name = string("op_1071_transpose_x_1"), val = bool(false)]; + bool var_1071_transpose_y_1 = const()[name = string("op_1071_transpose_y_1"), val = bool(true)]; + tensor var_1071_cast_fp16 = matmul(transpose_x = var_1071_transpose_x_1, transpose_y = var_1071_transpose_y_1, x = q_5_cast_fp16, y = k_5_cast_fp16)[name = string("op_1071_cast_fp16")]; + fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_3_cast_fp16 = mul(x = var_1071_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")]; + tensor input_171_cast_fp16 = sub(x = _inversed_scores_3_cast_fp16, y = var_530_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_681, x = input_171_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool var_1080_transpose_x_0 = const()[name = string("op_1080_transpose_x_0"), val = bool(false)]; + bool var_1080_transpose_y_0 = const()[name = string("op_1080_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1016_cast_fp16)[name = string("transpose_75")]; + tensor var_1080_cast_fp16 = matmul(transpose_x = var_1080_transpose_x_0, transpose_y = var_1080_transpose_y_0, x = attn_7_cast_fp16, y = v_5_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_perm_0 = const()[name = string("op_1081_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1082 = const()[name = string("op_1082"), val = tensor([2, 256, 512])]; + tensor var_1081_cast_fp16 = transpose(perm = var_1081_perm_0, x = var_1080_cast_fp16)[name = string("transpose_74")]; + tensor input_173_cast_fp16 = reshape(shape = var_1082, x = var_1081_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18859648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19056320))))[name = string("main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19056512)))]; + tensor linear_15_cast_fp16 = linear(bias = main_blocks_1_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor out_27_perm_0 = const()[name = string("out_27_perm_0"), val = tensor([0, 2, 1])]; + tensor out_27_cast_fp16 = transpose(perm = out_27_perm_0, x = linear_15_cast_fp16)[name = string("transpose_73")]; + tensor var_1089_cast_fp16 = mul(x = out_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor x_91_cast_fp16 = add(x = x_m_5_cast_fp16, y = var_1089_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_175_perm_0 = const()[name = string("input_175_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1096_axes_0 = const()[name = string("op_1096_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19057600)))]; + tensor main_blocks_1_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19058688)))]; + tensor input_175_cast_fp16 = transpose(perm = input_175_perm_0, x = x_91_cast_fp16)[name = string("transpose_72")]; + tensor var_1096_cast_fp16 = layer_norm(axes = var_1096_axes_0, beta = main_blocks_1_text_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_text_norm_norm_weight_to_fp16, x = input_175_cast_fp16)[name = string("op_1096_cast_fp16")]; + tensor var_1097_perm_0 = const()[name = string("op_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1097_cast_fp16 = transpose(perm = var_1097_perm_0, x = var_1096_cast_fp16)[name = string("transpose_71")]; + tensor x_93_cast_fp16 = mul(x = var_1097_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor x_95_cast_fp16 = mul(x = x_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_1110_cast_fp16 = mul(x = x_95_cast_fp16, y = last_1_cast_fp16)[name = string("op_1110_cast_fp16")]; + tensor x_last_23_axes_0 = const()[name = string("x_last_23_axes_0"), val = tensor([-1])]; + bool x_last_23_keep_dims_0 = const()[name = string("x_last_23_keep_dims_0"), val = bool(true)]; + tensor x_last_23_cast_fp16 = reduce_sum(axes = x_last_23_axes_0, keep_dims = x_last_23_keep_dims_0, x = var_1110_cast_fp16)[name = string("x_last_23_cast_fp16")]; + tensor var_1113_cast_fp16 = mul(x = x_95_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1115_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_23_cast_fp16)[name = string("op_1115_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = var_1113_cast_fp16, y = var_1115_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("replicate")]; + fp16 const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = fp16(0x0p+0)]; + tensor input_181_cast_fp16 = pad(constant_val = const_39_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + string h_69_pad_type_0 = const()[name = string("h_69_pad_type_0"), val = string("valid")]; + int32 h_69_groups_0 = const()[name = string("h_69_groups_0"), val = int32(512)]; + tensor h_69_strides_0 = const()[name = string("h_69_strides_0"), val = tensor([1])]; + tensor h_69_pad_0 = const()[name = string("h_69_pad_0"), val = tensor([0, 0])]; + tensor h_69_dilations_0 = const()[name = string("h_69_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19059776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19061760))))[name = string("main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19061952)))]; + tensor h_69_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_69_dilations_0, groups = h_69_groups_0, pad = h_69_pad_0, pad_type = h_69_pad_type_0, strides = h_69_strides_0, weight = main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = string("h_69_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = h_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_183_perm_0 = const()[name = string("input_183_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1133_axes_0 = const()[name = string("op_1133_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19063040)))]; + tensor main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19064128)))]; + tensor input_183_cast_fp16 = transpose(perm = input_183_perm_0, x = x_97_cast_fp16)[name = string("transpose_70")]; + tensor var_1133_cast_fp16 = layer_norm(axes = var_1133_axes_0, beta = main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16, x = input_183_cast_fp16)[name = string("op_1133_cast_fp16")]; + tensor input_185_perm_0 = const()[name = string("input_185_perm_0"), val = tensor([0, 2, 1])]; + string h_71_pad_type_0 = const()[name = string("h_71_pad_type_0"), val = string("valid")]; + tensor h_71_strides_0 = const()[name = string("h_71_strides_0"), val = tensor([1])]; + tensor h_71_pad_0 = const()[name = string("h_71_pad_0"), val = tensor([0, 0])]; + tensor h_71_dilations_0 = const()[name = string("h_71_dilations_0"), val = tensor([1])]; + int32 h_71_groups_0 = const()[name = string("h_71_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19851712))))[name = string("main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19851904)))]; + tensor input_185_cast_fp16 = transpose(perm = input_185_perm_0, x = var_1133_cast_fp16)[name = string("transpose_69")]; + tensor h_71_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_71_dilations_0, groups = h_71_groups_0, pad = h_71_pad_0, pad_type = h_71_pad_type_0, strides = h_71_strides_0, weight = main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = string("h_71_cast_fp16")]; + string input_187_mode_0 = const()[name = string("input_187_mode_0"), val = string("EXACT")]; + tensor input_187_cast_fp16 = gelu(mode = input_187_mode_0, x = h_71_cast_fp16)[name = string("input_187_cast_fp16")]; + string h_73_pad_type_0 = const()[name = string("h_73_pad_type_0"), val = string("valid")]; + tensor h_73_strides_0 = const()[name = string("h_73_strides_0"), val = tensor([1])]; + tensor h_73_pad_0 = const()[name = string("h_73_pad_0"), val = tensor([0, 0])]; + tensor h_73_dilations_0 = const()[name = string("h_73_dilations_0"), val = tensor([1])]; + int32 h_73_groups_0 = const()[name = string("h_73_groups_0"), val = int32(1)]; + tensor op_1150_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19856064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20642560))))[name = string("op_1150_weight_0_to_fp16_palettized")]; + tensor var_1150_bias_0_to_fp16 = const()[name = string("op_1150_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20642752)))]; + tensor var_1150_cast_fp16 = conv(bias = var_1150_bias_0_to_fp16, dilations = h_73_dilations_0, groups = h_73_groups_0, pad = h_73_pad_0, pad_type = h_73_pad_type_0, strides = h_73_strides_0, weight = op_1150_weight_0_to_fp16_palettized, x = input_187_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor out_29_cast_fp16 = add(x = x_95_cast_fp16, y = var_1150_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor x_99_cast_fp16 = mul(x = out_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor x_m_7_cast_fp16 = mul(x = x_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_7_cast_fp16")]; + tensor input_189_perm_0 = const()[name = string("input_189_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20643840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20742208))))[name = string("main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20742400)))]; + tensor input_189_cast_fp16 = transpose(perm = input_189_perm_0, x = x_99_cast_fp16)[name = string("transpose_68")]; + tensor linear_16_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_1166 = const()[name = string("op_1166"), val = tensor([2, 256, 2, 128])]; + tensor var_1167_cast_fp16 = reshape(shape = var_1166, x = linear_16_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20742976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20792192))))[name = string("main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20792384)))]; + tensor linear_18_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_1180 = const()[name = string("op_1180"), val = tensor([2, 50, 2, 128])]; + tensor var_1181_cast_fp16 = reshape(shape = var_1180, x = linear_18_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1185_transpose_x_0 = const()[name = string("op_1185_transpose_x_0"), val = bool(false)]; + bool var_1185_transpose_y_0 = const()[name = string("op_1185_transpose_y_0"), val = bool(false)]; + tensor op_1184_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20792960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20812224))))[name = string("op_1184_to_fp16_palettized")]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1167_cast_fp16)[name = string("transpose_67")]; + tensor var_1185_cast_fp16 = matmul(transpose_x = var_1185_transpose_x_0, transpose_y = var_1185_transpose_y_0, x = q_7_cast_fp16, y = op_1184_to_fp16_palettized)[name = string("op_1185_cast_fp16")]; + fp16 _inversed_input_191_y_0_to_fp16 = const()[name = string("_inversed_input_191_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_191_cast_fp16 = mul(x = var_1185_cast_fp16, y = _inversed_input_191_y_0_to_fp16)[name = string("_inversed_input_191_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_681, x = _inversed_input_191_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor attn_11_cast_fp16 = mul(x = attn_9_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool var_1192_transpose_x_0 = const()[name = string("op_1192_transpose_x_0"), val = bool(false)]; + bool var_1192_transpose_y_0 = const()[name = string("op_1192_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1181_cast_fp16)[name = string("transpose_66")]; + tensor var_1192_cast_fp16 = matmul(transpose_x = var_1192_transpose_x_0, transpose_y = var_1192_transpose_y_0, x = attn_11_cast_fp16, y = v_7_cast_fp16)[name = string("op_1192_cast_fp16")]; + tensor var_1193_perm_0 = const()[name = string("op_1193_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1194 = const()[name = string("op_1194"), val = tensor([2, 256, 256])]; + tensor var_1193_cast_fp16 = transpose(perm = var_1193_perm_0, x = var_1192_cast_fp16)[name = string("transpose_65")]; + tensor input_193_cast_fp16 = reshape(shape = var_1194, x = var_1193_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20812416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20910784))))[name = string("main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20910976)))]; + tensor linear_19_cast_fp16 = linear(bias = main_blocks_1_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_193_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor out_31_perm_0 = const()[name = string("out_31_perm_0"), val = tensor([0, 2, 1])]; + tensor out_31_cast_fp16 = transpose(perm = out_31_perm_0, x = linear_19_cast_fp16)[name = string("transpose_64")]; + tensor var_1201_cast_fp16 = mul(x = out_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1201_cast_fp16")]; + tensor x_101_cast_fp16 = add(x = x_m_7_cast_fp16, y = var_1201_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_195_perm_0 = const()[name = string("input_195_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1208_axes_0 = const()[name = string("op_1208_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20912064)))]; + tensor main_blocks_1_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20913152)))]; + tensor input_195_cast_fp16 = transpose(perm = input_195_perm_0, x = x_101_cast_fp16)[name = string("transpose_63")]; + tensor var_1208_cast_fp16 = layer_norm(axes = var_1208_axes_0, beta = main_blocks_1_style_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_style_norm_norm_weight_to_fp16, x = input_195_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_perm_0 = const()[name = string("op_1209_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1209_cast_fp16 = transpose(perm = var_1209_perm_0, x = var_1208_cast_fp16)[name = string("transpose_62")]; + tensor x_103_cast_fp16 = mul(x = var_1209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_103_cast_fp16")]; + int32 var_1228 = const()[name = string("op_1228"), val = int32(-1)]; + tensor x_105_cast_fp16 = mul(x = x_103_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_1263_cast_fp16 = mul(x = x_105_cast_fp16, y = last_1_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor x_last_25_axes_0 = const()[name = string("x_last_25_axes_0"), val = tensor([-1])]; + bool x_last_25_keep_dims_0 = const()[name = string("x_last_25_keep_dims_0"), val = bool(true)]; + tensor x_last_25_cast_fp16 = reduce_sum(axes = x_last_25_axes_0, keep_dims = x_last_25_keep_dims_0, x = var_1263_cast_fp16)[name = string("x_last_25_cast_fp16")]; + tensor var_1266_cast_fp16 = mul(x = x_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1266_cast_fp16")]; + tensor var_1268_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_25_cast_fp16)[name = string("op_1268_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1266_cast_fp16, y = var_1268_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_201_mode_0 = const()[name = string("input_201_mode_0"), val = string("replicate")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_201_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")]; + string h_75_pad_type_0 = const()[name = string("h_75_pad_type_0"), val = string("valid")]; + int32 h_75_groups_0 = const()[name = string("h_75_groups_0"), val = int32(512)]; + tensor h_75_strides_0 = const()[name = string("h_75_strides_0"), val = tensor([1])]; + tensor h_75_pad_0 = const()[name = string("h_75_pad_0"), val = tensor([0, 0])]; + tensor h_75_dilations_0 = const()[name = string("h_75_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20914240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20916224))))[name = string("main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20916416)))]; + tensor h_75_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_75_dilations_0, groups = h_75_groups_0, pad = h_75_pad_0, pad_type = h_75_pad_type_0, strides = h_75_strides_0, weight = main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_201_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = h_75_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_203_perm_0 = const()[name = string("input_203_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1286_axes_0 = const()[name = string("op_1286_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20917504)))]; + tensor main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20918592)))]; + fp16 var_1233_to_fp16 = const()[name = string("op_1233_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_203_cast_fp16 = transpose(perm = input_203_perm_0, x = x_107_cast_fp16)[name = string("transpose_61")]; + tensor var_1286_cast_fp16 = layer_norm(axes = var_1286_axes_0, beta = main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16, x = input_203_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string h_77_pad_type_0 = const()[name = string("h_77_pad_type_0"), val = string("valid")]; + tensor h_77_strides_0 = const()[name = string("h_77_strides_0"), val = tensor([1])]; + tensor h_77_pad_0 = const()[name = string("h_77_pad_0"), val = tensor([0, 0])]; + tensor h_77_dilations_0 = const()[name = string("h_77_dilations_0"), val = tensor([1])]; + int32 h_77_groups_0 = const()[name = string("h_77_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20919680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21706176))))[name = string("main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21706368)))]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = var_1286_cast_fp16)[name = string("transpose_60")]; + tensor h_77_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_77_dilations_0, groups = h_77_groups_0, pad = h_77_pad_0, pad_type = h_77_pad_type_0, strides = h_77_strides_0, weight = main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_205_cast_fp16)[name = string("h_77_cast_fp16")]; + string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")]; + tensor input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = h_77_cast_fp16)[name = string("input_207_cast_fp16")]; + string h_79_pad_type_0 = const()[name = string("h_79_pad_type_0"), val = string("valid")]; + tensor h_79_strides_0 = const()[name = string("h_79_strides_0"), val = tensor([1])]; + tensor h_79_pad_0 = const()[name = string("h_79_pad_0"), val = tensor([0, 0])]; + tensor h_79_dilations_0 = const()[name = string("h_79_dilations_0"), val = tensor([1])]; + int32 h_79_groups_0 = const()[name = string("h_79_groups_0"), val = int32(1)]; + tensor op_1303_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21710528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22497024))))[name = string("op_1303_weight_0_to_fp16_palettized")]; + tensor var_1303_bias_0_to_fp16 = const()[name = string("op_1303_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22497216)))]; + tensor var_1303_cast_fp16 = conv(bias = var_1303_bias_0_to_fp16, dilations = h_79_dilations_0, groups = h_79_groups_0, pad = h_79_pad_0, pad_type = h_79_pad_type_0, strides = h_79_strides_0, weight = op_1303_weight_0_to_fp16_palettized, x = input_207_cast_fp16)[name = string("op_1303_cast_fp16")]; + tensor out_33_cast_fp16 = add(x = x_105_cast_fp16, y = var_1303_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor x_109_cast_fp16 = mul(x = out_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor x_111_cast_fp16 = mul(x = x_109_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_1317_cast_fp16 = mul(x = x_111_cast_fp16, y = last_1_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor x_last_27_axes_0 = const()[name = string("x_last_27_axes_0"), val = tensor([-1])]; + bool x_last_27_keep_dims_0 = const()[name = string("x_last_27_keep_dims_0"), val = bool(true)]; + tensor x_last_27_cast_fp16 = reduce_sum(axes = x_last_27_axes_0, keep_dims = x_last_27_keep_dims_0, x = var_1317_cast_fp16)[name = string("x_last_27_cast_fp16")]; + tensor var_1320_cast_fp16 = mul(x = x_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1322_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_27_cast_fp16)[name = string("op_1322_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1320_cast_fp16, y = var_1322_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("replicate")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor input_213_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = input_213_mode_0, pad = input_213_pad_0, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + string h_81_pad_type_0 = const()[name = string("h_81_pad_type_0"), val = string("valid")]; + tensor h_81_dilations_0 = const()[name = string("h_81_dilations_0"), val = tensor([2])]; + int32 h_81_groups_0 = const()[name = string("h_81_groups_0"), val = int32(512)]; + tensor h_81_strides_0 = const()[name = string("h_81_strides_0"), val = tensor([1])]; + tensor h_81_pad_0 = const()[name = string("h_81_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22498304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22500288))))[name = string("main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22500480)))]; + tensor h_81_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_81_dilations_0, groups = h_81_groups_0, pad = h_81_pad_0, pad_type = h_81_pad_type_0, strides = h_81_strides_0, weight = main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_213_cast_fp16)[name = string("h_81_cast_fp16")]; + tensor x_113_cast_fp16 = mul(x = h_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor input_215_perm_0 = const()[name = string("input_215_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1340_axes_0 = const()[name = string("op_1340_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22501568)))]; + tensor main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22502656)))]; + tensor input_215_cast_fp16 = transpose(perm = input_215_perm_0, x = x_113_cast_fp16)[name = string("transpose_59")]; + tensor var_1340_cast_fp16 = layer_norm(axes = var_1340_axes_0, beta = main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16, x = input_215_cast_fp16)[name = string("op_1340_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + string h_83_pad_type_0 = const()[name = string("h_83_pad_type_0"), val = string("valid")]; + tensor h_83_strides_0 = const()[name = string("h_83_strides_0"), val = tensor([1])]; + tensor h_83_pad_0 = const()[name = string("h_83_pad_0"), val = tensor([0, 0])]; + tensor h_83_dilations_0 = const()[name = string("h_83_dilations_0"), val = tensor([1])]; + int32 h_83_groups_0 = const()[name = string("h_83_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22503744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23290240))))[name = string("main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23290432)))]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = var_1340_cast_fp16)[name = string("transpose_58")]; + tensor h_83_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_83_dilations_0, groups = h_83_groups_0, pad = h_83_pad_0, pad_type = h_83_pad_type_0, strides = h_83_strides_0, weight = main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = string("h_83_cast_fp16")]; + string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")]; + tensor input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = h_83_cast_fp16)[name = string("input_219_cast_fp16")]; + string h_85_pad_type_0 = const()[name = string("h_85_pad_type_0"), val = string("valid")]; + tensor h_85_strides_0 = const()[name = string("h_85_strides_0"), val = tensor([1])]; + tensor h_85_pad_0 = const()[name = string("h_85_pad_0"), val = tensor([0, 0])]; + tensor h_85_dilations_0 = const()[name = string("h_85_dilations_0"), val = tensor([1])]; + int32 h_85_groups_0 = const()[name = string("h_85_groups_0"), val = int32(1)]; + tensor op_1357_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23294592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24081088))))[name = string("op_1357_weight_0_to_fp16_palettized")]; + tensor var_1357_bias_0_to_fp16 = const()[name = string("op_1357_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24081280)))]; + tensor var_1357_cast_fp16 = conv(bias = var_1357_bias_0_to_fp16, dilations = h_85_dilations_0, groups = h_85_groups_0, pad = h_85_pad_0, pad_type = h_85_pad_type_0, strides = h_85_strides_0, weight = op_1357_weight_0_to_fp16_palettized, x = input_219_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor out_35_cast_fp16 = add(x = x_111_cast_fp16, y = var_1357_cast_fp16)[name = string("out_35_cast_fp16")]; + tensor x_115_cast_fp16 = mul(x = out_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_cast_fp16 = mul(x = x_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1371_cast_fp16 = mul(x = x_117_cast_fp16, y = last_1_cast_fp16)[name = string("op_1371_cast_fp16")]; + tensor x_last_29_axes_0 = const()[name = string("x_last_29_axes_0"), val = tensor([-1])]; + bool x_last_29_keep_dims_0 = const()[name = string("x_last_29_keep_dims_0"), val = bool(true)]; + tensor x_last_29_cast_fp16 = reduce_sum(axes = x_last_29_axes_0, keep_dims = x_last_29_keep_dims_0, x = var_1371_cast_fp16)[name = string("x_last_29_cast_fp16")]; + tensor var_1374_cast_fp16 = mul(x = x_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_29_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1376_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_225_mode_0 = const()[name = string("input_225_mode_0"), val = string("replicate")]; + fp16 const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = fp16(0x0p+0)]; + tensor input_225_cast_fp16 = pad(constant_val = const_48_to_fp16, mode = input_225_mode_0, pad = input_225_pad_0, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")]; + string h_87_pad_type_0 = const()[name = string("h_87_pad_type_0"), val = string("valid")]; + tensor h_87_dilations_0 = const()[name = string("h_87_dilations_0"), val = tensor([4])]; + int32 h_87_groups_0 = const()[name = string("h_87_groups_0"), val = int32(512)]; + tensor h_87_strides_0 = const()[name = string("h_87_strides_0"), val = tensor([1])]; + tensor h_87_pad_0 = const()[name = string("h_87_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24082368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24084352))))[name = string("main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24084544)))]; + tensor h_87_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_87_dilations_0, groups = h_87_groups_0, pad = h_87_pad_0, pad_type = h_87_pad_type_0, strides = h_87_strides_0, weight = main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor x_119_cast_fp16 = mul(x = h_87_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_227_perm_0 = const()[name = string("input_227_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1394_axes_0 = const()[name = string("op_1394_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24085632)))]; + tensor main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24086720)))]; + tensor input_227_cast_fp16 = transpose(perm = input_227_perm_0, x = x_119_cast_fp16)[name = string("transpose_57")]; + tensor var_1394_cast_fp16 = layer_norm(axes = var_1394_axes_0, beta = main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16, x = input_227_cast_fp16)[name = string("op_1394_cast_fp16")]; + tensor input_229_perm_0 = const()[name = string("input_229_perm_0"), val = tensor([0, 2, 1])]; + string h_89_pad_type_0 = const()[name = string("h_89_pad_type_0"), val = string("valid")]; + tensor h_89_strides_0 = const()[name = string("h_89_strides_0"), val = tensor([1])]; + tensor h_89_pad_0 = const()[name = string("h_89_pad_0"), val = tensor([0, 0])]; + tensor h_89_dilations_0 = const()[name = string("h_89_dilations_0"), val = tensor([1])]; + int32 h_89_groups_0 = const()[name = string("h_89_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24087808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24874304))))[name = string("main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24874496)))]; + tensor input_229_cast_fp16 = transpose(perm = input_229_perm_0, x = var_1394_cast_fp16)[name = string("transpose_56")]; + tensor h_89_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_89_dilations_0, groups = h_89_groups_0, pad = h_89_pad_0, pad_type = h_89_pad_type_0, strides = h_89_strides_0, weight = main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = string("h_89_cast_fp16")]; + string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")]; + tensor input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = h_89_cast_fp16)[name = string("input_231_cast_fp16")]; + string h_91_pad_type_0 = const()[name = string("h_91_pad_type_0"), val = string("valid")]; + tensor h_91_strides_0 = const()[name = string("h_91_strides_0"), val = tensor([1])]; + tensor h_91_pad_0 = const()[name = string("h_91_pad_0"), val = tensor([0, 0])]; + tensor h_91_dilations_0 = const()[name = string("h_91_dilations_0"), val = tensor([1])]; + int32 h_91_groups_0 = const()[name = string("h_91_groups_0"), val = int32(1)]; + tensor op_1411_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24878656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25665152))))[name = string("op_1411_weight_0_to_fp16_palettized")]; + tensor var_1411_bias_0_to_fp16 = const()[name = string("op_1411_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25665344)))]; + tensor var_1411_cast_fp16 = conv(bias = var_1411_bias_0_to_fp16, dilations = h_91_dilations_0, groups = h_91_groups_0, pad = h_91_pad_0, pad_type = h_91_pad_type_0, strides = h_91_strides_0, weight = op_1411_weight_0_to_fp16_palettized, x = input_231_cast_fp16)[name = string("op_1411_cast_fp16")]; + tensor out_37_cast_fp16 = add(x = x_117_cast_fp16, y = var_1411_cast_fp16)[name = string("out_37_cast_fp16")]; + tensor x_121_cast_fp16 = mul(x = out_37_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_121_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1425_cast_fp16 = mul(x = x_123_cast_fp16, y = last_1_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor x_last_31_axes_0 = const()[name = string("x_last_31_axes_0"), val = tensor([-1])]; + bool x_last_31_keep_dims_0 = const()[name = string("x_last_31_keep_dims_0"), val = bool(true)]; + tensor x_last_31_cast_fp16 = reduce_sum(axes = x_last_31_axes_0, keep_dims = x_last_31_keep_dims_0, x = var_1425_cast_fp16)[name = string("x_last_31_cast_fp16")]; + tensor var_1428_cast_fp16 = mul(x = x_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_31_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = var_1428_cast_fp16, y = var_1430_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("replicate")]; + fp16 const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = fp16(0x0p+0)]; + tensor input_237_cast_fp16 = pad(constant_val = const_50_to_fp16, mode = input_237_mode_0, pad = input_237_pad_0, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + string h_93_pad_type_0 = const()[name = string("h_93_pad_type_0"), val = string("valid")]; + tensor h_93_dilations_0 = const()[name = string("h_93_dilations_0"), val = tensor([8])]; + int32 h_93_groups_0 = const()[name = string("h_93_groups_0"), val = int32(512)]; + tensor h_93_strides_0 = const()[name = string("h_93_strides_0"), val = tensor([1])]; + tensor h_93_pad_0 = const()[name = string("h_93_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25666432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25668416))))[name = string("main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25668608)))]; + tensor h_93_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_93_dilations_0, groups = h_93_groups_0, pad = h_93_pad_0, pad_type = h_93_pad_type_0, strides = h_93_strides_0, weight = main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = string("h_93_cast_fp16")]; + tensor x_125_cast_fp16 = mul(x = h_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_239_perm_0 = const()[name = string("input_239_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1448_axes_0 = const()[name = string("op_1448_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25669696)))]; + tensor main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25670784)))]; + tensor input_239_cast_fp16 = transpose(perm = input_239_perm_0, x = x_125_cast_fp16)[name = string("transpose_55")]; + tensor var_1448_cast_fp16 = layer_norm(axes = var_1448_axes_0, beta = main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16, x = input_239_cast_fp16)[name = string("op_1448_cast_fp16")]; + tensor input_241_perm_0 = const()[name = string("input_241_perm_0"), val = tensor([0, 2, 1])]; + string h_95_pad_type_0 = const()[name = string("h_95_pad_type_0"), val = string("valid")]; + tensor h_95_strides_0 = const()[name = string("h_95_strides_0"), val = tensor([1])]; + tensor h_95_pad_0 = const()[name = string("h_95_pad_0"), val = tensor([0, 0])]; + tensor h_95_dilations_0 = const()[name = string("h_95_dilations_0"), val = tensor([1])]; + int32 h_95_groups_0 = const()[name = string("h_95_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25671872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26458368))))[name = string("main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26458560)))]; + tensor input_241_cast_fp16 = transpose(perm = input_241_perm_0, x = var_1448_cast_fp16)[name = string("transpose_54")]; + tensor h_95_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_95_dilations_0, groups = h_95_groups_0, pad = h_95_pad_0, pad_type = h_95_pad_type_0, strides = h_95_strides_0, weight = main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_241_cast_fp16)[name = string("h_95_cast_fp16")]; + string input_243_mode_0 = const()[name = string("input_243_mode_0"), val = string("EXACT")]; + tensor input_243_cast_fp16 = gelu(mode = input_243_mode_0, x = h_95_cast_fp16)[name = string("input_243_cast_fp16")]; + string h_97_pad_type_0 = const()[name = string("h_97_pad_type_0"), val = string("valid")]; + tensor h_97_strides_0 = const()[name = string("h_97_strides_0"), val = tensor([1])]; + tensor h_97_pad_0 = const()[name = string("h_97_pad_0"), val = tensor([0, 0])]; + tensor h_97_dilations_0 = const()[name = string("h_97_dilations_0"), val = tensor([1])]; + int32 h_97_groups_0 = const()[name = string("h_97_groups_0"), val = int32(1)]; + tensor op_1465_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26462720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27249216))))[name = string("op_1465_weight_0_to_fp16_palettized")]; + tensor var_1465_bias_0_to_fp16 = const()[name = string("op_1465_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27249408)))]; + tensor var_1465_cast_fp16 = conv(bias = var_1465_bias_0_to_fp16, dilations = h_97_dilations_0, groups = h_97_groups_0, pad = h_97_pad_0, pad_type = h_97_pad_type_0, strides = h_97_strides_0, weight = op_1465_weight_0_to_fp16_palettized, x = input_243_cast_fp16)[name = string("op_1465_cast_fp16")]; + tensor out_39_cast_fp16 = add(x = x_123_cast_fp16, y = var_1465_cast_fp16)[name = string("out_39_cast_fp16")]; + tensor x_127_cast_fp16 = mul(x = out_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27250496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27275136))))[name = string("main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27275328)))]; + tensor linear_20_cast_fp16 = linear(bias = main_blocks_2_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor t_9_axes_0 = const()[name = string("t_9_axes_0"), val = tensor([-1])]; + tensor t_9_cast_fp16 = expand_dims(axes = t_9_axes_0, x = linear_20_cast_fp16)[name = string("t_9_cast_fp16")]; + tensor var_1475_cast_fp16 = add(x = x_127_cast_fp16, y = t_9_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = var_1475_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor x_131_cast_fp16 = mul(x = x_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor var_1488_cast_fp16 = mul(x = x_131_cast_fp16, y = last_1_cast_fp16)[name = string("op_1488_cast_fp16")]; + tensor x_last_33_axes_0 = const()[name = string("x_last_33_axes_0"), val = tensor([-1])]; + bool x_last_33_keep_dims_0 = const()[name = string("x_last_33_keep_dims_0"), val = bool(true)]; + tensor x_last_33_cast_fp16 = reduce_sum(axes = x_last_33_axes_0, keep_dims = x_last_33_keep_dims_0, x = var_1488_cast_fp16)[name = string("x_last_33_cast_fp16")]; + tensor var_1491_cast_fp16 = mul(x = x_131_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1491_cast_fp16")]; + tensor var_1493_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_33_cast_fp16)[name = string("op_1493_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = var_1491_cast_fp16, y = var_1493_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_251_mode_0 = const()[name = string("input_251_mode_0"), val = string("replicate")]; + fp16 const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = fp16(0x0p+0)]; + tensor input_251_cast_fp16 = pad(constant_val = const_52_to_fp16, mode = input_251_mode_0, pad = input_251_pad_0, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + string h_99_pad_type_0 = const()[name = string("h_99_pad_type_0"), val = string("valid")]; + int32 h_99_groups_0 = const()[name = string("h_99_groups_0"), val = int32(512)]; + tensor h_99_strides_0 = const()[name = string("h_99_strides_0"), val = tensor([1])]; + tensor h_99_pad_0 = const()[name = string("h_99_pad_0"), val = tensor([0, 0])]; + tensor h_99_dilations_0 = const()[name = string("h_99_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27276416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27278400))))[name = string("main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27278592)))]; + tensor h_99_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_99_dilations_0, groups = h_99_groups_0, pad = h_99_pad_0, pad_type = h_99_pad_type_0, strides = h_99_strides_0, weight = main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor x_133_cast_fp16 = mul(x = h_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_253_perm_0 = const()[name = string("input_253_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1511_axes_0 = const()[name = string("op_1511_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27279680)))]; + tensor main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27280768)))]; + tensor input_253_cast_fp16 = transpose(perm = input_253_perm_0, x = x_133_cast_fp16)[name = string("transpose_53")]; + tensor var_1511_cast_fp16 = layer_norm(axes = var_1511_axes_0, beta = main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16, x = input_253_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor input_255_perm_0 = const()[name = string("input_255_perm_0"), val = tensor([0, 2, 1])]; + string h_101_pad_type_0 = const()[name = string("h_101_pad_type_0"), val = string("valid")]; + tensor h_101_strides_0 = const()[name = string("h_101_strides_0"), val = tensor([1])]; + tensor h_101_pad_0 = const()[name = string("h_101_pad_0"), val = tensor([0, 0])]; + tensor h_101_dilations_0 = const()[name = string("h_101_dilations_0"), val = tensor([1])]; + int32 h_101_groups_0 = const()[name = string("h_101_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27281856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28068352))))[name = string("main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28068544)))]; + tensor input_255_cast_fp16 = transpose(perm = input_255_perm_0, x = var_1511_cast_fp16)[name = string("transpose_52")]; + tensor h_101_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_101_dilations_0, groups = h_101_groups_0, pad = h_101_pad_0, pad_type = h_101_pad_type_0, strides = h_101_strides_0, weight = main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_255_cast_fp16)[name = string("h_101_cast_fp16")]; + string input_257_mode_0 = const()[name = string("input_257_mode_0"), val = string("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = h_101_cast_fp16)[name = string("input_257_cast_fp16")]; + string h_103_pad_type_0 = const()[name = string("h_103_pad_type_0"), val = string("valid")]; + tensor h_103_strides_0 = const()[name = string("h_103_strides_0"), val = tensor([1])]; + tensor h_103_pad_0 = const()[name = string("h_103_pad_0"), val = tensor([0, 0])]; + tensor h_103_dilations_0 = const()[name = string("h_103_dilations_0"), val = tensor([1])]; + int32 h_103_groups_0 = const()[name = string("h_103_groups_0"), val = int32(1)]; + tensor op_1528_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28072704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28859200))))[name = string("op_1528_weight_0_to_fp16_palettized")]; + tensor var_1528_bias_0_to_fp16 = const()[name = string("op_1528_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28859392)))]; + tensor var_1528_cast_fp16 = conv(bias = var_1528_bias_0_to_fp16, dilations = h_103_dilations_0, groups = h_103_groups_0, pad = h_103_pad_0, pad_type = h_103_pad_type_0, strides = h_103_strides_0, weight = op_1528_weight_0_to_fp16_palettized, x = input_257_cast_fp16)[name = string("op_1528_cast_fp16")]; + tensor out_41_cast_fp16 = add(x = x_131_cast_fp16, y = var_1528_cast_fp16)[name = string("out_41_cast_fp16")]; + tensor x_135_cast_fp16 = mul(x = out_41_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor x_m_9_cast_fp16 = mul(x = x_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_9_cast_fp16")]; + tensor input_259_perm_0 = const()[name = string("input_259_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28860480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29057152))))[name = string("main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29057344)))]; + tensor input_259_cast_fp16 = transpose(perm = input_259_perm_0, x = x_135_cast_fp16)[name = string("transpose_51")]; + tensor linear_21_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_259_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_1546 = const()[name = string("op_1546"), val = tensor([2, 256, 8, 64])]; + tensor var_1547_cast_fp16 = reshape(shape = var_1546, x = linear_21_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor x_137_perm_0 = const()[name = string("x_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29058432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29156800))))[name = string("main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29156992)))]; + tensor linear_22_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_1554 = const()[name = string("op_1554"), val = tensor([2, 128, 8, 64])]; + tensor var_1555_cast_fp16 = reshape(shape = var_1554, x = linear_22_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_139_perm_0 = const()[name = string("x_139_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29158080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29256448))))[name = string("main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29256640)))]; + tensor linear_23_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_1562 = const()[name = string("op_1562"), val = tensor([2, 128, 8, 64])]; + tensor var_1563_cast_fp16 = reshape(shape = var_1562, x = linear_23_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_9_begin_0 = const()[name = string("x_a_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_9_end_0 = const()[name = string("x_a_9_end_0"), val = tensor([2, 8, 256, 32])]; + tensor x_a_9_end_mask_0 = const()[name = string("x_a_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_137_cast_fp16 = transpose(perm = x_137_perm_0, x = var_1547_cast_fp16)[name = string("transpose_50")]; + tensor x_a_9_cast_fp16 = slice_by_index(begin = x_a_9_begin_0, end = x_a_9_end_0, end_mask = x_a_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_a_9_cast_fp16")]; + tensor x_b_9_begin_0 = const()[name = string("x_b_9_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_9_end_0 = const()[name = string("x_b_9_end_0"), val = tensor([2, 8, 256, 64])]; + tensor x_b_9_end_mask_0 = const()[name = string("x_b_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_9_cast_fp16 = slice_by_index(begin = x_b_9_begin_0, end = x_b_9_end_0, end_mask = x_b_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_b_9_cast_fp16")]; + tensor var_1591_cast_fp16 = mul(x = x_a_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1591_cast_fp16")]; + tensor var_1592_cast_fp16 = mul(x = x_b_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor rot_a_9_cast_fp16 = sub(x = var_1591_cast_fp16, y = var_1592_cast_fp16)[name = string("rot_a_9_cast_fp16")]; + tensor var_1594_cast_fp16 = mul(x = x_a_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595_cast_fp16 = mul(x = x_b_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1595_cast_fp16")]; + tensor rot_b_9_cast_fp16 = add(x = var_1594_cast_fp16, y = var_1595_cast_fp16)[name = string("rot_b_9_cast_fp16")]; + bool q_9_interleave_0 = const()[name = string("q_9_interleave_0"), val = bool(false)]; + tensor q_9_cast_fp16 = concat(axis = var_1228, interleave = q_9_interleave_0, values = (rot_a_9_cast_fp16, rot_b_9_cast_fp16))[name = string("q_9_cast_fp16")]; + tensor x_a_11_begin_0 = const()[name = string("x_a_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_11_end_0 = const()[name = string("x_a_11_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_11_end_mask_0 = const()[name = string("x_a_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_139_cast_fp16 = transpose(perm = x_139_perm_0, x = var_1555_cast_fp16)[name = string("transpose_49")]; + tensor x_a_11_cast_fp16 = slice_by_index(begin = x_a_11_begin_0, end = x_a_11_end_0, end_mask = x_a_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_a_11_cast_fp16")]; + tensor x_b_11_begin_0 = const()[name = string("x_b_11_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_11_end_0 = const()[name = string("x_b_11_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_11_end_mask_0 = const()[name = string("x_b_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_11_cast_fp16 = slice_by_index(begin = x_b_11_begin_0, end = x_b_11_end_0, end_mask = x_b_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_b_11_cast_fp16")]; + tensor var_1609_cast_fp16 = mul(x = x_a_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor var_1610_cast_fp16 = mul(x = x_b_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor rot_a_11_cast_fp16 = sub(x = var_1609_cast_fp16, y = var_1610_cast_fp16)[name = string("rot_a_11_cast_fp16")]; + tensor var_1612_cast_fp16 = mul(x = x_a_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1612_cast_fp16")]; + tensor var_1613_cast_fp16 = mul(x = x_b_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1613_cast_fp16")]; + tensor rot_b_11_cast_fp16 = add(x = var_1612_cast_fp16, y = var_1613_cast_fp16)[name = string("rot_b_11_cast_fp16")]; + bool k_9_interleave_0 = const()[name = string("k_9_interleave_0"), val = bool(false)]; + tensor k_9_cast_fp16 = concat(axis = var_1228, interleave = k_9_interleave_0, values = (rot_a_11_cast_fp16, rot_b_11_cast_fp16))[name = string("k_9_cast_fp16")]; + bool var_1618_transpose_x_1 = const()[name = string("op_1618_transpose_x_1"), val = bool(false)]; + bool var_1618_transpose_y_1 = const()[name = string("op_1618_transpose_y_1"), val = bool(true)]; + tensor var_1618_cast_fp16 = matmul(transpose_x = var_1618_transpose_x_1, transpose_y = var_1618_transpose_y_1, x = q_9_cast_fp16, y = k_9_cast_fp16)[name = string("op_1618_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_1618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor input_265_cast_fp16 = sub(x = _inversed_scores_5_cast_fp16, y = var_530_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1228, x = input_265_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool var_1627_transpose_x_0 = const()[name = string("op_1627_transpose_x_0"), val = bool(false)]; + bool var_1627_transpose_y_0 = const()[name = string("op_1627_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1563_cast_fp16)[name = string("transpose_48")]; + tensor var_1627_cast_fp16 = matmul(transpose_x = var_1627_transpose_x_0, transpose_y = var_1627_transpose_y_0, x = attn_13_cast_fp16, y = v_9_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor var_1628_perm_0 = const()[name = string("op_1628_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1629 = const()[name = string("op_1629"), val = tensor([2, 256, 512])]; + tensor var_1628_cast_fp16 = transpose(perm = var_1628_perm_0, x = var_1627_cast_fp16)[name = string("transpose_47")]; + tensor input_267_cast_fp16 = reshape(shape = var_1629, x = var_1628_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29257728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29454400))))[name = string("main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29454592)))]; + tensor linear_24_cast_fp16 = linear(bias = main_blocks_2_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor out_43_perm_0 = const()[name = string("out_43_perm_0"), val = tensor([0, 2, 1])]; + tensor out_43_cast_fp16 = transpose(perm = out_43_perm_0, x = linear_24_cast_fp16)[name = string("transpose_46")]; + tensor var_1636_cast_fp16 = mul(x = out_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1636_cast_fp16")]; + tensor x_141_cast_fp16 = add(x = x_m_9_cast_fp16, y = var_1636_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1643_axes_0 = const()[name = string("op_1643_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29455680)))]; + tensor main_blocks_2_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29456768)))]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_141_cast_fp16)[name = string("transpose_45")]; + tensor var_1643_cast_fp16 = layer_norm(axes = var_1643_axes_0, beta = main_blocks_2_text_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_text_norm_norm_weight_to_fp16, x = input_269_cast_fp16)[name = string("op_1643_cast_fp16")]; + tensor var_1644_perm_0 = const()[name = string("op_1644_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1644_cast_fp16 = transpose(perm = var_1644_perm_0, x = var_1643_cast_fp16)[name = string("transpose_44")]; + tensor x_143_cast_fp16 = mul(x = var_1644_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor x_145_cast_fp16 = mul(x = x_143_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1657_cast_fp16 = mul(x = x_145_cast_fp16, y = last_1_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor x_last_35_axes_0 = const()[name = string("x_last_35_axes_0"), val = tensor([-1])]; + bool x_last_35_keep_dims_0 = const()[name = string("x_last_35_keep_dims_0"), val = bool(true)]; + tensor x_last_35_cast_fp16 = reduce_sum(axes = x_last_35_axes_0, keep_dims = x_last_35_keep_dims_0, x = var_1657_cast_fp16)[name = string("x_last_35_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = x_145_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1662_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_35_cast_fp16)[name = string("op_1662_cast_fp16")]; + tensor input_273_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1662_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("replicate")]; + fp16 const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = fp16(0x0p+0)]; + tensor input_275_cast_fp16 = pad(constant_val = const_59_to_fp16, mode = input_275_mode_0, pad = input_275_pad_0, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")]; + string h_105_pad_type_0 = const()[name = string("h_105_pad_type_0"), val = string("valid")]; + int32 h_105_groups_0 = const()[name = string("h_105_groups_0"), val = int32(512)]; + tensor h_105_strides_0 = const()[name = string("h_105_strides_0"), val = tensor([1])]; + tensor h_105_pad_0 = const()[name = string("h_105_pad_0"), val = tensor([0, 0])]; + tensor h_105_dilations_0 = const()[name = string("h_105_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29457856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29459840))))[name = string("main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29460032)))]; + tensor h_105_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_105_dilations_0, groups = h_105_groups_0, pad = h_105_pad_0, pad_type = h_105_pad_type_0, strides = h_105_strides_0, weight = main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_275_cast_fp16)[name = string("h_105_cast_fp16")]; + tensor x_147_cast_fp16 = mul(x = h_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_277_perm_0 = const()[name = string("input_277_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1680_axes_0 = const()[name = string("op_1680_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29461120)))]; + tensor main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29462208)))]; + tensor input_277_cast_fp16 = transpose(perm = input_277_perm_0, x = x_147_cast_fp16)[name = string("transpose_43")]; + tensor var_1680_cast_fp16 = layer_norm(axes = var_1680_axes_0, beta = main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16, x = input_277_cast_fp16)[name = string("op_1680_cast_fp16")]; + tensor input_279_perm_0 = const()[name = string("input_279_perm_0"), val = tensor([0, 2, 1])]; + string h_107_pad_type_0 = const()[name = string("h_107_pad_type_0"), val = string("valid")]; + tensor h_107_strides_0 = const()[name = string("h_107_strides_0"), val = tensor([1])]; + tensor h_107_pad_0 = const()[name = string("h_107_pad_0"), val = tensor([0, 0])]; + tensor h_107_dilations_0 = const()[name = string("h_107_dilations_0"), val = tensor([1])]; + int32 h_107_groups_0 = const()[name = string("h_107_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29463296))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30249792))))[name = string("main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30249984)))]; + tensor input_279_cast_fp16 = transpose(perm = input_279_perm_0, x = var_1680_cast_fp16)[name = string("transpose_42")]; + tensor h_107_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_107_dilations_0, groups = h_107_groups_0, pad = h_107_pad_0, pad_type = h_107_pad_type_0, strides = h_107_strides_0, weight = main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_279_cast_fp16)[name = string("h_107_cast_fp16")]; + string input_281_mode_0 = const()[name = string("input_281_mode_0"), val = string("EXACT")]; + tensor input_281_cast_fp16 = gelu(mode = input_281_mode_0, x = h_107_cast_fp16)[name = string("input_281_cast_fp16")]; + string h_109_pad_type_0 = const()[name = string("h_109_pad_type_0"), val = string("valid")]; + tensor h_109_strides_0 = const()[name = string("h_109_strides_0"), val = tensor([1])]; + tensor h_109_pad_0 = const()[name = string("h_109_pad_0"), val = tensor([0, 0])]; + tensor h_109_dilations_0 = const()[name = string("h_109_dilations_0"), val = tensor([1])]; + int32 h_109_groups_0 = const()[name = string("h_109_groups_0"), val = int32(1)]; + tensor op_1697_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30254144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31040640))))[name = string("op_1697_weight_0_to_fp16_palettized")]; + tensor var_1697_bias_0_to_fp16 = const()[name = string("op_1697_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31040832)))]; + tensor var_1697_cast_fp16 = conv(bias = var_1697_bias_0_to_fp16, dilations = h_109_dilations_0, groups = h_109_groups_0, pad = h_109_pad_0, pad_type = h_109_pad_type_0, strides = h_109_strides_0, weight = op_1697_weight_0_to_fp16_palettized, x = input_281_cast_fp16)[name = string("op_1697_cast_fp16")]; + tensor out_45_cast_fp16 = add(x = x_145_cast_fp16, y = var_1697_cast_fp16)[name = string("out_45_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = out_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor x_m_11_cast_fp16 = mul(x = x_149_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_11_cast_fp16")]; + tensor input_283_perm_0 = const()[name = string("input_283_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31041920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31140288))))[name = string("main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31140480)))]; + tensor input_283_cast_fp16 = transpose(perm = input_283_perm_0, x = x_149_cast_fp16)[name = string("transpose_41")]; + tensor linear_25_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_283_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_1713 = const()[name = string("op_1713"), val = tensor([2, 256, 2, 128])]; + tensor var_1714_cast_fp16 = reshape(shape = var_1713, x = linear_25_cast_fp16)[name = string("op_1714_cast_fp16")]; + tensor q_11_perm_0 = const()[name = string("q_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31141056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31190272))))[name = string("main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31190464)))]; + tensor linear_27_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1727 = const()[name = string("op_1727"), val = tensor([2, 50, 2, 128])]; + tensor var_1728_cast_fp16 = reshape(shape = var_1727, x = linear_27_cast_fp16)[name = string("op_1728_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1732_transpose_x_0 = const()[name = string("op_1732_transpose_x_0"), val = bool(false)]; + bool var_1732_transpose_y_0 = const()[name = string("op_1732_transpose_y_0"), val = bool(false)]; + tensor op_1731_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31191040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31210304))))[name = string("op_1731_to_fp16_palettized")]; + tensor q_11_cast_fp16 = transpose(perm = q_11_perm_0, x = var_1714_cast_fp16)[name = string("transpose_40")]; + tensor var_1732_cast_fp16 = matmul(transpose_x = var_1732_transpose_x_0, transpose_y = var_1732_transpose_y_0, x = q_11_cast_fp16, y = op_1731_to_fp16_palettized)[name = string("op_1732_cast_fp16")]; + fp16 _inversed_input_285_y_0_to_fp16 = const()[name = string("_inversed_input_285_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_285_cast_fp16 = mul(x = var_1732_cast_fp16, y = _inversed_input_285_y_0_to_fp16)[name = string("_inversed_input_285_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1228, x = _inversed_input_285_cast_fp16)[name = string("attn_15_cast_fp16")]; + tensor attn_17_cast_fp16 = mul(x = attn_15_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool var_1739_transpose_x_0 = const()[name = string("op_1739_transpose_x_0"), val = bool(false)]; + bool var_1739_transpose_y_0 = const()[name = string("op_1739_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_1728_cast_fp16)[name = string("transpose_39")]; + tensor var_1739_cast_fp16 = matmul(transpose_x = var_1739_transpose_x_0, transpose_y = var_1739_transpose_y_0, x = attn_17_cast_fp16, y = v_11_cast_fp16)[name = string("op_1739_cast_fp16")]; + tensor var_1740_perm_0 = const()[name = string("op_1740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1741 = const()[name = string("op_1741"), val = tensor([2, 256, 256])]; + tensor var_1740_cast_fp16 = transpose(perm = var_1740_perm_0, x = var_1739_cast_fp16)[name = string("transpose_38")]; + tensor input_287_cast_fp16 = reshape(shape = var_1741, x = var_1740_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31210496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31308864))))[name = string("main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31309056)))]; + tensor linear_28_cast_fp16 = linear(bias = main_blocks_2_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor out_47_perm_0 = const()[name = string("out_47_perm_0"), val = tensor([0, 2, 1])]; + tensor out_47_cast_fp16 = transpose(perm = out_47_perm_0, x = linear_28_cast_fp16)[name = string("transpose_37")]; + tensor var_1748_cast_fp16 = mul(x = out_47_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1748_cast_fp16")]; + tensor x_151_cast_fp16 = add(x = x_m_11_cast_fp16, y = var_1748_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_289_perm_0 = const()[name = string("input_289_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1755_axes_0 = const()[name = string("op_1755_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31310144)))]; + tensor main_blocks_2_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31311232)))]; + tensor input_289_cast_fp16 = transpose(perm = input_289_perm_0, x = x_151_cast_fp16)[name = string("transpose_36")]; + tensor var_1755_cast_fp16 = layer_norm(axes = var_1755_axes_0, beta = main_blocks_2_style_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_style_norm_norm_weight_to_fp16, x = input_289_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor var_1756_perm_0 = const()[name = string("op_1756_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1756_cast_fp16 = transpose(perm = var_1756_perm_0, x = var_1755_cast_fp16)[name = string("transpose_35")]; + tensor x_153_cast_fp16 = mul(x = var_1756_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_153_cast_fp16")]; + int32 var_1775 = const()[name = string("op_1775"), val = int32(-1)]; + tensor x_155_cast_fp16 = mul(x = x_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor var_1810_cast_fp16 = mul(x = x_155_cast_fp16, y = last_1_cast_fp16)[name = string("op_1810_cast_fp16")]; + tensor x_last_37_axes_0 = const()[name = string("x_last_37_axes_0"), val = tensor([-1])]; + bool x_last_37_keep_dims_0 = const()[name = string("x_last_37_keep_dims_0"), val = bool(true)]; + tensor x_last_37_cast_fp16 = reduce_sum(axes = x_last_37_axes_0, keep_dims = x_last_37_keep_dims_0, x = var_1810_cast_fp16)[name = string("x_last_37_cast_fp16")]; + tensor var_1813_cast_fp16 = mul(x = x_155_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1815_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_37_cast_fp16)[name = string("op_1815_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = var_1813_cast_fp16, y = var_1815_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_295_mode_0 = const()[name = string("input_295_mode_0"), val = string("replicate")]; + fp16 const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = fp16(0x0p+0)]; + tensor input_295_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")]; + string h_111_pad_type_0 = const()[name = string("h_111_pad_type_0"), val = string("valid")]; + int32 h_111_groups_0 = const()[name = string("h_111_groups_0"), val = int32(512)]; + tensor h_111_strides_0 = const()[name = string("h_111_strides_0"), val = tensor([1])]; + tensor h_111_pad_0 = const()[name = string("h_111_pad_0"), val = tensor([0, 0])]; + tensor h_111_dilations_0 = const()[name = string("h_111_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31312320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31314304))))[name = string("main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31314496)))]; + tensor h_111_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_111_dilations_0, groups = h_111_groups_0, pad = h_111_pad_0, pad_type = h_111_pad_type_0, strides = h_111_strides_0, weight = main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_295_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor x_157_cast_fp16 = mul(x = h_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor input_297_perm_0 = const()[name = string("input_297_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1833_axes_0 = const()[name = string("op_1833_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31315584)))]; + tensor main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31316672)))]; + fp16 var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_297_cast_fp16 = transpose(perm = input_297_perm_0, x = x_157_cast_fp16)[name = string("transpose_34")]; + tensor var_1833_cast_fp16 = layer_norm(axes = var_1833_axes_0, beta = main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16, x = input_297_cast_fp16)[name = string("op_1833_cast_fp16")]; + tensor input_299_perm_0 = const()[name = string("input_299_perm_0"), val = tensor([0, 2, 1])]; + string h_113_pad_type_0 = const()[name = string("h_113_pad_type_0"), val = string("valid")]; + tensor h_113_strides_0 = const()[name = string("h_113_strides_0"), val = tensor([1])]; + tensor h_113_pad_0 = const()[name = string("h_113_pad_0"), val = tensor([0, 0])]; + tensor h_113_dilations_0 = const()[name = string("h_113_dilations_0"), val = tensor([1])]; + int32 h_113_groups_0 = const()[name = string("h_113_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31317760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32104256))))[name = string("main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32104448)))]; + tensor input_299_cast_fp16 = transpose(perm = input_299_perm_0, x = var_1833_cast_fp16)[name = string("transpose_33")]; + tensor h_113_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_113_dilations_0, groups = h_113_groups_0, pad = h_113_pad_0, pad_type = h_113_pad_type_0, strides = h_113_strides_0, weight = main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("h_113_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = h_113_cast_fp16)[name = string("input_301_cast_fp16")]; + string h_115_pad_type_0 = const()[name = string("h_115_pad_type_0"), val = string("valid")]; + tensor h_115_strides_0 = const()[name = string("h_115_strides_0"), val = tensor([1])]; + tensor h_115_pad_0 = const()[name = string("h_115_pad_0"), val = tensor([0, 0])]; + tensor h_115_dilations_0 = const()[name = string("h_115_dilations_0"), val = tensor([1])]; + int32 h_115_groups_0 = const()[name = string("h_115_groups_0"), val = int32(1)]; + tensor op_1850_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32108608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32895104))))[name = string("op_1850_weight_0_to_fp16_palettized")]; + tensor var_1850_bias_0_to_fp16 = const()[name = string("op_1850_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32895296)))]; + tensor var_1850_cast_fp16 = conv(bias = var_1850_bias_0_to_fp16, dilations = h_115_dilations_0, groups = h_115_groups_0, pad = h_115_pad_0, pad_type = h_115_pad_type_0, strides = h_115_strides_0, weight = op_1850_weight_0_to_fp16_palettized, x = input_301_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor out_49_cast_fp16 = add(x = x_155_cast_fp16, y = var_1850_cast_fp16)[name = string("out_49_cast_fp16")]; + tensor x_159_cast_fp16 = mul(x = out_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor x_161_cast_fp16 = mul(x = x_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor var_1864_cast_fp16 = mul(x = x_161_cast_fp16, y = last_1_cast_fp16)[name = string("op_1864_cast_fp16")]; + tensor x_last_39_axes_0 = const()[name = string("x_last_39_axes_0"), val = tensor([-1])]; + bool x_last_39_keep_dims_0 = const()[name = string("x_last_39_keep_dims_0"), val = bool(true)]; + tensor x_last_39_cast_fp16 = reduce_sum(axes = x_last_39_axes_0, keep_dims = x_last_39_keep_dims_0, x = var_1864_cast_fp16)[name = string("x_last_39_cast_fp16")]; + tensor var_1867_cast_fp16 = mul(x = x_161_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_39_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor input_305_cast_fp16 = add(x = var_1867_cast_fp16, y = var_1869_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("replicate")]; + fp16 const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_66_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string h_117_pad_type_0 = const()[name = string("h_117_pad_type_0"), val = string("valid")]; + tensor h_117_dilations_0 = const()[name = string("h_117_dilations_0"), val = tensor([2])]; + int32 h_117_groups_0 = const()[name = string("h_117_groups_0"), val = int32(512)]; + tensor h_117_strides_0 = const()[name = string("h_117_strides_0"), val = tensor([1])]; + tensor h_117_pad_0 = const()[name = string("h_117_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32896384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32898368))))[name = string("main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32898560)))]; + tensor h_117_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_117_dilations_0, groups = h_117_groups_0, pad = h_117_pad_0, pad_type = h_117_pad_type_0, strides = h_117_strides_0, weight = main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_307_cast_fp16)[name = string("h_117_cast_fp16")]; + tensor x_163_cast_fp16 = mul(x = h_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1887_axes_0 = const()[name = string("op_1887_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32899648)))]; + tensor main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32900736)))]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_163_cast_fp16)[name = string("transpose_32")]; + tensor var_1887_cast_fp16 = layer_norm(axes = var_1887_axes_0, beta = main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16, x = input_309_cast_fp16)[name = string("op_1887_cast_fp16")]; + tensor input_311_perm_0 = const()[name = string("input_311_perm_0"), val = tensor([0, 2, 1])]; + string h_119_pad_type_0 = const()[name = string("h_119_pad_type_0"), val = string("valid")]; + tensor h_119_strides_0 = const()[name = string("h_119_strides_0"), val = tensor([1])]; + tensor h_119_pad_0 = const()[name = string("h_119_pad_0"), val = tensor([0, 0])]; + tensor h_119_dilations_0 = const()[name = string("h_119_dilations_0"), val = tensor([1])]; + int32 h_119_groups_0 = const()[name = string("h_119_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32901824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33688320))))[name = string("main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33688512)))]; + tensor input_311_cast_fp16 = transpose(perm = input_311_perm_0, x = var_1887_cast_fp16)[name = string("transpose_31")]; + tensor h_119_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_119_dilations_0, groups = h_119_groups_0, pad = h_119_pad_0, pad_type = h_119_pad_type_0, strides = h_119_strides_0, weight = main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_311_cast_fp16)[name = string("h_119_cast_fp16")]; + string input_313_mode_0 = const()[name = string("input_313_mode_0"), val = string("EXACT")]; + tensor input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = h_119_cast_fp16)[name = string("input_313_cast_fp16")]; + string h_121_pad_type_0 = const()[name = string("h_121_pad_type_0"), val = string("valid")]; + tensor h_121_strides_0 = const()[name = string("h_121_strides_0"), val = tensor([1])]; + tensor h_121_pad_0 = const()[name = string("h_121_pad_0"), val = tensor([0, 0])]; + tensor h_121_dilations_0 = const()[name = string("h_121_dilations_0"), val = tensor([1])]; + int32 h_121_groups_0 = const()[name = string("h_121_groups_0"), val = int32(1)]; + tensor op_1904_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33692672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34479168))))[name = string("op_1904_weight_0_to_fp16_palettized")]; + tensor var_1904_bias_0_to_fp16 = const()[name = string("op_1904_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34479360)))]; + tensor var_1904_cast_fp16 = conv(bias = var_1904_bias_0_to_fp16, dilations = h_121_dilations_0, groups = h_121_groups_0, pad = h_121_pad_0, pad_type = h_121_pad_type_0, strides = h_121_strides_0, weight = op_1904_weight_0_to_fp16_palettized, x = input_313_cast_fp16)[name = string("op_1904_cast_fp16")]; + tensor out_51_cast_fp16 = add(x = x_161_cast_fp16, y = var_1904_cast_fp16)[name = string("out_51_cast_fp16")]; + tensor x_165_cast_fp16 = mul(x = out_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor x_167_cast_fp16 = mul(x = x_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1918_cast_fp16 = mul(x = x_167_cast_fp16, y = last_1_cast_fp16)[name = string("op_1918_cast_fp16")]; + tensor x_last_41_axes_0 = const()[name = string("x_last_41_axes_0"), val = tensor([-1])]; + bool x_last_41_keep_dims_0 = const()[name = string("x_last_41_keep_dims_0"), val = bool(true)]; + tensor x_last_41_cast_fp16 = reduce_sum(axes = x_last_41_axes_0, keep_dims = x_last_41_keep_dims_0, x = var_1918_cast_fp16)[name = string("x_last_41_cast_fp16")]; + tensor var_1921_cast_fp16 = mul(x = x_167_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1921_cast_fp16")]; + tensor var_1923_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_41_cast_fp16)[name = string("op_1923_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = var_1921_cast_fp16, y = var_1923_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("replicate")]; + fp16 const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = fp16(0x0p+0)]; + tensor input_319_cast_fp16 = pad(constant_val = const_68_to_fp16, mode = input_319_mode_0, pad = input_319_pad_0, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string h_123_pad_type_0 = const()[name = string("h_123_pad_type_0"), val = string("valid")]; + tensor h_123_dilations_0 = const()[name = string("h_123_dilations_0"), val = tensor([4])]; + int32 h_123_groups_0 = const()[name = string("h_123_groups_0"), val = int32(512)]; + tensor h_123_strides_0 = const()[name = string("h_123_strides_0"), val = tensor([1])]; + tensor h_123_pad_0 = const()[name = string("h_123_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34480448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34482432))))[name = string("main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34482624)))]; + tensor h_123_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_123_dilations_0, groups = h_123_groups_0, pad = h_123_pad_0, pad_type = h_123_pad_type_0, strides = h_123_strides_0, weight = main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_319_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor x_169_cast_fp16 = mul(x = h_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1941_axes_0 = const()[name = string("op_1941_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34483712)))]; + tensor main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34484800)))]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_169_cast_fp16)[name = string("transpose_30")]; + tensor var_1941_cast_fp16 = layer_norm(axes = var_1941_axes_0, beta = main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16, x = input_321_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor([0, 2, 1])]; + string h_125_pad_type_0 = const()[name = string("h_125_pad_type_0"), val = string("valid")]; + tensor h_125_strides_0 = const()[name = string("h_125_strides_0"), val = tensor([1])]; + tensor h_125_pad_0 = const()[name = string("h_125_pad_0"), val = tensor([0, 0])]; + tensor h_125_dilations_0 = const()[name = string("h_125_dilations_0"), val = tensor([1])]; + int32 h_125_groups_0 = const()[name = string("h_125_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34485888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35272384))))[name = string("main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35272576)))]; + tensor input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = var_1941_cast_fp16)[name = string("transpose_29")]; + tensor h_125_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_125_dilations_0, groups = h_125_groups_0, pad = h_125_pad_0, pad_type = h_125_pad_type_0, strides = h_125_strides_0, weight = main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = string("h_125_cast_fp16")]; + string input_325_mode_0 = const()[name = string("input_325_mode_0"), val = string("EXACT")]; + tensor input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = h_125_cast_fp16)[name = string("input_325_cast_fp16")]; + string h_127_pad_type_0 = const()[name = string("h_127_pad_type_0"), val = string("valid")]; + tensor h_127_strides_0 = const()[name = string("h_127_strides_0"), val = tensor([1])]; + tensor h_127_pad_0 = const()[name = string("h_127_pad_0"), val = tensor([0, 0])]; + tensor h_127_dilations_0 = const()[name = string("h_127_dilations_0"), val = tensor([1])]; + int32 h_127_groups_0 = const()[name = string("h_127_groups_0"), val = int32(1)]; + tensor op_1958_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35276736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36063232))))[name = string("op_1958_weight_0_to_fp16_palettized")]; + tensor var_1958_bias_0_to_fp16 = const()[name = string("op_1958_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36063424)))]; + tensor var_1958_cast_fp16 = conv(bias = var_1958_bias_0_to_fp16, dilations = h_127_dilations_0, groups = h_127_groups_0, pad = h_127_pad_0, pad_type = h_127_pad_type_0, strides = h_127_strides_0, weight = op_1958_weight_0_to_fp16_palettized, x = input_325_cast_fp16)[name = string("op_1958_cast_fp16")]; + tensor out_53_cast_fp16 = add(x = x_167_cast_fp16, y = var_1958_cast_fp16)[name = string("out_53_cast_fp16")]; + tensor x_171_cast_fp16 = mul(x = out_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_171_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor var_1972_cast_fp16 = mul(x = x_173_cast_fp16, y = last_1_cast_fp16)[name = string("op_1972_cast_fp16")]; + tensor x_last_43_axes_0 = const()[name = string("x_last_43_axes_0"), val = tensor([-1])]; + bool x_last_43_keep_dims_0 = const()[name = string("x_last_43_keep_dims_0"), val = bool(true)]; + tensor x_last_43_cast_fp16 = reduce_sum(axes = x_last_43_axes_0, keep_dims = x_last_43_keep_dims_0, x = var_1972_cast_fp16)[name = string("x_last_43_cast_fp16")]; + tensor var_1975_cast_fp16 = mul(x = x_173_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1975_cast_fp16")]; + tensor var_1977_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_43_cast_fp16)[name = string("op_1977_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1977_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("replicate")]; + fp16 const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = fp16(0x0p+0)]; + tensor input_331_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = input_331_mode_0, pad = input_331_pad_0, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + string h_129_pad_type_0 = const()[name = string("h_129_pad_type_0"), val = string("valid")]; + tensor h_129_dilations_0 = const()[name = string("h_129_dilations_0"), val = tensor([8])]; + int32 h_129_groups_0 = const()[name = string("h_129_groups_0"), val = int32(512)]; + tensor h_129_strides_0 = const()[name = string("h_129_strides_0"), val = tensor([1])]; + tensor h_129_pad_0 = const()[name = string("h_129_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36064512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36066496))))[name = string("main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36066688)))]; + tensor h_129_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_129_dilations_0, groups = h_129_groups_0, pad = h_129_pad_0, pad_type = h_129_pad_type_0, strides = h_129_strides_0, weight = main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = string("h_129_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = h_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_333_perm_0 = const()[name = string("input_333_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1995_axes_0 = const()[name = string("op_1995_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36067776)))]; + tensor main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36068864)))]; + tensor input_333_cast_fp16 = transpose(perm = input_333_perm_0, x = x_175_cast_fp16)[name = string("transpose_28")]; + tensor var_1995_cast_fp16 = layer_norm(axes = var_1995_axes_0, beta = main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16, x = input_333_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor input_335_perm_0 = const()[name = string("input_335_perm_0"), val = tensor([0, 2, 1])]; + string h_131_pad_type_0 = const()[name = string("h_131_pad_type_0"), val = string("valid")]; + tensor h_131_strides_0 = const()[name = string("h_131_strides_0"), val = tensor([1])]; + tensor h_131_pad_0 = const()[name = string("h_131_pad_0"), val = tensor([0, 0])]; + tensor h_131_dilations_0 = const()[name = string("h_131_dilations_0"), val = tensor([1])]; + int32 h_131_groups_0 = const()[name = string("h_131_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36069952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36856448))))[name = string("main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36856640)))]; + tensor input_335_cast_fp16 = transpose(perm = input_335_perm_0, x = var_1995_cast_fp16)[name = string("transpose_27")]; + tensor h_131_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_131_dilations_0, groups = h_131_groups_0, pad = h_131_pad_0, pad_type = h_131_pad_type_0, strides = h_131_strides_0, weight = main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_335_cast_fp16)[name = string("h_131_cast_fp16")]; + string input_337_mode_0 = const()[name = string("input_337_mode_0"), val = string("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = h_131_cast_fp16)[name = string("input_337_cast_fp16")]; + string h_133_pad_type_0 = const()[name = string("h_133_pad_type_0"), val = string("valid")]; + tensor h_133_strides_0 = const()[name = string("h_133_strides_0"), val = tensor([1])]; + tensor h_133_pad_0 = const()[name = string("h_133_pad_0"), val = tensor([0, 0])]; + tensor h_133_dilations_0 = const()[name = string("h_133_dilations_0"), val = tensor([1])]; + int32 h_133_groups_0 = const()[name = string("h_133_groups_0"), val = int32(1)]; + tensor op_2012_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36860800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37647296))))[name = string("op_2012_weight_0_to_fp16_palettized")]; + tensor var_2012_bias_0_to_fp16 = const()[name = string("op_2012_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37647488)))]; + tensor var_2012_cast_fp16 = conv(bias = var_2012_bias_0_to_fp16, dilations = h_133_dilations_0, groups = h_133_groups_0, pad = h_133_pad_0, pad_type = h_133_pad_type_0, strides = h_133_strides_0, weight = op_2012_weight_0_to_fp16_palettized, x = input_337_cast_fp16)[name = string("op_2012_cast_fp16")]; + tensor out_55_cast_fp16 = add(x = x_173_cast_fp16, y = var_2012_cast_fp16)[name = string("out_55_cast_fp16")]; + tensor x_177_cast_fp16 = mul(x = out_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37648576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37673216))))[name = string("main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37673408)))]; + tensor linear_29_cast_fp16 = linear(bias = main_blocks_3_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor t_axes_0 = const()[name = string("t_axes_0"), val = tensor([-1])]; + tensor t_cast_fp16 = expand_dims(axes = t_axes_0, x = linear_29_cast_fp16)[name = string("t_cast_fp16")]; + tensor var_2022_cast_fp16 = add(x = x_177_cast_fp16, y = t_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor x_179_cast_fp16 = mul(x = var_2022_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor x_181_cast_fp16 = mul(x = x_179_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor var_2035_cast_fp16 = mul(x = x_181_cast_fp16, y = last_1_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor x_last_45_axes_0 = const()[name = string("x_last_45_axes_0"), val = tensor([-1])]; + bool x_last_45_keep_dims_0 = const()[name = string("x_last_45_keep_dims_0"), val = bool(true)]; + tensor x_last_45_cast_fp16 = reduce_sum(axes = x_last_45_axes_0, keep_dims = x_last_45_keep_dims_0, x = var_2035_cast_fp16)[name = string("x_last_45_cast_fp16")]; + tensor var_2038_cast_fp16 = mul(x = x_181_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2038_cast_fp16")]; + tensor var_2040_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_45_cast_fp16)[name = string("op_2040_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = var_2038_cast_fp16, y = var_2040_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_345_mode_0 = const()[name = string("input_345_mode_0"), val = string("replicate")]; + fp16 const_72_to_fp16 = const()[name = string("const_72_to_fp16"), val = fp16(0x0p+0)]; + tensor input_345_cast_fp16 = pad(constant_val = const_72_to_fp16, mode = input_345_mode_0, pad = input_345_pad_0, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + string h_135_pad_type_0 = const()[name = string("h_135_pad_type_0"), val = string("valid")]; + int32 h_135_groups_0 = const()[name = string("h_135_groups_0"), val = int32(512)]; + tensor h_135_strides_0 = const()[name = string("h_135_strides_0"), val = tensor([1])]; + tensor h_135_pad_0 = const()[name = string("h_135_pad_0"), val = tensor([0, 0])]; + tensor h_135_dilations_0 = const()[name = string("h_135_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37674496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37676480))))[name = string("main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37676672)))]; + tensor h_135_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_135_dilations_0, groups = h_135_groups_0, pad = h_135_pad_0, pad_type = h_135_pad_type_0, strides = h_135_strides_0, weight = main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_345_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor x_183_cast_fp16 = mul(x = h_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_183_cast_fp16")]; + tensor input_347_perm_0 = const()[name = string("input_347_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2058_axes_0 = const()[name = string("op_2058_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37677760)))]; + tensor main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37678848)))]; + tensor input_347_cast_fp16 = transpose(perm = input_347_perm_0, x = x_183_cast_fp16)[name = string("transpose_26")]; + tensor var_2058_cast_fp16 = layer_norm(axes = var_2058_axes_0, beta = main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16, x = input_347_cast_fp16)[name = string("op_2058_cast_fp16")]; + tensor input_349_perm_0 = const()[name = string("input_349_perm_0"), val = tensor([0, 2, 1])]; + string h_137_pad_type_0 = const()[name = string("h_137_pad_type_0"), val = string("valid")]; + tensor h_137_strides_0 = const()[name = string("h_137_strides_0"), val = tensor([1])]; + tensor h_137_pad_0 = const()[name = string("h_137_pad_0"), val = tensor([0, 0])]; + tensor h_137_dilations_0 = const()[name = string("h_137_dilations_0"), val = tensor([1])]; + int32 h_137_groups_0 = const()[name = string("h_137_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37679936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38466432))))[name = string("main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38466624)))]; + tensor input_349_cast_fp16 = transpose(perm = input_349_perm_0, x = var_2058_cast_fp16)[name = string("transpose_25")]; + tensor h_137_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_137_dilations_0, groups = h_137_groups_0, pad = h_137_pad_0, pad_type = h_137_pad_type_0, strides = h_137_strides_0, weight = main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_349_cast_fp16)[name = string("h_137_cast_fp16")]; + string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")]; + tensor input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = h_137_cast_fp16)[name = string("input_351_cast_fp16")]; + string h_139_pad_type_0 = const()[name = string("h_139_pad_type_0"), val = string("valid")]; + tensor h_139_strides_0 = const()[name = string("h_139_strides_0"), val = tensor([1])]; + tensor h_139_pad_0 = const()[name = string("h_139_pad_0"), val = tensor([0, 0])]; + tensor h_139_dilations_0 = const()[name = string("h_139_dilations_0"), val = tensor([1])]; + int32 h_139_groups_0 = const()[name = string("h_139_groups_0"), val = int32(1)]; + tensor op_2075_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38470784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39257280))))[name = string("op_2075_weight_0_to_fp16_palettized")]; + tensor var_2075_bias_0_to_fp16 = const()[name = string("op_2075_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39257472)))]; + tensor var_2075_cast_fp16 = conv(bias = var_2075_bias_0_to_fp16, dilations = h_139_dilations_0, groups = h_139_groups_0, pad = h_139_pad_0, pad_type = h_139_pad_type_0, strides = h_139_strides_0, weight = op_2075_weight_0_to_fp16_palettized, x = input_351_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor out_57_cast_fp16 = add(x = x_181_cast_fp16, y = var_2075_cast_fp16)[name = string("out_57_cast_fp16")]; + tensor x_185_cast_fp16 = mul(x = out_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor x_m_13_cast_fp16 = mul(x = x_185_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_13_cast_fp16")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39258560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39455232))))[name = string("main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39455424)))]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_185_cast_fp16)[name = string("transpose_24")]; + tensor linear_30_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_2093 = const()[name = string("op_2093"), val = tensor([2, 256, 8, 64])]; + tensor var_2094_cast_fp16 = reshape(shape = var_2093, x = linear_30_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor x_187_perm_0 = const()[name = string("x_187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39554880))))[name = string("main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39555072)))]; + tensor linear_31_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_2101 = const()[name = string("op_2101"), val = tensor([2, 128, 8, 64])]; + tensor var_2102_cast_fp16 = reshape(shape = var_2101, x = linear_31_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor x_189_perm_0 = const()[name = string("x_189_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39556160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39654528))))[name = string("main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39654720)))]; + tensor linear_32_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_2109 = const()[name = string("op_2109"), val = tensor([2, 128, 8, 64])]; + tensor var_2110_cast_fp16 = reshape(shape = var_2109, x = linear_32_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_13_begin_0 = const()[name = string("x_a_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_13_end_0 = const()[name = string("x_a_13_end_0"), val = tensor([2, 8, 256, 32])]; + tensor x_a_13_end_mask_0 = const()[name = string("x_a_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_187_cast_fp16 = transpose(perm = x_187_perm_0, x = var_2094_cast_fp16)[name = string("transpose_23")]; + tensor x_a_13_cast_fp16 = slice_by_index(begin = x_a_13_begin_0, end = x_a_13_end_0, end_mask = x_a_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_a_13_cast_fp16")]; + tensor x_b_13_begin_0 = const()[name = string("x_b_13_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_13_end_0 = const()[name = string("x_b_13_end_0"), val = tensor([2, 8, 256, 64])]; + tensor x_b_13_end_mask_0 = const()[name = string("x_b_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_13_cast_fp16 = slice_by_index(begin = x_b_13_begin_0, end = x_b_13_end_0, end_mask = x_b_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_b_13_cast_fp16")]; + tensor var_2138_cast_fp16 = mul(x = x_a_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2138_cast_fp16")]; + tensor var_2139_cast_fp16 = mul(x = x_b_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2139_cast_fp16")]; + tensor rot_a_13_cast_fp16 = sub(x = var_2138_cast_fp16, y = var_2139_cast_fp16)[name = string("rot_a_13_cast_fp16")]; + tensor var_2141_cast_fp16 = mul(x = x_a_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2141_cast_fp16")]; + tensor var_2142_cast_fp16 = mul(x = x_b_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2142_cast_fp16")]; + tensor rot_b_13_cast_fp16 = add(x = var_2141_cast_fp16, y = var_2142_cast_fp16)[name = string("rot_b_13_cast_fp16")]; + bool q_13_interleave_0 = const()[name = string("q_13_interleave_0"), val = bool(false)]; + tensor q_13_cast_fp16 = concat(axis = var_1775, interleave = q_13_interleave_0, values = (rot_a_13_cast_fp16, rot_b_13_cast_fp16))[name = string("q_13_cast_fp16")]; + tensor x_a_begin_0 = const()[name = string("x_a_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_end_0 = const()[name = string("x_a_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_end_mask_0 = const()[name = string("x_a_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_189_cast_fp16 = transpose(perm = x_189_perm_0, x = var_2102_cast_fp16)[name = string("transpose_22")]; + tensor x_a_cast_fp16 = slice_by_index(begin = x_a_begin_0, end = x_a_end_0, end_mask = x_a_end_mask_0, x = x_189_cast_fp16)[name = string("x_a_cast_fp16")]; + tensor x_b_begin_0 = const()[name = string("x_b_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_end_0 = const()[name = string("x_b_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_end_mask_0 = const()[name = string("x_b_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_cast_fp16 = slice_by_index(begin = x_b_begin_0, end = x_b_end_0, end_mask = x_b_end_mask_0, x = x_189_cast_fp16)[name = string("x_b_cast_fp16")]; + tensor var_2156_cast_fp16 = mul(x = x_a_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2156_cast_fp16")]; + tensor var_2157_cast_fp16 = mul(x = x_b_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2157_cast_fp16")]; + tensor rot_a_cast_fp16 = sub(x = var_2156_cast_fp16, y = var_2157_cast_fp16)[name = string("rot_a_cast_fp16")]; + tensor var_2159_cast_fp16 = mul(x = x_a_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2159_cast_fp16")]; + tensor var_2160_cast_fp16 = mul(x = x_b_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2160_cast_fp16")]; + tensor rot_b_cast_fp16 = add(x = var_2159_cast_fp16, y = var_2160_cast_fp16)[name = string("rot_b_cast_fp16")]; + bool k_13_interleave_0 = const()[name = string("k_13_interleave_0"), val = bool(false)]; + tensor k_13_cast_fp16 = concat(axis = var_1775, interleave = k_13_interleave_0, values = (rot_a_cast_fp16, rot_b_cast_fp16))[name = string("k_13_cast_fp16")]; + bool var_2165_transpose_x_1 = const()[name = string("op_2165_transpose_x_1"), val = bool(false)]; + bool var_2165_transpose_y_1 = const()[name = string("op_2165_transpose_y_1"), val = bool(true)]; + tensor var_2165_cast_fp16 = matmul(transpose_x = var_2165_transpose_x_1, transpose_y = var_2165_transpose_y_1, x = q_13_cast_fp16, y = k_13_cast_fp16)[name = string("op_2165_cast_fp16")]; + fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_cast_fp16 = mul(x = var_2165_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")]; + tensor input_359_cast_fp16 = sub(x = _inversed_scores_cast_fp16, y = var_530_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_1775, x = input_359_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool var_2174_transpose_x_0 = const()[name = string("op_2174_transpose_x_0"), val = bool(false)]; + bool var_2174_transpose_y_0 = const()[name = string("op_2174_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2110_cast_fp16)[name = string("transpose_21")]; + tensor var_2174_cast_fp16 = matmul(transpose_x = var_2174_transpose_x_0, transpose_y = var_2174_transpose_y_0, x = attn_19_cast_fp16, y = v_13_cast_fp16)[name = string("op_2174_cast_fp16")]; + tensor var_2175_perm_0 = const()[name = string("op_2175_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2176 = const()[name = string("op_2176"), val = tensor([2, 256, 512])]; + tensor var_2175_cast_fp16 = transpose(perm = var_2175_perm_0, x = var_2174_cast_fp16)[name = string("transpose_20")]; + tensor input_361_cast_fp16 = reshape(shape = var_2176, x = var_2175_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39655808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39852480))))[name = string("main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39852672)))]; + tensor linear_33_cast_fp16 = linear(bias = main_blocks_3_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor out_59_perm_0 = const()[name = string("out_59_perm_0"), val = tensor([0, 2, 1])]; + tensor out_59_cast_fp16 = transpose(perm = out_59_perm_0, x = linear_33_cast_fp16)[name = string("transpose_19")]; + tensor var_2183_cast_fp16 = mul(x = out_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_m_13_cast_fp16, y = var_2183_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor input_363_perm_0 = const()[name = string("input_363_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2190_axes_0 = const()[name = string("op_2190_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39853760)))]; + tensor main_blocks_3_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39854848)))]; + tensor input_363_cast_fp16 = transpose(perm = input_363_perm_0, x = x_191_cast_fp16)[name = string("transpose_18")]; + tensor var_2190_cast_fp16 = layer_norm(axes = var_2190_axes_0, beta = main_blocks_3_text_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_text_norm_norm_weight_to_fp16, x = input_363_cast_fp16)[name = string("op_2190_cast_fp16")]; + tensor var_2191_perm_0 = const()[name = string("op_2191_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2191_perm_0, x = var_2190_cast_fp16)[name = string("transpose_17")]; + tensor x_193_cast_fp16 = mul(x = var_2191_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_193_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2204_cast_fp16 = mul(x = x_195_cast_fp16, y = last_1_cast_fp16)[name = string("op_2204_cast_fp16")]; + tensor x_last_47_axes_0 = const()[name = string("x_last_47_axes_0"), val = tensor([-1])]; + bool x_last_47_keep_dims_0 = const()[name = string("x_last_47_keep_dims_0"), val = bool(true)]; + tensor x_last_47_cast_fp16 = reduce_sum(axes = x_last_47_axes_0, keep_dims = x_last_47_keep_dims_0, x = var_2204_cast_fp16)[name = string("x_last_47_cast_fp16")]; + tensor var_2207_cast_fp16 = mul(x = x_195_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2207_cast_fp16")]; + tensor var_2209_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_47_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor input_367_cast_fp16 = add(x = var_2207_cast_fp16, y = var_2209_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_369_mode_0 = const()[name = string("input_369_mode_0"), val = string("replicate")]; + fp16 const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = fp16(0x0p+0)]; + tensor input_369_cast_fp16 = pad(constant_val = const_79_to_fp16, mode = input_369_mode_0, pad = input_369_pad_0, x = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + string h_141_pad_type_0 = const()[name = string("h_141_pad_type_0"), val = string("valid")]; + int32 h_141_groups_0 = const()[name = string("h_141_groups_0"), val = int32(512)]; + tensor h_141_strides_0 = const()[name = string("h_141_strides_0"), val = tensor([1])]; + tensor h_141_pad_0 = const()[name = string("h_141_pad_0"), val = tensor([0, 0])]; + tensor h_141_dilations_0 = const()[name = string("h_141_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39855936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39857920))))[name = string("main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39858112)))]; + tensor h_141_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_141_dilations_0, groups = h_141_groups_0, pad = h_141_pad_0, pad_type = h_141_pad_type_0, strides = h_141_strides_0, weight = main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_369_cast_fp16)[name = string("h_141_cast_fp16")]; + tensor x_197_cast_fp16 = mul(x = h_141_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_371_perm_0 = const()[name = string("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2227_axes_0 = const()[name = string("op_2227_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39859200)))]; + tensor main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39860288)))]; + tensor input_371_cast_fp16 = transpose(perm = input_371_perm_0, x = x_197_cast_fp16)[name = string("transpose_16")]; + tensor var_2227_cast_fp16 = layer_norm(axes = var_2227_axes_0, beta = main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16, x = input_371_cast_fp16)[name = string("op_2227_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + string h_143_pad_type_0 = const()[name = string("h_143_pad_type_0"), val = string("valid")]; + tensor h_143_strides_0 = const()[name = string("h_143_strides_0"), val = tensor([1])]; + tensor h_143_pad_0 = const()[name = string("h_143_pad_0"), val = tensor([0, 0])]; + tensor h_143_dilations_0 = const()[name = string("h_143_dilations_0"), val = tensor([1])]; + int32 h_143_groups_0 = const()[name = string("h_143_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39861376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40647872))))[name = string("main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40648064)))]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = var_2227_cast_fp16)[name = string("transpose_15")]; + tensor h_143_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_143_dilations_0, groups = h_143_groups_0, pad = h_143_pad_0, pad_type = h_143_pad_type_0, strides = h_143_strides_0, weight = main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = string("h_143_cast_fp16")]; + string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")]; + tensor input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = h_143_cast_fp16)[name = string("input_375_cast_fp16")]; + string h_145_pad_type_0 = const()[name = string("h_145_pad_type_0"), val = string("valid")]; + tensor h_145_strides_0 = const()[name = string("h_145_strides_0"), val = tensor([1])]; + tensor h_145_pad_0 = const()[name = string("h_145_pad_0"), val = tensor([0, 0])]; + tensor h_145_dilations_0 = const()[name = string("h_145_dilations_0"), val = tensor([1])]; + int32 h_145_groups_0 = const()[name = string("h_145_groups_0"), val = int32(1)]; + tensor op_2244_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40652224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41438720))))[name = string("op_2244_weight_0_to_fp16_palettized")]; + tensor var_2244_bias_0_to_fp16 = const()[name = string("op_2244_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41438912)))]; + tensor var_2244_cast_fp16 = conv(bias = var_2244_bias_0_to_fp16, dilations = h_145_dilations_0, groups = h_145_groups_0, pad = h_145_pad_0, pad_type = h_145_pad_type_0, strides = h_145_strides_0, weight = op_2244_weight_0_to_fp16_palettized, x = input_375_cast_fp16)[name = string("op_2244_cast_fp16")]; + tensor out_61_cast_fp16 = add(x = x_195_cast_fp16, y = var_2244_cast_fp16)[name = string("out_61_cast_fp16")]; + tensor x_199_cast_fp16 = mul(x = out_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor x_m_cast_fp16 = mul(x = x_199_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_cast_fp16")]; + tensor input_377_perm_0 = const()[name = string("input_377_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41440000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41538368))))[name = string("main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41538560)))]; + tensor input_377_cast_fp16 = transpose(perm = input_377_perm_0, x = x_199_cast_fp16)[name = string("transpose_14")]; + tensor linear_34_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_2260 = const()[name = string("op_2260"), val = tensor([2, 256, 2, 128])]; + tensor var_2261_cast_fp16 = reshape(shape = var_2260, x = linear_34_cast_fp16)[name = string("op_2261_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41539136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41588352))))[name = string("main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41588544)))]; + tensor linear_36_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_2274 = const()[name = string("op_2274"), val = tensor([2, 50, 2, 128])]; + tensor var_2275_cast_fp16 = reshape(shape = var_2274, x = linear_36_cast_fp16)[name = string("op_2275_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_2279_transpose_x_0 = const()[name = string("op_2279_transpose_x_0"), val = bool(false)]; + bool var_2279_transpose_y_0 = const()[name = string("op_2279_transpose_y_0"), val = bool(false)]; + tensor op_2278_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41589120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41608384))))[name = string("op_2278_to_fp16_palettized")]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_2261_cast_fp16)[name = string("transpose_13")]; + tensor var_2279_cast_fp16 = matmul(transpose_x = var_2279_transpose_x_0, transpose_y = var_2279_transpose_y_0, x = q_cast_fp16, y = op_2278_to_fp16_palettized)[name = string("op_2279_cast_fp16")]; + fp16 _inversed_input_379_y_0_to_fp16 = const()[name = string("_inversed_input_379_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_379_cast_fp16 = mul(x = var_2279_cast_fp16, y = _inversed_input_379_y_0_to_fp16)[name = string("_inversed_input_379_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_1775, x = _inversed_input_379_cast_fp16)[name = string("attn_21_cast_fp16")]; + tensor attn_cast_fp16 = mul(x = attn_21_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_cast_fp16")]; + bool var_2286_transpose_x_0 = const()[name = string("op_2286_transpose_x_0"), val = bool(false)]; + bool var_2286_transpose_y_0 = const()[name = string("op_2286_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_2275_cast_fp16)[name = string("transpose_12")]; + tensor var_2286_cast_fp16 = matmul(transpose_x = var_2286_transpose_x_0, transpose_y = var_2286_transpose_y_0, x = attn_cast_fp16, y = v_15_cast_fp16)[name = string("op_2286_cast_fp16")]; + tensor var_2287_perm_0 = const()[name = string("op_2287_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2288 = const()[name = string("op_2288"), val = tensor([2, 256, 256])]; + tensor var_2287_cast_fp16 = transpose(perm = var_2287_perm_0, x = var_2286_cast_fp16)[name = string("transpose_11")]; + tensor input_381_cast_fp16 = reshape(shape = var_2288, x = var_2287_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41608576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41706944))))[name = string("main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41707136)))]; + tensor linear_37_cast_fp16 = linear(bias = main_blocks_3_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor out_63_perm_0 = const()[name = string("out_63_perm_0"), val = tensor([0, 2, 1])]; + tensor out_63_cast_fp16 = transpose(perm = out_63_perm_0, x = linear_37_cast_fp16)[name = string("transpose_10")]; + tensor var_2295_cast_fp16 = mul(x = out_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2295_cast_fp16")]; + tensor x_201_cast_fp16 = add(x = x_m_cast_fp16, y = var_2295_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_383_perm_0 = const()[name = string("input_383_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2302_axes_0 = const()[name = string("op_2302_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41708224)))]; + tensor main_blocks_3_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41709312)))]; + tensor input_383_cast_fp16 = transpose(perm = input_383_perm_0, x = x_201_cast_fp16)[name = string("transpose_9")]; + tensor var_2302_cast_fp16 = layer_norm(axes = var_2302_axes_0, beta = main_blocks_3_style_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_style_norm_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_perm_0 = const()[name = string("op_2303_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2303_cast_fp16 = transpose(perm = var_2303_perm_0, x = var_2302_cast_fp16)[name = string("transpose_8")]; + tensor x_203_cast_fp16 = mul(x = var_2303_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor x_205_cast_fp16 = mul(x = x_203_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor var_2330_cast_fp16 = mul(x = x_205_cast_fp16, y = last_1_cast_fp16)[name = string("op_2330_cast_fp16")]; + tensor x_last_49_axes_0 = const()[name = string("x_last_49_axes_0"), val = tensor([-1])]; + bool x_last_49_keep_dims_0 = const()[name = string("x_last_49_keep_dims_0"), val = bool(true)]; + tensor x_last_49_cast_fp16 = reduce_sum(axes = x_last_49_axes_0, keep_dims = x_last_49_keep_dims_0, x = var_2330_cast_fp16)[name = string("x_last_49_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = x_205_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor var_2335_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_49_cast_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2335_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_pad_0 = const()[name = string("input_389_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("replicate")]; + fp16 const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = fp16(0x0p+0)]; + tensor input_389_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = input_389_mode_0, pad = input_389_pad_0, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + string h_147_pad_type_0 = const()[name = string("h_147_pad_type_0"), val = string("valid")]; + int32 h_147_groups_0 = const()[name = string("h_147_groups_0"), val = int32(512)]; + tensor h_147_strides_0 = const()[name = string("h_147_strides_0"), val = tensor([1])]; + tensor h_147_pad_0 = const()[name = string("h_147_pad_0"), val = tensor([0, 0])]; + tensor h_147_dilations_0 = const()[name = string("h_147_dilations_0"), val = tensor([1])]; + tensor last_convnext_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41710400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41712384))))[name = string("last_convnext_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41712576)))]; + tensor h_147_cast_fp16 = conv(bias = last_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_147_dilations_0, groups = h_147_groups_0, pad = h_147_pad_0, pad_type = h_147_pad_type_0, strides = h_147_strides_0, weight = last_convnext_0_dwconv__conv_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor x_207_cast_fp16 = mul(x = h_147_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_391_perm_0 = const()[name = string("input_391_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2353_axes_0 = const()[name = string("op_2353_axes_0"), val = tensor([-1])]; + tensor last_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41713664)))]; + tensor last_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41714752)))]; + fp16 var_2307_to_fp16 = const()[name = string("op_2307_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_391_cast_fp16 = transpose(perm = input_391_perm_0, x = x_207_cast_fp16)[name = string("transpose_7")]; + tensor var_2353_cast_fp16 = layer_norm(axes = var_2353_axes_0, beta = last_convnext_0_norm_norm_bias_to_fp16, epsilon = var_2307_to_fp16, gamma = last_convnext_0_norm_norm_weight_to_fp16, x = input_391_cast_fp16)[name = string("op_2353_cast_fp16")]; + tensor input_393_perm_0 = const()[name = string("input_393_perm_0"), val = tensor([0, 2, 1])]; + string h_149_pad_type_0 = const()[name = string("h_149_pad_type_0"), val = string("valid")]; + tensor h_149_strides_0 = const()[name = string("h_149_strides_0"), val = tensor([1])]; + tensor h_149_pad_0 = const()[name = string("h_149_pad_0"), val = tensor([0, 0])]; + tensor h_149_dilations_0 = const()[name = string("h_149_dilations_0"), val = tensor([1])]; + int32 h_149_groups_0 = const()[name = string("h_149_groups_0"), val = int32(1)]; + tensor last_convnext_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41715840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42502336))))[name = string("last_convnext_0_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42502528)))]; + tensor input_393_cast_fp16 = transpose(perm = input_393_perm_0, x = var_2353_cast_fp16)[name = string("transpose_6")]; + tensor h_149_cast_fp16 = conv(bias = last_convnext_0_pwconv1_bias_to_fp16, dilations = h_149_dilations_0, groups = h_149_groups_0, pad = h_149_pad_0, pad_type = h_149_pad_type_0, strides = h_149_strides_0, weight = last_convnext_0_pwconv1_weight_to_fp16_palettized, x = input_393_cast_fp16)[name = string("h_149_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = h_149_cast_fp16)[name = string("input_395_cast_fp16")]; + string h_151_pad_type_0 = const()[name = string("h_151_pad_type_0"), val = string("valid")]; + tensor h_151_strides_0 = const()[name = string("h_151_strides_0"), val = tensor([1])]; + tensor h_151_pad_0 = const()[name = string("h_151_pad_0"), val = tensor([0, 0])]; + tensor h_151_dilations_0 = const()[name = string("h_151_dilations_0"), val = tensor([1])]; + int32 h_151_groups_0 = const()[name = string("h_151_groups_0"), val = int32(1)]; + tensor op_2370_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42506688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43293184))))[name = string("op_2370_weight_0_to_fp16_palettized")]; + tensor var_2370_bias_0_to_fp16 = const()[name = string("op_2370_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43293376)))]; + tensor var_2370_cast_fp16 = conv(bias = var_2370_bias_0_to_fp16, dilations = h_151_dilations_0, groups = h_151_groups_0, pad = h_151_pad_0, pad_type = h_151_pad_type_0, strides = h_151_strides_0, weight = op_2370_weight_0_to_fp16_palettized, x = input_395_cast_fp16)[name = string("op_2370_cast_fp16")]; + tensor out_65_cast_fp16 = add(x = x_205_cast_fp16, y = var_2370_cast_fp16)[name = string("out_65_cast_fp16")]; + tensor x_209_cast_fp16 = mul(x = out_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor x_211_cast_fp16 = mul(x = x_209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_2398_cast_fp16 = mul(x = x_211_cast_fp16, y = last_1_cast_fp16)[name = string("op_2398_cast_fp16")]; + tensor x_last_51_axes_0 = const()[name = string("x_last_51_axes_0"), val = tensor([-1])]; + bool x_last_51_keep_dims_0 = const()[name = string("x_last_51_keep_dims_0"), val = bool(true)]; + tensor x_last_51_cast_fp16 = reduce_sum(axes = x_last_51_axes_0, keep_dims = x_last_51_keep_dims_0, x = var_2398_cast_fp16)[name = string("x_last_51_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = x_211_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_51_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor input_399_cast_fp16 = add(x = var_2401_cast_fp16, y = var_2403_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor input_401_pad_0 = const()[name = string("input_401_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_401_mode_0 = const()[name = string("input_401_mode_0"), val = string("replicate")]; + fp16 const_86_to_fp16 = const()[name = string("const_86_to_fp16"), val = fp16(0x0p+0)]; + tensor input_401_cast_fp16 = pad(constant_val = const_86_to_fp16, mode = input_401_mode_0, pad = input_401_pad_0, x = input_399_cast_fp16)[name = string("input_401_cast_fp16")]; + string h_153_pad_type_0 = const()[name = string("h_153_pad_type_0"), val = string("valid")]; + int32 h_153_groups_0 = const()[name = string("h_153_groups_0"), val = int32(512)]; + tensor h_153_strides_0 = const()[name = string("h_153_strides_0"), val = tensor([1])]; + tensor h_153_pad_0 = const()[name = string("h_153_pad_0"), val = tensor([0, 0])]; + tensor h_153_dilations_0 = const()[name = string("h_153_dilations_0"), val = tensor([1])]; + tensor last_convnext_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43294464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43296448))))[name = string("last_convnext_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43296640)))]; + tensor h_153_cast_fp16 = conv(bias = last_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_153_dilations_0, groups = h_153_groups_0, pad = h_153_pad_0, pad_type = h_153_pad_type_0, strides = h_153_strides_0, weight = last_convnext_1_dwconv__conv_weight_to_fp16_palettized, x = input_401_cast_fp16)[name = string("h_153_cast_fp16")]; + tensor x_213_cast_fp16 = mul(x = h_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_213_cast_fp16")]; + tensor input_403_perm_0 = const()[name = string("input_403_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2421_axes_0 = const()[name = string("op_2421_axes_0"), val = tensor([-1])]; + tensor last_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43297728)))]; + tensor last_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43298816)))]; + fp16 var_2375_to_fp16 = const()[name = string("op_2375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_403_cast_fp16 = transpose(perm = input_403_perm_0, x = x_213_cast_fp16)[name = string("transpose_5")]; + tensor var_2421_cast_fp16 = layer_norm(axes = var_2421_axes_0, beta = last_convnext_1_norm_norm_bias_to_fp16, epsilon = var_2375_to_fp16, gamma = last_convnext_1_norm_norm_weight_to_fp16, x = input_403_cast_fp16)[name = string("op_2421_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string h_155_pad_type_0 = const()[name = string("h_155_pad_type_0"), val = string("valid")]; + tensor h_155_strides_0 = const()[name = string("h_155_strides_0"), val = tensor([1])]; + tensor h_155_pad_0 = const()[name = string("h_155_pad_0"), val = tensor([0, 0])]; + tensor h_155_dilations_0 = const()[name = string("h_155_dilations_0"), val = tensor([1])]; + int32 h_155_groups_0 = const()[name = string("h_155_groups_0"), val = int32(1)]; + tensor last_convnext_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43299904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44086400))))[name = string("last_convnext_1_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44086592)))]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = var_2421_cast_fp16)[name = string("transpose_4")]; + tensor h_155_cast_fp16 = conv(bias = last_convnext_1_pwconv1_bias_to_fp16, dilations = h_155_dilations_0, groups = h_155_groups_0, pad = h_155_pad_0, pad_type = h_155_pad_type_0, strides = h_155_strides_0, weight = last_convnext_1_pwconv1_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = string("h_155_cast_fp16")]; + string input_407_mode_0 = const()[name = string("input_407_mode_0"), val = string("EXACT")]; + tensor input_407_cast_fp16 = gelu(mode = input_407_mode_0, x = h_155_cast_fp16)[name = string("input_407_cast_fp16")]; + string h_157_pad_type_0 = const()[name = string("h_157_pad_type_0"), val = string("valid")]; + tensor h_157_strides_0 = const()[name = string("h_157_strides_0"), val = tensor([1])]; + tensor h_157_pad_0 = const()[name = string("h_157_pad_0"), val = tensor([0, 0])]; + tensor h_157_dilations_0 = const()[name = string("h_157_dilations_0"), val = tensor([1])]; + int32 h_157_groups_0 = const()[name = string("h_157_groups_0"), val = int32(1)]; + tensor op_2438_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44090752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44877248))))[name = string("op_2438_weight_0_to_fp16_palettized")]; + tensor var_2438_bias_0_to_fp16 = const()[name = string("op_2438_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44877440)))]; + tensor var_2438_cast_fp16 = conv(bias = var_2438_bias_0_to_fp16, dilations = h_157_dilations_0, groups = h_157_groups_0, pad = h_157_pad_0, pad_type = h_157_pad_type_0, strides = h_157_strides_0, weight = op_2438_weight_0_to_fp16_palettized, x = input_407_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor out_67_cast_fp16 = add(x = x_211_cast_fp16, y = var_2438_cast_fp16)[name = string("out_67_cast_fp16")]; + tensor x_215_cast_fp16 = mul(x = out_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_215_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2466_cast_fp16 = mul(x = x_217_cast_fp16, y = last_1_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor x_last_53_axes_0 = const()[name = string("x_last_53_axes_0"), val = tensor([-1])]; + bool x_last_53_keep_dims_0 = const()[name = string("x_last_53_keep_dims_0"), val = bool(true)]; + tensor x_last_53_cast_fp16 = reduce_sum(axes = x_last_53_axes_0, keep_dims = x_last_53_keep_dims_0, x = var_2466_cast_fp16)[name = string("x_last_53_cast_fp16")]; + tensor var_2469_cast_fp16 = mul(x = x_217_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor var_2471_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_53_cast_fp16)[name = string("op_2471_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = var_2469_cast_fp16, y = var_2471_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("replicate")]; + fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)]; + tensor input_413_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = input_413_mode_0, pad = input_413_pad_0, x = input_411_cast_fp16)[name = string("input_413_cast_fp16")]; + string h_159_pad_type_0 = const()[name = string("h_159_pad_type_0"), val = string("valid")]; + int32 h_159_groups_0 = const()[name = string("h_159_groups_0"), val = int32(512)]; + tensor h_159_strides_0 = const()[name = string("h_159_strides_0"), val = tensor([1])]; + tensor h_159_pad_0 = const()[name = string("h_159_pad_0"), val = tensor([0, 0])]; + tensor h_159_dilations_0 = const()[name = string("h_159_dilations_0"), val = tensor([1])]; + tensor last_convnext_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44878528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44880512))))[name = string("last_convnext_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44880704)))]; + tensor h_159_cast_fp16 = conv(bias = last_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_159_dilations_0, groups = h_159_groups_0, pad = h_159_pad_0, pad_type = h_159_pad_type_0, strides = h_159_strides_0, weight = last_convnext_2_dwconv__conv_weight_to_fp16_palettized, x = input_413_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor x_219_cast_fp16 = mul(x = h_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_415_perm_0 = const()[name = string("input_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2489_axes_0 = const()[name = string("op_2489_axes_0"), val = tensor([-1])]; + tensor last_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44881792)))]; + tensor last_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44882880)))]; + fp16 var_2443_to_fp16 = const()[name = string("op_2443_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_415_cast_fp16 = transpose(perm = input_415_perm_0, x = x_219_cast_fp16)[name = string("transpose_3")]; + tensor var_2489_cast_fp16 = layer_norm(axes = var_2489_axes_0, beta = last_convnext_2_norm_norm_bias_to_fp16, epsilon = var_2443_to_fp16, gamma = last_convnext_2_norm_norm_weight_to_fp16, x = input_415_cast_fp16)[name = string("op_2489_cast_fp16")]; + tensor input_417_perm_0 = const()[name = string("input_417_perm_0"), val = tensor([0, 2, 1])]; + string h_161_pad_type_0 = const()[name = string("h_161_pad_type_0"), val = string("valid")]; + tensor h_161_strides_0 = const()[name = string("h_161_strides_0"), val = tensor([1])]; + tensor h_161_pad_0 = const()[name = string("h_161_pad_0"), val = tensor([0, 0])]; + tensor h_161_dilations_0 = const()[name = string("h_161_dilations_0"), val = tensor([1])]; + int32 h_161_groups_0 = const()[name = string("h_161_groups_0"), val = int32(1)]; + tensor last_convnext_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44883968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45670464))))[name = string("last_convnext_2_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45670656)))]; + tensor input_417_cast_fp16 = transpose(perm = input_417_perm_0, x = var_2489_cast_fp16)[name = string("transpose_2")]; + tensor h_161_cast_fp16 = conv(bias = last_convnext_2_pwconv1_bias_to_fp16, dilations = h_161_dilations_0, groups = h_161_groups_0, pad = h_161_pad_0, pad_type = h_161_pad_type_0, strides = h_161_strides_0, weight = last_convnext_2_pwconv1_weight_to_fp16_palettized, x = input_417_cast_fp16)[name = string("h_161_cast_fp16")]; + string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("EXACT")]; + tensor input_419_cast_fp16 = gelu(mode = input_419_mode_0, x = h_161_cast_fp16)[name = string("input_419_cast_fp16")]; + string h_163_pad_type_0 = const()[name = string("h_163_pad_type_0"), val = string("valid")]; + tensor h_163_strides_0 = const()[name = string("h_163_strides_0"), val = tensor([1])]; + tensor h_163_pad_0 = const()[name = string("h_163_pad_0"), val = tensor([0, 0])]; + tensor h_163_dilations_0 = const()[name = string("h_163_dilations_0"), val = tensor([1])]; + int32 h_163_groups_0 = const()[name = string("h_163_groups_0"), val = int32(1)]; + tensor op_2506_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45674816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46461312))))[name = string("op_2506_weight_0_to_fp16_palettized")]; + tensor var_2506_bias_0_to_fp16 = const()[name = string("op_2506_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46461504)))]; + tensor var_2506_cast_fp16 = conv(bias = var_2506_bias_0_to_fp16, dilations = h_163_dilations_0, groups = h_163_groups_0, pad = h_163_pad_0, pad_type = h_163_pad_type_0, strides = h_163_strides_0, weight = op_2506_weight_0_to_fp16_palettized, x = input_419_cast_fp16)[name = string("op_2506_cast_fp16")]; + tensor out_69_cast_fp16 = add(x = x_217_cast_fp16, y = var_2506_cast_fp16)[name = string("out_69_cast_fp16")]; + tensor x_221_cast_fp16 = mul(x = out_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor x_223_cast_fp16 = mul(x = x_221_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_2534_cast_fp16 = mul(x = x_223_cast_fp16, y = last_1_cast_fp16)[name = string("op_2534_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_2534_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_2537_cast_fp16 = mul(x = x_223_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2537_cast_fp16")]; + tensor var_2539_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_cast_fp16)[name = string("op_2539_cast_fp16")]; + tensor input_423_cast_fp16 = add(x = var_2537_cast_fp16, y = var_2539_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_425_mode_0 = const()[name = string("input_425_mode_0"), val = string("replicate")]; + fp16 const_90_to_fp16 = const()[name = string("const_90_to_fp16"), val = fp16(0x0p+0)]; + tensor input_425_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = input_425_mode_0, pad = input_425_pad_0, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")]; + string h_165_pad_type_0 = const()[name = string("h_165_pad_type_0"), val = string("valid")]; + int32 h_165_groups_0 = const()[name = string("h_165_groups_0"), val = int32(512)]; + tensor h_165_strides_0 = const()[name = string("h_165_strides_0"), val = tensor([1])]; + tensor h_165_pad_0 = const()[name = string("h_165_pad_0"), val = tensor([0, 0])]; + tensor h_165_dilations_0 = const()[name = string("h_165_dilations_0"), val = tensor([1])]; + tensor last_convnext_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46462592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46464576))))[name = string("last_convnext_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46464768)))]; + tensor h_165_cast_fp16 = conv(bias = last_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_165_dilations_0, groups = h_165_groups_0, pad = h_165_pad_0, pad_type = h_165_pad_type_0, strides = h_165_strides_0, weight = last_convnext_3_dwconv__conv_weight_to_fp16_palettized, x = input_425_cast_fp16)[name = string("h_165_cast_fp16")]; + tensor x_cast_fp16 = mul(x = h_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2557_axes_0 = const()[name = string("op_2557_axes_0"), val = tensor([-1])]; + tensor last_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46465856)))]; + tensor last_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46466944)))]; + fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_cast_fp16)[name = string("transpose_1")]; + tensor var_2557_cast_fp16 = layer_norm(axes = var_2557_axes_0, beta = last_convnext_3_norm_norm_bias_to_fp16, epsilon = var_2511_to_fp16, gamma = last_convnext_3_norm_norm_weight_to_fp16, x = input_427_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor input_429_perm_0 = const()[name = string("input_429_perm_0"), val = tensor([0, 2, 1])]; + string h_167_pad_type_0 = const()[name = string("h_167_pad_type_0"), val = string("valid")]; + tensor h_167_strides_0 = const()[name = string("h_167_strides_0"), val = tensor([1])]; + tensor h_167_pad_0 = const()[name = string("h_167_pad_0"), val = tensor([0, 0])]; + tensor h_167_dilations_0 = const()[name = string("h_167_dilations_0"), val = tensor([1])]; + int32 h_167_groups_0 = const()[name = string("h_167_groups_0"), val = int32(1)]; + tensor last_convnext_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46468032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47254528))))[name = string("last_convnext_3_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47254720)))]; + tensor input_429_cast_fp16 = transpose(perm = input_429_perm_0, x = var_2557_cast_fp16)[name = string("transpose_0")]; + tensor h_167_cast_fp16 = conv(bias = last_convnext_3_pwconv1_bias_to_fp16, dilations = h_167_dilations_0, groups = h_167_groups_0, pad = h_167_pad_0, pad_type = h_167_pad_type_0, strides = h_167_strides_0, weight = last_convnext_3_pwconv1_weight_to_fp16_palettized, x = input_429_cast_fp16)[name = string("h_167_cast_fp16")]; + string input_431_mode_0 = const()[name = string("input_431_mode_0"), val = string("EXACT")]; + tensor input_431_cast_fp16 = gelu(mode = input_431_mode_0, x = h_167_cast_fp16)[name = string("input_431_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor op_2574_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47258880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48045376))))[name = string("op_2574_weight_0_to_fp16_palettized")]; + tensor var_2574_bias_0_to_fp16 = const()[name = string("op_2574_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48045568)))]; + tensor var_2574_cast_fp16 = conv(bias = var_2574_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = op_2574_weight_0_to_fp16_palettized, x = input_431_cast_fp16)[name = string("op_2574_cast_fp16")]; + tensor out_cast_fp16 = add(x = x_223_cast_fp16, y = var_2574_cast_fp16)[name = string("out_cast_fp16")]; + tensor input_cast_fp16 = mul(x = out_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("input_cast_fp16")]; + string var_2587_pad_type_0 = const()[name = string("op_2587_pad_type_0"), val = string("valid")]; + tensor var_2587_strides_0 = const()[name = string("op_2587_strides_0"), val = tensor([1])]; + tensor var_2587_pad_0 = const()[name = string("op_2587_pad_0"), val = tensor([0, 0])]; + tensor var_2587_dilations_0 = const()[name = string("op_2587_dilations_0"), val = tensor([1])]; + int32 var_2587_groups_0 = const()[name = string("op_2587_groups_0"), val = int32(1)]; + tensor proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48046656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48102016))))[name = string("proj_out_weight_to_fp16_palettized")]; + tensor var_2587_cast_fp16 = conv(dilations = var_2587_dilations_0, groups = var_2587_groups_0, pad = var_2587_pad_0, pad_type = var_2587_pad_type_0, strides = var_2587_strides_0, weight = proj_out_weight_to_fp16_palettized, x = input_cast_fp16)[name = string("op_2587_cast_fp16")]; + tensor v_cast_fp16 = mul(x = var_2587_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("v_cast_fp16")]; + tensor cond_begin_0 = const()[name = string("cond_begin_0"), val = tensor([0, 0, 0])]; + tensor cond_end_0 = const()[name = string("cond_end_0"), val = tensor([1, 144, 256])]; + tensor cond_end_mask_0 = const()[name = string("cond_end_mask_0"), val = tensor([false, true, true])]; + tensor cond_cast_fp16 = slice_by_index(begin = cond_begin_0, end = cond_end_0, end_mask = cond_end_mask_0, x = v_cast_fp16)[name = string("cond_cast_fp16")]; + tensor uncond_begin_0 = const()[name = string("uncond_begin_0"), val = tensor([1, 0, 0])]; + tensor uncond_end_0 = const()[name = string("uncond_end_0"), val = tensor([2, 144, 256])]; + tensor uncond_end_mask_0 = const()[name = string("uncond_end_mask_0"), val = tensor([true, true, true])]; + tensor uncond_cast_fp16 = slice_by_index(begin = uncond_begin_0, end = uncond_end_0, end_mask = uncond_end_mask_0, x = v_cast_fp16)[name = string("uncond_cast_fp16")]; + fp32 var_2597_epsilon_0 = const()[name = string("op_2597_epsilon_0"), val = fp32(0x1.a36e2ep-14)]; + tensor var_2597_cast_fp16 = inverse(epsilon = var_2597_epsilon_0, x = total_step_to_fp16)[name = string("op_2597_cast_fp16")]; + tensor var_2603 = const()[name = string("op_2603"), val = tensor([-1, 1, 1])]; + tensor step_cast_fp16 = reshape(shape = var_2603, x = var_2597_cast_fp16)[name = string("step_cast_fp16")]; + fp16 var_2605_to_fp16 = const()[name = string("op_2605_to_fp16"), val = fp16(0x1p+2)]; + tensor var_2606_cast_fp16 = mul(x = cond_cast_fp16, y = var_2605_to_fp16)[name = string("op_2606_cast_fp16")]; + fp16 var_2607_to_fp16 = const()[name = string("op_2607_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_2608_cast_fp16 = mul(x = uncond_cast_fp16, y = var_2607_to_fp16)[name = string("op_2608_cast_fp16")]; + tensor var_2610_cast_fp16 = sub(x = var_2606_cast_fp16, y = var_2608_cast_fp16)[name = string("op_2610_cast_fp16")]; + tensor var_2611_cast_fp16 = mul(x = step_cast_fp16, y = var_2610_cast_fp16)[name = string("op_2611_cast_fp16")]; + tensor var_2613_cast_fp16 = add(x = noisy_latent_to_fp16, y = var_2611_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor var_2614_cast_fp16 = mul(x = var_2613_cast_fp16, y = latent_mask_to_fp16)[name = string("op_2614_cast_fp16")]; + string var_2614_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2614_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor denoised_latent = cast(dtype = var_2614_cast_fp16_to_fp32_dtype_0, x = var_2614_cast_fp16)[name = string("cast_0")]; + } -> (denoised_latent); +} \ No newline at end of file diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlmodelc/weights/weight.bin b/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..6b77b328a12f2992fa9803aca58061621fe9e401 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c420cab08b8d875652e366c2e9a9cdcc3d5845ba4f2a944330fc7d6ede4bbb2e +size 48102208 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlpackage/Data/com.apple.CoreML/model.mlmodel b/VectorEstimatorVariants/VectorEstimator_v2_L256_int6.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 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a/VectorEstimatorVariants/VectorEstimator_v2_L256_int8.mlmodelc/model.mil b/VectorEstimatorVariants/VectorEstimator_v2_L256_int8.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..9701519c1467c828c1e1e207fa9dd6870a7cb9df --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L256_int8.mlmodelc/model.mil @@ -0,0 +1,2102 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor current_step, tensor latent_mask, tensor noisy_latent, tensor style_ttl, tensor text_emb, tensor text_mask, tensor total_step) { + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + bool text_emb_interleave_0 = const()[name = string("text_emb_interleave_0"), val = bool(false)]; + string text_emb_to_fp16_dtype_0 = const()[name = string("text_emb_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_uncond_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor([[[0x1.9b8p-10]]]))[name = string("text_uncond_to_fp16_quantized")]; + tensor text_emb_to_fp16 = cast(dtype = text_emb_to_fp16_dtype_0, x = text_emb)[name = string("cast_7")]; + tensor text_emb_cast_fp16 = concat(axis = var_39, interleave = text_emb_interleave_0, values = (text_emb_to_fp16, text_uncond_to_fp16_quantized))[name = string("text_emb_cast_fp16")]; + bool input_95_interleave_0 = const()[name = string("input_95_interleave_0"), val = bool(false)]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor uncond_masker_style_value_special_token_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32896))), scale = tensor([[[0x1.bb4p-9]]]))[name = string("uncond_masker_style_value_special_token_to_fp16_quantized")]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_6")]; + tensor input_95_cast_fp16 = concat(axis = var_39, interleave = input_95_interleave_0, values = (style_ttl_to_fp16, uncond_masker_style_value_special_token_to_fp16_quantized))[name = string("input_95_cast_fp16")]; + int32 var_64 = const()[name = string("op_64"), val = int32(0)]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + string noisy_latent_to_fp16_dtype_0 = const()[name = string("noisy_latent_to_fp16_dtype_0"), val = string("fp16")]; + tensor noisy_latent_to_fp16 = cast(dtype = noisy_latent_to_fp16_dtype_0, x = noisy_latent)[name = string("cast_5")]; + tensor input_5_cast_fp16 = concat(axis = var_64, interleave = input_5_interleave_0, values = (noisy_latent_to_fp16, noisy_latent_to_fp16))[name = string("input_5_cast_fp16")]; + int32 var_67 = const()[name = string("op_67"), val = int32(0)]; + bool latent_mask_b_interleave_0 = const()[name = string("latent_mask_b_interleave_0"), val = bool(false)]; + string latent_mask_to_fp16_dtype_0 = const()[name = string("latent_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor latent_mask_to_fp16 = cast(dtype = latent_mask_to_fp16_dtype_0, x = latent_mask)[name = string("cast_4")]; + tensor latent_mask_b_cast_fp16 = concat(axis = var_67, interleave = latent_mask_b_interleave_0, values = (latent_mask_to_fp16, latent_mask_to_fp16))[name = string("latent_mask_b_cast_fp16")]; + int32 var_70 = const()[name = string("op_70"), val = int32(0)]; + bool text_mask_interleave_0 = const()[name = string("text_mask_interleave_0"), val = bool(false)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_3")]; + tensor text_mask_cast_fp16 = concat(axis = var_70, interleave = text_mask_interleave_0, values = (text_mask_to_fp16, text_mask_to_fp16))[name = string("text_mask_cast_fp16")]; + string current_step_to_fp16_dtype_0 = const()[name = string("current_step_to_fp16_dtype_0"), val = string("fp16")]; + string total_step_to_fp16_dtype_0 = const()[name = string("total_step_to_fp16_dtype_0"), val = string("fp16")]; + tensor current_step_to_fp16 = cast(dtype = current_step_to_fp16_dtype_0, x = current_step)[name = string("cast_1")]; + tensor total_step_to_fp16 = cast(dtype = total_step_to_fp16_dtype_0, x = total_step)[name = string("cast_2")]; + tensor t_1_cast_fp16 = real_div(x = current_step_to_fp16, y = total_step_to_fp16)[name = string("t_1_cast_fp16")]; + int32 var_74 = const()[name = string("op_74"), val = int32(0)]; + bool t_3_interleave_0 = const()[name = string("t_3_interleave_0"), val = bool(false)]; + tensor t_3_cast_fp16 = concat(axis = var_74, interleave = t_3_interleave_0, values = (t_1_cast_fp16, t_1_cast_fp16))[name = string("t_3_cast_fp16")]; + int32 var_78 = const()[name = string("op_78"), val = int32(-1)]; + tensor var_85 = const()[name = string("op_85"), val = tensor([-1, 1])]; + tensor var_86_cast_fp16 = reshape(shape = var_85, x = t_3_cast_fp16)[name = string("op_86_cast_fp16")]; + fp16 time_encoder_time_scale_to_fp16 = const()[name = string("time_encoder_time_scale_to_fp16"), val = fp16(0x1.f4p+9)]; + tensor t_scaled_cast_fp16 = mul(x = var_86_cast_fp16, y = time_encoder_time_scale_to_fp16)[name = string("t_scaled_cast_fp16")]; + tensor time_encoder_omegas_to_fp16 = const()[name = string("time_encoder_omegas_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45760)))]; + tensor phase_cast_fp16 = mul(x = t_scaled_cast_fp16, y = time_encoder_omegas_to_fp16)[name = string("phase_cast_fp16")]; + tensor var_89_cast_fp16 = sin(x = phase_cast_fp16)[name = string("op_89_cast_fp16")]; + tensor var_90_cast_fp16 = cos(x = phase_cast_fp16)[name = string("op_90_cast_fp16")]; + bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)]; + tensor input_1_cast_fp16 = concat(axis = var_78, interleave = input_1_interleave_0, values = (var_89_cast_fp16, var_90_cast_fp16))[name = string("input_1_cast_fp16")]; + tensor time_encoder_mlp_0_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62336))))[name = string("time_encoder_mlp_0_linear_weight_to_fp16_quantized")]; + tensor time_encoder_mlp_0_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62912)))]; + tensor linear_0_cast_fp16 = linear(bias = time_encoder_mlp_0_linear_bias_to_fp16, weight = time_encoder_mlp_0_linear_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_97_cast_fp16 = softplus(x = linear_0_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = tanh(x = var_97_cast_fp16)[name = string("op_98_cast_fp16")]; + tensor input_3_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_98_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor time_encoder_mlp_2_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79936))))[name = string("time_encoder_mlp_2_linear_weight_to_fp16_quantized")]; + tensor time_encoder_mlp_2_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80128)))]; + tensor linear_1_cast_fp16 = linear(bias = time_encoder_mlp_2_linear_bias_to_fp16, weight = time_encoder_mlp_2_linear_weight_to_fp16_quantized, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor time_emb_axes_0 = const()[name = string("time_emb_axes_0"), val = tensor([-1])]; + tensor time_emb_cast_fp16 = expand_dims(axes = time_emb_axes_0, x = linear_1_cast_fp16)[name = string("time_emb_cast_fp16")]; + string var_115_pad_type_0 = const()[name = string("op_115_pad_type_0"), val = string("valid")]; + tensor var_115_strides_0 = const()[name = string("op_115_strides_0"), val = tensor([1])]; + tensor var_115_pad_0 = const()[name = string("op_115_pad_0"), val = tensor([0, 0])]; + tensor var_115_dilations_0 = const()[name = string("op_115_dilations_0"), val = tensor([1])]; + int32 var_115_groups_0 = const()[name = string("op_115_groups_0"), val = int32(1)]; + tensor proj_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154112))))[name = string("proj_in_weight_to_fp16_quantized")]; + tensor var_115_cast_fp16 = conv(dilations = var_115_dilations_0, groups = var_115_groups_0, pad = var_115_pad_0, pad_type = var_115_pad_type_0, strides = var_115_strides_0, weight = proj_in_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = var_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_3_cast_fp16")]; + int32 var_134 = const()[name = string("op_134"), val = int32(-1)]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([0, 0, 1])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 256])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([true, true, true])]; + tensor input_7_cast_fp16 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, x = latent_mask_b_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_166_pad_0 = const()[name = string("op_166_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_166_mode_0 = const()[name = string("op_166_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_166_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_166_mode_0, pad = var_166_pad_0, x = input_7_cast_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_133_to_fp16 = const()[name = string("op_133_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = sub(x = var_133_to_fp16, y = var_166_cast_fp16)[name = string("op_167_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = latent_mask_b_cast_fp16, y = var_167_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_169_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_172_cast_fp16 = mul(x = x_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor var_173_cast_fp16 = sub(x = var_133_to_fp16, y = latent_mask_b_cast_fp16)[name = string("op_173_cast_fp16")]; + tensor var_174_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_174_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_174_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("replicate")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor input_11_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_11_mode_0, pad = input_11_pad_0, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(512)]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157824))))[name = string("main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158912)))]; + tensor h_3_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, 2, 1])]; + tensor var_192_axes_0 = const()[name = string("op_192_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160000)))]; + tensor main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161088)))]; + fp16 var_139_to_fp16 = const()[name = string("op_139_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_7_cast_fp16)[name = string("transpose_117")]; + tensor var_192_cast_fp16 = layer_norm(axes = var_192_axes_0, beta = main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16, x = input_13_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, 2, 1])]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1210816))))[name = string("main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214976)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = var_192_cast_fp16)[name = string("transpose_116")]; + tensor h_5_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("h_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = h_5_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)]; + tensor op_209_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1219136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2267776))))[name = string("op_209_weight_0_to_fp16_quantized")]; + tensor var_209_bias_0_to_fp16 = const()[name = string("op_209_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2268864)))]; + tensor var_209_cast_fp16 = conv(bias = var_209_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = op_209_weight_0_to_fp16_quantized, x = input_17_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_209_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_223_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_223_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_226_cast_fp16 = mul(x = x_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_226_cast_fp16")]; + tensor var_228_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_228_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_226_cast_fp16, y = var_228_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")]; + fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)]; + tensor input_23_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([2])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(512)]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2269952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2272576))))[name = string("main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2273664)))]; + tensor h_9_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor var_246_axes_0 = const()[name = string("op_246_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2274752)))]; + tensor main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2275840)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_13_cast_fp16)[name = string("transpose_115")]; + tensor var_246_cast_fp16 = layer_norm(axes = var_246_axes_0, beta = main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_246_cast_fp16")]; + tensor input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor([0, 2, 1])]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2276928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3325568))))[name = string("main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3329728)))]; + tensor input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_246_cast_fp16)[name = string("transpose_114")]; + tensor h_11_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("h_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_11_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)]; + tensor op_263_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3333888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4382528))))[name = string("op_263_weight_0_to_fp16_quantized")]; + tensor var_263_bias_0_to_fp16 = const()[name = string("op_263_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4383616)))]; + tensor var_263_cast_fp16 = conv(bias = var_263_bias_0_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = op_263_weight_0_to_fp16_quantized, x = input_29_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_263_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_277_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_277_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_277_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_280_cast_fp16 = mul(x = x_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_280_cast_fp16")]; + tensor var_282_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_280_cast_fp16, y = var_282_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("replicate")]; + fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)]; + tensor input_35_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = input_35_mode_0, pad = input_35_pad_0, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([4])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(512)]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4384704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4387328))))[name = string("main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4388416)))]; + tensor h_15_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor var_300_axes_0 = const()[name = string("op_300_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4389504)))]; + tensor main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4390592)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_19_cast_fp16)[name = string("transpose_113")]; + tensor var_300_cast_fp16 = layer_norm(axes = var_300_axes_0, beta = main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16, x = input_37_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor([0, 2, 1])]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4391680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5440320))))[name = string("main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5444480)))]; + tensor input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_300_cast_fp16)[name = string("transpose_112")]; + tensor h_17_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("h_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = h_17_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)]; + tensor op_317_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5448640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6497280))))[name = string("op_317_weight_0_to_fp16_quantized")]; + tensor var_317_bias_0_to_fp16 = const()[name = string("op_317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6498368)))]; + tensor var_317_cast_fp16 = conv(bias = var_317_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = op_317_weight_0_to_fp16_quantized, x = input_41_cast_fp16)[name = string("op_317_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_317_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor var_331_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_331_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_331_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = x_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor var_336_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_336_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_334_cast_fp16, y = var_336_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")]; + fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([8])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(512)]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6499456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6502080))))[name = string("main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6503168)))]; + tensor h_21_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6504256)))]; + tensor main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6505344)))]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_25_cast_fp16)[name = string("transpose_111")]; + tensor var_354_cast_fp16 = layer_norm(axes = var_354_axes_0, beta = main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_354_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6506432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7555072))))[name = string("main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7559232)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_354_cast_fp16)[name = string("transpose_110")]; + tensor h_23_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("h_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_23_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)]; + tensor op_371_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7563392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8612032))))[name = string("op_371_weight_0_to_fp16_quantized")]; + tensor var_371_bias_0_to_fp16 = const()[name = string("op_371_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8613120)))]; + tensor var_371_cast_fp16 = conv(bias = var_371_bias_0_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = op_371_weight_0_to_fp16_quantized, x = input_53_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_371_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor input_55_cast_fp16 = squeeze(axes = input_55_axes_0, x = time_emb_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor main_blocks_0_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8614208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8647040))))[name = string("main_blocks_0_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8648128)))]; + tensor linear_2_cast_fp16 = linear(bias = main_blocks_0_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_0_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor t_5_axes_0 = const()[name = string("t_5_axes_0"), val = tensor([-1])]; + tensor t_5_cast_fp16 = expand_dims(axes = t_5_axes_0, x = linear_2_cast_fp16)[name = string("t_5_cast_fp16")]; + tensor var_381_cast_fp16 = add(x = x_27_cast_fp16, y = t_5_cast_fp16)[name = string("op_381_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_381_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_394_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_394_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_394_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_397_cast_fp16 = mul(x = x_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = var_397_cast_fp16, y = var_399_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("replicate")]; + fp16 const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = fp16(0x0p+0)]; + tensor input_61_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = input_61_mode_0, pad = input_61_pad_0, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(512)]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8649216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8651840))))[name = string("main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8652928)))]; + tensor h_27_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_61_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417_axes_0 = const()[name = string("op_417_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8654016)))]; + tensor main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8655104)))]; + tensor input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_33_cast_fp16)[name = string("transpose_109")]; + tensor var_417_cast_fp16 = layer_norm(axes = var_417_axes_0, beta = main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16, x = input_63_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8656192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9704832))))[name = string("main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9708992)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = var_417_cast_fp16)[name = string("transpose_108")]; + tensor h_29_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("h_29_cast_fp16")]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("EXACT")]; + tensor input_67_cast_fp16 = gelu(mode = input_67_mode_0, x = h_29_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)]; + tensor op_434_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9713152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10761792))))[name = string("op_434_weight_0_to_fp16_quantized")]; + tensor var_434_bias_0_to_fp16 = const()[name = string("op_434_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10762880)))]; + tensor var_434_cast_fp16 = conv(bias = var_434_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = op_434_weight_0_to_fp16_quantized, x = input_67_cast_fp16)[name = string("op_434_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_434_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_m_1_cast_fp16 = mul(x = x_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_1_cast_fp16")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10763968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11026176))))[name = string("main_blocks_0_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11027264)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_35_cast_fp16)[name = string("transpose_107")]; + tensor linear_3_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([2, 256, 8, 64])]; + tensor var_453_cast_fp16 = reshape(shape = var_452, x = linear_3_cast_fp16)[name = string("op_453_cast_fp16")]; + tensor x_37_perm_0 = const()[name = string("x_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11028352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11159488))))[name = string("main_blocks_0_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11160576)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = text_emb_cast_fp16)[name = string("transpose_106")]; + tensor linear_4_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([2, 128, 8, 64])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = linear_4_cast_fp16)[name = string("op_461_cast_fp16")]; + tensor x_39_perm_0 = const()[name = string("x_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11161664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11292800))))[name = string("main_blocks_0_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11293888)))]; + tensor linear_5_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_468 = const()[name = string("op_468"), val = tensor([2, 128, 8, 64])]; + tensor var_469_cast_fp16 = reshape(shape = var_468, x = linear_5_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor divisor_1_axes_0 = const()[name = string("divisor_1_axes_0"), val = tensor([1, 2])]; + bool divisor_1_keep_dims_0 = const()[name = string("divisor_1_keep_dims_0"), val = bool(false)]; + tensor divisor_1_cast_fp16 = reduce_sum(axes = divisor_1_axes_0, keep_dims = divisor_1_keep_dims_0, x = latent_mask_b_cast_fp16)[name = string("divisor_1_cast_fp16")]; + tensor divisor_3_axes_0 = const()[name = string("divisor_3_axes_0"), val = tensor([1, 2])]; + bool divisor_3_keep_dims_0 = const()[name = string("divisor_3_keep_dims_0"), val = bool(false)]; + tensor divisor_3_cast_fp16 = reduce_sum(axes = divisor_3_axes_0, keep_dims = divisor_3_keep_dims_0, x = text_mask_cast_fp16)[name = string("divisor_3_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([-1, 1, 1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = divisor_1_cast_fp16)[name = string("op_488_cast_fp16")]; + tensor const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11294976)))]; + tensor scaled_1_cast_fp16 = real_div(x = const_16_to_fp16, y = var_488_cast_fp16)[name = string("scaled_1_cast_fp16")]; + tensor main_blocks_0_text_attn_theta_to_fp16 = const()[name = string("main_blocks_0_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11296064)))]; + tensor angles_1_cast_fp16 = mul(x = scaled_1_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_1_cast_fp16")]; + tensor var_491_cast_fp16 = cos(x = angles_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = var_491_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor var_493_cast_fp16 = sin(x = angles_1_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = var_493_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor x_a_1_begin_0 = const()[name = string("x_a_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_1_end_0 = const()[name = string("x_a_1_end_0"), val = tensor([2, 8, 256, 32])]; + tensor x_a_1_end_mask_0 = const()[name = string("x_a_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_37_cast_fp16 = transpose(perm = x_37_perm_0, x = var_453_cast_fp16)[name = string("transpose_105")]; + tensor x_a_1_cast_fp16 = slice_by_index(begin = x_a_1_begin_0, end = x_a_1_end_0, end_mask = x_a_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_a_1_cast_fp16")]; + tensor x_b_1_begin_0 = const()[name = string("x_b_1_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_1_end_0 = const()[name = string("x_b_1_end_0"), val = tensor([2, 8, 256, 64])]; + tensor x_b_1_end_mask_0 = const()[name = string("x_b_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_1_cast_fp16 = slice_by_index(begin = x_b_1_begin_0, end = x_b_1_end_0, end_mask = x_b_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_b_1_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = x_a_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor var_498_cast_fp16 = mul(x = x_b_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_498_cast_fp16")]; + tensor rot_a_1_cast_fp16 = sub(x = var_497_cast_fp16, y = var_498_cast_fp16)[name = string("rot_a_1_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = x_a_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = x_b_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor rot_b_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("rot_b_1_cast_fp16")]; + bool q_1_interleave_0 = const()[name = string("q_1_interleave_0"), val = bool(false)]; + tensor q_1_cast_fp16 = concat(axis = var_134, interleave = q_1_interleave_0, values = (rot_a_1_cast_fp16, rot_b_1_cast_fp16))[name = string("q_1_cast_fp16")]; + tensor var_505 = const()[name = string("op_505"), val = tensor([-1, 1, 1])]; + tensor var_506_cast_fp16 = reshape(shape = var_505, x = divisor_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11296192)))]; + tensor scaled_3_cast_fp16 = real_div(x = const_17_to_fp16, y = var_506_cast_fp16)[name = string("scaled_3_cast_fp16")]; + tensor angles_3_cast_fp16 = mul(x = scaled_3_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_3_cast_fp16")]; + tensor var_509_cast_fp16 = cos(x = angles_3_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor cos_3_axes_0 = const()[name = string("cos_3_axes_0"), val = tensor([1])]; + tensor cos_3_cast_fp16 = expand_dims(axes = cos_3_axes_0, x = var_509_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor var_511_cast_fp16 = sin(x = angles_3_cast_fp16)[name = string("op_511_cast_fp16")]; + tensor sin_3_axes_0 = const()[name = string("sin_3_axes_0"), val = tensor([1])]; + tensor sin_3_cast_fp16 = expand_dims(axes = sin_3_axes_0, x = var_511_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor x_a_3_begin_0 = const()[name = string("x_a_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_3_end_0 = const()[name = string("x_a_3_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_3_end_mask_0 = const()[name = string("x_a_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_39_cast_fp16 = transpose(perm = x_39_perm_0, x = var_461_cast_fp16)[name = string("transpose_104")]; + tensor x_a_3_cast_fp16 = slice_by_index(begin = x_a_3_begin_0, end = x_a_3_end_0, end_mask = x_a_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_a_3_cast_fp16")]; + tensor x_b_3_begin_0 = const()[name = string("x_b_3_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_3_end_0 = const()[name = string("x_b_3_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_3_end_mask_0 = const()[name = string("x_b_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_3_cast_fp16 = slice_by_index(begin = x_b_3_begin_0, end = x_b_3_end_0, end_mask = x_b_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_b_3_cast_fp16")]; + tensor var_515_cast_fp16 = mul(x = x_a_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_515_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = x_b_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor rot_a_3_cast_fp16 = sub(x = var_515_cast_fp16, y = var_516_cast_fp16)[name = string("rot_a_3_cast_fp16")]; + tensor var_518_cast_fp16 = mul(x = x_a_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = mul(x = x_b_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor rot_b_3_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = string("rot_b_3_cast_fp16")]; + bool k_1_interleave_0 = const()[name = string("k_1_interleave_0"), val = bool(false)]; + tensor k_1_cast_fp16 = concat(axis = var_134, interleave = k_1_interleave_0, values = (rot_a_3_cast_fp16, rot_b_3_cast_fp16))[name = string("k_1_cast_fp16")]; + bool var_524_transpose_x_1 = const()[name = string("op_524_transpose_x_1"), val = bool(false)]; + bool var_524_transpose_y_1 = const()[name = string("op_524_transpose_y_1"), val = bool(true)]; + tensor var_524_cast_fp16 = matmul(transpose_x = var_524_transpose_x_1, transpose_y = var_524_transpose_y_1, x = q_1_cast_fp16, y = k_1_cast_fp16)[name = string("op_524_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_524_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([2])]; + tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = text_mask_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528_cast_fp16 = sub(x = var_133_to_fp16, y = var_527_cast_fp16)[name = string("op_528_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_530_cast_fp16 = mul(x = var_528_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_75_cast_fp16 = sub(x = _inversed_scores_1_cast_fp16, y = var_530_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_134, x = input_75_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool var_533_transpose_x_0 = const()[name = string("op_533_transpose_x_0"), val = bool(false)]; + bool var_533_transpose_y_0 = const()[name = string("op_533_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_469_cast_fp16)[name = string("transpose_103")]; + tensor var_533_cast_fp16 = matmul(transpose_x = var_533_transpose_x_0, transpose_y = var_533_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("op_533_cast_fp16")]; + tensor var_534_perm_0 = const()[name = string("op_534_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_535 = const()[name = string("op_535"), val = tensor([2, 256, 512])]; + tensor var_534_cast_fp16 = transpose(perm = var_534_perm_0, x = var_533_cast_fp16)[name = string("transpose_102")]; + tensor input_77_cast_fp16 = reshape(shape = var_535, x = var_534_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11296768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11558976))))[name = string("main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11560064)))]; + tensor linear_6_cast_fp16 = linear(bias = main_blocks_0_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor out_11_perm_0 = const()[name = string("out_11_perm_0"), val = tensor([0, 2, 1])]; + tensor out_11_cast_fp16 = transpose(perm = out_11_perm_0, x = linear_6_cast_fp16)[name = string("transpose_101")]; + tensor var_542_cast_fp16 = mul(x = out_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_542_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = x_m_1_cast_fp16, y = var_542_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor var_549_axes_0 = const()[name = string("op_549_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11561152)))]; + tensor main_blocks_0_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11562240)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_41_cast_fp16)[name = string("transpose_100")]; + tensor var_549_cast_fp16 = layer_norm(axes = var_549_axes_0, beta = main_blocks_0_text_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_text_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor var_550_perm_0 = const()[name = string("op_550_perm_0"), val = tensor([0, 2, 1])]; + tensor var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = var_549_cast_fp16)[name = string("transpose_99")]; + tensor x_43_cast_fp16 = mul(x = var_550_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = x_45_cast_fp16, y = last_1_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor x_last_11_axes_0 = const()[name = string("x_last_11_axes_0"), val = tensor([-1])]; + bool x_last_11_keep_dims_0 = const()[name = string("x_last_11_keep_dims_0"), val = bool(true)]; + tensor x_last_11_cast_fp16 = reduce_sum(axes = x_last_11_axes_0, keep_dims = x_last_11_keep_dims_0, x = var_563_cast_fp16)[name = string("x_last_11_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = x_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor var_568_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_11_cast_fp16)[name = string("op_568_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = var_566_cast_fp16, y = var_568_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_85_mode_0 = const()[name = string("input_85_mode_0"), val = string("replicate")]; + fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)]; + tensor input_85_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(512)]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11563328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11565952))))[name = string("main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11567040)))]; + tensor h_33_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("h_33_cast_fp16")]; + tensor x_47_cast_fp16 = mul(x = h_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11568128)))]; + tensor main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11569216)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_47_cast_fp16)[name = string("transpose_98")]; + tensor var_586_cast_fp16 = layer_norm(axes = var_586_axes_0, beta = main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_586_cast_fp16")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11570304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12618944))))[name = string("main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12623104)))]; + tensor input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = var_586_cast_fp16)[name = string("transpose_97")]; + tensor h_35_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("h_35_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = h_35_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")]; + tensor h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor([1])]; + tensor h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor([0, 0])]; + tensor h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor([1])]; + int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)]; + tensor op_603_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12627264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13675904))))[name = string("op_603_weight_0_to_fp16_quantized")]; + tensor var_603_bias_0_to_fp16 = const()[name = string("op_603_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13676992)))]; + tensor var_603_cast_fp16 = conv(bias = var_603_bias_0_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = op_603_weight_0_to_fp16_quantized, x = input_91_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor out_13_cast_fp16 = add(x = x_45_cast_fp16, y = var_603_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_49_cast_fp16 = mul(x = out_13_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_m_3_cast_fp16 = mul(x = x_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_3_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13678080))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13809216))))[name = string("main_blocks_0_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13809792)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_49_cast_fp16)[name = string("transpose_96")]; + tensor linear_7_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([2, 256, 2, 128])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = linear_7_cast_fp16)[name = string("op_620_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_0_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13810368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13875968))))[name = string("main_blocks_0_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13876544)))]; + tensor linear_9_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_633 = const()[name = string("op_633"), val = tensor([2, 50, 2, 128])]; + tensor var_634_cast_fp16 = reshape(shape = var_633, x = linear_9_cast_fp16)[name = string("op_634_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_638_transpose_x_0 = const()[name = string("op_638_transpose_x_0"), val = bool(false)]; + bool var_638_transpose_y_0 = const()[name = string("op_638_transpose_y_0"), val = bool(false)]; + tensor op_637_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13877120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13902784))))[name = string("op_637_to_fp16_quantized")]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_620_cast_fp16)[name = string("transpose_95")]; + tensor var_638_cast_fp16 = matmul(transpose_x = var_638_transpose_x_0, transpose_y = var_638_transpose_y_0, x = q_3_cast_fp16, y = op_637_to_fp16_quantized)[name = string("op_638_cast_fp16")]; + fp16 _inversed_input_97_y_0_to_fp16 = const()[name = string("_inversed_input_97_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_97_cast_fp16 = mul(x = var_638_cast_fp16, y = _inversed_input_97_y_0_to_fp16)[name = string("_inversed_input_97_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_134, x = _inversed_input_97_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_642_perm_0 = const()[name = string("op_642_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_1_axes_0 = const()[name = string("mask_1_axes_0"), val = tensor([1])]; + tensor var_642_cast_fp16 = transpose(perm = var_642_perm_0, x = latent_mask_b_cast_fp16)[name = string("transpose_94")]; + tensor mask_1_cast_fp16 = expand_dims(axes = mask_1_axes_0, x = var_642_cast_fp16)[name = string("mask_1_cast_fp16")]; + tensor attn_5_cast_fp16 = mul(x = attn_3_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool var_645_transpose_x_0 = const()[name = string("op_645_transpose_x_0"), val = bool(false)]; + bool var_645_transpose_y_0 = const()[name = string("op_645_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_634_cast_fp16)[name = string("transpose_93")]; + tensor var_645_cast_fp16 = matmul(transpose_x = var_645_transpose_x_0, transpose_y = var_645_transpose_y_0, x = attn_5_cast_fp16, y = v_3_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_646_perm_0 = const()[name = string("op_646_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_647 = const()[name = string("op_647"), val = tensor([2, 256, 256])]; + tensor var_646_cast_fp16 = transpose(perm = var_646_perm_0, x = var_645_cast_fp16)[name = string("transpose_92")]; + tensor input_99_cast_fp16 = reshape(shape = var_647, x = var_646_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13902976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14034112))))[name = string("main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14035200)))]; + tensor linear_10_cast_fp16 = linear(bias = main_blocks_0_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor out_15_perm_0 = const()[name = string("out_15_perm_0"), val = tensor([0, 2, 1])]; + tensor out_15_cast_fp16 = transpose(perm = out_15_perm_0, x = linear_10_cast_fp16)[name = string("transpose_91")]; + tensor var_654_cast_fp16 = mul(x = out_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor x_51_cast_fp16 = add(x = x_m_3_cast_fp16, y = var_654_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14036288)))]; + tensor main_blocks_0_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14037376)))]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_51_cast_fp16)[name = string("transpose_90")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = main_blocks_0_style_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_style_norm_norm_weight_to_fp16, x = input_101_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_662_perm_0 = const()[name = string("op_662_perm_0"), val = tensor([0, 2, 1])]; + tensor var_662_cast_fp16 = transpose(perm = var_662_perm_0, x = var_661_cast_fp16)[name = string("transpose_89")]; + tensor x_53_cast_fp16 = mul(x = var_662_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_53_cast_fp16")]; + int32 var_681 = const()[name = string("op_681"), val = int32(-1)]; + tensor x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = x_55_cast_fp16, y = last_1_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor x_last_13_axes_0 = const()[name = string("x_last_13_axes_0"), val = tensor([-1])]; + bool x_last_13_keep_dims_0 = const()[name = string("x_last_13_keep_dims_0"), val = bool(true)]; + tensor x_last_13_cast_fp16 = reduce_sum(axes = x_last_13_axes_0, keep_dims = x_last_13_keep_dims_0, x = var_716_cast_fp16)[name = string("x_last_13_cast_fp16")]; + tensor var_719_cast_fp16 = mul(x = x_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_721_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_13_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_719_cast_fp16, y = var_721_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("replicate")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(512)]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14038464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14041088))))[name = string("main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14042176)))]; + tensor h_39_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor x_57_cast_fp16 = mul(x = h_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_739_axes_0 = const()[name = string("op_739_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14043264)))]; + tensor main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14044352)))]; + fp16 var_686_to_fp16 = const()[name = string("op_686_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_57_cast_fp16)[name = string("transpose_88")]; + tensor var_739_cast_fp16 = layer_norm(axes = var_739_axes_0, beta = main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_739_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + string h_41_pad_type_0 = const()[name = string("h_41_pad_type_0"), val = string("valid")]; + tensor h_41_strides_0 = const()[name = string("h_41_strides_0"), val = tensor([1])]; + tensor h_41_pad_0 = const()[name = string("h_41_pad_0"), val = tensor([0, 0])]; + tensor h_41_dilations_0 = const()[name = string("h_41_dilations_0"), val = tensor([1])]; + int32 h_41_groups_0 = const()[name = string("h_41_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14045440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15094080))))[name = string("main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15098240)))]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = var_739_cast_fp16)[name = string("transpose_87")]; + tensor h_41_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_41_dilations_0, groups = h_41_groups_0, pad = h_41_pad_0, pad_type = h_41_pad_type_0, strides = h_41_strides_0, weight = main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = h_41_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor op_756_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15102400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16151040))))[name = string("op_756_weight_0_to_fp16_quantized")]; + tensor var_756_bias_0_to_fp16 = const()[name = string("op_756_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16152128)))]; + tensor var_756_cast_fp16 = conv(bias = var_756_bias_0_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = op_756_weight_0_to_fp16_quantized, x = input_113_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor out_17_cast_fp16 = add(x = x_55_cast_fp16, y = var_756_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_59_cast_fp16 = mul(x = out_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor x_61_cast_fp16 = mul(x = x_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_770_cast_fp16 = mul(x = x_61_cast_fp16, y = last_1_cast_fp16)[name = string("op_770_cast_fp16")]; + tensor x_last_15_axes_0 = const()[name = string("x_last_15_axes_0"), val = tensor([-1])]; + bool x_last_15_keep_dims_0 = const()[name = string("x_last_15_keep_dims_0"), val = bool(true)]; + tensor x_last_15_cast_fp16 = reduce_sum(axes = x_last_15_axes_0, keep_dims = x_last_15_keep_dims_0, x = var_770_cast_fp16)[name = string("x_last_15_cast_fp16")]; + tensor var_773_cast_fp16 = mul(x = x_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor var_775_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_15_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = var_773_cast_fp16, y = var_775_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("replicate")]; + fp16 const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = fp16(0x0p+0)]; + tensor input_119_cast_fp16 = pad(constant_val = const_26_to_fp16, mode = input_119_mode_0, pad = input_119_pad_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")]; + string h_45_pad_type_0 = const()[name = string("h_45_pad_type_0"), val = string("valid")]; + tensor h_45_dilations_0 = const()[name = string("h_45_dilations_0"), val = tensor([2])]; + int32 h_45_groups_0 = const()[name = string("h_45_groups_0"), val = int32(512)]; + tensor h_45_strides_0 = const()[name = string("h_45_strides_0"), val = tensor([1])]; + tensor h_45_pad_0 = const()[name = string("h_45_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16153216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16155840))))[name = string("main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16156928)))]; + tensor h_45_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_45_dilations_0, groups = h_45_groups_0, pad = h_45_pad_0, pad_type = h_45_pad_type_0, strides = h_45_strides_0, weight = main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = h_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16158016)))]; + tensor main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16159104)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_63_cast_fp16)[name = string("transpose_86")]; + tensor var_793_cast_fp16 = layer_norm(axes = var_793_axes_0, beta = main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor input_123_perm_0 = const()[name = string("input_123_perm_0"), val = tensor([0, 2, 1])]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16160192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17208832))))[name = string("main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17212992)))]; + tensor input_123_cast_fp16 = transpose(perm = input_123_perm_0, x = var_793_cast_fp16)[name = string("transpose_85")]; + tensor h_47_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_123_cast_fp16)[name = string("h_47_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = h_47_cast_fp16)[name = string("input_125_cast_fp16")]; + string h_49_pad_type_0 = const()[name = string("h_49_pad_type_0"), val = string("valid")]; + tensor h_49_strides_0 = const()[name = string("h_49_strides_0"), val = tensor([1])]; + tensor h_49_pad_0 = const()[name = string("h_49_pad_0"), val = tensor([0, 0])]; + tensor h_49_dilations_0 = const()[name = string("h_49_dilations_0"), val = tensor([1])]; + int32 h_49_groups_0 = const()[name = string("h_49_groups_0"), val = int32(1)]; + tensor op_810_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17217152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18265792))))[name = string("op_810_weight_0_to_fp16_quantized")]; + tensor var_810_bias_0_to_fp16 = const()[name = string("op_810_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18266880)))]; + tensor var_810_cast_fp16 = conv(bias = var_810_bias_0_to_fp16, dilations = h_49_dilations_0, groups = h_49_groups_0, pad = h_49_pad_0, pad_type = h_49_pad_type_0, strides = h_49_strides_0, weight = op_810_weight_0_to_fp16_quantized, x = input_125_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = x_61_cast_fp16, y = var_810_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor x_65_cast_fp16 = mul(x = out_19_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = x_67_cast_fp16, y = last_1_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_last_17_axes_0 = const()[name = string("x_last_17_axes_0"), val = tensor([-1])]; + bool x_last_17_keep_dims_0 = const()[name = string("x_last_17_keep_dims_0"), val = bool(true)]; + tensor x_last_17_cast_fp16 = reduce_sum(axes = x_last_17_axes_0, keep_dims = x_last_17_keep_dims_0, x = var_824_cast_fp16)[name = string("x_last_17_cast_fp16")]; + tensor var_827_cast_fp16 = mul(x = x_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_829_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_17_cast_fp16)[name = string("op_829_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = var_827_cast_fp16, y = var_829_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("replicate")]; + fp16 const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = fp16(0x0p+0)]; + tensor input_131_cast_fp16 = pad(constant_val = const_28_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + string h_51_pad_type_0 = const()[name = string("h_51_pad_type_0"), val = string("valid")]; + tensor h_51_dilations_0 = const()[name = string("h_51_dilations_0"), val = tensor([4])]; + int32 h_51_groups_0 = const()[name = string("h_51_groups_0"), val = int32(512)]; + tensor h_51_strides_0 = const()[name = string("h_51_strides_0"), val = tensor([1])]; + tensor h_51_pad_0 = const()[name = string("h_51_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18267968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18270592))))[name = string("main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18271680)))]; + tensor h_51_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_51_dilations_0, groups = h_51_groups_0, pad = h_51_pad_0, pad_type = h_51_pad_type_0, strides = h_51_strides_0, weight = main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor x_69_cast_fp16 = mul(x = h_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_847_axes_0 = const()[name = string("op_847_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18272768)))]; + tensor main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18273856)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_69_cast_fp16)[name = string("transpose_84")]; + tensor var_847_cast_fp16 = layer_norm(axes = var_847_axes_0, beta = main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_847_cast_fp16")]; + tensor input_135_perm_0 = const()[name = string("input_135_perm_0"), val = tensor([0, 2, 1])]; + string h_53_pad_type_0 = const()[name = string("h_53_pad_type_0"), val = string("valid")]; + tensor h_53_strides_0 = const()[name = string("h_53_strides_0"), val = tensor([1])]; + tensor h_53_pad_0 = const()[name = string("h_53_pad_0"), val = tensor([0, 0])]; + tensor h_53_dilations_0 = const()[name = string("h_53_dilations_0"), val = tensor([1])]; + int32 h_53_groups_0 = const()[name = string("h_53_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18274944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19323584))))[name = string("main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19327744)))]; + tensor input_135_cast_fp16 = transpose(perm = input_135_perm_0, x = var_847_cast_fp16)[name = string("transpose_83")]; + tensor h_53_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_53_dilations_0, groups = h_53_groups_0, pad = h_53_pad_0, pad_type = h_53_pad_type_0, strides = h_53_strides_0, weight = main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("h_53_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = h_53_cast_fp16)[name = string("input_137_cast_fp16")]; + string h_55_pad_type_0 = const()[name = string("h_55_pad_type_0"), val = string("valid")]; + tensor h_55_strides_0 = const()[name = string("h_55_strides_0"), val = tensor([1])]; + tensor h_55_pad_0 = const()[name = string("h_55_pad_0"), val = tensor([0, 0])]; + tensor h_55_dilations_0 = const()[name = string("h_55_dilations_0"), val = tensor([1])]; + int32 h_55_groups_0 = const()[name = string("h_55_groups_0"), val = int32(1)]; + tensor op_864_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19331904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20380544))))[name = string("op_864_weight_0_to_fp16_quantized")]; + tensor var_864_bias_0_to_fp16 = const()[name = string("op_864_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20381632)))]; + tensor var_864_cast_fp16 = conv(bias = var_864_bias_0_to_fp16, dilations = h_55_dilations_0, groups = h_55_groups_0, pad = h_55_pad_0, pad_type = h_55_pad_type_0, strides = h_55_strides_0, weight = op_864_weight_0_to_fp16_quantized, x = input_137_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor out_21_cast_fp16 = add(x = x_67_cast_fp16, y = var_864_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = out_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor x_73_cast_fp16 = mul(x = x_71_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_878_cast_fp16 = mul(x = x_73_cast_fp16, y = last_1_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor x_last_19_axes_0 = const()[name = string("x_last_19_axes_0"), val = tensor([-1])]; + bool x_last_19_keep_dims_0 = const()[name = string("x_last_19_keep_dims_0"), val = bool(true)]; + tensor x_last_19_cast_fp16 = reduce_sum(axes = x_last_19_axes_0, keep_dims = x_last_19_keep_dims_0, x = var_878_cast_fp16)[name = string("x_last_19_cast_fp16")]; + tensor var_881_cast_fp16 = mul(x = x_73_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_881_cast_fp16")]; + tensor var_883_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_19_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = var_881_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("replicate")]; + fp16 const_30_to_fp16 = const()[name = string("const_30_to_fp16"), val = fp16(0x0p+0)]; + tensor input_143_cast_fp16 = pad(constant_val = const_30_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + string h_57_pad_type_0 = const()[name = string("h_57_pad_type_0"), val = string("valid")]; + tensor h_57_dilations_0 = const()[name = string("h_57_dilations_0"), val = tensor([8])]; + int32 h_57_groups_0 = const()[name = string("h_57_groups_0"), val = int32(512)]; + tensor h_57_strides_0 = const()[name = string("h_57_strides_0"), val = tensor([1])]; + tensor h_57_pad_0 = const()[name = string("h_57_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20382720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20385344))))[name = string("main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20386432)))]; + tensor h_57_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_57_dilations_0, groups = h_57_groups_0, pad = h_57_pad_0, pad_type = h_57_pad_type_0, strides = h_57_strides_0, weight = main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_143_cast_fp16)[name = string("h_57_cast_fp16")]; + tensor x_75_cast_fp16 = mul(x = h_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_901_axes_0 = const()[name = string("op_901_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20387520)))]; + tensor main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20388608)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_75_cast_fp16)[name = string("transpose_82")]; + tensor var_901_cast_fp16 = layer_norm(axes = var_901_axes_0, beta = main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor input_147_perm_0 = const()[name = string("input_147_perm_0"), val = tensor([0, 2, 1])]; + string h_59_pad_type_0 = const()[name = string("h_59_pad_type_0"), val = string("valid")]; + tensor h_59_strides_0 = const()[name = string("h_59_strides_0"), val = tensor([1])]; + tensor h_59_pad_0 = const()[name = string("h_59_pad_0"), val = tensor([0, 0])]; + tensor h_59_dilations_0 = const()[name = string("h_59_dilations_0"), val = tensor([1])]; + int32 h_59_groups_0 = const()[name = string("h_59_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20389696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21438336))))[name = string("main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21442496)))]; + tensor input_147_cast_fp16 = transpose(perm = input_147_perm_0, x = var_901_cast_fp16)[name = string("transpose_81")]; + tensor h_59_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_59_dilations_0, groups = h_59_groups_0, pad = h_59_pad_0, pad_type = h_59_pad_type_0, strides = h_59_strides_0, weight = main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_147_cast_fp16)[name = string("h_59_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = h_59_cast_fp16)[name = string("input_149_cast_fp16")]; + string h_61_pad_type_0 = const()[name = string("h_61_pad_type_0"), val = string("valid")]; + tensor h_61_strides_0 = const()[name = string("h_61_strides_0"), val = tensor([1])]; + tensor h_61_pad_0 = const()[name = string("h_61_pad_0"), val = tensor([0, 0])]; + tensor h_61_dilations_0 = const()[name = string("h_61_dilations_0"), val = tensor([1])]; + int32 h_61_groups_0 = const()[name = string("h_61_groups_0"), val = int32(1)]; + tensor op_918_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21446656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22495296))))[name = string("op_918_weight_0_to_fp16_quantized")]; + tensor var_918_bias_0_to_fp16 = const()[name = string("op_918_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22496384)))]; + tensor var_918_cast_fp16 = conv(bias = var_918_bias_0_to_fp16, dilations = h_61_dilations_0, groups = h_61_groups_0, pad = h_61_pad_0, pad_type = h_61_pad_type_0, strides = h_61_strides_0, weight = op_918_weight_0_to_fp16_quantized, x = input_149_cast_fp16)[name = string("op_918_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = x_73_cast_fp16, y = var_918_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor x_77_cast_fp16 = mul(x = out_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor main_blocks_1_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22497472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22530304))))[name = string("main_blocks_1_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22531392)))]; + tensor linear_11_cast_fp16 = linear(bias = main_blocks_1_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_1_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor t_7_axes_0 = const()[name = string("t_7_axes_0"), val = tensor([-1])]; + tensor t_7_cast_fp16 = expand_dims(axes = t_7_axes_0, x = linear_11_cast_fp16)[name = string("t_7_cast_fp16")]; + tensor var_928_cast_fp16 = add(x = x_77_cast_fp16, y = t_7_cast_fp16)[name = string("op_928_cast_fp16")]; + tensor x_79_cast_fp16 = mul(x = var_928_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor x_81_cast_fp16 = mul(x = x_79_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = x_81_cast_fp16, y = last_1_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor x_last_21_axes_0 = const()[name = string("x_last_21_axes_0"), val = tensor([-1])]; + bool x_last_21_keep_dims_0 = const()[name = string("x_last_21_keep_dims_0"), val = bool(true)]; + tensor x_last_21_cast_fp16 = reduce_sum(axes = x_last_21_axes_0, keep_dims = x_last_21_keep_dims_0, x = var_941_cast_fp16)[name = string("x_last_21_cast_fp16")]; + tensor var_944_cast_fp16 = mul(x = x_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor var_946_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_21_cast_fp16)[name = string("op_946_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = var_944_cast_fp16, y = var_946_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("replicate")]; + fp16 const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = fp16(0x0p+0)]; + tensor input_157_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string h_63_pad_type_0 = const()[name = string("h_63_pad_type_0"), val = string("valid")]; + int32 h_63_groups_0 = const()[name = string("h_63_groups_0"), val = int32(512)]; + tensor h_63_strides_0 = const()[name = string("h_63_strides_0"), val = tensor([1])]; + tensor h_63_pad_0 = const()[name = string("h_63_pad_0"), val = tensor([0, 0])]; + tensor h_63_dilations_0 = const()[name = string("h_63_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22532480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22535104))))[name = string("main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22536192)))]; + tensor h_63_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_63_dilations_0, groups = h_63_groups_0, pad = h_63_pad_0, pad_type = h_63_pad_type_0, strides = h_63_strides_0, weight = main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_157_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor x_83_cast_fp16 = mul(x = h_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor var_964_axes_0 = const()[name = string("op_964_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22537280)))]; + tensor main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22538368)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_83_cast_fp16)[name = string("transpose_80")]; + tensor var_964_cast_fp16 = layer_norm(axes = var_964_axes_0, beta = main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + string h_65_pad_type_0 = const()[name = string("h_65_pad_type_0"), val = string("valid")]; + tensor h_65_strides_0 = const()[name = string("h_65_strides_0"), val = tensor([1])]; + tensor h_65_pad_0 = const()[name = string("h_65_pad_0"), val = tensor([0, 0])]; + tensor h_65_dilations_0 = const()[name = string("h_65_dilations_0"), val = tensor([1])]; + int32 h_65_groups_0 = const()[name = string("h_65_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22539456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23588096))))[name = string("main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23592256)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = var_964_cast_fp16)[name = string("transpose_79")]; + tensor h_65_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_65_dilations_0, groups = h_65_groups_0, pad = h_65_pad_0, pad_type = h_65_pad_type_0, strides = h_65_strides_0, weight = main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_161_cast_fp16)[name = string("h_65_cast_fp16")]; + string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("EXACT")]; + tensor input_163_cast_fp16 = gelu(mode = input_163_mode_0, x = h_65_cast_fp16)[name = string("input_163_cast_fp16")]; + string h_67_pad_type_0 = const()[name = string("h_67_pad_type_0"), val = string("valid")]; + tensor h_67_strides_0 = const()[name = string("h_67_strides_0"), val = tensor([1])]; + tensor h_67_pad_0 = const()[name = string("h_67_pad_0"), val = tensor([0, 0])]; + tensor h_67_dilations_0 = const()[name = string("h_67_dilations_0"), val = tensor([1])]; + int32 h_67_groups_0 = const()[name = string("h_67_groups_0"), val = int32(1)]; + tensor op_981_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23596416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24645056))))[name = string("op_981_weight_0_to_fp16_quantized")]; + tensor var_981_bias_0_to_fp16 = const()[name = string("op_981_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24646144)))]; + tensor var_981_cast_fp16 = conv(bias = var_981_bias_0_to_fp16, dilations = h_67_dilations_0, groups = h_67_groups_0, pad = h_67_pad_0, pad_type = h_67_pad_type_0, strides = h_67_strides_0, weight = op_981_weight_0_to_fp16_quantized, x = input_163_cast_fp16)[name = string("op_981_cast_fp16")]; + tensor out_25_cast_fp16 = add(x = x_81_cast_fp16, y = var_981_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = out_25_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor x_m_5_cast_fp16 = mul(x = x_85_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_5_cast_fp16")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24647232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24909440))))[name = string("main_blocks_1_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24910528)))]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_85_cast_fp16)[name = string("transpose_78")]; + tensor linear_12_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_999 = const()[name = string("op_999"), val = tensor([2, 256, 8, 64])]; + tensor var_1000_cast_fp16 = reshape(shape = var_999, x = linear_12_cast_fp16)[name = string("op_1000_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24911616))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25042752))))[name = string("main_blocks_1_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25043840)))]; + tensor linear_13_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_1007 = const()[name = string("op_1007"), val = tensor([2, 128, 8, 64])]; + tensor var_1008_cast_fp16 = reshape(shape = var_1007, x = linear_13_cast_fp16)[name = string("op_1008_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25044928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25176064))))[name = string("main_blocks_1_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25177152)))]; + tensor linear_14_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_1015 = const()[name = string("op_1015"), val = tensor([2, 128, 8, 64])]; + tensor var_1016_cast_fp16 = reshape(shape = var_1015, x = linear_14_cast_fp16)[name = string("op_1016_cast_fp16")]; + tensor v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_5_begin_0 = const()[name = string("x_a_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_5_end_0 = const()[name = string("x_a_5_end_0"), val = tensor([2, 8, 256, 32])]; + tensor x_a_5_end_mask_0 = const()[name = string("x_a_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_1000_cast_fp16)[name = string("transpose_77")]; + tensor x_a_5_cast_fp16 = slice_by_index(begin = x_a_5_begin_0, end = x_a_5_end_0, end_mask = x_a_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_a_5_cast_fp16")]; + tensor x_b_5_begin_0 = const()[name = string("x_b_5_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_5_end_0 = const()[name = string("x_b_5_end_0"), val = tensor([2, 8, 256, 64])]; + tensor x_b_5_end_mask_0 = const()[name = string("x_b_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_5_cast_fp16 = slice_by_index(begin = x_b_5_begin_0, end = x_b_5_end_0, end_mask = x_b_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_b_5_cast_fp16")]; + tensor var_1044_cast_fp16 = mul(x = x_a_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1044_cast_fp16")]; + tensor var_1045_cast_fp16 = mul(x = x_b_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor rot_a_5_cast_fp16 = sub(x = var_1044_cast_fp16, y = var_1045_cast_fp16)[name = string("rot_a_5_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_a_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_cast_fp16 = mul(x = x_b_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1048_cast_fp16")]; + tensor rot_b_5_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("rot_b_5_cast_fp16")]; + bool q_5_interleave_0 = const()[name = string("q_5_interleave_0"), val = bool(false)]; + tensor q_5_cast_fp16 = concat(axis = var_681, interleave = q_5_interleave_0, values = (rot_a_5_cast_fp16, rot_b_5_cast_fp16))[name = string("q_5_cast_fp16")]; + tensor x_a_7_begin_0 = const()[name = string("x_a_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_7_end_0 = const()[name = string("x_a_7_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_7_end_mask_0 = const()[name = string("x_a_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_1008_cast_fp16)[name = string("transpose_76")]; + tensor x_a_7_cast_fp16 = slice_by_index(begin = x_a_7_begin_0, end = x_a_7_end_0, end_mask = x_a_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_a_7_cast_fp16")]; + tensor x_b_7_begin_0 = const()[name = string("x_b_7_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_7_end_0 = const()[name = string("x_b_7_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_7_end_mask_0 = const()[name = string("x_b_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_7_cast_fp16 = slice_by_index(begin = x_b_7_begin_0, end = x_b_7_end_0, end_mask = x_b_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_b_7_cast_fp16")]; + tensor var_1062_cast_fp16 = mul(x = x_a_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = x_b_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor rot_a_7_cast_fp16 = sub(x = var_1062_cast_fp16, y = var_1063_cast_fp16)[name = string("rot_a_7_cast_fp16")]; + tensor var_1065_cast_fp16 = mul(x = x_a_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1065_cast_fp16")]; + tensor var_1066_cast_fp16 = mul(x = x_b_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1066_cast_fp16")]; + tensor rot_b_7_cast_fp16 = add(x = var_1065_cast_fp16, y = var_1066_cast_fp16)[name = string("rot_b_7_cast_fp16")]; + bool k_5_interleave_0 = const()[name = string("k_5_interleave_0"), val = bool(false)]; + tensor k_5_cast_fp16 = concat(axis = var_681, interleave = k_5_interleave_0, values = (rot_a_7_cast_fp16, rot_b_7_cast_fp16))[name = string("k_5_cast_fp16")]; + bool var_1071_transpose_x_1 = const()[name = string("op_1071_transpose_x_1"), val = bool(false)]; + bool var_1071_transpose_y_1 = const()[name = string("op_1071_transpose_y_1"), val = bool(true)]; + tensor var_1071_cast_fp16 = matmul(transpose_x = var_1071_transpose_x_1, transpose_y = var_1071_transpose_y_1, x = q_5_cast_fp16, y = k_5_cast_fp16)[name = string("op_1071_cast_fp16")]; + fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_3_cast_fp16 = mul(x = var_1071_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")]; + tensor input_171_cast_fp16 = sub(x = _inversed_scores_3_cast_fp16, y = var_530_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_681, x = input_171_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool var_1080_transpose_x_0 = const()[name = string("op_1080_transpose_x_0"), val = bool(false)]; + bool var_1080_transpose_y_0 = const()[name = string("op_1080_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1016_cast_fp16)[name = string("transpose_75")]; + tensor var_1080_cast_fp16 = matmul(transpose_x = var_1080_transpose_x_0, transpose_y = var_1080_transpose_y_0, x = attn_7_cast_fp16, y = v_5_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_perm_0 = const()[name = string("op_1081_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1082 = const()[name = string("op_1082"), val = tensor([2, 256, 512])]; + tensor var_1081_cast_fp16 = transpose(perm = var_1081_perm_0, x = var_1080_cast_fp16)[name = string("transpose_74")]; + tensor input_173_cast_fp16 = reshape(shape = var_1082, x = var_1081_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25178240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25440448))))[name = string("main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25441536)))]; + tensor linear_15_cast_fp16 = linear(bias = main_blocks_1_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_173_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor out_27_perm_0 = const()[name = string("out_27_perm_0"), val = tensor([0, 2, 1])]; + tensor out_27_cast_fp16 = transpose(perm = out_27_perm_0, x = linear_15_cast_fp16)[name = string("transpose_73")]; + tensor var_1089_cast_fp16 = mul(x = out_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor x_91_cast_fp16 = add(x = x_m_5_cast_fp16, y = var_1089_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_175_perm_0 = const()[name = string("input_175_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1096_axes_0 = const()[name = string("op_1096_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25442624)))]; + tensor main_blocks_1_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25443712)))]; + tensor input_175_cast_fp16 = transpose(perm = input_175_perm_0, x = x_91_cast_fp16)[name = string("transpose_72")]; + tensor var_1096_cast_fp16 = layer_norm(axes = var_1096_axes_0, beta = main_blocks_1_text_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_text_norm_norm_weight_to_fp16, x = input_175_cast_fp16)[name = string("op_1096_cast_fp16")]; + tensor var_1097_perm_0 = const()[name = string("op_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1097_cast_fp16 = transpose(perm = var_1097_perm_0, x = var_1096_cast_fp16)[name = string("transpose_71")]; + tensor x_93_cast_fp16 = mul(x = var_1097_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor x_95_cast_fp16 = mul(x = x_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_1110_cast_fp16 = mul(x = x_95_cast_fp16, y = last_1_cast_fp16)[name = string("op_1110_cast_fp16")]; + tensor x_last_23_axes_0 = const()[name = string("x_last_23_axes_0"), val = tensor([-1])]; + bool x_last_23_keep_dims_0 = const()[name = string("x_last_23_keep_dims_0"), val = bool(true)]; + tensor x_last_23_cast_fp16 = reduce_sum(axes = x_last_23_axes_0, keep_dims = x_last_23_keep_dims_0, x = var_1110_cast_fp16)[name = string("x_last_23_cast_fp16")]; + tensor var_1113_cast_fp16 = mul(x = x_95_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1115_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_23_cast_fp16)[name = string("op_1115_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = var_1113_cast_fp16, y = var_1115_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("replicate")]; + fp16 const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = fp16(0x0p+0)]; + tensor input_181_cast_fp16 = pad(constant_val = const_39_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + string h_69_pad_type_0 = const()[name = string("h_69_pad_type_0"), val = string("valid")]; + int32 h_69_groups_0 = const()[name = string("h_69_groups_0"), val = int32(512)]; + tensor h_69_strides_0 = const()[name = string("h_69_strides_0"), val = tensor([1])]; + tensor h_69_pad_0 = const()[name = string("h_69_pad_0"), val = tensor([0, 0])]; + tensor h_69_dilations_0 = const()[name = string("h_69_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25444800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25447424))))[name = string("main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25448512)))]; + tensor h_69_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_69_dilations_0, groups = h_69_groups_0, pad = h_69_pad_0, pad_type = h_69_pad_type_0, strides = h_69_strides_0, weight = main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_181_cast_fp16)[name = string("h_69_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = h_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_183_perm_0 = const()[name = string("input_183_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1133_axes_0 = const()[name = string("op_1133_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25449600)))]; + tensor main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25450688)))]; + tensor input_183_cast_fp16 = transpose(perm = input_183_perm_0, x = x_97_cast_fp16)[name = string("transpose_70")]; + tensor var_1133_cast_fp16 = layer_norm(axes = var_1133_axes_0, beta = main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16, x = input_183_cast_fp16)[name = string("op_1133_cast_fp16")]; + tensor input_185_perm_0 = const()[name = string("input_185_perm_0"), val = tensor([0, 2, 1])]; + string h_71_pad_type_0 = const()[name = string("h_71_pad_type_0"), val = string("valid")]; + tensor h_71_strides_0 = const()[name = string("h_71_strides_0"), val = tensor([1])]; + tensor h_71_pad_0 = const()[name = string("h_71_pad_0"), val = tensor([0, 0])]; + tensor h_71_dilations_0 = const()[name = string("h_71_dilations_0"), val = tensor([1])]; + int32 h_71_groups_0 = const()[name = string("h_71_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25451776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26500416))))[name = string("main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26504576)))]; + tensor input_185_cast_fp16 = transpose(perm = input_185_perm_0, x = var_1133_cast_fp16)[name = string("transpose_69")]; + tensor h_71_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_71_dilations_0, groups = h_71_groups_0, pad = h_71_pad_0, pad_type = h_71_pad_type_0, strides = h_71_strides_0, weight = main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("h_71_cast_fp16")]; + string input_187_mode_0 = const()[name = string("input_187_mode_0"), val = string("EXACT")]; + tensor input_187_cast_fp16 = gelu(mode = input_187_mode_0, x = h_71_cast_fp16)[name = string("input_187_cast_fp16")]; + string h_73_pad_type_0 = const()[name = string("h_73_pad_type_0"), val = string("valid")]; + tensor h_73_strides_0 = const()[name = string("h_73_strides_0"), val = tensor([1])]; + tensor h_73_pad_0 = const()[name = string("h_73_pad_0"), val = tensor([0, 0])]; + tensor h_73_dilations_0 = const()[name = string("h_73_dilations_0"), val = tensor([1])]; + int32 h_73_groups_0 = const()[name = string("h_73_groups_0"), val = int32(1)]; + tensor op_1150_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26508736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27557376))))[name = string("op_1150_weight_0_to_fp16_quantized")]; + tensor var_1150_bias_0_to_fp16 = const()[name = string("op_1150_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27558464)))]; + tensor var_1150_cast_fp16 = conv(bias = var_1150_bias_0_to_fp16, dilations = h_73_dilations_0, groups = h_73_groups_0, pad = h_73_pad_0, pad_type = h_73_pad_type_0, strides = h_73_strides_0, weight = op_1150_weight_0_to_fp16_quantized, x = input_187_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor out_29_cast_fp16 = add(x = x_95_cast_fp16, y = var_1150_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor x_99_cast_fp16 = mul(x = out_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor x_m_7_cast_fp16 = mul(x = x_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_7_cast_fp16")]; + tensor input_189_perm_0 = const()[name = string("input_189_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27559552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27690688))))[name = string("main_blocks_1_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27691264)))]; + tensor input_189_cast_fp16 = transpose(perm = input_189_perm_0, x = x_99_cast_fp16)[name = string("transpose_68")]; + tensor linear_16_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_1166 = const()[name = string("op_1166"), val = tensor([2, 256, 2, 128])]; + tensor var_1167_cast_fp16 = reshape(shape = var_1166, x = linear_16_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_1_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27691840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27757440))))[name = string("main_blocks_1_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27758016)))]; + tensor linear_18_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_1180 = const()[name = string("op_1180"), val = tensor([2, 50, 2, 128])]; + tensor var_1181_cast_fp16 = reshape(shape = var_1180, x = linear_18_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1185_transpose_x_0 = const()[name = string("op_1185_transpose_x_0"), val = bool(false)]; + bool var_1185_transpose_y_0 = const()[name = string("op_1185_transpose_y_0"), val = bool(false)]; + tensor op_1184_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27758592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27784256))))[name = string("op_1184_to_fp16_quantized")]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1167_cast_fp16)[name = string("transpose_67")]; + tensor var_1185_cast_fp16 = matmul(transpose_x = var_1185_transpose_x_0, transpose_y = var_1185_transpose_y_0, x = q_7_cast_fp16, y = op_1184_to_fp16_quantized)[name = string("op_1185_cast_fp16")]; + fp16 _inversed_input_191_y_0_to_fp16 = const()[name = string("_inversed_input_191_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_191_cast_fp16 = mul(x = var_1185_cast_fp16, y = _inversed_input_191_y_0_to_fp16)[name = string("_inversed_input_191_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_681, x = _inversed_input_191_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor attn_11_cast_fp16 = mul(x = attn_9_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool var_1192_transpose_x_0 = const()[name = string("op_1192_transpose_x_0"), val = bool(false)]; + bool var_1192_transpose_y_0 = const()[name = string("op_1192_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1181_cast_fp16)[name = string("transpose_66")]; + tensor var_1192_cast_fp16 = matmul(transpose_x = var_1192_transpose_x_0, transpose_y = var_1192_transpose_y_0, x = attn_11_cast_fp16, y = v_7_cast_fp16)[name = string("op_1192_cast_fp16")]; + tensor var_1193_perm_0 = const()[name = string("op_1193_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1194 = const()[name = string("op_1194"), val = tensor([2, 256, 256])]; + tensor var_1193_cast_fp16 = transpose(perm = var_1193_perm_0, x = var_1192_cast_fp16)[name = string("transpose_65")]; + tensor input_193_cast_fp16 = reshape(shape = var_1194, x = var_1193_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27784448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27915584))))[name = string("main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27916672)))]; + tensor linear_19_cast_fp16 = linear(bias = main_blocks_1_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_193_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor out_31_perm_0 = const()[name = string("out_31_perm_0"), val = tensor([0, 2, 1])]; + tensor out_31_cast_fp16 = transpose(perm = out_31_perm_0, x = linear_19_cast_fp16)[name = string("transpose_64")]; + tensor var_1201_cast_fp16 = mul(x = out_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1201_cast_fp16")]; + tensor x_101_cast_fp16 = add(x = x_m_7_cast_fp16, y = var_1201_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_195_perm_0 = const()[name = string("input_195_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1208_axes_0 = const()[name = string("op_1208_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27917760)))]; + tensor main_blocks_1_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27918848)))]; + tensor input_195_cast_fp16 = transpose(perm = input_195_perm_0, x = x_101_cast_fp16)[name = string("transpose_63")]; + tensor var_1208_cast_fp16 = layer_norm(axes = var_1208_axes_0, beta = main_blocks_1_style_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_style_norm_norm_weight_to_fp16, x = input_195_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_perm_0 = const()[name = string("op_1209_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1209_cast_fp16 = transpose(perm = var_1209_perm_0, x = var_1208_cast_fp16)[name = string("transpose_62")]; + tensor x_103_cast_fp16 = mul(x = var_1209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_103_cast_fp16")]; + int32 var_1228 = const()[name = string("op_1228"), val = int32(-1)]; + tensor x_105_cast_fp16 = mul(x = x_103_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_1263_cast_fp16 = mul(x = x_105_cast_fp16, y = last_1_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor x_last_25_axes_0 = const()[name = string("x_last_25_axes_0"), val = tensor([-1])]; + bool x_last_25_keep_dims_0 = const()[name = string("x_last_25_keep_dims_0"), val = bool(true)]; + tensor x_last_25_cast_fp16 = reduce_sum(axes = x_last_25_axes_0, keep_dims = x_last_25_keep_dims_0, x = var_1263_cast_fp16)[name = string("x_last_25_cast_fp16")]; + tensor var_1266_cast_fp16 = mul(x = x_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1266_cast_fp16")]; + tensor var_1268_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_25_cast_fp16)[name = string("op_1268_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1266_cast_fp16, y = var_1268_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_201_mode_0 = const()[name = string("input_201_mode_0"), val = string("replicate")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_201_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")]; + string h_75_pad_type_0 = const()[name = string("h_75_pad_type_0"), val = string("valid")]; + int32 h_75_groups_0 = const()[name = string("h_75_groups_0"), val = int32(512)]; + tensor h_75_strides_0 = const()[name = string("h_75_strides_0"), val = tensor([1])]; + tensor h_75_pad_0 = const()[name = string("h_75_pad_0"), val = tensor([0, 0])]; + tensor h_75_dilations_0 = const()[name = string("h_75_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27919936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27922560))))[name = string("main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27923648)))]; + tensor h_75_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_75_dilations_0, groups = h_75_groups_0, pad = h_75_pad_0, pad_type = h_75_pad_type_0, strides = h_75_strides_0, weight = main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_201_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = h_75_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_203_perm_0 = const()[name = string("input_203_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1286_axes_0 = const()[name = string("op_1286_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27924736)))]; + tensor main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27925824)))]; + fp16 var_1233_to_fp16 = const()[name = string("op_1233_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_203_cast_fp16 = transpose(perm = input_203_perm_0, x = x_107_cast_fp16)[name = string("transpose_61")]; + tensor var_1286_cast_fp16 = layer_norm(axes = var_1286_axes_0, beta = main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16, x = input_203_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string h_77_pad_type_0 = const()[name = string("h_77_pad_type_0"), val = string("valid")]; + tensor h_77_strides_0 = const()[name = string("h_77_strides_0"), val = tensor([1])]; + tensor h_77_pad_0 = const()[name = string("h_77_pad_0"), val = tensor([0, 0])]; + tensor h_77_dilations_0 = const()[name = string("h_77_dilations_0"), val = tensor([1])]; + int32 h_77_groups_0 = const()[name = string("h_77_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27926912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28975552))))[name = string("main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28979712)))]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = var_1286_cast_fp16)[name = string("transpose_60")]; + tensor h_77_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_77_dilations_0, groups = h_77_groups_0, pad = h_77_pad_0, pad_type = h_77_pad_type_0, strides = h_77_strides_0, weight = main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("h_77_cast_fp16")]; + string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")]; + tensor input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = h_77_cast_fp16)[name = string("input_207_cast_fp16")]; + string h_79_pad_type_0 = const()[name = string("h_79_pad_type_0"), val = string("valid")]; + tensor h_79_strides_0 = const()[name = string("h_79_strides_0"), val = tensor([1])]; + tensor h_79_pad_0 = const()[name = string("h_79_pad_0"), val = tensor([0, 0])]; + tensor h_79_dilations_0 = const()[name = string("h_79_dilations_0"), val = tensor([1])]; + int32 h_79_groups_0 = const()[name = string("h_79_groups_0"), val = int32(1)]; + tensor op_1303_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28983872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30032512))))[name = string("op_1303_weight_0_to_fp16_quantized")]; + tensor var_1303_bias_0_to_fp16 = const()[name = string("op_1303_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30033600)))]; + tensor var_1303_cast_fp16 = conv(bias = var_1303_bias_0_to_fp16, dilations = h_79_dilations_0, groups = h_79_groups_0, pad = h_79_pad_0, pad_type = h_79_pad_type_0, strides = h_79_strides_0, weight = op_1303_weight_0_to_fp16_quantized, x = input_207_cast_fp16)[name = string("op_1303_cast_fp16")]; + tensor out_33_cast_fp16 = add(x = x_105_cast_fp16, y = var_1303_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor x_109_cast_fp16 = mul(x = out_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor x_111_cast_fp16 = mul(x = x_109_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_1317_cast_fp16 = mul(x = x_111_cast_fp16, y = last_1_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor x_last_27_axes_0 = const()[name = string("x_last_27_axes_0"), val = tensor([-1])]; + bool x_last_27_keep_dims_0 = const()[name = string("x_last_27_keep_dims_0"), val = bool(true)]; + tensor x_last_27_cast_fp16 = reduce_sum(axes = x_last_27_axes_0, keep_dims = x_last_27_keep_dims_0, x = var_1317_cast_fp16)[name = string("x_last_27_cast_fp16")]; + tensor var_1320_cast_fp16 = mul(x = x_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1322_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_27_cast_fp16)[name = string("op_1322_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1320_cast_fp16, y = var_1322_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("replicate")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor input_213_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = input_213_mode_0, pad = input_213_pad_0, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + string h_81_pad_type_0 = const()[name = string("h_81_pad_type_0"), val = string("valid")]; + tensor h_81_dilations_0 = const()[name = string("h_81_dilations_0"), val = tensor([2])]; + int32 h_81_groups_0 = const()[name = string("h_81_groups_0"), val = int32(512)]; + tensor h_81_strides_0 = const()[name = string("h_81_strides_0"), val = tensor([1])]; + tensor h_81_pad_0 = const()[name = string("h_81_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30034688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30037312))))[name = string("main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30038400)))]; + tensor h_81_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_81_dilations_0, groups = h_81_groups_0, pad = h_81_pad_0, pad_type = h_81_pad_type_0, strides = h_81_strides_0, weight = main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_213_cast_fp16)[name = string("h_81_cast_fp16")]; + tensor x_113_cast_fp16 = mul(x = h_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor input_215_perm_0 = const()[name = string("input_215_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1340_axes_0 = const()[name = string("op_1340_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30039488)))]; + tensor main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30040576)))]; + tensor input_215_cast_fp16 = transpose(perm = input_215_perm_0, x = x_113_cast_fp16)[name = string("transpose_59")]; + tensor var_1340_cast_fp16 = layer_norm(axes = var_1340_axes_0, beta = main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16, x = input_215_cast_fp16)[name = string("op_1340_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + string h_83_pad_type_0 = const()[name = string("h_83_pad_type_0"), val = string("valid")]; + tensor h_83_strides_0 = const()[name = string("h_83_strides_0"), val = tensor([1])]; + tensor h_83_pad_0 = const()[name = string("h_83_pad_0"), val = tensor([0, 0])]; + tensor h_83_dilations_0 = const()[name = string("h_83_dilations_0"), val = tensor([1])]; + int32 h_83_groups_0 = const()[name = string("h_83_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30041664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31090304))))[name = string("main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31094464)))]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = var_1340_cast_fp16)[name = string("transpose_58")]; + tensor h_83_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_83_dilations_0, groups = h_83_groups_0, pad = h_83_pad_0, pad_type = h_83_pad_type_0, strides = h_83_strides_0, weight = main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_217_cast_fp16)[name = string("h_83_cast_fp16")]; + string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")]; + tensor input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = h_83_cast_fp16)[name = string("input_219_cast_fp16")]; + string h_85_pad_type_0 = const()[name = string("h_85_pad_type_0"), val = string("valid")]; + tensor h_85_strides_0 = const()[name = string("h_85_strides_0"), val = tensor([1])]; + tensor h_85_pad_0 = const()[name = string("h_85_pad_0"), val = tensor([0, 0])]; + tensor h_85_dilations_0 = const()[name = string("h_85_dilations_0"), val = tensor([1])]; + int32 h_85_groups_0 = const()[name = string("h_85_groups_0"), val = int32(1)]; + tensor op_1357_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31098624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32147264))))[name = string("op_1357_weight_0_to_fp16_quantized")]; + tensor var_1357_bias_0_to_fp16 = const()[name = string("op_1357_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32148352)))]; + tensor var_1357_cast_fp16 = conv(bias = var_1357_bias_0_to_fp16, dilations = h_85_dilations_0, groups = h_85_groups_0, pad = h_85_pad_0, pad_type = h_85_pad_type_0, strides = h_85_strides_0, weight = op_1357_weight_0_to_fp16_quantized, x = input_219_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor out_35_cast_fp16 = add(x = x_111_cast_fp16, y = var_1357_cast_fp16)[name = string("out_35_cast_fp16")]; + tensor x_115_cast_fp16 = mul(x = out_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_cast_fp16 = mul(x = x_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1371_cast_fp16 = mul(x = x_117_cast_fp16, y = last_1_cast_fp16)[name = string("op_1371_cast_fp16")]; + tensor x_last_29_axes_0 = const()[name = string("x_last_29_axes_0"), val = tensor([-1])]; + bool x_last_29_keep_dims_0 = const()[name = string("x_last_29_keep_dims_0"), val = bool(true)]; + tensor x_last_29_cast_fp16 = reduce_sum(axes = x_last_29_axes_0, keep_dims = x_last_29_keep_dims_0, x = var_1371_cast_fp16)[name = string("x_last_29_cast_fp16")]; + tensor var_1374_cast_fp16 = mul(x = x_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_29_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1376_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_225_mode_0 = const()[name = string("input_225_mode_0"), val = string("replicate")]; + fp16 const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = fp16(0x0p+0)]; + tensor input_225_cast_fp16 = pad(constant_val = const_48_to_fp16, mode = input_225_mode_0, pad = input_225_pad_0, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")]; + string h_87_pad_type_0 = const()[name = string("h_87_pad_type_0"), val = string("valid")]; + tensor h_87_dilations_0 = const()[name = string("h_87_dilations_0"), val = tensor([4])]; + int32 h_87_groups_0 = const()[name = string("h_87_groups_0"), val = int32(512)]; + tensor h_87_strides_0 = const()[name = string("h_87_strides_0"), val = tensor([1])]; + tensor h_87_pad_0 = const()[name = string("h_87_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32149440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32152064))))[name = string("main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32153152)))]; + tensor h_87_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_87_dilations_0, groups = h_87_groups_0, pad = h_87_pad_0, pad_type = h_87_pad_type_0, strides = h_87_strides_0, weight = main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor x_119_cast_fp16 = mul(x = h_87_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_227_perm_0 = const()[name = string("input_227_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1394_axes_0 = const()[name = string("op_1394_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32154240)))]; + tensor main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32155328)))]; + tensor input_227_cast_fp16 = transpose(perm = input_227_perm_0, x = x_119_cast_fp16)[name = string("transpose_57")]; + tensor var_1394_cast_fp16 = layer_norm(axes = var_1394_axes_0, beta = main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16, x = input_227_cast_fp16)[name = string("op_1394_cast_fp16")]; + tensor input_229_perm_0 = const()[name = string("input_229_perm_0"), val = tensor([0, 2, 1])]; + string h_89_pad_type_0 = const()[name = string("h_89_pad_type_0"), val = string("valid")]; + tensor h_89_strides_0 = const()[name = string("h_89_strides_0"), val = tensor([1])]; + tensor h_89_pad_0 = const()[name = string("h_89_pad_0"), val = tensor([0, 0])]; + tensor h_89_dilations_0 = const()[name = string("h_89_dilations_0"), val = tensor([1])]; + int32 h_89_groups_0 = const()[name = string("h_89_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32156416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33205056))))[name = string("main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33209216)))]; + tensor input_229_cast_fp16 = transpose(perm = input_229_perm_0, x = var_1394_cast_fp16)[name = string("transpose_56")]; + tensor h_89_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_89_dilations_0, groups = h_89_groups_0, pad = h_89_pad_0, pad_type = h_89_pad_type_0, strides = h_89_strides_0, weight = main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_229_cast_fp16)[name = string("h_89_cast_fp16")]; + string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")]; + tensor input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = h_89_cast_fp16)[name = string("input_231_cast_fp16")]; + string h_91_pad_type_0 = const()[name = string("h_91_pad_type_0"), val = string("valid")]; + tensor h_91_strides_0 = const()[name = string("h_91_strides_0"), val = tensor([1])]; + tensor h_91_pad_0 = const()[name = string("h_91_pad_0"), val = tensor([0, 0])]; + tensor h_91_dilations_0 = const()[name = string("h_91_dilations_0"), val = tensor([1])]; + int32 h_91_groups_0 = const()[name = string("h_91_groups_0"), val = int32(1)]; + tensor op_1411_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33213376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34262016))))[name = string("op_1411_weight_0_to_fp16_quantized")]; + tensor var_1411_bias_0_to_fp16 = const()[name = string("op_1411_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34263104)))]; + tensor var_1411_cast_fp16 = conv(bias = var_1411_bias_0_to_fp16, dilations = h_91_dilations_0, groups = h_91_groups_0, pad = h_91_pad_0, pad_type = h_91_pad_type_0, strides = h_91_strides_0, weight = op_1411_weight_0_to_fp16_quantized, x = input_231_cast_fp16)[name = string("op_1411_cast_fp16")]; + tensor out_37_cast_fp16 = add(x = x_117_cast_fp16, y = var_1411_cast_fp16)[name = string("out_37_cast_fp16")]; + tensor x_121_cast_fp16 = mul(x = out_37_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_121_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1425_cast_fp16 = mul(x = x_123_cast_fp16, y = last_1_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor x_last_31_axes_0 = const()[name = string("x_last_31_axes_0"), val = tensor([-1])]; + bool x_last_31_keep_dims_0 = const()[name = string("x_last_31_keep_dims_0"), val = bool(true)]; + tensor x_last_31_cast_fp16 = reduce_sum(axes = x_last_31_axes_0, keep_dims = x_last_31_keep_dims_0, x = var_1425_cast_fp16)[name = string("x_last_31_cast_fp16")]; + tensor var_1428_cast_fp16 = mul(x = x_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_31_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = var_1428_cast_fp16, y = var_1430_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("replicate")]; + fp16 const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = fp16(0x0p+0)]; + tensor input_237_cast_fp16 = pad(constant_val = const_50_to_fp16, mode = input_237_mode_0, pad = input_237_pad_0, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + string h_93_pad_type_0 = const()[name = string("h_93_pad_type_0"), val = string("valid")]; + tensor h_93_dilations_0 = const()[name = string("h_93_dilations_0"), val = tensor([8])]; + int32 h_93_groups_0 = const()[name = string("h_93_groups_0"), val = int32(512)]; + tensor h_93_strides_0 = const()[name = string("h_93_strides_0"), val = tensor([1])]; + tensor h_93_pad_0 = const()[name = string("h_93_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34264192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34266816))))[name = string("main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34267904)))]; + tensor h_93_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_93_dilations_0, groups = h_93_groups_0, pad = h_93_pad_0, pad_type = h_93_pad_type_0, strides = h_93_strides_0, weight = main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_237_cast_fp16)[name = string("h_93_cast_fp16")]; + tensor x_125_cast_fp16 = mul(x = h_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_239_perm_0 = const()[name = string("input_239_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1448_axes_0 = const()[name = string("op_1448_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34268992)))]; + tensor main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34270080)))]; + tensor input_239_cast_fp16 = transpose(perm = input_239_perm_0, x = x_125_cast_fp16)[name = string("transpose_55")]; + tensor var_1448_cast_fp16 = layer_norm(axes = var_1448_axes_0, beta = main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16, x = input_239_cast_fp16)[name = string("op_1448_cast_fp16")]; + tensor input_241_perm_0 = const()[name = string("input_241_perm_0"), val = tensor([0, 2, 1])]; + string h_95_pad_type_0 = const()[name = string("h_95_pad_type_0"), val = string("valid")]; + tensor h_95_strides_0 = const()[name = string("h_95_strides_0"), val = tensor([1])]; + tensor h_95_pad_0 = const()[name = string("h_95_pad_0"), val = tensor([0, 0])]; + tensor h_95_dilations_0 = const()[name = string("h_95_dilations_0"), val = tensor([1])]; + int32 h_95_groups_0 = const()[name = string("h_95_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34271168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35319808))))[name = string("main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35323968)))]; + tensor input_241_cast_fp16 = transpose(perm = input_241_perm_0, x = var_1448_cast_fp16)[name = string("transpose_54")]; + tensor h_95_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_95_dilations_0, groups = h_95_groups_0, pad = h_95_pad_0, pad_type = h_95_pad_type_0, strides = h_95_strides_0, weight = main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_241_cast_fp16)[name = string("h_95_cast_fp16")]; + string input_243_mode_0 = const()[name = string("input_243_mode_0"), val = string("EXACT")]; + tensor input_243_cast_fp16 = gelu(mode = input_243_mode_0, x = h_95_cast_fp16)[name = string("input_243_cast_fp16")]; + string h_97_pad_type_0 = const()[name = string("h_97_pad_type_0"), val = string("valid")]; + tensor h_97_strides_0 = const()[name = string("h_97_strides_0"), val = tensor([1])]; + tensor h_97_pad_0 = const()[name = string("h_97_pad_0"), val = tensor([0, 0])]; + tensor h_97_dilations_0 = const()[name = string("h_97_dilations_0"), val = tensor([1])]; + int32 h_97_groups_0 = const()[name = string("h_97_groups_0"), val = int32(1)]; + tensor op_1465_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35328128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36376768))))[name = string("op_1465_weight_0_to_fp16_quantized")]; + tensor var_1465_bias_0_to_fp16 = const()[name = string("op_1465_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36377856)))]; + tensor var_1465_cast_fp16 = conv(bias = var_1465_bias_0_to_fp16, dilations = h_97_dilations_0, groups = h_97_groups_0, pad = h_97_pad_0, pad_type = h_97_pad_type_0, strides = h_97_strides_0, weight = op_1465_weight_0_to_fp16_quantized, x = input_243_cast_fp16)[name = string("op_1465_cast_fp16")]; + tensor out_39_cast_fp16 = add(x = x_123_cast_fp16, y = var_1465_cast_fp16)[name = string("out_39_cast_fp16")]; + tensor x_127_cast_fp16 = mul(x = out_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor main_blocks_2_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36378944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36411776))))[name = string("main_blocks_2_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36412864)))]; + tensor linear_20_cast_fp16 = linear(bias = main_blocks_2_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_2_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor t_9_axes_0 = const()[name = string("t_9_axes_0"), val = tensor([-1])]; + tensor t_9_cast_fp16 = expand_dims(axes = t_9_axes_0, x = linear_20_cast_fp16)[name = string("t_9_cast_fp16")]; + tensor var_1475_cast_fp16 = add(x = x_127_cast_fp16, y = t_9_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = var_1475_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor x_131_cast_fp16 = mul(x = x_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor var_1488_cast_fp16 = mul(x = x_131_cast_fp16, y = last_1_cast_fp16)[name = string("op_1488_cast_fp16")]; + tensor x_last_33_axes_0 = const()[name = string("x_last_33_axes_0"), val = tensor([-1])]; + bool x_last_33_keep_dims_0 = const()[name = string("x_last_33_keep_dims_0"), val = bool(true)]; + tensor x_last_33_cast_fp16 = reduce_sum(axes = x_last_33_axes_0, keep_dims = x_last_33_keep_dims_0, x = var_1488_cast_fp16)[name = string("x_last_33_cast_fp16")]; + tensor var_1491_cast_fp16 = mul(x = x_131_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1491_cast_fp16")]; + tensor var_1493_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_33_cast_fp16)[name = string("op_1493_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = var_1491_cast_fp16, y = var_1493_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_251_mode_0 = const()[name = string("input_251_mode_0"), val = string("replicate")]; + fp16 const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = fp16(0x0p+0)]; + tensor input_251_cast_fp16 = pad(constant_val = const_52_to_fp16, mode = input_251_mode_0, pad = input_251_pad_0, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + string h_99_pad_type_0 = const()[name = string("h_99_pad_type_0"), val = string("valid")]; + int32 h_99_groups_0 = const()[name = string("h_99_groups_0"), val = int32(512)]; + tensor h_99_strides_0 = const()[name = string("h_99_strides_0"), val = tensor([1])]; + tensor h_99_pad_0 = const()[name = string("h_99_pad_0"), val = tensor([0, 0])]; + tensor h_99_dilations_0 = const()[name = string("h_99_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36413952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36416576))))[name = string("main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36417664)))]; + tensor h_99_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_99_dilations_0, groups = h_99_groups_0, pad = h_99_pad_0, pad_type = h_99_pad_type_0, strides = h_99_strides_0, weight = main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor x_133_cast_fp16 = mul(x = h_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_253_perm_0 = const()[name = string("input_253_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1511_axes_0 = const()[name = string("op_1511_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36418752)))]; + tensor main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36419840)))]; + tensor input_253_cast_fp16 = transpose(perm = input_253_perm_0, x = x_133_cast_fp16)[name = string("transpose_53")]; + tensor var_1511_cast_fp16 = layer_norm(axes = var_1511_axes_0, beta = main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16, x = input_253_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor input_255_perm_0 = const()[name = string("input_255_perm_0"), val = tensor([0, 2, 1])]; + string h_101_pad_type_0 = const()[name = string("h_101_pad_type_0"), val = string("valid")]; + tensor h_101_strides_0 = const()[name = string("h_101_strides_0"), val = tensor([1])]; + tensor h_101_pad_0 = const()[name = string("h_101_pad_0"), val = tensor([0, 0])]; + tensor h_101_dilations_0 = const()[name = string("h_101_dilations_0"), val = tensor([1])]; + int32 h_101_groups_0 = const()[name = string("h_101_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36420928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37469568))))[name = string("main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37473728)))]; + tensor input_255_cast_fp16 = transpose(perm = input_255_perm_0, x = var_1511_cast_fp16)[name = string("transpose_52")]; + tensor h_101_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_101_dilations_0, groups = h_101_groups_0, pad = h_101_pad_0, pad_type = h_101_pad_type_0, strides = h_101_strides_0, weight = main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("h_101_cast_fp16")]; + string input_257_mode_0 = const()[name = string("input_257_mode_0"), val = string("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = h_101_cast_fp16)[name = string("input_257_cast_fp16")]; + string h_103_pad_type_0 = const()[name = string("h_103_pad_type_0"), val = string("valid")]; + tensor h_103_strides_0 = const()[name = string("h_103_strides_0"), val = tensor([1])]; + tensor h_103_pad_0 = const()[name = string("h_103_pad_0"), val = tensor([0, 0])]; + tensor h_103_dilations_0 = const()[name = string("h_103_dilations_0"), val = tensor([1])]; + int32 h_103_groups_0 = const()[name = string("h_103_groups_0"), val = int32(1)]; + tensor op_1528_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37477888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38526528))))[name = string("op_1528_weight_0_to_fp16_quantized")]; + tensor var_1528_bias_0_to_fp16 = const()[name = string("op_1528_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38527616)))]; + tensor var_1528_cast_fp16 = conv(bias = var_1528_bias_0_to_fp16, dilations = h_103_dilations_0, groups = h_103_groups_0, pad = h_103_pad_0, pad_type = h_103_pad_type_0, strides = h_103_strides_0, weight = op_1528_weight_0_to_fp16_quantized, x = input_257_cast_fp16)[name = string("op_1528_cast_fp16")]; + tensor out_41_cast_fp16 = add(x = x_131_cast_fp16, y = var_1528_cast_fp16)[name = string("out_41_cast_fp16")]; + tensor x_135_cast_fp16 = mul(x = out_41_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor x_m_9_cast_fp16 = mul(x = x_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_9_cast_fp16")]; + tensor input_259_perm_0 = const()[name = string("input_259_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38528704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38790912))))[name = string("main_blocks_2_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38792000)))]; + tensor input_259_cast_fp16 = transpose(perm = input_259_perm_0, x = x_135_cast_fp16)[name = string("transpose_51")]; + tensor linear_21_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_259_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_1546 = const()[name = string("op_1546"), val = tensor([2, 256, 8, 64])]; + tensor var_1547_cast_fp16 = reshape(shape = var_1546, x = linear_21_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor x_137_perm_0 = const()[name = string("x_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38793088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38924224))))[name = string("main_blocks_2_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38925312)))]; + tensor linear_22_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_1554 = const()[name = string("op_1554"), val = tensor([2, 128, 8, 64])]; + tensor var_1555_cast_fp16 = reshape(shape = var_1554, x = linear_22_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_139_perm_0 = const()[name = string("x_139_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38926400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39057536))))[name = string("main_blocks_2_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39058624)))]; + tensor linear_23_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_1562 = const()[name = string("op_1562"), val = tensor([2, 128, 8, 64])]; + tensor var_1563_cast_fp16 = reshape(shape = var_1562, x = linear_23_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_9_begin_0 = const()[name = string("x_a_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_9_end_0 = const()[name = string("x_a_9_end_0"), val = tensor([2, 8, 256, 32])]; + tensor x_a_9_end_mask_0 = const()[name = string("x_a_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_137_cast_fp16 = transpose(perm = x_137_perm_0, x = var_1547_cast_fp16)[name = string("transpose_50")]; + tensor x_a_9_cast_fp16 = slice_by_index(begin = x_a_9_begin_0, end = x_a_9_end_0, end_mask = x_a_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_a_9_cast_fp16")]; + tensor x_b_9_begin_0 = const()[name = string("x_b_9_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_9_end_0 = const()[name = string("x_b_9_end_0"), val = tensor([2, 8, 256, 64])]; + tensor x_b_9_end_mask_0 = const()[name = string("x_b_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_9_cast_fp16 = slice_by_index(begin = x_b_9_begin_0, end = x_b_9_end_0, end_mask = x_b_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_b_9_cast_fp16")]; + tensor var_1591_cast_fp16 = mul(x = x_a_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1591_cast_fp16")]; + tensor var_1592_cast_fp16 = mul(x = x_b_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor rot_a_9_cast_fp16 = sub(x = var_1591_cast_fp16, y = var_1592_cast_fp16)[name = string("rot_a_9_cast_fp16")]; + tensor var_1594_cast_fp16 = mul(x = x_a_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595_cast_fp16 = mul(x = x_b_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1595_cast_fp16")]; + tensor rot_b_9_cast_fp16 = add(x = var_1594_cast_fp16, y = var_1595_cast_fp16)[name = string("rot_b_9_cast_fp16")]; + bool q_9_interleave_0 = const()[name = string("q_9_interleave_0"), val = bool(false)]; + tensor q_9_cast_fp16 = concat(axis = var_1228, interleave = q_9_interleave_0, values = (rot_a_9_cast_fp16, rot_b_9_cast_fp16))[name = string("q_9_cast_fp16")]; + tensor x_a_11_begin_0 = const()[name = string("x_a_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_11_end_0 = const()[name = string("x_a_11_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_11_end_mask_0 = const()[name = string("x_a_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_139_cast_fp16 = transpose(perm = x_139_perm_0, x = var_1555_cast_fp16)[name = string("transpose_49")]; + tensor x_a_11_cast_fp16 = slice_by_index(begin = x_a_11_begin_0, end = x_a_11_end_0, end_mask = x_a_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_a_11_cast_fp16")]; + tensor x_b_11_begin_0 = const()[name = string("x_b_11_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_11_end_0 = const()[name = string("x_b_11_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_11_end_mask_0 = const()[name = string("x_b_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_11_cast_fp16 = slice_by_index(begin = x_b_11_begin_0, end = x_b_11_end_0, end_mask = x_b_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_b_11_cast_fp16")]; + tensor var_1609_cast_fp16 = mul(x = x_a_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor var_1610_cast_fp16 = mul(x = x_b_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor rot_a_11_cast_fp16 = sub(x = var_1609_cast_fp16, y = var_1610_cast_fp16)[name = string("rot_a_11_cast_fp16")]; + tensor var_1612_cast_fp16 = mul(x = x_a_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1612_cast_fp16")]; + tensor var_1613_cast_fp16 = mul(x = x_b_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1613_cast_fp16")]; + tensor rot_b_11_cast_fp16 = add(x = var_1612_cast_fp16, y = var_1613_cast_fp16)[name = string("rot_b_11_cast_fp16")]; + bool k_9_interleave_0 = const()[name = string("k_9_interleave_0"), val = bool(false)]; + tensor k_9_cast_fp16 = concat(axis = var_1228, interleave = k_9_interleave_0, values = (rot_a_11_cast_fp16, rot_b_11_cast_fp16))[name = string("k_9_cast_fp16")]; + bool var_1618_transpose_x_1 = const()[name = string("op_1618_transpose_x_1"), val = bool(false)]; + bool var_1618_transpose_y_1 = const()[name = string("op_1618_transpose_y_1"), val = bool(true)]; + tensor var_1618_cast_fp16 = matmul(transpose_x = var_1618_transpose_x_1, transpose_y = var_1618_transpose_y_1, x = q_9_cast_fp16, y = k_9_cast_fp16)[name = string("op_1618_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_1618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor input_265_cast_fp16 = sub(x = _inversed_scores_5_cast_fp16, y = var_530_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1228, x = input_265_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool var_1627_transpose_x_0 = const()[name = string("op_1627_transpose_x_0"), val = bool(false)]; + bool var_1627_transpose_y_0 = const()[name = string("op_1627_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1563_cast_fp16)[name = string("transpose_48")]; + tensor var_1627_cast_fp16 = matmul(transpose_x = var_1627_transpose_x_0, transpose_y = var_1627_transpose_y_0, x = attn_13_cast_fp16, y = v_9_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor var_1628_perm_0 = const()[name = string("op_1628_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1629 = const()[name = string("op_1629"), val = tensor([2, 256, 512])]; + tensor var_1628_cast_fp16 = transpose(perm = var_1628_perm_0, x = var_1627_cast_fp16)[name = string("transpose_47")]; + tensor input_267_cast_fp16 = reshape(shape = var_1629, x = var_1628_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39059712))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39321920))))[name = string("main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39323008)))]; + tensor linear_24_cast_fp16 = linear(bias = main_blocks_2_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_267_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor out_43_perm_0 = const()[name = string("out_43_perm_0"), val = tensor([0, 2, 1])]; + tensor out_43_cast_fp16 = transpose(perm = out_43_perm_0, x = linear_24_cast_fp16)[name = string("transpose_46")]; + tensor var_1636_cast_fp16 = mul(x = out_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1636_cast_fp16")]; + tensor x_141_cast_fp16 = add(x = x_m_9_cast_fp16, y = var_1636_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1643_axes_0 = const()[name = string("op_1643_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39324096)))]; + tensor main_blocks_2_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39325184)))]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_141_cast_fp16)[name = string("transpose_45")]; + tensor var_1643_cast_fp16 = layer_norm(axes = var_1643_axes_0, beta = main_blocks_2_text_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_text_norm_norm_weight_to_fp16, x = input_269_cast_fp16)[name = string("op_1643_cast_fp16")]; + tensor var_1644_perm_0 = const()[name = string("op_1644_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1644_cast_fp16 = transpose(perm = var_1644_perm_0, x = var_1643_cast_fp16)[name = string("transpose_44")]; + tensor x_143_cast_fp16 = mul(x = var_1644_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor x_145_cast_fp16 = mul(x = x_143_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1657_cast_fp16 = mul(x = x_145_cast_fp16, y = last_1_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor x_last_35_axes_0 = const()[name = string("x_last_35_axes_0"), val = tensor([-1])]; + bool x_last_35_keep_dims_0 = const()[name = string("x_last_35_keep_dims_0"), val = bool(true)]; + tensor x_last_35_cast_fp16 = reduce_sum(axes = x_last_35_axes_0, keep_dims = x_last_35_keep_dims_0, x = var_1657_cast_fp16)[name = string("x_last_35_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = x_145_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1662_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_35_cast_fp16)[name = string("op_1662_cast_fp16")]; + tensor input_273_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1662_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("replicate")]; + fp16 const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = fp16(0x0p+0)]; + tensor input_275_cast_fp16 = pad(constant_val = const_59_to_fp16, mode = input_275_mode_0, pad = input_275_pad_0, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")]; + string h_105_pad_type_0 = const()[name = string("h_105_pad_type_0"), val = string("valid")]; + int32 h_105_groups_0 = const()[name = string("h_105_groups_0"), val = int32(512)]; + tensor h_105_strides_0 = const()[name = string("h_105_strides_0"), val = tensor([1])]; + tensor h_105_pad_0 = const()[name = string("h_105_pad_0"), val = tensor([0, 0])]; + tensor h_105_dilations_0 = const()[name = string("h_105_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39326272))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39328896))))[name = string("main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39329984)))]; + tensor h_105_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_105_dilations_0, groups = h_105_groups_0, pad = h_105_pad_0, pad_type = h_105_pad_type_0, strides = h_105_strides_0, weight = main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("h_105_cast_fp16")]; + tensor x_147_cast_fp16 = mul(x = h_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_277_perm_0 = const()[name = string("input_277_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1680_axes_0 = const()[name = string("op_1680_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39331072)))]; + tensor main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39332160)))]; + tensor input_277_cast_fp16 = transpose(perm = input_277_perm_0, x = x_147_cast_fp16)[name = string("transpose_43")]; + tensor var_1680_cast_fp16 = layer_norm(axes = var_1680_axes_0, beta = main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16, x = input_277_cast_fp16)[name = string("op_1680_cast_fp16")]; + tensor input_279_perm_0 = const()[name = string("input_279_perm_0"), val = tensor([0, 2, 1])]; + string h_107_pad_type_0 = const()[name = string("h_107_pad_type_0"), val = string("valid")]; + tensor h_107_strides_0 = const()[name = string("h_107_strides_0"), val = tensor([1])]; + tensor h_107_pad_0 = const()[name = string("h_107_pad_0"), val = tensor([0, 0])]; + tensor h_107_dilations_0 = const()[name = string("h_107_dilations_0"), val = tensor([1])]; + int32 h_107_groups_0 = const()[name = string("h_107_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39333248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40381888))))[name = string("main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40386048)))]; + tensor input_279_cast_fp16 = transpose(perm = input_279_perm_0, x = var_1680_cast_fp16)[name = string("transpose_42")]; + tensor h_107_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_107_dilations_0, groups = h_107_groups_0, pad = h_107_pad_0, pad_type = h_107_pad_type_0, strides = h_107_strides_0, weight = main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_279_cast_fp16)[name = string("h_107_cast_fp16")]; + string input_281_mode_0 = const()[name = string("input_281_mode_0"), val = string("EXACT")]; + tensor input_281_cast_fp16 = gelu(mode = input_281_mode_0, x = h_107_cast_fp16)[name = string("input_281_cast_fp16")]; + string h_109_pad_type_0 = const()[name = string("h_109_pad_type_0"), val = string("valid")]; + tensor h_109_strides_0 = const()[name = string("h_109_strides_0"), val = tensor([1])]; + tensor h_109_pad_0 = const()[name = string("h_109_pad_0"), val = tensor([0, 0])]; + tensor h_109_dilations_0 = const()[name = string("h_109_dilations_0"), val = tensor([1])]; + int32 h_109_groups_0 = const()[name = string("h_109_groups_0"), val = int32(1)]; + tensor op_1697_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40390208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41438848))))[name = string("op_1697_weight_0_to_fp16_quantized")]; + tensor var_1697_bias_0_to_fp16 = const()[name = string("op_1697_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41439936)))]; + tensor var_1697_cast_fp16 = conv(bias = var_1697_bias_0_to_fp16, dilations = h_109_dilations_0, groups = h_109_groups_0, pad = h_109_pad_0, pad_type = h_109_pad_type_0, strides = h_109_strides_0, weight = op_1697_weight_0_to_fp16_quantized, x = input_281_cast_fp16)[name = string("op_1697_cast_fp16")]; + tensor out_45_cast_fp16 = add(x = x_145_cast_fp16, y = var_1697_cast_fp16)[name = string("out_45_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = out_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor x_m_11_cast_fp16 = mul(x = x_149_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_11_cast_fp16")]; + tensor input_283_perm_0 = const()[name = string("input_283_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41441024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41572160))))[name = string("main_blocks_2_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41572736)))]; + tensor input_283_cast_fp16 = transpose(perm = input_283_perm_0, x = x_149_cast_fp16)[name = string("transpose_41")]; + tensor linear_25_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_283_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_1713 = const()[name = string("op_1713"), val = tensor([2, 256, 2, 128])]; + tensor var_1714_cast_fp16 = reshape(shape = var_1713, x = linear_25_cast_fp16)[name = string("op_1714_cast_fp16")]; + tensor q_11_perm_0 = const()[name = string("q_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_2_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41573312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41638912))))[name = string("main_blocks_2_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41639488)))]; + tensor linear_27_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1727 = const()[name = string("op_1727"), val = tensor([2, 50, 2, 128])]; + tensor var_1728_cast_fp16 = reshape(shape = var_1727, x = linear_27_cast_fp16)[name = string("op_1728_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1732_transpose_x_0 = const()[name = string("op_1732_transpose_x_0"), val = bool(false)]; + bool var_1732_transpose_y_0 = const()[name = string("op_1732_transpose_y_0"), val = bool(false)]; + tensor op_1731_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41640064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41665728))))[name = string("op_1731_to_fp16_quantized")]; + tensor q_11_cast_fp16 = transpose(perm = q_11_perm_0, x = var_1714_cast_fp16)[name = string("transpose_40")]; + tensor var_1732_cast_fp16 = matmul(transpose_x = var_1732_transpose_x_0, transpose_y = var_1732_transpose_y_0, x = q_11_cast_fp16, y = op_1731_to_fp16_quantized)[name = string("op_1732_cast_fp16")]; + fp16 _inversed_input_285_y_0_to_fp16 = const()[name = string("_inversed_input_285_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_285_cast_fp16 = mul(x = var_1732_cast_fp16, y = _inversed_input_285_y_0_to_fp16)[name = string("_inversed_input_285_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1228, x = _inversed_input_285_cast_fp16)[name = string("attn_15_cast_fp16")]; + tensor attn_17_cast_fp16 = mul(x = attn_15_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool var_1739_transpose_x_0 = const()[name = string("op_1739_transpose_x_0"), val = bool(false)]; + bool var_1739_transpose_y_0 = const()[name = string("op_1739_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_1728_cast_fp16)[name = string("transpose_39")]; + tensor var_1739_cast_fp16 = matmul(transpose_x = var_1739_transpose_x_0, transpose_y = var_1739_transpose_y_0, x = attn_17_cast_fp16, y = v_11_cast_fp16)[name = string("op_1739_cast_fp16")]; + tensor var_1740_perm_0 = const()[name = string("op_1740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1741 = const()[name = string("op_1741"), val = tensor([2, 256, 256])]; + tensor var_1740_cast_fp16 = transpose(perm = var_1740_perm_0, x = var_1739_cast_fp16)[name = string("transpose_38")]; + tensor input_287_cast_fp16 = reshape(shape = var_1741, x = var_1740_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41665920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41797056))))[name = string("main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41798144)))]; + tensor linear_28_cast_fp16 = linear(bias = main_blocks_2_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_287_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor out_47_perm_0 = const()[name = string("out_47_perm_0"), val = tensor([0, 2, 1])]; + tensor out_47_cast_fp16 = transpose(perm = out_47_perm_0, x = linear_28_cast_fp16)[name = string("transpose_37")]; + tensor var_1748_cast_fp16 = mul(x = out_47_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1748_cast_fp16")]; + tensor x_151_cast_fp16 = add(x = x_m_11_cast_fp16, y = var_1748_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_289_perm_0 = const()[name = string("input_289_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1755_axes_0 = const()[name = string("op_1755_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41799232)))]; + tensor main_blocks_2_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41800320)))]; + tensor input_289_cast_fp16 = transpose(perm = input_289_perm_0, x = x_151_cast_fp16)[name = string("transpose_36")]; + tensor var_1755_cast_fp16 = layer_norm(axes = var_1755_axes_0, beta = main_blocks_2_style_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_style_norm_norm_weight_to_fp16, x = input_289_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor var_1756_perm_0 = const()[name = string("op_1756_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1756_cast_fp16 = transpose(perm = var_1756_perm_0, x = var_1755_cast_fp16)[name = string("transpose_35")]; + tensor x_153_cast_fp16 = mul(x = var_1756_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_153_cast_fp16")]; + int32 var_1775 = const()[name = string("op_1775"), val = int32(-1)]; + tensor x_155_cast_fp16 = mul(x = x_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor var_1810_cast_fp16 = mul(x = x_155_cast_fp16, y = last_1_cast_fp16)[name = string("op_1810_cast_fp16")]; + tensor x_last_37_axes_0 = const()[name = string("x_last_37_axes_0"), val = tensor([-1])]; + bool x_last_37_keep_dims_0 = const()[name = string("x_last_37_keep_dims_0"), val = bool(true)]; + tensor x_last_37_cast_fp16 = reduce_sum(axes = x_last_37_axes_0, keep_dims = x_last_37_keep_dims_0, x = var_1810_cast_fp16)[name = string("x_last_37_cast_fp16")]; + tensor var_1813_cast_fp16 = mul(x = x_155_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1815_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_37_cast_fp16)[name = string("op_1815_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = var_1813_cast_fp16, y = var_1815_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_295_mode_0 = const()[name = string("input_295_mode_0"), val = string("replicate")]; + fp16 const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = fp16(0x0p+0)]; + tensor input_295_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")]; + string h_111_pad_type_0 = const()[name = string("h_111_pad_type_0"), val = string("valid")]; + int32 h_111_groups_0 = const()[name = string("h_111_groups_0"), val = int32(512)]; + tensor h_111_strides_0 = const()[name = string("h_111_strides_0"), val = tensor([1])]; + tensor h_111_pad_0 = const()[name = string("h_111_pad_0"), val = tensor([0, 0])]; + tensor h_111_dilations_0 = const()[name = string("h_111_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41801408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41804032))))[name = string("main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41805120)))]; + tensor h_111_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_111_dilations_0, groups = h_111_groups_0, pad = h_111_pad_0, pad_type = h_111_pad_type_0, strides = h_111_strides_0, weight = main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_295_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor x_157_cast_fp16 = mul(x = h_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor input_297_perm_0 = const()[name = string("input_297_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1833_axes_0 = const()[name = string("op_1833_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41806208)))]; + tensor main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41807296)))]; + fp16 var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_297_cast_fp16 = transpose(perm = input_297_perm_0, x = x_157_cast_fp16)[name = string("transpose_34")]; + tensor var_1833_cast_fp16 = layer_norm(axes = var_1833_axes_0, beta = main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16, x = input_297_cast_fp16)[name = string("op_1833_cast_fp16")]; + tensor input_299_perm_0 = const()[name = string("input_299_perm_0"), val = tensor([0, 2, 1])]; + string h_113_pad_type_0 = const()[name = string("h_113_pad_type_0"), val = string("valid")]; + tensor h_113_strides_0 = const()[name = string("h_113_strides_0"), val = tensor([1])]; + tensor h_113_pad_0 = const()[name = string("h_113_pad_0"), val = tensor([0, 0])]; + tensor h_113_dilations_0 = const()[name = string("h_113_dilations_0"), val = tensor([1])]; + int32 h_113_groups_0 = const()[name = string("h_113_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41808384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42857024))))[name = string("main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42861184)))]; + tensor input_299_cast_fp16 = transpose(perm = input_299_perm_0, x = var_1833_cast_fp16)[name = string("transpose_33")]; + tensor h_113_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_113_dilations_0, groups = h_113_groups_0, pad = h_113_pad_0, pad_type = h_113_pad_type_0, strides = h_113_strides_0, weight = main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_299_cast_fp16)[name = string("h_113_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = h_113_cast_fp16)[name = string("input_301_cast_fp16")]; + string h_115_pad_type_0 = const()[name = string("h_115_pad_type_0"), val = string("valid")]; + tensor h_115_strides_0 = const()[name = string("h_115_strides_0"), val = tensor([1])]; + tensor h_115_pad_0 = const()[name = string("h_115_pad_0"), val = tensor([0, 0])]; + tensor h_115_dilations_0 = const()[name = string("h_115_dilations_0"), val = tensor([1])]; + int32 h_115_groups_0 = const()[name = string("h_115_groups_0"), val = int32(1)]; + tensor op_1850_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42865344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43913984))))[name = string("op_1850_weight_0_to_fp16_quantized")]; + tensor var_1850_bias_0_to_fp16 = const()[name = string("op_1850_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43915072)))]; + tensor var_1850_cast_fp16 = conv(bias = var_1850_bias_0_to_fp16, dilations = h_115_dilations_0, groups = h_115_groups_0, pad = h_115_pad_0, pad_type = h_115_pad_type_0, strides = h_115_strides_0, weight = op_1850_weight_0_to_fp16_quantized, x = input_301_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor out_49_cast_fp16 = add(x = x_155_cast_fp16, y = var_1850_cast_fp16)[name = string("out_49_cast_fp16")]; + tensor x_159_cast_fp16 = mul(x = out_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor x_161_cast_fp16 = mul(x = x_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor var_1864_cast_fp16 = mul(x = x_161_cast_fp16, y = last_1_cast_fp16)[name = string("op_1864_cast_fp16")]; + tensor x_last_39_axes_0 = const()[name = string("x_last_39_axes_0"), val = tensor([-1])]; + bool x_last_39_keep_dims_0 = const()[name = string("x_last_39_keep_dims_0"), val = bool(true)]; + tensor x_last_39_cast_fp16 = reduce_sum(axes = x_last_39_axes_0, keep_dims = x_last_39_keep_dims_0, x = var_1864_cast_fp16)[name = string("x_last_39_cast_fp16")]; + tensor var_1867_cast_fp16 = mul(x = x_161_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_39_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor input_305_cast_fp16 = add(x = var_1867_cast_fp16, y = var_1869_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("replicate")]; + fp16 const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_66_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string h_117_pad_type_0 = const()[name = string("h_117_pad_type_0"), val = string("valid")]; + tensor h_117_dilations_0 = const()[name = string("h_117_dilations_0"), val = tensor([2])]; + int32 h_117_groups_0 = const()[name = string("h_117_groups_0"), val = int32(512)]; + tensor h_117_strides_0 = const()[name = string("h_117_strides_0"), val = tensor([1])]; + tensor h_117_pad_0 = const()[name = string("h_117_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43916160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43918784))))[name = string("main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43919872)))]; + tensor h_117_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_117_dilations_0, groups = h_117_groups_0, pad = h_117_pad_0, pad_type = h_117_pad_type_0, strides = h_117_strides_0, weight = main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_307_cast_fp16)[name = string("h_117_cast_fp16")]; + tensor x_163_cast_fp16 = mul(x = h_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1887_axes_0 = const()[name = string("op_1887_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43920960)))]; + tensor main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43922048)))]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_163_cast_fp16)[name = string("transpose_32")]; + tensor var_1887_cast_fp16 = layer_norm(axes = var_1887_axes_0, beta = main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16, x = input_309_cast_fp16)[name = string("op_1887_cast_fp16")]; + tensor input_311_perm_0 = const()[name = string("input_311_perm_0"), val = tensor([0, 2, 1])]; + string h_119_pad_type_0 = const()[name = string("h_119_pad_type_0"), val = string("valid")]; + tensor h_119_strides_0 = const()[name = string("h_119_strides_0"), val = tensor([1])]; + tensor h_119_pad_0 = const()[name = string("h_119_pad_0"), val = tensor([0, 0])]; + tensor h_119_dilations_0 = const()[name = string("h_119_dilations_0"), val = tensor([1])]; + int32 h_119_groups_0 = const()[name = string("h_119_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43923136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44971776))))[name = string("main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44975936)))]; + tensor input_311_cast_fp16 = transpose(perm = input_311_perm_0, x = var_1887_cast_fp16)[name = string("transpose_31")]; + tensor h_119_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_119_dilations_0, groups = h_119_groups_0, pad = h_119_pad_0, pad_type = h_119_pad_type_0, strides = h_119_strides_0, weight = main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_311_cast_fp16)[name = string("h_119_cast_fp16")]; + string input_313_mode_0 = const()[name = string("input_313_mode_0"), val = string("EXACT")]; + tensor input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = h_119_cast_fp16)[name = string("input_313_cast_fp16")]; + string h_121_pad_type_0 = const()[name = string("h_121_pad_type_0"), val = string("valid")]; + tensor h_121_strides_0 = const()[name = string("h_121_strides_0"), val = tensor([1])]; + tensor h_121_pad_0 = const()[name = string("h_121_pad_0"), val = tensor([0, 0])]; + tensor h_121_dilations_0 = const()[name = string("h_121_dilations_0"), val = tensor([1])]; + int32 h_121_groups_0 = const()[name = string("h_121_groups_0"), val = int32(1)]; + tensor op_1904_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44980096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46028736))))[name = string("op_1904_weight_0_to_fp16_quantized")]; + tensor var_1904_bias_0_to_fp16 = const()[name = string("op_1904_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46029824)))]; + tensor var_1904_cast_fp16 = conv(bias = var_1904_bias_0_to_fp16, dilations = h_121_dilations_0, groups = h_121_groups_0, pad = h_121_pad_0, pad_type = h_121_pad_type_0, strides = h_121_strides_0, weight = op_1904_weight_0_to_fp16_quantized, x = input_313_cast_fp16)[name = string("op_1904_cast_fp16")]; + tensor out_51_cast_fp16 = add(x = x_161_cast_fp16, y = var_1904_cast_fp16)[name = string("out_51_cast_fp16")]; + tensor x_165_cast_fp16 = mul(x = out_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor x_167_cast_fp16 = mul(x = x_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1918_cast_fp16 = mul(x = x_167_cast_fp16, y = last_1_cast_fp16)[name = string("op_1918_cast_fp16")]; + tensor x_last_41_axes_0 = const()[name = string("x_last_41_axes_0"), val = tensor([-1])]; + bool x_last_41_keep_dims_0 = const()[name = string("x_last_41_keep_dims_0"), val = bool(true)]; + tensor x_last_41_cast_fp16 = reduce_sum(axes = x_last_41_axes_0, keep_dims = x_last_41_keep_dims_0, x = var_1918_cast_fp16)[name = string("x_last_41_cast_fp16")]; + tensor var_1921_cast_fp16 = mul(x = x_167_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1921_cast_fp16")]; + tensor var_1923_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_41_cast_fp16)[name = string("op_1923_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = var_1921_cast_fp16, y = var_1923_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("replicate")]; + fp16 const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = fp16(0x0p+0)]; + tensor input_319_cast_fp16 = pad(constant_val = const_68_to_fp16, mode = input_319_mode_0, pad = input_319_pad_0, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string h_123_pad_type_0 = const()[name = string("h_123_pad_type_0"), val = string("valid")]; + tensor h_123_dilations_0 = const()[name = string("h_123_dilations_0"), val = tensor([4])]; + int32 h_123_groups_0 = const()[name = string("h_123_groups_0"), val = int32(512)]; + tensor h_123_strides_0 = const()[name = string("h_123_strides_0"), val = tensor([1])]; + tensor h_123_pad_0 = const()[name = string("h_123_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46030912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46033536))))[name = string("main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46034624)))]; + tensor h_123_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_123_dilations_0, groups = h_123_groups_0, pad = h_123_pad_0, pad_type = h_123_pad_type_0, strides = h_123_strides_0, weight = main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_319_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor x_169_cast_fp16 = mul(x = h_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1941_axes_0 = const()[name = string("op_1941_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46035712)))]; + tensor main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46036800)))]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_169_cast_fp16)[name = string("transpose_30")]; + tensor var_1941_cast_fp16 = layer_norm(axes = var_1941_axes_0, beta = main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16, x = input_321_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor([0, 2, 1])]; + string h_125_pad_type_0 = const()[name = string("h_125_pad_type_0"), val = string("valid")]; + tensor h_125_strides_0 = const()[name = string("h_125_strides_0"), val = tensor([1])]; + tensor h_125_pad_0 = const()[name = string("h_125_pad_0"), val = tensor([0, 0])]; + tensor h_125_dilations_0 = const()[name = string("h_125_dilations_0"), val = tensor([1])]; + int32 h_125_groups_0 = const()[name = string("h_125_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46037888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47086528))))[name = string("main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47090688)))]; + tensor input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = var_1941_cast_fp16)[name = string("transpose_29")]; + tensor h_125_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_125_dilations_0, groups = h_125_groups_0, pad = h_125_pad_0, pad_type = h_125_pad_type_0, strides = h_125_strides_0, weight = main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_323_cast_fp16)[name = string("h_125_cast_fp16")]; + string input_325_mode_0 = const()[name = string("input_325_mode_0"), val = string("EXACT")]; + tensor input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = h_125_cast_fp16)[name = string("input_325_cast_fp16")]; + string h_127_pad_type_0 = const()[name = string("h_127_pad_type_0"), val = string("valid")]; + tensor h_127_strides_0 = const()[name = string("h_127_strides_0"), val = tensor([1])]; + tensor h_127_pad_0 = const()[name = string("h_127_pad_0"), val = tensor([0, 0])]; + tensor h_127_dilations_0 = const()[name = string("h_127_dilations_0"), val = tensor([1])]; + int32 h_127_groups_0 = const()[name = string("h_127_groups_0"), val = int32(1)]; + tensor op_1958_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47094848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48143488))))[name = string("op_1958_weight_0_to_fp16_quantized")]; + tensor var_1958_bias_0_to_fp16 = const()[name = string("op_1958_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48144576)))]; + tensor var_1958_cast_fp16 = conv(bias = var_1958_bias_0_to_fp16, dilations = h_127_dilations_0, groups = h_127_groups_0, pad = h_127_pad_0, pad_type = h_127_pad_type_0, strides = h_127_strides_0, weight = op_1958_weight_0_to_fp16_quantized, x = input_325_cast_fp16)[name = string("op_1958_cast_fp16")]; + tensor out_53_cast_fp16 = add(x = x_167_cast_fp16, y = var_1958_cast_fp16)[name = string("out_53_cast_fp16")]; + tensor x_171_cast_fp16 = mul(x = out_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_171_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor var_1972_cast_fp16 = mul(x = x_173_cast_fp16, y = last_1_cast_fp16)[name = string("op_1972_cast_fp16")]; + tensor x_last_43_axes_0 = const()[name = string("x_last_43_axes_0"), val = tensor([-1])]; + bool x_last_43_keep_dims_0 = const()[name = string("x_last_43_keep_dims_0"), val = bool(true)]; + tensor x_last_43_cast_fp16 = reduce_sum(axes = x_last_43_axes_0, keep_dims = x_last_43_keep_dims_0, x = var_1972_cast_fp16)[name = string("x_last_43_cast_fp16")]; + tensor var_1975_cast_fp16 = mul(x = x_173_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1975_cast_fp16")]; + tensor var_1977_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_43_cast_fp16)[name = string("op_1977_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1977_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("replicate")]; + fp16 const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = fp16(0x0p+0)]; + tensor input_331_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = input_331_mode_0, pad = input_331_pad_0, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + string h_129_pad_type_0 = const()[name = string("h_129_pad_type_0"), val = string("valid")]; + tensor h_129_dilations_0 = const()[name = string("h_129_dilations_0"), val = tensor([8])]; + int32 h_129_groups_0 = const()[name = string("h_129_groups_0"), val = int32(512)]; + tensor h_129_strides_0 = const()[name = string("h_129_strides_0"), val = tensor([1])]; + tensor h_129_pad_0 = const()[name = string("h_129_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48145664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48148288))))[name = string("main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48149376)))]; + tensor h_129_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_129_dilations_0, groups = h_129_groups_0, pad = h_129_pad_0, pad_type = h_129_pad_type_0, strides = h_129_strides_0, weight = main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_331_cast_fp16)[name = string("h_129_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = h_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_333_perm_0 = const()[name = string("input_333_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1995_axes_0 = const()[name = string("op_1995_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48150464)))]; + tensor main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48151552)))]; + tensor input_333_cast_fp16 = transpose(perm = input_333_perm_0, x = x_175_cast_fp16)[name = string("transpose_28")]; + tensor var_1995_cast_fp16 = layer_norm(axes = var_1995_axes_0, beta = main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16, x = input_333_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor input_335_perm_0 = const()[name = string("input_335_perm_0"), val = tensor([0, 2, 1])]; + string h_131_pad_type_0 = const()[name = string("h_131_pad_type_0"), val = string("valid")]; + tensor h_131_strides_0 = const()[name = string("h_131_strides_0"), val = tensor([1])]; + tensor h_131_pad_0 = const()[name = string("h_131_pad_0"), val = tensor([0, 0])]; + tensor h_131_dilations_0 = const()[name = string("h_131_dilations_0"), val = tensor([1])]; + int32 h_131_groups_0 = const()[name = string("h_131_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48152640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49201280))))[name = string("main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49205440)))]; + tensor input_335_cast_fp16 = transpose(perm = input_335_perm_0, x = var_1995_cast_fp16)[name = string("transpose_27")]; + tensor h_131_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_131_dilations_0, groups = h_131_groups_0, pad = h_131_pad_0, pad_type = h_131_pad_type_0, strides = h_131_strides_0, weight = main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_335_cast_fp16)[name = string("h_131_cast_fp16")]; + string input_337_mode_0 = const()[name = string("input_337_mode_0"), val = string("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = h_131_cast_fp16)[name = string("input_337_cast_fp16")]; + string h_133_pad_type_0 = const()[name = string("h_133_pad_type_0"), val = string("valid")]; + tensor h_133_strides_0 = const()[name = string("h_133_strides_0"), val = tensor([1])]; + tensor h_133_pad_0 = const()[name = string("h_133_pad_0"), val = tensor([0, 0])]; + tensor h_133_dilations_0 = const()[name = string("h_133_dilations_0"), val = tensor([1])]; + int32 h_133_groups_0 = const()[name = string("h_133_groups_0"), val = int32(1)]; + tensor op_2012_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49209600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50258240))))[name = string("op_2012_weight_0_to_fp16_quantized")]; + tensor var_2012_bias_0_to_fp16 = const()[name = string("op_2012_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50259328)))]; + tensor var_2012_cast_fp16 = conv(bias = var_2012_bias_0_to_fp16, dilations = h_133_dilations_0, groups = h_133_groups_0, pad = h_133_pad_0, pad_type = h_133_pad_type_0, strides = h_133_strides_0, weight = op_2012_weight_0_to_fp16_quantized, x = input_337_cast_fp16)[name = string("op_2012_cast_fp16")]; + tensor out_55_cast_fp16 = add(x = x_173_cast_fp16, y = var_2012_cast_fp16)[name = string("out_55_cast_fp16")]; + tensor x_177_cast_fp16 = mul(x = out_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor main_blocks_3_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50260416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50293248))))[name = string("main_blocks_3_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50294336)))]; + tensor linear_29_cast_fp16 = linear(bias = main_blocks_3_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_3_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor t_axes_0 = const()[name = string("t_axes_0"), val = tensor([-1])]; + tensor t_cast_fp16 = expand_dims(axes = t_axes_0, x = linear_29_cast_fp16)[name = string("t_cast_fp16")]; + tensor var_2022_cast_fp16 = add(x = x_177_cast_fp16, y = t_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor x_179_cast_fp16 = mul(x = var_2022_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor x_181_cast_fp16 = mul(x = x_179_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor var_2035_cast_fp16 = mul(x = x_181_cast_fp16, y = last_1_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor x_last_45_axes_0 = const()[name = string("x_last_45_axes_0"), val = tensor([-1])]; + bool x_last_45_keep_dims_0 = const()[name = string("x_last_45_keep_dims_0"), val = bool(true)]; + tensor x_last_45_cast_fp16 = reduce_sum(axes = x_last_45_axes_0, keep_dims = x_last_45_keep_dims_0, x = var_2035_cast_fp16)[name = string("x_last_45_cast_fp16")]; + tensor var_2038_cast_fp16 = mul(x = x_181_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2038_cast_fp16")]; + tensor var_2040_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_45_cast_fp16)[name = string("op_2040_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = var_2038_cast_fp16, y = var_2040_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_345_mode_0 = const()[name = string("input_345_mode_0"), val = string("replicate")]; + fp16 const_72_to_fp16 = const()[name = string("const_72_to_fp16"), val = fp16(0x0p+0)]; + tensor input_345_cast_fp16 = pad(constant_val = const_72_to_fp16, mode = input_345_mode_0, pad = input_345_pad_0, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + string h_135_pad_type_0 = const()[name = string("h_135_pad_type_0"), val = string("valid")]; + int32 h_135_groups_0 = const()[name = string("h_135_groups_0"), val = int32(512)]; + tensor h_135_strides_0 = const()[name = string("h_135_strides_0"), val = tensor([1])]; + tensor h_135_pad_0 = const()[name = string("h_135_pad_0"), val = tensor([0, 0])]; + tensor h_135_dilations_0 = const()[name = string("h_135_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50295424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50298048))))[name = string("main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50299136)))]; + tensor h_135_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_135_dilations_0, groups = h_135_groups_0, pad = h_135_pad_0, pad_type = h_135_pad_type_0, strides = h_135_strides_0, weight = main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_345_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor x_183_cast_fp16 = mul(x = h_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_183_cast_fp16")]; + tensor input_347_perm_0 = const()[name = string("input_347_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2058_axes_0 = const()[name = string("op_2058_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50300224)))]; + tensor main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50301312)))]; + tensor input_347_cast_fp16 = transpose(perm = input_347_perm_0, x = x_183_cast_fp16)[name = string("transpose_26")]; + tensor var_2058_cast_fp16 = layer_norm(axes = var_2058_axes_0, beta = main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16, x = input_347_cast_fp16)[name = string("op_2058_cast_fp16")]; + tensor input_349_perm_0 = const()[name = string("input_349_perm_0"), val = tensor([0, 2, 1])]; + string h_137_pad_type_0 = const()[name = string("h_137_pad_type_0"), val = string("valid")]; + tensor h_137_strides_0 = const()[name = string("h_137_strides_0"), val = tensor([1])]; + tensor h_137_pad_0 = const()[name = string("h_137_pad_0"), val = tensor([0, 0])]; + tensor h_137_dilations_0 = const()[name = string("h_137_dilations_0"), val = tensor([1])]; + int32 h_137_groups_0 = const()[name = string("h_137_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50302400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51351040))))[name = string("main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51355200)))]; + tensor input_349_cast_fp16 = transpose(perm = input_349_perm_0, x = var_2058_cast_fp16)[name = string("transpose_25")]; + tensor h_137_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_137_dilations_0, groups = h_137_groups_0, pad = h_137_pad_0, pad_type = h_137_pad_type_0, strides = h_137_strides_0, weight = main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_349_cast_fp16)[name = string("h_137_cast_fp16")]; + string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")]; + tensor input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = h_137_cast_fp16)[name = string("input_351_cast_fp16")]; + string h_139_pad_type_0 = const()[name = string("h_139_pad_type_0"), val = string("valid")]; + tensor h_139_strides_0 = const()[name = string("h_139_strides_0"), val = tensor([1])]; + tensor h_139_pad_0 = const()[name = string("h_139_pad_0"), val = tensor([0, 0])]; + tensor h_139_dilations_0 = const()[name = string("h_139_dilations_0"), val = tensor([1])]; + int32 h_139_groups_0 = const()[name = string("h_139_groups_0"), val = int32(1)]; + tensor op_2075_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51359360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52408000))))[name = string("op_2075_weight_0_to_fp16_quantized")]; + tensor var_2075_bias_0_to_fp16 = const()[name = string("op_2075_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52409088)))]; + tensor var_2075_cast_fp16 = conv(bias = var_2075_bias_0_to_fp16, dilations = h_139_dilations_0, groups = h_139_groups_0, pad = h_139_pad_0, pad_type = h_139_pad_type_0, strides = h_139_strides_0, weight = op_2075_weight_0_to_fp16_quantized, x = input_351_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor out_57_cast_fp16 = add(x = x_181_cast_fp16, y = var_2075_cast_fp16)[name = string("out_57_cast_fp16")]; + tensor x_185_cast_fp16 = mul(x = out_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor x_m_13_cast_fp16 = mul(x = x_185_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_13_cast_fp16")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52410176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52672384))))[name = string("main_blocks_3_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52673472)))]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_185_cast_fp16)[name = string("transpose_24")]; + tensor linear_30_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_353_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_2093 = const()[name = string("op_2093"), val = tensor([2, 256, 8, 64])]; + tensor var_2094_cast_fp16 = reshape(shape = var_2093, x = linear_30_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor x_187_perm_0 = const()[name = string("x_187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52674560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52805696))))[name = string("main_blocks_3_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52806784)))]; + tensor linear_31_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_2101 = const()[name = string("op_2101"), val = tensor([2, 128, 8, 64])]; + tensor var_2102_cast_fp16 = reshape(shape = var_2101, x = linear_31_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor x_189_perm_0 = const()[name = string("x_189_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52807872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52939008))))[name = string("main_blocks_3_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52940096)))]; + tensor linear_32_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_2109 = const()[name = string("op_2109"), val = tensor([2, 128, 8, 64])]; + tensor var_2110_cast_fp16 = reshape(shape = var_2109, x = linear_32_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_13_begin_0 = const()[name = string("x_a_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_13_end_0 = const()[name = string("x_a_13_end_0"), val = tensor([2, 8, 256, 32])]; + tensor x_a_13_end_mask_0 = const()[name = string("x_a_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_187_cast_fp16 = transpose(perm = x_187_perm_0, x = var_2094_cast_fp16)[name = string("transpose_23")]; + tensor x_a_13_cast_fp16 = slice_by_index(begin = x_a_13_begin_0, end = x_a_13_end_0, end_mask = x_a_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_a_13_cast_fp16")]; + tensor x_b_13_begin_0 = const()[name = string("x_b_13_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_13_end_0 = const()[name = string("x_b_13_end_0"), val = tensor([2, 8, 256, 64])]; + tensor x_b_13_end_mask_0 = const()[name = string("x_b_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_13_cast_fp16 = slice_by_index(begin = x_b_13_begin_0, end = x_b_13_end_0, end_mask = x_b_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_b_13_cast_fp16")]; + tensor var_2138_cast_fp16 = mul(x = x_a_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2138_cast_fp16")]; + tensor var_2139_cast_fp16 = mul(x = x_b_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2139_cast_fp16")]; + tensor rot_a_13_cast_fp16 = sub(x = var_2138_cast_fp16, y = var_2139_cast_fp16)[name = string("rot_a_13_cast_fp16")]; + tensor var_2141_cast_fp16 = mul(x = x_a_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2141_cast_fp16")]; + tensor var_2142_cast_fp16 = mul(x = x_b_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2142_cast_fp16")]; + tensor rot_b_13_cast_fp16 = add(x = var_2141_cast_fp16, y = var_2142_cast_fp16)[name = string("rot_b_13_cast_fp16")]; + bool q_13_interleave_0 = const()[name = string("q_13_interleave_0"), val = bool(false)]; + tensor q_13_cast_fp16 = concat(axis = var_1775, interleave = q_13_interleave_0, values = (rot_a_13_cast_fp16, rot_b_13_cast_fp16))[name = string("q_13_cast_fp16")]; + tensor x_a_begin_0 = const()[name = string("x_a_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_end_0 = const()[name = string("x_a_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_end_mask_0 = const()[name = string("x_a_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_189_cast_fp16 = transpose(perm = x_189_perm_0, x = var_2102_cast_fp16)[name = string("transpose_22")]; + tensor x_a_cast_fp16 = slice_by_index(begin = x_a_begin_0, end = x_a_end_0, end_mask = x_a_end_mask_0, x = x_189_cast_fp16)[name = string("x_a_cast_fp16")]; + tensor x_b_begin_0 = const()[name = string("x_b_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_end_0 = const()[name = string("x_b_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_end_mask_0 = const()[name = string("x_b_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_cast_fp16 = slice_by_index(begin = x_b_begin_0, end = x_b_end_0, end_mask = x_b_end_mask_0, x = x_189_cast_fp16)[name = string("x_b_cast_fp16")]; + tensor var_2156_cast_fp16 = mul(x = x_a_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2156_cast_fp16")]; + tensor var_2157_cast_fp16 = mul(x = x_b_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2157_cast_fp16")]; + tensor rot_a_cast_fp16 = sub(x = var_2156_cast_fp16, y = var_2157_cast_fp16)[name = string("rot_a_cast_fp16")]; + tensor var_2159_cast_fp16 = mul(x = x_a_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2159_cast_fp16")]; + tensor var_2160_cast_fp16 = mul(x = x_b_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2160_cast_fp16")]; + tensor rot_b_cast_fp16 = add(x = var_2159_cast_fp16, y = var_2160_cast_fp16)[name = string("rot_b_cast_fp16")]; + bool k_13_interleave_0 = const()[name = string("k_13_interleave_0"), val = bool(false)]; + tensor k_13_cast_fp16 = concat(axis = var_1775, interleave = k_13_interleave_0, values = (rot_a_cast_fp16, rot_b_cast_fp16))[name = string("k_13_cast_fp16")]; + bool var_2165_transpose_x_1 = const()[name = string("op_2165_transpose_x_1"), val = bool(false)]; + bool var_2165_transpose_y_1 = const()[name = string("op_2165_transpose_y_1"), val = bool(true)]; + tensor var_2165_cast_fp16 = matmul(transpose_x = var_2165_transpose_x_1, transpose_y = var_2165_transpose_y_1, x = q_13_cast_fp16, y = k_13_cast_fp16)[name = string("op_2165_cast_fp16")]; + fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_cast_fp16 = mul(x = var_2165_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")]; + tensor input_359_cast_fp16 = sub(x = _inversed_scores_cast_fp16, y = var_530_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_1775, x = input_359_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool var_2174_transpose_x_0 = const()[name = string("op_2174_transpose_x_0"), val = bool(false)]; + bool var_2174_transpose_y_0 = const()[name = string("op_2174_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2110_cast_fp16)[name = string("transpose_21")]; + tensor var_2174_cast_fp16 = matmul(transpose_x = var_2174_transpose_x_0, transpose_y = var_2174_transpose_y_0, x = attn_19_cast_fp16, y = v_13_cast_fp16)[name = string("op_2174_cast_fp16")]; + tensor var_2175_perm_0 = const()[name = string("op_2175_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2176 = const()[name = string("op_2176"), val = tensor([2, 256, 512])]; + tensor var_2175_cast_fp16 = transpose(perm = var_2175_perm_0, x = var_2174_cast_fp16)[name = string("transpose_20")]; + tensor input_361_cast_fp16 = reshape(shape = var_2176, x = var_2175_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52941184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53203392))))[name = string("main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53204480)))]; + tensor linear_33_cast_fp16 = linear(bias = main_blocks_3_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_361_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor out_59_perm_0 = const()[name = string("out_59_perm_0"), val = tensor([0, 2, 1])]; + tensor out_59_cast_fp16 = transpose(perm = out_59_perm_0, x = linear_33_cast_fp16)[name = string("transpose_19")]; + tensor var_2183_cast_fp16 = mul(x = out_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_m_13_cast_fp16, y = var_2183_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor input_363_perm_0 = const()[name = string("input_363_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2190_axes_0 = const()[name = string("op_2190_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53205568)))]; + tensor main_blocks_3_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53206656)))]; + tensor input_363_cast_fp16 = transpose(perm = input_363_perm_0, x = x_191_cast_fp16)[name = string("transpose_18")]; + tensor var_2190_cast_fp16 = layer_norm(axes = var_2190_axes_0, beta = main_blocks_3_text_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_text_norm_norm_weight_to_fp16, x = input_363_cast_fp16)[name = string("op_2190_cast_fp16")]; + tensor var_2191_perm_0 = const()[name = string("op_2191_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2191_perm_0, x = var_2190_cast_fp16)[name = string("transpose_17")]; + tensor x_193_cast_fp16 = mul(x = var_2191_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_193_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2204_cast_fp16 = mul(x = x_195_cast_fp16, y = last_1_cast_fp16)[name = string("op_2204_cast_fp16")]; + tensor x_last_47_axes_0 = const()[name = string("x_last_47_axes_0"), val = tensor([-1])]; + bool x_last_47_keep_dims_0 = const()[name = string("x_last_47_keep_dims_0"), val = bool(true)]; + tensor x_last_47_cast_fp16 = reduce_sum(axes = x_last_47_axes_0, keep_dims = x_last_47_keep_dims_0, x = var_2204_cast_fp16)[name = string("x_last_47_cast_fp16")]; + tensor var_2207_cast_fp16 = mul(x = x_195_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2207_cast_fp16")]; + tensor var_2209_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_47_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor input_367_cast_fp16 = add(x = var_2207_cast_fp16, y = var_2209_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_369_mode_0 = const()[name = string("input_369_mode_0"), val = string("replicate")]; + fp16 const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = fp16(0x0p+0)]; + tensor input_369_cast_fp16 = pad(constant_val = const_79_to_fp16, mode = input_369_mode_0, pad = input_369_pad_0, x = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + string h_141_pad_type_0 = const()[name = string("h_141_pad_type_0"), val = string("valid")]; + int32 h_141_groups_0 = const()[name = string("h_141_groups_0"), val = int32(512)]; + tensor h_141_strides_0 = const()[name = string("h_141_strides_0"), val = tensor([1])]; + tensor h_141_pad_0 = const()[name = string("h_141_pad_0"), val = tensor([0, 0])]; + tensor h_141_dilations_0 = const()[name = string("h_141_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53207744))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53210368))))[name = string("main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53211456)))]; + tensor h_141_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_141_dilations_0, groups = h_141_groups_0, pad = h_141_pad_0, pad_type = h_141_pad_type_0, strides = h_141_strides_0, weight = main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_369_cast_fp16)[name = string("h_141_cast_fp16")]; + tensor x_197_cast_fp16 = mul(x = h_141_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_371_perm_0 = const()[name = string("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2227_axes_0 = const()[name = string("op_2227_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53212544)))]; + tensor main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53213632)))]; + tensor input_371_cast_fp16 = transpose(perm = input_371_perm_0, x = x_197_cast_fp16)[name = string("transpose_16")]; + tensor var_2227_cast_fp16 = layer_norm(axes = var_2227_axes_0, beta = main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16, x = input_371_cast_fp16)[name = string("op_2227_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + string h_143_pad_type_0 = const()[name = string("h_143_pad_type_0"), val = string("valid")]; + tensor h_143_strides_0 = const()[name = string("h_143_strides_0"), val = tensor([1])]; + tensor h_143_pad_0 = const()[name = string("h_143_pad_0"), val = tensor([0, 0])]; + tensor h_143_dilations_0 = const()[name = string("h_143_dilations_0"), val = tensor([1])]; + int32 h_143_groups_0 = const()[name = string("h_143_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53214720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54263360))))[name = string("main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54267520)))]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = var_2227_cast_fp16)[name = string("transpose_15")]; + tensor h_143_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_143_dilations_0, groups = h_143_groups_0, pad = h_143_pad_0, pad_type = h_143_pad_type_0, strides = h_143_strides_0, weight = main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_373_cast_fp16)[name = string("h_143_cast_fp16")]; + string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")]; + tensor input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = h_143_cast_fp16)[name = string("input_375_cast_fp16")]; + string h_145_pad_type_0 = const()[name = string("h_145_pad_type_0"), val = string("valid")]; + tensor h_145_strides_0 = const()[name = string("h_145_strides_0"), val = tensor([1])]; + tensor h_145_pad_0 = const()[name = string("h_145_pad_0"), val = tensor([0, 0])]; + tensor h_145_dilations_0 = const()[name = string("h_145_dilations_0"), val = tensor([1])]; + int32 h_145_groups_0 = const()[name = string("h_145_groups_0"), val = int32(1)]; + tensor op_2244_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54271680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55320320))))[name = string("op_2244_weight_0_to_fp16_quantized")]; + tensor var_2244_bias_0_to_fp16 = const()[name = string("op_2244_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55321408)))]; + tensor var_2244_cast_fp16 = conv(bias = var_2244_bias_0_to_fp16, dilations = h_145_dilations_0, groups = h_145_groups_0, pad = h_145_pad_0, pad_type = h_145_pad_type_0, strides = h_145_strides_0, weight = op_2244_weight_0_to_fp16_quantized, x = input_375_cast_fp16)[name = string("op_2244_cast_fp16")]; + tensor out_61_cast_fp16 = add(x = x_195_cast_fp16, y = var_2244_cast_fp16)[name = string("out_61_cast_fp16")]; + tensor x_199_cast_fp16 = mul(x = out_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor x_m_cast_fp16 = mul(x = x_199_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_cast_fp16")]; + tensor input_377_perm_0 = const()[name = string("input_377_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55322496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55453632))))[name = string("main_blocks_3_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55454208)))]; + tensor input_377_cast_fp16 = transpose(perm = input_377_perm_0, x = x_199_cast_fp16)[name = string("transpose_14")]; + tensor linear_34_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_377_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_2260 = const()[name = string("op_2260"), val = tensor([2, 256, 2, 128])]; + tensor var_2261_cast_fp16 = reshape(shape = var_2260, x = linear_34_cast_fp16)[name = string("op_2261_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_3_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55454784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55520384))))[name = string("main_blocks_3_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55520960)))]; + tensor linear_36_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_2274 = const()[name = string("op_2274"), val = tensor([2, 50, 2, 128])]; + tensor var_2275_cast_fp16 = reshape(shape = var_2274, x = linear_36_cast_fp16)[name = string("op_2275_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_2279_transpose_x_0 = const()[name = string("op_2279_transpose_x_0"), val = bool(false)]; + bool var_2279_transpose_y_0 = const()[name = string("op_2279_transpose_y_0"), val = bool(false)]; + tensor op_2278_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55521536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55547200))))[name = string("op_2278_to_fp16_quantized")]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_2261_cast_fp16)[name = string("transpose_13")]; + tensor var_2279_cast_fp16 = matmul(transpose_x = var_2279_transpose_x_0, transpose_y = var_2279_transpose_y_0, x = q_cast_fp16, y = op_2278_to_fp16_quantized)[name = string("op_2279_cast_fp16")]; + fp16 _inversed_input_379_y_0_to_fp16 = const()[name = string("_inversed_input_379_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_379_cast_fp16 = mul(x = var_2279_cast_fp16, y = _inversed_input_379_y_0_to_fp16)[name = string("_inversed_input_379_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_1775, x = _inversed_input_379_cast_fp16)[name = string("attn_21_cast_fp16")]; + tensor attn_cast_fp16 = mul(x = attn_21_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_cast_fp16")]; + bool var_2286_transpose_x_0 = const()[name = string("op_2286_transpose_x_0"), val = bool(false)]; + bool var_2286_transpose_y_0 = const()[name = string("op_2286_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_2275_cast_fp16)[name = string("transpose_12")]; + tensor var_2286_cast_fp16 = matmul(transpose_x = var_2286_transpose_x_0, transpose_y = var_2286_transpose_y_0, x = attn_cast_fp16, y = v_15_cast_fp16)[name = string("op_2286_cast_fp16")]; + tensor var_2287_perm_0 = const()[name = string("op_2287_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2288 = const()[name = string("op_2288"), val = tensor([2, 256, 256])]; + tensor var_2287_cast_fp16 = transpose(perm = var_2287_perm_0, x = var_2286_cast_fp16)[name = string("transpose_11")]; + tensor input_381_cast_fp16 = reshape(shape = var_2288, x = var_2287_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55547392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55678528))))[name = string("main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55679616)))]; + tensor linear_37_cast_fp16 = linear(bias = main_blocks_3_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_381_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor out_63_perm_0 = const()[name = string("out_63_perm_0"), val = tensor([0, 2, 1])]; + tensor out_63_cast_fp16 = transpose(perm = out_63_perm_0, x = linear_37_cast_fp16)[name = string("transpose_10")]; + tensor var_2295_cast_fp16 = mul(x = out_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2295_cast_fp16")]; + tensor x_201_cast_fp16 = add(x = x_m_cast_fp16, y = var_2295_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_383_perm_0 = const()[name = string("input_383_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2302_axes_0 = const()[name = string("op_2302_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55680704)))]; + tensor main_blocks_3_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55681792)))]; + tensor input_383_cast_fp16 = transpose(perm = input_383_perm_0, x = x_201_cast_fp16)[name = string("transpose_9")]; + tensor var_2302_cast_fp16 = layer_norm(axes = var_2302_axes_0, beta = main_blocks_3_style_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_style_norm_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_perm_0 = const()[name = string("op_2303_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2303_cast_fp16 = transpose(perm = var_2303_perm_0, x = var_2302_cast_fp16)[name = string("transpose_8")]; + tensor x_203_cast_fp16 = mul(x = var_2303_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor x_205_cast_fp16 = mul(x = x_203_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor var_2330_cast_fp16 = mul(x = x_205_cast_fp16, y = last_1_cast_fp16)[name = string("op_2330_cast_fp16")]; + tensor x_last_49_axes_0 = const()[name = string("x_last_49_axes_0"), val = tensor([-1])]; + bool x_last_49_keep_dims_0 = const()[name = string("x_last_49_keep_dims_0"), val = bool(true)]; + tensor x_last_49_cast_fp16 = reduce_sum(axes = x_last_49_axes_0, keep_dims = x_last_49_keep_dims_0, x = var_2330_cast_fp16)[name = string("x_last_49_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = x_205_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor var_2335_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_49_cast_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2335_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_pad_0 = const()[name = string("input_389_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("replicate")]; + fp16 const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = fp16(0x0p+0)]; + tensor input_389_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = input_389_mode_0, pad = input_389_pad_0, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + string h_147_pad_type_0 = const()[name = string("h_147_pad_type_0"), val = string("valid")]; + int32 h_147_groups_0 = const()[name = string("h_147_groups_0"), val = int32(512)]; + tensor h_147_strides_0 = const()[name = string("h_147_strides_0"), val = tensor([1])]; + tensor h_147_pad_0 = const()[name = string("h_147_pad_0"), val = tensor([0, 0])]; + tensor h_147_dilations_0 = const()[name = string("h_147_dilations_0"), val = tensor([1])]; + tensor last_convnext_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55682880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55685504))))[name = string("last_convnext_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55686592)))]; + tensor h_147_cast_fp16 = conv(bias = last_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_147_dilations_0, groups = h_147_groups_0, pad = h_147_pad_0, pad_type = h_147_pad_type_0, strides = h_147_strides_0, weight = last_convnext_0_dwconv__conv_weight_to_fp16_quantized, x = input_389_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor x_207_cast_fp16 = mul(x = h_147_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_391_perm_0 = const()[name = string("input_391_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2353_axes_0 = const()[name = string("op_2353_axes_0"), val = tensor([-1])]; + tensor last_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55687680)))]; + tensor last_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55688768)))]; + fp16 var_2307_to_fp16 = const()[name = string("op_2307_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_391_cast_fp16 = transpose(perm = input_391_perm_0, x = x_207_cast_fp16)[name = string("transpose_7")]; + tensor var_2353_cast_fp16 = layer_norm(axes = var_2353_axes_0, beta = last_convnext_0_norm_norm_bias_to_fp16, epsilon = var_2307_to_fp16, gamma = last_convnext_0_norm_norm_weight_to_fp16, x = input_391_cast_fp16)[name = string("op_2353_cast_fp16")]; + tensor input_393_perm_0 = const()[name = string("input_393_perm_0"), val = tensor([0, 2, 1])]; + string h_149_pad_type_0 = const()[name = string("h_149_pad_type_0"), val = string("valid")]; + tensor h_149_strides_0 = const()[name = string("h_149_strides_0"), val = tensor([1])]; + tensor h_149_pad_0 = const()[name = string("h_149_pad_0"), val = tensor([0, 0])]; + tensor h_149_dilations_0 = const()[name = string("h_149_dilations_0"), val = tensor([1])]; + int32 h_149_groups_0 = const()[name = string("h_149_groups_0"), val = int32(1)]; + tensor last_convnext_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55689856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56738496))))[name = string("last_convnext_0_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56742656)))]; + tensor input_393_cast_fp16 = transpose(perm = input_393_perm_0, x = var_2353_cast_fp16)[name = string("transpose_6")]; + tensor h_149_cast_fp16 = conv(bias = last_convnext_0_pwconv1_bias_to_fp16, dilations = h_149_dilations_0, groups = h_149_groups_0, pad = h_149_pad_0, pad_type = h_149_pad_type_0, strides = h_149_strides_0, weight = last_convnext_0_pwconv1_weight_to_fp16_quantized, x = input_393_cast_fp16)[name = string("h_149_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = h_149_cast_fp16)[name = string("input_395_cast_fp16")]; + string h_151_pad_type_0 = const()[name = string("h_151_pad_type_0"), val = string("valid")]; + tensor h_151_strides_0 = const()[name = string("h_151_strides_0"), val = tensor([1])]; + tensor h_151_pad_0 = const()[name = string("h_151_pad_0"), val = tensor([0, 0])]; + tensor h_151_dilations_0 = const()[name = string("h_151_dilations_0"), val = tensor([1])]; + int32 h_151_groups_0 = const()[name = string("h_151_groups_0"), val = int32(1)]; + tensor op_2370_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56746816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57795456))))[name = string("op_2370_weight_0_to_fp16_quantized")]; + tensor var_2370_bias_0_to_fp16 = const()[name = string("op_2370_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57796544)))]; + tensor var_2370_cast_fp16 = conv(bias = var_2370_bias_0_to_fp16, dilations = h_151_dilations_0, groups = h_151_groups_0, pad = h_151_pad_0, pad_type = h_151_pad_type_0, strides = h_151_strides_0, weight = op_2370_weight_0_to_fp16_quantized, x = input_395_cast_fp16)[name = string("op_2370_cast_fp16")]; + tensor out_65_cast_fp16 = add(x = x_205_cast_fp16, y = var_2370_cast_fp16)[name = string("out_65_cast_fp16")]; + tensor x_209_cast_fp16 = mul(x = out_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor x_211_cast_fp16 = mul(x = x_209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_2398_cast_fp16 = mul(x = x_211_cast_fp16, y = last_1_cast_fp16)[name = string("op_2398_cast_fp16")]; + tensor x_last_51_axes_0 = const()[name = string("x_last_51_axes_0"), val = tensor([-1])]; + bool x_last_51_keep_dims_0 = const()[name = string("x_last_51_keep_dims_0"), val = bool(true)]; + tensor x_last_51_cast_fp16 = reduce_sum(axes = x_last_51_axes_0, keep_dims = x_last_51_keep_dims_0, x = var_2398_cast_fp16)[name = string("x_last_51_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = x_211_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_51_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor input_399_cast_fp16 = add(x = var_2401_cast_fp16, y = var_2403_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor input_401_pad_0 = const()[name = string("input_401_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_401_mode_0 = const()[name = string("input_401_mode_0"), val = string("replicate")]; + fp16 const_86_to_fp16 = const()[name = string("const_86_to_fp16"), val = fp16(0x0p+0)]; + tensor input_401_cast_fp16 = pad(constant_val = const_86_to_fp16, mode = input_401_mode_0, pad = input_401_pad_0, x = input_399_cast_fp16)[name = string("input_401_cast_fp16")]; + string h_153_pad_type_0 = const()[name = string("h_153_pad_type_0"), val = string("valid")]; + int32 h_153_groups_0 = const()[name = string("h_153_groups_0"), val = int32(512)]; + tensor h_153_strides_0 = const()[name = string("h_153_strides_0"), val = tensor([1])]; + tensor h_153_pad_0 = const()[name = string("h_153_pad_0"), val = tensor([0, 0])]; + tensor h_153_dilations_0 = const()[name = string("h_153_dilations_0"), val = tensor([1])]; + tensor last_convnext_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57797632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57800256))))[name = string("last_convnext_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57801344)))]; + tensor h_153_cast_fp16 = conv(bias = last_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_153_dilations_0, groups = h_153_groups_0, pad = h_153_pad_0, pad_type = h_153_pad_type_0, strides = h_153_strides_0, weight = last_convnext_1_dwconv__conv_weight_to_fp16_quantized, x = input_401_cast_fp16)[name = string("h_153_cast_fp16")]; + tensor x_213_cast_fp16 = mul(x = h_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_213_cast_fp16")]; + tensor input_403_perm_0 = const()[name = string("input_403_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2421_axes_0 = const()[name = string("op_2421_axes_0"), val = tensor([-1])]; + tensor last_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57802432)))]; + tensor last_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57803520)))]; + fp16 var_2375_to_fp16 = const()[name = string("op_2375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_403_cast_fp16 = transpose(perm = input_403_perm_0, x = x_213_cast_fp16)[name = string("transpose_5")]; + tensor var_2421_cast_fp16 = layer_norm(axes = var_2421_axes_0, beta = last_convnext_1_norm_norm_bias_to_fp16, epsilon = var_2375_to_fp16, gamma = last_convnext_1_norm_norm_weight_to_fp16, x = input_403_cast_fp16)[name = string("op_2421_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string h_155_pad_type_0 = const()[name = string("h_155_pad_type_0"), val = string("valid")]; + tensor h_155_strides_0 = const()[name = string("h_155_strides_0"), val = tensor([1])]; + tensor h_155_pad_0 = const()[name = string("h_155_pad_0"), val = tensor([0, 0])]; + tensor h_155_dilations_0 = const()[name = string("h_155_dilations_0"), val = tensor([1])]; + int32 h_155_groups_0 = const()[name = string("h_155_groups_0"), val = int32(1)]; + tensor last_convnext_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57804608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58853248))))[name = string("last_convnext_1_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58857408)))]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = var_2421_cast_fp16)[name = string("transpose_4")]; + tensor h_155_cast_fp16 = conv(bias = last_convnext_1_pwconv1_bias_to_fp16, dilations = h_155_dilations_0, groups = h_155_groups_0, pad = h_155_pad_0, pad_type = h_155_pad_type_0, strides = h_155_strides_0, weight = last_convnext_1_pwconv1_weight_to_fp16_quantized, x = input_405_cast_fp16)[name = string("h_155_cast_fp16")]; + string input_407_mode_0 = const()[name = string("input_407_mode_0"), val = string("EXACT")]; + tensor input_407_cast_fp16 = gelu(mode = input_407_mode_0, x = h_155_cast_fp16)[name = string("input_407_cast_fp16")]; + string h_157_pad_type_0 = const()[name = string("h_157_pad_type_0"), val = string("valid")]; + tensor h_157_strides_0 = const()[name = string("h_157_strides_0"), val = tensor([1])]; + tensor h_157_pad_0 = const()[name = string("h_157_pad_0"), val = tensor([0, 0])]; + tensor h_157_dilations_0 = const()[name = string("h_157_dilations_0"), val = tensor([1])]; + int32 h_157_groups_0 = const()[name = string("h_157_groups_0"), val = int32(1)]; + tensor op_2438_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58861568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59910208))))[name = string("op_2438_weight_0_to_fp16_quantized")]; + tensor var_2438_bias_0_to_fp16 = const()[name = string("op_2438_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59911296)))]; + tensor var_2438_cast_fp16 = conv(bias = var_2438_bias_0_to_fp16, dilations = h_157_dilations_0, groups = h_157_groups_0, pad = h_157_pad_0, pad_type = h_157_pad_type_0, strides = h_157_strides_0, weight = op_2438_weight_0_to_fp16_quantized, x = input_407_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor out_67_cast_fp16 = add(x = x_211_cast_fp16, y = var_2438_cast_fp16)[name = string("out_67_cast_fp16")]; + tensor x_215_cast_fp16 = mul(x = out_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_215_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2466_cast_fp16 = mul(x = x_217_cast_fp16, y = last_1_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor x_last_53_axes_0 = const()[name = string("x_last_53_axes_0"), val = tensor([-1])]; + bool x_last_53_keep_dims_0 = const()[name = string("x_last_53_keep_dims_0"), val = bool(true)]; + tensor x_last_53_cast_fp16 = reduce_sum(axes = x_last_53_axes_0, keep_dims = x_last_53_keep_dims_0, x = var_2466_cast_fp16)[name = string("x_last_53_cast_fp16")]; + tensor var_2469_cast_fp16 = mul(x = x_217_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor var_2471_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_53_cast_fp16)[name = string("op_2471_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = var_2469_cast_fp16, y = var_2471_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("replicate")]; + fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)]; + tensor input_413_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = input_413_mode_0, pad = input_413_pad_0, x = input_411_cast_fp16)[name = string("input_413_cast_fp16")]; + string h_159_pad_type_0 = const()[name = string("h_159_pad_type_0"), val = string("valid")]; + int32 h_159_groups_0 = const()[name = string("h_159_groups_0"), val = int32(512)]; + tensor h_159_strides_0 = const()[name = string("h_159_strides_0"), val = tensor([1])]; + tensor h_159_pad_0 = const()[name = string("h_159_pad_0"), val = tensor([0, 0])]; + tensor h_159_dilations_0 = const()[name = string("h_159_dilations_0"), val = tensor([1])]; + tensor last_convnext_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59912384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59915008))))[name = string("last_convnext_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59916096)))]; + tensor h_159_cast_fp16 = conv(bias = last_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_159_dilations_0, groups = h_159_groups_0, pad = h_159_pad_0, pad_type = h_159_pad_type_0, strides = h_159_strides_0, weight = last_convnext_2_dwconv__conv_weight_to_fp16_quantized, x = input_413_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor x_219_cast_fp16 = mul(x = h_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_415_perm_0 = const()[name = string("input_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2489_axes_0 = const()[name = string("op_2489_axes_0"), val = tensor([-1])]; + tensor last_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59917184)))]; + tensor last_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59918272)))]; + fp16 var_2443_to_fp16 = const()[name = string("op_2443_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_415_cast_fp16 = transpose(perm = input_415_perm_0, x = x_219_cast_fp16)[name = string("transpose_3")]; + tensor var_2489_cast_fp16 = layer_norm(axes = var_2489_axes_0, beta = last_convnext_2_norm_norm_bias_to_fp16, epsilon = var_2443_to_fp16, gamma = last_convnext_2_norm_norm_weight_to_fp16, x = input_415_cast_fp16)[name = string("op_2489_cast_fp16")]; + tensor input_417_perm_0 = const()[name = string("input_417_perm_0"), val = tensor([0, 2, 1])]; + string h_161_pad_type_0 = const()[name = string("h_161_pad_type_0"), val = string("valid")]; + tensor h_161_strides_0 = const()[name = string("h_161_strides_0"), val = tensor([1])]; + tensor h_161_pad_0 = const()[name = string("h_161_pad_0"), val = tensor([0, 0])]; + tensor h_161_dilations_0 = const()[name = string("h_161_dilations_0"), val = tensor([1])]; + int32 h_161_groups_0 = const()[name = string("h_161_groups_0"), val = int32(1)]; + tensor last_convnext_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59919360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60968000))))[name = string("last_convnext_2_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60972160)))]; + tensor input_417_cast_fp16 = transpose(perm = input_417_perm_0, x = var_2489_cast_fp16)[name = string("transpose_2")]; + tensor h_161_cast_fp16 = conv(bias = last_convnext_2_pwconv1_bias_to_fp16, dilations = h_161_dilations_0, groups = h_161_groups_0, pad = h_161_pad_0, pad_type = h_161_pad_type_0, strides = h_161_strides_0, weight = last_convnext_2_pwconv1_weight_to_fp16_quantized, x = input_417_cast_fp16)[name = string("h_161_cast_fp16")]; + string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("EXACT")]; + tensor input_419_cast_fp16 = gelu(mode = input_419_mode_0, x = h_161_cast_fp16)[name = string("input_419_cast_fp16")]; + string h_163_pad_type_0 = const()[name = string("h_163_pad_type_0"), val = string("valid")]; + tensor h_163_strides_0 = const()[name = string("h_163_strides_0"), val = tensor([1])]; + tensor h_163_pad_0 = const()[name = string("h_163_pad_0"), val = tensor([0, 0])]; + tensor h_163_dilations_0 = const()[name = string("h_163_dilations_0"), val = tensor([1])]; + int32 h_163_groups_0 = const()[name = string("h_163_groups_0"), val = int32(1)]; + tensor op_2506_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60976320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62024960))))[name = string("op_2506_weight_0_to_fp16_quantized")]; + tensor var_2506_bias_0_to_fp16 = const()[name = string("op_2506_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62026048)))]; + tensor var_2506_cast_fp16 = conv(bias = var_2506_bias_0_to_fp16, dilations = h_163_dilations_0, groups = h_163_groups_0, pad = h_163_pad_0, pad_type = h_163_pad_type_0, strides = h_163_strides_0, weight = op_2506_weight_0_to_fp16_quantized, x = input_419_cast_fp16)[name = string("op_2506_cast_fp16")]; + tensor out_69_cast_fp16 = add(x = x_217_cast_fp16, y = var_2506_cast_fp16)[name = string("out_69_cast_fp16")]; + tensor x_221_cast_fp16 = mul(x = out_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor x_223_cast_fp16 = mul(x = x_221_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_2534_cast_fp16 = mul(x = x_223_cast_fp16, y = last_1_cast_fp16)[name = string("op_2534_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_2534_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_2537_cast_fp16 = mul(x = x_223_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2537_cast_fp16")]; + tensor var_2539_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_cast_fp16)[name = string("op_2539_cast_fp16")]; + tensor input_423_cast_fp16 = add(x = var_2537_cast_fp16, y = var_2539_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_425_mode_0 = const()[name = string("input_425_mode_0"), val = string("replicate")]; + fp16 const_90_to_fp16 = const()[name = string("const_90_to_fp16"), val = fp16(0x0p+0)]; + tensor input_425_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = input_425_mode_0, pad = input_425_pad_0, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")]; + string h_165_pad_type_0 = const()[name = string("h_165_pad_type_0"), val = string("valid")]; + int32 h_165_groups_0 = const()[name = string("h_165_groups_0"), val = int32(512)]; + tensor h_165_strides_0 = const()[name = string("h_165_strides_0"), val = tensor([1])]; + tensor h_165_pad_0 = const()[name = string("h_165_pad_0"), val = tensor([0, 0])]; + tensor h_165_dilations_0 = const()[name = string("h_165_dilations_0"), val = tensor([1])]; + tensor last_convnext_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62027136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62029760))))[name = string("last_convnext_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62030848)))]; + tensor h_165_cast_fp16 = conv(bias = last_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_165_dilations_0, groups = h_165_groups_0, pad = h_165_pad_0, pad_type = h_165_pad_type_0, strides = h_165_strides_0, weight = last_convnext_3_dwconv__conv_weight_to_fp16_quantized, x = input_425_cast_fp16)[name = string("h_165_cast_fp16")]; + tensor x_cast_fp16 = mul(x = h_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2557_axes_0 = const()[name = string("op_2557_axes_0"), val = tensor([-1])]; + tensor last_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62031936)))]; + tensor last_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62033024)))]; + fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_cast_fp16)[name = string("transpose_1")]; + tensor var_2557_cast_fp16 = layer_norm(axes = var_2557_axes_0, beta = last_convnext_3_norm_norm_bias_to_fp16, epsilon = var_2511_to_fp16, gamma = last_convnext_3_norm_norm_weight_to_fp16, x = input_427_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor input_429_perm_0 = const()[name = string("input_429_perm_0"), val = tensor([0, 2, 1])]; + string h_167_pad_type_0 = const()[name = string("h_167_pad_type_0"), val = string("valid")]; + tensor h_167_strides_0 = const()[name = string("h_167_strides_0"), val = tensor([1])]; + tensor h_167_pad_0 = const()[name = string("h_167_pad_0"), val = tensor([0, 0])]; + tensor h_167_dilations_0 = const()[name = string("h_167_dilations_0"), val = tensor([1])]; + int32 h_167_groups_0 = const()[name = string("h_167_groups_0"), val = int32(1)]; + tensor last_convnext_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62034112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63082752))))[name = string("last_convnext_3_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63086912)))]; + tensor input_429_cast_fp16 = transpose(perm = input_429_perm_0, x = var_2557_cast_fp16)[name = string("transpose_0")]; + tensor h_167_cast_fp16 = conv(bias = last_convnext_3_pwconv1_bias_to_fp16, dilations = h_167_dilations_0, groups = h_167_groups_0, pad = h_167_pad_0, pad_type = h_167_pad_type_0, strides = h_167_strides_0, weight = last_convnext_3_pwconv1_weight_to_fp16_quantized, x = input_429_cast_fp16)[name = string("h_167_cast_fp16")]; + string input_431_mode_0 = const()[name = string("input_431_mode_0"), val = string("EXACT")]; + tensor input_431_cast_fp16 = gelu(mode = input_431_mode_0, x = h_167_cast_fp16)[name = string("input_431_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor op_2574_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63091072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64139712))))[name = string("op_2574_weight_0_to_fp16_quantized")]; + tensor var_2574_bias_0_to_fp16 = const()[name = string("op_2574_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64140800)))]; + tensor var_2574_cast_fp16 = conv(bias = var_2574_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = op_2574_weight_0_to_fp16_quantized, x = input_431_cast_fp16)[name = string("op_2574_cast_fp16")]; + tensor out_cast_fp16 = add(x = x_223_cast_fp16, y = var_2574_cast_fp16)[name = string("out_cast_fp16")]; + tensor input_cast_fp16 = mul(x = out_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("input_cast_fp16")]; + string var_2587_pad_type_0 = const()[name = string("op_2587_pad_type_0"), val = string("valid")]; + tensor var_2587_strides_0 = const()[name = string("op_2587_strides_0"), val = tensor([1])]; + tensor var_2587_pad_0 = const()[name = string("op_2587_pad_0"), val = tensor([0, 0])]; + tensor var_2587_dilations_0 = const()[name = string("op_2587_dilations_0"), val = tensor([1])]; + int32 var_2587_groups_0 = const()[name = string("op_2587_groups_0"), val = int32(1)]; + tensor proj_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64141888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64215680))))[name = string("proj_out_weight_to_fp16_quantized")]; + tensor var_2587_cast_fp16 = conv(dilations = var_2587_dilations_0, groups = var_2587_groups_0, pad = var_2587_pad_0, pad_type = var_2587_pad_type_0, strides = var_2587_strides_0, weight = proj_out_weight_to_fp16_quantized, x = input_cast_fp16)[name = string("op_2587_cast_fp16")]; + tensor v_cast_fp16 = mul(x = var_2587_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("v_cast_fp16")]; + tensor cond_begin_0 = const()[name = string("cond_begin_0"), val = tensor([0, 0, 0])]; + tensor cond_end_0 = const()[name = string("cond_end_0"), val = tensor([1, 144, 256])]; + tensor cond_end_mask_0 = const()[name = string("cond_end_mask_0"), val = tensor([false, true, true])]; + tensor cond_cast_fp16 = slice_by_index(begin = cond_begin_0, end = cond_end_0, end_mask = cond_end_mask_0, x = v_cast_fp16)[name = string("cond_cast_fp16")]; + tensor uncond_begin_0 = const()[name = string("uncond_begin_0"), val = tensor([1, 0, 0])]; + tensor uncond_end_0 = const()[name = string("uncond_end_0"), val = tensor([2, 144, 256])]; + tensor uncond_end_mask_0 = const()[name = string("uncond_end_mask_0"), val = tensor([true, true, true])]; + tensor uncond_cast_fp16 = slice_by_index(begin = uncond_begin_0, end = uncond_end_0, end_mask = uncond_end_mask_0, x = v_cast_fp16)[name = string("uncond_cast_fp16")]; + fp32 var_2597_epsilon_0 = const()[name = string("op_2597_epsilon_0"), val = fp32(0x1.a36e2ep-14)]; + tensor var_2597_cast_fp16 = inverse(epsilon = var_2597_epsilon_0, x = total_step_to_fp16)[name = string("op_2597_cast_fp16")]; + tensor var_2603 = const()[name = string("op_2603"), val = tensor([-1, 1, 1])]; + tensor step_cast_fp16 = reshape(shape = var_2603, x = var_2597_cast_fp16)[name = string("step_cast_fp16")]; + fp16 var_2605_to_fp16 = const()[name = string("op_2605_to_fp16"), val = fp16(0x1p+2)]; + tensor var_2606_cast_fp16 = mul(x = cond_cast_fp16, y = var_2605_to_fp16)[name = string("op_2606_cast_fp16")]; + fp16 var_2607_to_fp16 = const()[name = string("op_2607_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_2608_cast_fp16 = mul(x = uncond_cast_fp16, y = var_2607_to_fp16)[name = string("op_2608_cast_fp16")]; + tensor var_2610_cast_fp16 = sub(x = var_2606_cast_fp16, y = var_2608_cast_fp16)[name = string("op_2610_cast_fp16")]; + tensor var_2611_cast_fp16 = mul(x = step_cast_fp16, y = var_2610_cast_fp16)[name = string("op_2611_cast_fp16")]; + tensor var_2613_cast_fp16 = add(x = noisy_latent_to_fp16, y = var_2611_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor var_2614_cast_fp16 = mul(x = var_2613_cast_fp16, y = latent_mask_to_fp16)[name = string("op_2614_cast_fp16")]; + string var_2614_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2614_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor denoised_latent = cast(dtype = var_2614_cast_fp16_to_fp32_dtype_0, x = var_2614_cast_fp16)[name = string("cast_0")]; + } -> (denoised_latent); +} \ No newline at end of file diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L256_int8.mlmodelc/weights/weight.bin b/VectorEstimatorVariants/VectorEstimator_v2_L256_int8.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..1a20d26bd676bb6f199edf0ed1d15f109dae3215 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L256_int8.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/VectorEstimatorVariants/VectorEstimator_v2_L512_int4.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..0e124a8e692519b933d500b1df92f137d655d87e --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L512_int4.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8f370e473dafa80fdb00ba827eb87b46b123e5fab8c7c41f7e014140c4d9fce +size 553 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L512_int4.mlmodelc/model.mil b/VectorEstimatorVariants/VectorEstimator_v2_L512_int4.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..06471e32e07cb206c3bb40bd035156ea5d89c59f --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L512_int4.mlmodelc/model.mil @@ -0,0 +1,2102 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor current_step, tensor latent_mask, tensor noisy_latent, tensor style_ttl, tensor text_emb, tensor text_mask, tensor total_step) { + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + bool text_emb_interleave_0 = const()[name = string("text_emb_interleave_0"), val = bool(false)]; + string text_emb_to_fp16_dtype_0 = const()[name = string("text_emb_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_uncond_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16512))))[name = string("text_uncond_to_fp16_palettized")]; + tensor text_emb_to_fp16 = cast(dtype = text_emb_to_fp16_dtype_0, x = text_emb)[name = string("cast_7")]; + tensor text_emb_cast_fp16 = concat(axis = var_39, interleave = text_emb_interleave_0, values = (text_emb_to_fp16, text_uncond_to_fp16_palettized))[name = string("text_emb_cast_fp16")]; + bool input_95_interleave_0 = const()[name = string("input_95_interleave_0"), val = bool(false)]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor uncond_masker_style_value_special_token_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23104))))[name = string("uncond_masker_style_value_special_token_to_fp16_palettized")]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_6")]; + tensor input_95_cast_fp16 = concat(axis = var_39, interleave = input_95_interleave_0, values = (style_ttl_to_fp16, uncond_masker_style_value_special_token_to_fp16_palettized))[name = string("input_95_cast_fp16")]; + int32 var_64 = const()[name = string("op_64"), val = int32(0)]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + string noisy_latent_to_fp16_dtype_0 = const()[name = string("noisy_latent_to_fp16_dtype_0"), val = string("fp16")]; + tensor noisy_latent_to_fp16 = cast(dtype = noisy_latent_to_fp16_dtype_0, x = noisy_latent)[name = string("cast_5")]; + tensor input_5_cast_fp16 = concat(axis = var_64, interleave = input_5_interleave_0, values = (noisy_latent_to_fp16, noisy_latent_to_fp16))[name = string("input_5_cast_fp16")]; + int32 var_67 = const()[name = string("op_67"), val = int32(0)]; + bool latent_mask_b_interleave_0 = const()[name = string("latent_mask_b_interleave_0"), val = bool(false)]; + string latent_mask_to_fp16_dtype_0 = const()[name = string("latent_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor latent_mask_to_fp16 = cast(dtype = latent_mask_to_fp16_dtype_0, x = latent_mask)[name = string("cast_4")]; + tensor latent_mask_b_cast_fp16 = concat(axis = var_67, interleave = latent_mask_b_interleave_0, values = (latent_mask_to_fp16, latent_mask_to_fp16))[name = string("latent_mask_b_cast_fp16")]; + int32 var_70 = const()[name = string("op_70"), val = int32(0)]; + bool text_mask_interleave_0 = const()[name = string("text_mask_interleave_0"), val = bool(false)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_3")]; + tensor text_mask_cast_fp16 = concat(axis = var_70, interleave = text_mask_interleave_0, values = (text_mask_to_fp16, text_mask_to_fp16))[name = string("text_mask_cast_fp16")]; + string current_step_to_fp16_dtype_0 = const()[name = string("current_step_to_fp16_dtype_0"), val = string("fp16")]; + string total_step_to_fp16_dtype_0 = const()[name = string("total_step_to_fp16_dtype_0"), val = string("fp16")]; + tensor current_step_to_fp16 = cast(dtype = current_step_to_fp16_dtype_0, x = current_step)[name = string("cast_1")]; + tensor total_step_to_fp16 = cast(dtype = total_step_to_fp16_dtype_0, x = total_step)[name = string("cast_2")]; + tensor t_1_cast_fp16 = real_div(x = current_step_to_fp16, y = total_step_to_fp16)[name = string("t_1_cast_fp16")]; + int32 var_74 = const()[name = string("op_74"), val = int32(0)]; + bool t_3_interleave_0 = const()[name = string("t_3_interleave_0"), val = bool(false)]; + tensor t_3_cast_fp16 = concat(axis = var_74, interleave = t_3_interleave_0, values = (t_1_cast_fp16, t_1_cast_fp16))[name = string("t_3_cast_fp16")]; + int32 var_78 = const()[name = string("op_78"), val = int32(-1)]; + tensor var_85 = const()[name = string("op_85"), val = tensor([-1, 1])]; + tensor var_86_cast_fp16 = reshape(shape = var_85, x = t_3_cast_fp16)[name = string("op_86_cast_fp16")]; + fp16 time_encoder_time_scale_to_fp16 = const()[name = string("time_encoder_time_scale_to_fp16"), val = fp16(0x1.f4p+9)]; + tensor t_scaled_cast_fp16 = mul(x = var_86_cast_fp16, y = time_encoder_time_scale_to_fp16)[name = string("t_scaled_cast_fp16")]; + tensor time_encoder_omegas_to_fp16 = const()[name = string("time_encoder_omegas_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23232)))]; + tensor phase_cast_fp16 = mul(x = t_scaled_cast_fp16, y = time_encoder_omegas_to_fp16)[name = string("phase_cast_fp16")]; + tensor var_89_cast_fp16 = sin(x = phase_cast_fp16)[name = string("op_89_cast_fp16")]; + tensor var_90_cast_fp16 = cos(x = phase_cast_fp16)[name = string("op_90_cast_fp16")]; + bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)]; + tensor input_1_cast_fp16 = concat(axis = var_78, interleave = input_1_interleave_0, values = (var_89_cast_fp16, var_90_cast_fp16))[name = string("input_1_cast_fp16")]; + tensor time_encoder_mlp_0_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31616))))[name = string("time_encoder_mlp_0_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_0_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31744)))]; + tensor linear_0_cast_fp16 = linear(bias = time_encoder_mlp_0_linear_bias_to_fp16, weight = time_encoder_mlp_0_linear_weight_to_fp16_palettized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_97_cast_fp16 = softplus(x = linear_0_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = tanh(x = var_97_cast_fp16)[name = string("op_98_cast_fp16")]; + tensor input_3_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_98_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor time_encoder_mlp_2_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40576))))[name = string("time_encoder_mlp_2_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_2_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40704)))]; + tensor linear_1_cast_fp16 = linear(bias = time_encoder_mlp_2_linear_bias_to_fp16, weight = time_encoder_mlp_2_linear_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor time_emb_axes_0 = const()[name = string("time_emb_axes_0"), val = tensor([-1])]; + tensor time_emb_cast_fp16 = expand_dims(axes = time_emb_axes_0, x = linear_1_cast_fp16)[name = string("time_emb_cast_fp16")]; + string var_115_pad_type_0 = const()[name = string("op_115_pad_type_0"), val = string("valid")]; + tensor var_115_strides_0 = const()[name = string("op_115_strides_0"), val = tensor([1])]; + tensor var_115_pad_0 = const()[name = string("op_115_pad_0"), val = tensor([0, 0])]; + tensor var_115_dilations_0 = const()[name = string("op_115_dilations_0"), val = tensor([1])]; + int32 var_115_groups_0 = const()[name = string("op_115_groups_0"), val = int32(1)]; + tensor proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77824))))[name = string("proj_in_weight_to_fp16_palettized")]; + tensor var_115_cast_fp16 = conv(dilations = var_115_dilations_0, groups = var_115_groups_0, pad = var_115_pad_0, pad_type = var_115_pad_type_0, strides = var_115_strides_0, weight = proj_in_weight_to_fp16_palettized, x = input_5_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = var_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_3_cast_fp16")]; + int32 var_134 = const()[name = string("op_134"), val = int32(-1)]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([0, 0, 1])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 512])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([true, true, true])]; + tensor input_7_cast_fp16 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, x = latent_mask_b_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_166_pad_0 = const()[name = string("op_166_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_166_mode_0 = const()[name = string("op_166_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_166_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_166_mode_0, pad = var_166_pad_0, x = input_7_cast_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_133_to_fp16 = const()[name = string("op_133_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = sub(x = var_133_to_fp16, y = var_166_cast_fp16)[name = string("op_167_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = latent_mask_b_cast_fp16, y = var_167_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_169_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_172_cast_fp16 = mul(x = x_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor var_173_cast_fp16 = sub(x = var_133_to_fp16, y = latent_mask_b_cast_fp16)[name = string("op_173_cast_fp16")]; + tensor var_174_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_174_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_174_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("replicate")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor input_11_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_11_mode_0, pad = input_11_pad_0, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(512)]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79296))))[name = string("main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79424)))]; + tensor h_3_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, 2, 1])]; + tensor var_192_axes_0 = const()[name = string("op_192_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80512)))]; + tensor main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81600)))]; + fp16 var_139_to_fp16 = const()[name = string("op_139_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_7_cast_fp16)[name = string("transpose_117")]; + tensor var_192_cast_fp16 = layer_norm(axes = var_192_axes_0, beta = main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16, x = input_13_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, 2, 1])]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(607040))))[name = string("main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(607168)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = var_192_cast_fp16)[name = string("transpose_116")]; + tensor h_5_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = string("h_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = h_5_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)]; + tensor op_209_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(611328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135680))))[name = string("op_209_weight_0_to_fp16_palettized")]; + tensor var_209_bias_0_to_fp16 = const()[name = string("op_209_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135808)))]; + tensor var_209_cast_fp16 = conv(bias = var_209_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = op_209_weight_0_to_fp16_palettized, x = input_17_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_209_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_223_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_223_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_226_cast_fp16 = mul(x = x_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_226_cast_fp16")]; + tensor var_228_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_228_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_226_cast_fp16, y = var_228_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")]; + fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)]; + tensor input_23_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([2])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(512)]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1138240))))[name = string("main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1138368)))]; + tensor h_9_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor var_246_axes_0 = const()[name = string("op_246_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1139456)))]; + tensor main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1140544)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_13_cast_fp16)[name = string("transpose_115")]; + tensor var_246_cast_fp16 = layer_norm(axes = var_246_axes_0, beta = main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_246_cast_fp16")]; + tensor input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor([0, 2, 1])]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1141632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1665984))))[name = string("main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1666112)))]; + tensor input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_246_cast_fp16)[name = string("transpose_114")]; + tensor h_11_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_27_cast_fp16)[name = string("h_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_11_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)]; + tensor op_263_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1670272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2194624))))[name = string("op_263_weight_0_to_fp16_palettized")]; + tensor var_263_bias_0_to_fp16 = const()[name = string("op_263_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2194752)))]; + tensor var_263_cast_fp16 = conv(bias = var_263_bias_0_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = op_263_weight_0_to_fp16_palettized, x = input_29_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_263_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_277_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_277_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_277_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_280_cast_fp16 = mul(x = x_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_280_cast_fp16")]; + tensor var_282_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_280_cast_fp16, y = var_282_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("replicate")]; + fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)]; + tensor input_35_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = input_35_mode_0, pad = input_35_pad_0, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([4])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(512)]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2195840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2197184))))[name = string("main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2197312)))]; + tensor h_15_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor var_300_axes_0 = const()[name = string("op_300_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2198400)))]; + tensor main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2199488)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_19_cast_fp16)[name = string("transpose_113")]; + tensor var_300_cast_fp16 = layer_norm(axes = var_300_axes_0, beta = main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16, x = input_37_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor([0, 2, 1])]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2200576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2724928))))[name = string("main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2725056)))]; + tensor input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_300_cast_fp16)[name = string("transpose_112")]; + tensor h_17_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = string("h_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = h_17_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)]; + tensor op_317_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2729216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3253568))))[name = string("op_317_weight_0_to_fp16_palettized")]; + tensor var_317_bias_0_to_fp16 = const()[name = string("op_317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3253696)))]; + tensor var_317_cast_fp16 = conv(bias = var_317_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = op_317_weight_0_to_fp16_palettized, x = input_41_cast_fp16)[name = string("op_317_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_317_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor var_331_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_331_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_331_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = x_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor var_336_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_336_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_334_cast_fp16, y = var_336_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")]; + fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([8])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(512)]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3254784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3256128))))[name = string("main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3256256)))]; + tensor h_21_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3257344)))]; + tensor main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3258432)))]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_25_cast_fp16)[name = string("transpose_111")]; + tensor var_354_cast_fp16 = layer_norm(axes = var_354_axes_0, beta = main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_354_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3259520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3783872))))[name = string("main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3784000)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_354_cast_fp16)[name = string("transpose_110")]; + tensor h_23_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("h_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_23_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)]; + tensor op_371_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3788160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4312512))))[name = string("op_371_weight_0_to_fp16_palettized")]; + tensor var_371_bias_0_to_fp16 = const()[name = string("op_371_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4312640)))]; + tensor var_371_cast_fp16 = conv(bias = var_371_bias_0_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = op_371_weight_0_to_fp16_palettized, x = input_53_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_371_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor input_55_cast_fp16 = squeeze(axes = input_55_axes_0, x = time_emb_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4313728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4330176))))[name = string("main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4330304)))]; + tensor linear_2_cast_fp16 = linear(bias = main_blocks_0_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor t_5_axes_0 = const()[name = string("t_5_axes_0"), val = tensor([-1])]; + tensor t_5_cast_fp16 = expand_dims(axes = t_5_axes_0, x = linear_2_cast_fp16)[name = string("t_5_cast_fp16")]; + tensor var_381_cast_fp16 = add(x = x_27_cast_fp16, y = t_5_cast_fp16)[name = string("op_381_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_381_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_394_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_394_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_394_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_397_cast_fp16 = mul(x = x_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = var_397_cast_fp16, y = var_399_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("replicate")]; + fp16 const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = fp16(0x0p+0)]; + tensor input_61_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = input_61_mode_0, pad = input_61_pad_0, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(512)]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4331392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4332736))))[name = string("main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4332864)))]; + tensor h_27_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417_axes_0 = const()[name = string("op_417_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4333952)))]; + tensor main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4335040)))]; + tensor input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_33_cast_fp16)[name = string("transpose_109")]; + tensor var_417_cast_fp16 = layer_norm(axes = var_417_axes_0, beta = main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16, x = input_63_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4336128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4860480))))[name = string("main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4860608)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = var_417_cast_fp16)[name = string("transpose_108")]; + tensor h_29_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_65_cast_fp16)[name = string("h_29_cast_fp16")]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("EXACT")]; + tensor input_67_cast_fp16 = gelu(mode = input_67_mode_0, x = h_29_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)]; + tensor op_434_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4864768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5389120))))[name = string("op_434_weight_0_to_fp16_palettized")]; + tensor var_434_bias_0_to_fp16 = const()[name = string("op_434_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5389248)))]; + tensor var_434_cast_fp16 = conv(bias = var_434_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = op_434_weight_0_to_fp16_palettized, x = input_67_cast_fp16)[name = string("op_434_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_434_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_m_1_cast_fp16 = mul(x = x_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_1_cast_fp16")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5390336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5521472))))[name = string("main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5521600)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_35_cast_fp16)[name = string("transpose_107")]; + tensor linear_3_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([2, 512, 8, 64])]; + tensor var_453_cast_fp16 = reshape(shape = var_452, x = linear_3_cast_fp16)[name = string("op_453_cast_fp16")]; + tensor x_37_perm_0 = const()[name = string("x_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5522688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5588288))))[name = string("main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5588416)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = text_emb_cast_fp16)[name = string("transpose_106")]; + tensor linear_4_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([2, 128, 8, 64])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = linear_4_cast_fp16)[name = string("op_461_cast_fp16")]; + tensor x_39_perm_0 = const()[name = string("x_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5589504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5655104))))[name = string("main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5655232)))]; + tensor linear_5_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_468 = const()[name = string("op_468"), val = tensor([2, 128, 8, 64])]; + tensor var_469_cast_fp16 = reshape(shape = var_468, x = linear_5_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor divisor_1_axes_0 = const()[name = string("divisor_1_axes_0"), val = tensor([1, 2])]; + bool divisor_1_keep_dims_0 = const()[name = string("divisor_1_keep_dims_0"), val = bool(false)]; + tensor divisor_1_cast_fp16 = reduce_sum(axes = divisor_1_axes_0, keep_dims = divisor_1_keep_dims_0, x = latent_mask_b_cast_fp16)[name = string("divisor_1_cast_fp16")]; + tensor divisor_3_axes_0 = const()[name = string("divisor_3_axes_0"), val = tensor([1, 2])]; + bool divisor_3_keep_dims_0 = const()[name = string("divisor_3_keep_dims_0"), val = bool(false)]; + tensor divisor_3_cast_fp16 = reduce_sum(axes = divisor_3_axes_0, keep_dims = divisor_3_keep_dims_0, x = text_mask_cast_fp16)[name = string("divisor_3_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([-1, 1, 1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = divisor_1_cast_fp16)[name = string("op_488_cast_fp16")]; + tensor const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5656320)))]; + tensor scaled_1_cast_fp16 = real_div(x = const_16_to_fp16, y = var_488_cast_fp16)[name = string("scaled_1_cast_fp16")]; + tensor main_blocks_0_text_attn_theta_to_fp16 = const()[name = string("main_blocks_0_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5658432)))]; + tensor angles_1_cast_fp16 = mul(x = scaled_1_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_1_cast_fp16")]; + tensor var_491_cast_fp16 = cos(x = angles_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = var_491_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor var_493_cast_fp16 = sin(x = angles_1_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = var_493_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor x_a_1_begin_0 = const()[name = string("x_a_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_1_end_0 = const()[name = string("x_a_1_end_0"), val = tensor([2, 8, 512, 32])]; + tensor x_a_1_end_mask_0 = const()[name = string("x_a_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_37_cast_fp16 = transpose(perm = x_37_perm_0, x = var_453_cast_fp16)[name = string("transpose_105")]; + tensor x_a_1_cast_fp16 = slice_by_index(begin = x_a_1_begin_0, end = x_a_1_end_0, end_mask = x_a_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_a_1_cast_fp16")]; + tensor x_b_1_begin_0 = const()[name = string("x_b_1_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_1_end_0 = const()[name = string("x_b_1_end_0"), val = tensor([2, 8, 512, 64])]; + tensor x_b_1_end_mask_0 = const()[name = string("x_b_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_1_cast_fp16 = slice_by_index(begin = x_b_1_begin_0, end = x_b_1_end_0, end_mask = x_b_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_b_1_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = x_a_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor var_498_cast_fp16 = mul(x = x_b_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_498_cast_fp16")]; + tensor rot_a_1_cast_fp16 = sub(x = var_497_cast_fp16, y = var_498_cast_fp16)[name = string("rot_a_1_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = x_a_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = x_b_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor rot_b_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("rot_b_1_cast_fp16")]; + bool q_1_interleave_0 = const()[name = string("q_1_interleave_0"), val = bool(false)]; + tensor q_1_cast_fp16 = concat(axis = var_134, interleave = q_1_interleave_0, values = (rot_a_1_cast_fp16, rot_b_1_cast_fp16))[name = string("q_1_cast_fp16")]; + tensor var_505 = const()[name = string("op_505"), val = tensor([-1, 1, 1])]; + tensor var_506_cast_fp16 = reshape(shape = var_505, x = divisor_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5658560)))]; + tensor scaled_3_cast_fp16 = real_div(x = const_17_to_fp16, y = var_506_cast_fp16)[name = string("scaled_3_cast_fp16")]; + tensor angles_3_cast_fp16 = mul(x = scaled_3_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_3_cast_fp16")]; + tensor var_509_cast_fp16 = cos(x = angles_3_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor cos_3_axes_0 = const()[name = string("cos_3_axes_0"), val = tensor([1])]; + tensor cos_3_cast_fp16 = expand_dims(axes = cos_3_axes_0, x = var_509_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor var_511_cast_fp16 = sin(x = angles_3_cast_fp16)[name = string("op_511_cast_fp16")]; + tensor sin_3_axes_0 = const()[name = string("sin_3_axes_0"), val = tensor([1])]; + tensor sin_3_cast_fp16 = expand_dims(axes = sin_3_axes_0, x = var_511_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor x_a_3_begin_0 = const()[name = string("x_a_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_3_end_0 = const()[name = string("x_a_3_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_3_end_mask_0 = const()[name = string("x_a_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_39_cast_fp16 = transpose(perm = x_39_perm_0, x = var_461_cast_fp16)[name = string("transpose_104")]; + tensor x_a_3_cast_fp16 = slice_by_index(begin = x_a_3_begin_0, end = x_a_3_end_0, end_mask = x_a_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_a_3_cast_fp16")]; + tensor x_b_3_begin_0 = const()[name = string("x_b_3_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_3_end_0 = const()[name = string("x_b_3_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_3_end_mask_0 = const()[name = string("x_b_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_3_cast_fp16 = slice_by_index(begin = x_b_3_begin_0, end = x_b_3_end_0, end_mask = x_b_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_b_3_cast_fp16")]; + tensor var_515_cast_fp16 = mul(x = x_a_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_515_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = x_b_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor rot_a_3_cast_fp16 = sub(x = var_515_cast_fp16, y = var_516_cast_fp16)[name = string("rot_a_3_cast_fp16")]; + tensor var_518_cast_fp16 = mul(x = x_a_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = mul(x = x_b_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor rot_b_3_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = string("rot_b_3_cast_fp16")]; + bool k_1_interleave_0 = const()[name = string("k_1_interleave_0"), val = bool(false)]; + tensor k_1_cast_fp16 = concat(axis = var_134, interleave = k_1_interleave_0, values = (rot_a_3_cast_fp16, rot_b_3_cast_fp16))[name = string("k_1_cast_fp16")]; + bool var_524_transpose_x_1 = const()[name = string("op_524_transpose_x_1"), val = bool(false)]; + bool var_524_transpose_y_1 = const()[name = string("op_524_transpose_y_1"), val = bool(true)]; + tensor var_524_cast_fp16 = matmul(transpose_x = var_524_transpose_x_1, transpose_y = var_524_transpose_y_1, x = q_1_cast_fp16, y = k_1_cast_fp16)[name = string("op_524_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_524_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([2])]; + tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = text_mask_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528_cast_fp16 = sub(x = var_133_to_fp16, y = var_527_cast_fp16)[name = string("op_528_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_530_cast_fp16 = mul(x = var_528_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_75_cast_fp16 = sub(x = _inversed_scores_1_cast_fp16, y = var_530_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_134, x = input_75_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool var_533_transpose_x_0 = const()[name = string("op_533_transpose_x_0"), val = bool(false)]; + bool var_533_transpose_y_0 = const()[name = string("op_533_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_469_cast_fp16)[name = string("transpose_103")]; + tensor var_533_cast_fp16 = matmul(transpose_x = var_533_transpose_x_0, transpose_y = var_533_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("op_533_cast_fp16")]; + tensor var_534_perm_0 = const()[name = string("op_534_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_535 = const()[name = string("op_535"), val = tensor([2, 512, 512])]; + tensor var_534_cast_fp16 = transpose(perm = var_534_perm_0, x = var_533_cast_fp16)[name = string("transpose_102")]; + tensor input_77_cast_fp16 = reshape(shape = var_535, x = var_534_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5659136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5790272))))[name = string("main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5790400)))]; + tensor linear_6_cast_fp16 = linear(bias = main_blocks_0_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor out_11_perm_0 = const()[name = string("out_11_perm_0"), val = tensor([0, 2, 1])]; + tensor out_11_cast_fp16 = transpose(perm = out_11_perm_0, x = linear_6_cast_fp16)[name = string("transpose_101")]; + tensor var_542_cast_fp16 = mul(x = out_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_542_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = x_m_1_cast_fp16, y = var_542_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor var_549_axes_0 = const()[name = string("op_549_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5791488)))]; + tensor main_blocks_0_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5792576)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_41_cast_fp16)[name = string("transpose_100")]; + tensor var_549_cast_fp16 = layer_norm(axes = var_549_axes_0, beta = main_blocks_0_text_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_text_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor var_550_perm_0 = const()[name = string("op_550_perm_0"), val = tensor([0, 2, 1])]; + tensor var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = var_549_cast_fp16)[name = string("transpose_99")]; + tensor x_43_cast_fp16 = mul(x = var_550_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = x_45_cast_fp16, y = last_1_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor x_last_11_axes_0 = const()[name = string("x_last_11_axes_0"), val = tensor([-1])]; + bool x_last_11_keep_dims_0 = const()[name = string("x_last_11_keep_dims_0"), val = bool(true)]; + tensor x_last_11_cast_fp16 = reduce_sum(axes = x_last_11_axes_0, keep_dims = x_last_11_keep_dims_0, x = var_563_cast_fp16)[name = string("x_last_11_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = x_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor var_568_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_11_cast_fp16)[name = string("op_568_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = var_566_cast_fp16, y = var_568_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_85_mode_0 = const()[name = string("input_85_mode_0"), val = string("replicate")]; + fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)]; + tensor input_85_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(512)]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5793664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5795008))))[name = string("main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5795136)))]; + tensor h_33_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("h_33_cast_fp16")]; + tensor x_47_cast_fp16 = mul(x = h_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5796224)))]; + tensor main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5797312)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_47_cast_fp16)[name = string("transpose_98")]; + tensor var_586_cast_fp16 = layer_norm(axes = var_586_axes_0, beta = main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_586_cast_fp16")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5798400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6322752))))[name = string("main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6322880)))]; + tensor input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = var_586_cast_fp16)[name = string("transpose_97")]; + tensor h_35_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_89_cast_fp16)[name = string("h_35_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = h_35_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")]; + tensor h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor([1])]; + tensor h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor([0, 0])]; + tensor h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor([1])]; + int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)]; + tensor op_603_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6327040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6851392))))[name = string("op_603_weight_0_to_fp16_palettized")]; + tensor var_603_bias_0_to_fp16 = const()[name = string("op_603_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6851520)))]; + tensor var_603_cast_fp16 = conv(bias = var_603_bias_0_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = op_603_weight_0_to_fp16_palettized, x = input_91_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor out_13_cast_fp16 = add(x = x_45_cast_fp16, y = var_603_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_49_cast_fp16 = mul(x = out_13_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_m_3_cast_fp16 = mul(x = x_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_3_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6852608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6918208))))[name = string("main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6918336)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_49_cast_fp16)[name = string("transpose_96")]; + tensor linear_7_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([2, 512, 2, 128])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = linear_7_cast_fp16)[name = string("op_620_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6918912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6951744))))[name = string("main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6951872)))]; + tensor linear_9_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_633 = const()[name = string("op_633"), val = tensor([2, 50, 2, 128])]; + tensor var_634_cast_fp16 = reshape(shape = var_633, x = linear_9_cast_fp16)[name = string("op_634_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_638_transpose_x_0 = const()[name = string("op_638_transpose_x_0"), val = bool(false)]; + bool var_638_transpose_y_0 = const()[name = string("op_638_transpose_y_0"), val = bool(false)]; + tensor op_637_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6952448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6965312))))[name = string("op_637_to_fp16_palettized")]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_620_cast_fp16)[name = string("transpose_95")]; + tensor var_638_cast_fp16 = matmul(transpose_x = var_638_transpose_x_0, transpose_y = var_638_transpose_y_0, x = q_3_cast_fp16, y = op_637_to_fp16_palettized)[name = string("op_638_cast_fp16")]; + fp16 _inversed_input_97_y_0_to_fp16 = const()[name = string("_inversed_input_97_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_97_cast_fp16 = mul(x = var_638_cast_fp16, y = _inversed_input_97_y_0_to_fp16)[name = string("_inversed_input_97_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_134, x = _inversed_input_97_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_642_perm_0 = const()[name = string("op_642_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_1_axes_0 = const()[name = string("mask_1_axes_0"), val = tensor([1])]; + tensor var_642_cast_fp16 = transpose(perm = var_642_perm_0, x = latent_mask_b_cast_fp16)[name = string("transpose_94")]; + tensor mask_1_cast_fp16 = expand_dims(axes = mask_1_axes_0, x = var_642_cast_fp16)[name = string("mask_1_cast_fp16")]; + tensor attn_5_cast_fp16 = mul(x = attn_3_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool var_645_transpose_x_0 = const()[name = string("op_645_transpose_x_0"), val = bool(false)]; + bool var_645_transpose_y_0 = const()[name = string("op_645_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_634_cast_fp16)[name = string("transpose_93")]; + tensor var_645_cast_fp16 = matmul(transpose_x = var_645_transpose_x_0, transpose_y = var_645_transpose_y_0, x = attn_5_cast_fp16, y = v_3_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_646_perm_0 = const()[name = string("op_646_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_647 = const()[name = string("op_647"), val = tensor([2, 512, 256])]; + tensor var_646_cast_fp16 = transpose(perm = var_646_perm_0, x = var_645_cast_fp16)[name = string("transpose_92")]; + tensor input_99_cast_fp16 = reshape(shape = var_647, x = var_646_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6965440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7031040))))[name = string("main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7031168)))]; + tensor linear_10_cast_fp16 = linear(bias = main_blocks_0_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor out_15_perm_0 = const()[name = string("out_15_perm_0"), val = tensor([0, 2, 1])]; + tensor out_15_cast_fp16 = transpose(perm = out_15_perm_0, x = linear_10_cast_fp16)[name = string("transpose_91")]; + tensor var_654_cast_fp16 = mul(x = out_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor x_51_cast_fp16 = add(x = x_m_3_cast_fp16, y = var_654_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7032256)))]; + tensor main_blocks_0_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7033344)))]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_51_cast_fp16)[name = string("transpose_90")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = main_blocks_0_style_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_style_norm_norm_weight_to_fp16, x = input_101_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_662_perm_0 = const()[name = string("op_662_perm_0"), val = tensor([0, 2, 1])]; + tensor var_662_cast_fp16 = transpose(perm = var_662_perm_0, x = var_661_cast_fp16)[name = string("transpose_89")]; + tensor x_53_cast_fp16 = mul(x = var_662_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_53_cast_fp16")]; + int32 var_681 = const()[name = string("op_681"), val = int32(-1)]; + tensor x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = x_55_cast_fp16, y = last_1_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor x_last_13_axes_0 = const()[name = string("x_last_13_axes_0"), val = tensor([-1])]; + bool x_last_13_keep_dims_0 = const()[name = string("x_last_13_keep_dims_0"), val = bool(true)]; + tensor x_last_13_cast_fp16 = reduce_sum(axes = x_last_13_axes_0, keep_dims = x_last_13_keep_dims_0, x = var_716_cast_fp16)[name = string("x_last_13_cast_fp16")]; + tensor var_719_cast_fp16 = mul(x = x_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_721_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_13_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_719_cast_fp16, y = var_721_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("replicate")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(512)]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7034432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7035776))))[name = string("main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7035904)))]; + tensor h_39_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor x_57_cast_fp16 = mul(x = h_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_739_axes_0 = const()[name = string("op_739_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7036992)))]; + tensor main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7038080)))]; + fp16 var_686_to_fp16 = const()[name = string("op_686_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_57_cast_fp16)[name = string("transpose_88")]; + tensor var_739_cast_fp16 = layer_norm(axes = var_739_axes_0, beta = main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_739_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + string h_41_pad_type_0 = const()[name = string("h_41_pad_type_0"), val = string("valid")]; + tensor h_41_strides_0 = const()[name = string("h_41_strides_0"), val = tensor([1])]; + tensor h_41_pad_0 = const()[name = string("h_41_pad_0"), val = tensor([0, 0])]; + tensor h_41_dilations_0 = const()[name = string("h_41_dilations_0"), val = tensor([1])]; + int32 h_41_groups_0 = const()[name = string("h_41_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7039168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7563520))))[name = string("main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7563648)))]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = var_739_cast_fp16)[name = string("transpose_87")]; + tensor h_41_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_41_dilations_0, groups = h_41_groups_0, pad = h_41_pad_0, pad_type = h_41_pad_type_0, strides = h_41_strides_0, weight = main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = h_41_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor op_756_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7567808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8092160))))[name = string("op_756_weight_0_to_fp16_palettized")]; + tensor var_756_bias_0_to_fp16 = const()[name = string("op_756_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8092288)))]; + tensor var_756_cast_fp16 = conv(bias = var_756_bias_0_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = op_756_weight_0_to_fp16_palettized, x = input_113_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor out_17_cast_fp16 = add(x = x_55_cast_fp16, y = var_756_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_59_cast_fp16 = mul(x = out_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor x_61_cast_fp16 = mul(x = x_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_770_cast_fp16 = mul(x = x_61_cast_fp16, y = last_1_cast_fp16)[name = string("op_770_cast_fp16")]; + tensor x_last_15_axes_0 = const()[name = string("x_last_15_axes_0"), val = tensor([-1])]; + bool x_last_15_keep_dims_0 = const()[name = string("x_last_15_keep_dims_0"), val = bool(true)]; + tensor x_last_15_cast_fp16 = reduce_sum(axes = x_last_15_axes_0, keep_dims = x_last_15_keep_dims_0, x = var_770_cast_fp16)[name = string("x_last_15_cast_fp16")]; + tensor var_773_cast_fp16 = mul(x = x_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor var_775_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_15_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = var_773_cast_fp16, y = var_775_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("replicate")]; + fp16 const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = fp16(0x0p+0)]; + tensor input_119_cast_fp16 = pad(constant_val = const_26_to_fp16, mode = input_119_mode_0, pad = input_119_pad_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")]; + string h_45_pad_type_0 = const()[name = string("h_45_pad_type_0"), val = string("valid")]; + tensor h_45_dilations_0 = const()[name = string("h_45_dilations_0"), val = tensor([2])]; + int32 h_45_groups_0 = const()[name = string("h_45_groups_0"), val = int32(512)]; + tensor h_45_strides_0 = const()[name = string("h_45_strides_0"), val = tensor([1])]; + tensor h_45_pad_0 = const()[name = string("h_45_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8093376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8094720))))[name = string("main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8094848)))]; + tensor h_45_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_45_dilations_0, groups = h_45_groups_0, pad = h_45_pad_0, pad_type = h_45_pad_type_0, strides = h_45_strides_0, weight = main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = h_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095936)))]; + tensor main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8097024)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_63_cast_fp16)[name = string("transpose_86")]; + tensor var_793_cast_fp16 = layer_norm(axes = var_793_axes_0, beta = main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor input_123_perm_0 = const()[name = string("input_123_perm_0"), val = tensor([0, 2, 1])]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8098112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8622464))))[name = string("main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8622592)))]; + tensor input_123_cast_fp16 = transpose(perm = input_123_perm_0, x = var_793_cast_fp16)[name = string("transpose_85")]; + tensor h_47_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = string("h_47_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = h_47_cast_fp16)[name = string("input_125_cast_fp16")]; + string h_49_pad_type_0 = const()[name = string("h_49_pad_type_0"), val = string("valid")]; + tensor h_49_strides_0 = const()[name = string("h_49_strides_0"), val = tensor([1])]; + tensor h_49_pad_0 = const()[name = string("h_49_pad_0"), val = tensor([0, 0])]; + tensor h_49_dilations_0 = const()[name = string("h_49_dilations_0"), val = tensor([1])]; + int32 h_49_groups_0 = const()[name = string("h_49_groups_0"), val = int32(1)]; + tensor op_810_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8626752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9151104))))[name = string("op_810_weight_0_to_fp16_palettized")]; + tensor var_810_bias_0_to_fp16 = const()[name = string("op_810_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9151232)))]; + tensor var_810_cast_fp16 = conv(bias = var_810_bias_0_to_fp16, dilations = h_49_dilations_0, groups = h_49_groups_0, pad = h_49_pad_0, pad_type = h_49_pad_type_0, strides = h_49_strides_0, weight = op_810_weight_0_to_fp16_palettized, x = input_125_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = x_61_cast_fp16, y = var_810_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor x_65_cast_fp16 = mul(x = out_19_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = x_67_cast_fp16, y = last_1_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_last_17_axes_0 = const()[name = string("x_last_17_axes_0"), val = tensor([-1])]; + bool x_last_17_keep_dims_0 = const()[name = string("x_last_17_keep_dims_0"), val = bool(true)]; + tensor x_last_17_cast_fp16 = reduce_sum(axes = x_last_17_axes_0, keep_dims = x_last_17_keep_dims_0, x = var_824_cast_fp16)[name = string("x_last_17_cast_fp16")]; + tensor var_827_cast_fp16 = mul(x = x_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_829_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_17_cast_fp16)[name = string("op_829_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = var_827_cast_fp16, y = var_829_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("replicate")]; + fp16 const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = fp16(0x0p+0)]; + tensor input_131_cast_fp16 = pad(constant_val = const_28_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + string h_51_pad_type_0 = const()[name = string("h_51_pad_type_0"), val = string("valid")]; + tensor h_51_dilations_0 = const()[name = string("h_51_dilations_0"), val = tensor([4])]; + int32 h_51_groups_0 = const()[name = string("h_51_groups_0"), val = int32(512)]; + tensor h_51_strides_0 = const()[name = string("h_51_strides_0"), val = tensor([1])]; + tensor h_51_pad_0 = const()[name = string("h_51_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9152320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9153664))))[name = string("main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9153792)))]; + tensor h_51_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_51_dilations_0, groups = h_51_groups_0, pad = h_51_pad_0, pad_type = h_51_pad_type_0, strides = h_51_strides_0, weight = main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor x_69_cast_fp16 = mul(x = h_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_847_axes_0 = const()[name = string("op_847_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9154880)))]; + tensor main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9155968)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_69_cast_fp16)[name = string("transpose_84")]; + tensor var_847_cast_fp16 = layer_norm(axes = var_847_axes_0, beta = main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_847_cast_fp16")]; + tensor input_135_perm_0 = const()[name = string("input_135_perm_0"), val = tensor([0, 2, 1])]; + string h_53_pad_type_0 = const()[name = string("h_53_pad_type_0"), val = string("valid")]; + tensor h_53_strides_0 = const()[name = string("h_53_strides_0"), val = tensor([1])]; + tensor h_53_pad_0 = const()[name = string("h_53_pad_0"), val = tensor([0, 0])]; + tensor h_53_dilations_0 = const()[name = string("h_53_dilations_0"), val = tensor([1])]; + int32 h_53_groups_0 = const()[name = string("h_53_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9157056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681408))))[name = string("main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681536)))]; + tensor input_135_cast_fp16 = transpose(perm = input_135_perm_0, x = var_847_cast_fp16)[name = string("transpose_83")]; + tensor h_53_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_53_dilations_0, groups = h_53_groups_0, pad = h_53_pad_0, pad_type = h_53_pad_type_0, strides = h_53_strides_0, weight = main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = string("h_53_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = h_53_cast_fp16)[name = string("input_137_cast_fp16")]; + string h_55_pad_type_0 = const()[name = string("h_55_pad_type_0"), val = string("valid")]; + tensor h_55_strides_0 = const()[name = string("h_55_strides_0"), val = tensor([1])]; + tensor h_55_pad_0 = const()[name = string("h_55_pad_0"), val = tensor([0, 0])]; + tensor h_55_dilations_0 = const()[name = string("h_55_dilations_0"), val = tensor([1])]; + int32 h_55_groups_0 = const()[name = string("h_55_groups_0"), val = int32(1)]; + tensor op_864_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9685696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10210048))))[name = string("op_864_weight_0_to_fp16_palettized")]; + tensor var_864_bias_0_to_fp16 = const()[name = string("op_864_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10210176)))]; + tensor var_864_cast_fp16 = conv(bias = var_864_bias_0_to_fp16, dilations = h_55_dilations_0, groups = h_55_groups_0, pad = h_55_pad_0, pad_type = h_55_pad_type_0, strides = h_55_strides_0, weight = op_864_weight_0_to_fp16_palettized, x = input_137_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor out_21_cast_fp16 = add(x = x_67_cast_fp16, y = var_864_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = out_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor x_73_cast_fp16 = mul(x = x_71_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_878_cast_fp16 = mul(x = x_73_cast_fp16, y = last_1_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor x_last_19_axes_0 = const()[name = string("x_last_19_axes_0"), val = tensor([-1])]; + bool x_last_19_keep_dims_0 = const()[name = string("x_last_19_keep_dims_0"), val = bool(true)]; + tensor x_last_19_cast_fp16 = reduce_sum(axes = x_last_19_axes_0, keep_dims = x_last_19_keep_dims_0, x = var_878_cast_fp16)[name = string("x_last_19_cast_fp16")]; + tensor var_881_cast_fp16 = mul(x = x_73_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_881_cast_fp16")]; + tensor var_883_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_19_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = var_881_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("replicate")]; + fp16 const_30_to_fp16 = const()[name = string("const_30_to_fp16"), val = fp16(0x0p+0)]; + tensor input_143_cast_fp16 = pad(constant_val = const_30_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + string h_57_pad_type_0 = const()[name = string("h_57_pad_type_0"), val = string("valid")]; + tensor h_57_dilations_0 = const()[name = string("h_57_dilations_0"), val = tensor([8])]; + int32 h_57_groups_0 = const()[name = string("h_57_groups_0"), val = int32(512)]; + tensor h_57_strides_0 = const()[name = string("h_57_strides_0"), val = tensor([1])]; + tensor h_57_pad_0 = const()[name = string("h_57_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10211264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10212608))))[name = string("main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10212736)))]; + tensor h_57_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_57_dilations_0, groups = h_57_groups_0, pad = h_57_pad_0, pad_type = h_57_pad_type_0, strides = h_57_strides_0, weight = main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = string("h_57_cast_fp16")]; + tensor x_75_cast_fp16 = mul(x = h_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_901_axes_0 = const()[name = string("op_901_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10213824)))]; + tensor main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10214912)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_75_cast_fp16)[name = string("transpose_82")]; + tensor var_901_cast_fp16 = layer_norm(axes = var_901_axes_0, beta = main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor input_147_perm_0 = const()[name = string("input_147_perm_0"), val = tensor([0, 2, 1])]; + string h_59_pad_type_0 = const()[name = string("h_59_pad_type_0"), val = string("valid")]; + tensor h_59_strides_0 = const()[name = string("h_59_strides_0"), val = tensor([1])]; + tensor h_59_pad_0 = const()[name = string("h_59_pad_0"), val = tensor([0, 0])]; + tensor h_59_dilations_0 = const()[name = string("h_59_dilations_0"), val = tensor([1])]; + int32 h_59_groups_0 = const()[name = string("h_59_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10216000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10740352))))[name = string("main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10740480)))]; + tensor input_147_cast_fp16 = transpose(perm = input_147_perm_0, x = var_901_cast_fp16)[name = string("transpose_81")]; + tensor h_59_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_59_dilations_0, groups = h_59_groups_0, pad = h_59_pad_0, pad_type = h_59_pad_type_0, strides = h_59_strides_0, weight = main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("h_59_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = h_59_cast_fp16)[name = string("input_149_cast_fp16")]; + string h_61_pad_type_0 = const()[name = string("h_61_pad_type_0"), val = string("valid")]; + tensor h_61_strides_0 = const()[name = string("h_61_strides_0"), val = tensor([1])]; + tensor h_61_pad_0 = const()[name = string("h_61_pad_0"), val = tensor([0, 0])]; + tensor h_61_dilations_0 = const()[name = string("h_61_dilations_0"), val = tensor([1])]; + int32 h_61_groups_0 = const()[name = string("h_61_groups_0"), val = int32(1)]; + tensor op_918_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10744640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11268992))))[name = string("op_918_weight_0_to_fp16_palettized")]; + tensor var_918_bias_0_to_fp16 = const()[name = string("op_918_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11269120)))]; + tensor var_918_cast_fp16 = conv(bias = var_918_bias_0_to_fp16, dilations = h_61_dilations_0, groups = h_61_groups_0, pad = h_61_pad_0, pad_type = h_61_pad_type_0, strides = h_61_strides_0, weight = op_918_weight_0_to_fp16_palettized, x = input_149_cast_fp16)[name = string("op_918_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = x_73_cast_fp16, y = var_918_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor x_77_cast_fp16 = mul(x = out_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11270208))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11286656))))[name = string("main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11286784)))]; + tensor linear_11_cast_fp16 = linear(bias = main_blocks_1_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor t_7_axes_0 = const()[name = string("t_7_axes_0"), val = tensor([-1])]; + tensor t_7_cast_fp16 = expand_dims(axes = t_7_axes_0, x = linear_11_cast_fp16)[name = string("t_7_cast_fp16")]; + tensor var_928_cast_fp16 = add(x = x_77_cast_fp16, y = t_7_cast_fp16)[name = string("op_928_cast_fp16")]; + tensor x_79_cast_fp16 = mul(x = var_928_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor x_81_cast_fp16 = mul(x = x_79_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = x_81_cast_fp16, y = last_1_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor x_last_21_axes_0 = const()[name = string("x_last_21_axes_0"), val = tensor([-1])]; + bool x_last_21_keep_dims_0 = const()[name = string("x_last_21_keep_dims_0"), val = bool(true)]; + tensor x_last_21_cast_fp16 = reduce_sum(axes = x_last_21_axes_0, keep_dims = x_last_21_keep_dims_0, x = var_941_cast_fp16)[name = string("x_last_21_cast_fp16")]; + tensor var_944_cast_fp16 = mul(x = x_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor var_946_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_21_cast_fp16)[name = string("op_946_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = var_944_cast_fp16, y = var_946_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("replicate")]; + fp16 const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = fp16(0x0p+0)]; + tensor input_157_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string h_63_pad_type_0 = const()[name = string("h_63_pad_type_0"), val = string("valid")]; + int32 h_63_groups_0 = const()[name = string("h_63_groups_0"), val = int32(512)]; + tensor h_63_strides_0 = const()[name = string("h_63_strides_0"), val = tensor([1])]; + tensor h_63_pad_0 = const()[name = string("h_63_pad_0"), val = tensor([0, 0])]; + tensor h_63_dilations_0 = const()[name = string("h_63_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11287872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11289216))))[name = string("main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11289344)))]; + tensor h_63_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_63_dilations_0, groups = h_63_groups_0, pad = h_63_pad_0, pad_type = h_63_pad_type_0, strides = h_63_strides_0, weight = main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor x_83_cast_fp16 = mul(x = h_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor var_964_axes_0 = const()[name = string("op_964_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11290432)))]; + tensor main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11291520)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_83_cast_fp16)[name = string("transpose_80")]; + tensor var_964_cast_fp16 = layer_norm(axes = var_964_axes_0, beta = main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + string h_65_pad_type_0 = const()[name = string("h_65_pad_type_0"), val = string("valid")]; + tensor h_65_strides_0 = const()[name = string("h_65_strides_0"), val = tensor([1])]; + tensor h_65_pad_0 = const()[name = string("h_65_pad_0"), val = tensor([0, 0])]; + tensor h_65_dilations_0 = const()[name = string("h_65_dilations_0"), val = tensor([1])]; + int32 h_65_groups_0 = const()[name = string("h_65_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11292608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11816960))))[name = string("main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11817088)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = var_964_cast_fp16)[name = string("transpose_79")]; + tensor h_65_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_65_dilations_0, groups = h_65_groups_0, pad = h_65_pad_0, pad_type = h_65_pad_type_0, strides = h_65_strides_0, weight = main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = string("h_65_cast_fp16")]; + string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("EXACT")]; + tensor input_163_cast_fp16 = gelu(mode = input_163_mode_0, x = h_65_cast_fp16)[name = string("input_163_cast_fp16")]; + string h_67_pad_type_0 = const()[name = string("h_67_pad_type_0"), val = string("valid")]; + tensor h_67_strides_0 = const()[name = string("h_67_strides_0"), val = tensor([1])]; + tensor h_67_pad_0 = const()[name = string("h_67_pad_0"), val = tensor([0, 0])]; + tensor h_67_dilations_0 = const()[name = string("h_67_dilations_0"), val = tensor([1])]; + int32 h_67_groups_0 = const()[name = string("h_67_groups_0"), val = int32(1)]; + tensor op_981_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11821248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12345600))))[name = string("op_981_weight_0_to_fp16_palettized")]; + tensor var_981_bias_0_to_fp16 = const()[name = string("op_981_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12345728)))]; + tensor var_981_cast_fp16 = conv(bias = var_981_bias_0_to_fp16, dilations = h_67_dilations_0, groups = h_67_groups_0, pad = h_67_pad_0, pad_type = h_67_pad_type_0, strides = h_67_strides_0, weight = op_981_weight_0_to_fp16_palettized, x = input_163_cast_fp16)[name = string("op_981_cast_fp16")]; + tensor out_25_cast_fp16 = add(x = x_81_cast_fp16, y = var_981_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = out_25_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor x_m_5_cast_fp16 = mul(x = x_85_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_5_cast_fp16")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12346816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12477952))))[name = string("main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12478080)))]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_85_cast_fp16)[name = string("transpose_78")]; + tensor linear_12_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_999 = const()[name = string("op_999"), val = tensor([2, 512, 8, 64])]; + tensor var_1000_cast_fp16 = reshape(shape = var_999, x = linear_12_cast_fp16)[name = string("op_1000_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12479168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12544768))))[name = string("main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12544896)))]; + tensor linear_13_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_1007 = const()[name = string("op_1007"), val = tensor([2, 128, 8, 64])]; + tensor var_1008_cast_fp16 = reshape(shape = var_1007, x = linear_13_cast_fp16)[name = string("op_1008_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12545984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12611584))))[name = string("main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12611712)))]; + tensor linear_14_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_1015 = const()[name = string("op_1015"), val = tensor([2, 128, 8, 64])]; + tensor var_1016_cast_fp16 = reshape(shape = var_1015, x = linear_14_cast_fp16)[name = string("op_1016_cast_fp16")]; + tensor v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_5_begin_0 = const()[name = string("x_a_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_5_end_0 = const()[name = string("x_a_5_end_0"), val = tensor([2, 8, 512, 32])]; + tensor x_a_5_end_mask_0 = const()[name = string("x_a_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_1000_cast_fp16)[name = string("transpose_77")]; + tensor x_a_5_cast_fp16 = slice_by_index(begin = x_a_5_begin_0, end = x_a_5_end_0, end_mask = x_a_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_a_5_cast_fp16")]; + tensor x_b_5_begin_0 = const()[name = string("x_b_5_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_5_end_0 = const()[name = string("x_b_5_end_0"), val = tensor([2, 8, 512, 64])]; + tensor x_b_5_end_mask_0 = const()[name = string("x_b_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_5_cast_fp16 = slice_by_index(begin = x_b_5_begin_0, end = x_b_5_end_0, end_mask = x_b_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_b_5_cast_fp16")]; + tensor var_1044_cast_fp16 = mul(x = x_a_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1044_cast_fp16")]; + tensor var_1045_cast_fp16 = mul(x = x_b_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor rot_a_5_cast_fp16 = sub(x = var_1044_cast_fp16, y = var_1045_cast_fp16)[name = string("rot_a_5_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_a_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_cast_fp16 = mul(x = x_b_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1048_cast_fp16")]; + tensor rot_b_5_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("rot_b_5_cast_fp16")]; + bool q_5_interleave_0 = const()[name = string("q_5_interleave_0"), val = bool(false)]; + tensor q_5_cast_fp16 = concat(axis = var_681, interleave = q_5_interleave_0, values = (rot_a_5_cast_fp16, rot_b_5_cast_fp16))[name = string("q_5_cast_fp16")]; + tensor x_a_7_begin_0 = const()[name = string("x_a_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_7_end_0 = const()[name = string("x_a_7_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_7_end_mask_0 = const()[name = string("x_a_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_1008_cast_fp16)[name = string("transpose_76")]; + tensor x_a_7_cast_fp16 = slice_by_index(begin = x_a_7_begin_0, end = x_a_7_end_0, end_mask = x_a_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_a_7_cast_fp16")]; + tensor x_b_7_begin_0 = const()[name = string("x_b_7_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_7_end_0 = const()[name = string("x_b_7_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_7_end_mask_0 = const()[name = string("x_b_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_7_cast_fp16 = slice_by_index(begin = x_b_7_begin_0, end = x_b_7_end_0, end_mask = x_b_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_b_7_cast_fp16")]; + tensor var_1062_cast_fp16 = mul(x = x_a_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = x_b_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor rot_a_7_cast_fp16 = sub(x = var_1062_cast_fp16, y = var_1063_cast_fp16)[name = string("rot_a_7_cast_fp16")]; + tensor var_1065_cast_fp16 = mul(x = x_a_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1065_cast_fp16")]; + tensor var_1066_cast_fp16 = mul(x = x_b_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1066_cast_fp16")]; + tensor rot_b_7_cast_fp16 = add(x = var_1065_cast_fp16, y = var_1066_cast_fp16)[name = string("rot_b_7_cast_fp16")]; + bool k_5_interleave_0 = const()[name = string("k_5_interleave_0"), val = bool(false)]; + tensor k_5_cast_fp16 = concat(axis = var_681, interleave = k_5_interleave_0, values = (rot_a_7_cast_fp16, rot_b_7_cast_fp16))[name = string("k_5_cast_fp16")]; + bool var_1071_transpose_x_1 = const()[name = string("op_1071_transpose_x_1"), val = bool(false)]; + bool var_1071_transpose_y_1 = const()[name = string("op_1071_transpose_y_1"), val = bool(true)]; + tensor var_1071_cast_fp16 = matmul(transpose_x = var_1071_transpose_x_1, transpose_y = var_1071_transpose_y_1, x = q_5_cast_fp16, y = k_5_cast_fp16)[name = string("op_1071_cast_fp16")]; + fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_3_cast_fp16 = mul(x = var_1071_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")]; + tensor input_171_cast_fp16 = sub(x = _inversed_scores_3_cast_fp16, y = var_530_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_681, x = input_171_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool var_1080_transpose_x_0 = const()[name = string("op_1080_transpose_x_0"), val = bool(false)]; + bool var_1080_transpose_y_0 = const()[name = string("op_1080_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1016_cast_fp16)[name = string("transpose_75")]; + tensor var_1080_cast_fp16 = matmul(transpose_x = var_1080_transpose_x_0, transpose_y = var_1080_transpose_y_0, x = attn_7_cast_fp16, y = v_5_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_perm_0 = const()[name = string("op_1081_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1082 = const()[name = string("op_1082"), val = tensor([2, 512, 512])]; + tensor var_1081_cast_fp16 = transpose(perm = var_1081_perm_0, x = var_1080_cast_fp16)[name = string("transpose_74")]; + tensor input_173_cast_fp16 = reshape(shape = var_1082, x = var_1081_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12612800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12743936))))[name = string("main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12744064)))]; + tensor linear_15_cast_fp16 = linear(bias = main_blocks_1_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor out_27_perm_0 = const()[name = string("out_27_perm_0"), val = tensor([0, 2, 1])]; + tensor out_27_cast_fp16 = transpose(perm = out_27_perm_0, x = linear_15_cast_fp16)[name = string("transpose_73")]; + tensor var_1089_cast_fp16 = mul(x = out_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor x_91_cast_fp16 = add(x = x_m_5_cast_fp16, y = var_1089_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_175_perm_0 = const()[name = string("input_175_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1096_axes_0 = const()[name = string("op_1096_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12745152)))]; + tensor main_blocks_1_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12746240)))]; + tensor input_175_cast_fp16 = transpose(perm = input_175_perm_0, x = x_91_cast_fp16)[name = string("transpose_72")]; + tensor var_1096_cast_fp16 = layer_norm(axes = var_1096_axes_0, beta = main_blocks_1_text_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_text_norm_norm_weight_to_fp16, x = input_175_cast_fp16)[name = string("op_1096_cast_fp16")]; + tensor var_1097_perm_0 = const()[name = string("op_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1097_cast_fp16 = transpose(perm = var_1097_perm_0, x = var_1096_cast_fp16)[name = string("transpose_71")]; + tensor x_93_cast_fp16 = mul(x = var_1097_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor x_95_cast_fp16 = mul(x = x_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_1110_cast_fp16 = mul(x = x_95_cast_fp16, y = last_1_cast_fp16)[name = string("op_1110_cast_fp16")]; + tensor x_last_23_axes_0 = const()[name = string("x_last_23_axes_0"), val = tensor([-1])]; + bool x_last_23_keep_dims_0 = const()[name = string("x_last_23_keep_dims_0"), val = bool(true)]; + tensor x_last_23_cast_fp16 = reduce_sum(axes = x_last_23_axes_0, keep_dims = x_last_23_keep_dims_0, x = var_1110_cast_fp16)[name = string("x_last_23_cast_fp16")]; + tensor var_1113_cast_fp16 = mul(x = x_95_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1115_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_23_cast_fp16)[name = string("op_1115_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = var_1113_cast_fp16, y = var_1115_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("replicate")]; + fp16 const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = fp16(0x0p+0)]; + tensor input_181_cast_fp16 = pad(constant_val = const_39_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + string h_69_pad_type_0 = const()[name = string("h_69_pad_type_0"), val = string("valid")]; + int32 h_69_groups_0 = const()[name = string("h_69_groups_0"), val = int32(512)]; + tensor h_69_strides_0 = const()[name = string("h_69_strides_0"), val = tensor([1])]; + tensor h_69_pad_0 = const()[name = string("h_69_pad_0"), val = tensor([0, 0])]; + tensor h_69_dilations_0 = const()[name = string("h_69_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12747328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12748672))))[name = string("main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12748800)))]; + tensor h_69_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_69_dilations_0, groups = h_69_groups_0, pad = h_69_pad_0, pad_type = h_69_pad_type_0, strides = h_69_strides_0, weight = main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = string("h_69_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = h_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_183_perm_0 = const()[name = string("input_183_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1133_axes_0 = const()[name = string("op_1133_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12749888)))]; + tensor main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12750976)))]; + tensor input_183_cast_fp16 = transpose(perm = input_183_perm_0, x = x_97_cast_fp16)[name = string("transpose_70")]; + tensor var_1133_cast_fp16 = layer_norm(axes = var_1133_axes_0, beta = main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16, x = input_183_cast_fp16)[name = string("op_1133_cast_fp16")]; + tensor input_185_perm_0 = const()[name = string("input_185_perm_0"), val = tensor([0, 2, 1])]; + string h_71_pad_type_0 = const()[name = string("h_71_pad_type_0"), val = string("valid")]; + tensor h_71_strides_0 = const()[name = string("h_71_strides_0"), val = tensor([1])]; + tensor h_71_pad_0 = const()[name = string("h_71_pad_0"), val = tensor([0, 0])]; + tensor h_71_dilations_0 = const()[name = string("h_71_dilations_0"), val = tensor([1])]; + int32 h_71_groups_0 = const()[name = string("h_71_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12752064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13276416))))[name = string("main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13276544)))]; + tensor input_185_cast_fp16 = transpose(perm = input_185_perm_0, x = var_1133_cast_fp16)[name = string("transpose_69")]; + tensor h_71_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_71_dilations_0, groups = h_71_groups_0, pad = h_71_pad_0, pad_type = h_71_pad_type_0, strides = h_71_strides_0, weight = main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = string("h_71_cast_fp16")]; + string input_187_mode_0 = const()[name = string("input_187_mode_0"), val = string("EXACT")]; + tensor input_187_cast_fp16 = gelu(mode = input_187_mode_0, x = h_71_cast_fp16)[name = string("input_187_cast_fp16")]; + string h_73_pad_type_0 = const()[name = string("h_73_pad_type_0"), val = string("valid")]; + tensor h_73_strides_0 = const()[name = string("h_73_strides_0"), val = tensor([1])]; + tensor h_73_pad_0 = const()[name = string("h_73_pad_0"), val = tensor([0, 0])]; + tensor h_73_dilations_0 = const()[name = string("h_73_dilations_0"), val = tensor([1])]; + int32 h_73_groups_0 = const()[name = string("h_73_groups_0"), val = int32(1)]; + tensor op_1150_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13280704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13805056))))[name = string("op_1150_weight_0_to_fp16_palettized")]; + tensor var_1150_bias_0_to_fp16 = const()[name = string("op_1150_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13805184)))]; + tensor var_1150_cast_fp16 = conv(bias = var_1150_bias_0_to_fp16, dilations = h_73_dilations_0, groups = h_73_groups_0, pad = h_73_pad_0, pad_type = h_73_pad_type_0, strides = h_73_strides_0, weight = op_1150_weight_0_to_fp16_palettized, x = input_187_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor out_29_cast_fp16 = add(x = x_95_cast_fp16, y = var_1150_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor x_99_cast_fp16 = mul(x = out_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor x_m_7_cast_fp16 = mul(x = x_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_7_cast_fp16")]; + tensor input_189_perm_0 = const()[name = string("input_189_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13806272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13871872))))[name = string("main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13872000)))]; + tensor input_189_cast_fp16 = transpose(perm = input_189_perm_0, x = x_99_cast_fp16)[name = string("transpose_68")]; + tensor linear_16_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_1166 = const()[name = string("op_1166"), val = tensor([2, 512, 2, 128])]; + tensor var_1167_cast_fp16 = reshape(shape = var_1166, x = linear_16_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13872576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13905408))))[name = string("main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13905536)))]; + tensor linear_18_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_1180 = const()[name = string("op_1180"), val = tensor([2, 50, 2, 128])]; + tensor var_1181_cast_fp16 = reshape(shape = var_1180, x = linear_18_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1185_transpose_x_0 = const()[name = string("op_1185_transpose_x_0"), val = bool(false)]; + bool var_1185_transpose_y_0 = const()[name = string("op_1185_transpose_y_0"), val = bool(false)]; + tensor op_1184_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13906112))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13918976))))[name = string("op_1184_to_fp16_palettized")]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1167_cast_fp16)[name = string("transpose_67")]; + tensor var_1185_cast_fp16 = matmul(transpose_x = var_1185_transpose_x_0, transpose_y = var_1185_transpose_y_0, x = q_7_cast_fp16, y = op_1184_to_fp16_palettized)[name = string("op_1185_cast_fp16")]; + fp16 _inversed_input_191_y_0_to_fp16 = const()[name = string("_inversed_input_191_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_191_cast_fp16 = mul(x = var_1185_cast_fp16, y = _inversed_input_191_y_0_to_fp16)[name = string("_inversed_input_191_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_681, x = _inversed_input_191_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor attn_11_cast_fp16 = mul(x = attn_9_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool var_1192_transpose_x_0 = const()[name = string("op_1192_transpose_x_0"), val = bool(false)]; + bool var_1192_transpose_y_0 = const()[name = string("op_1192_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1181_cast_fp16)[name = string("transpose_66")]; + tensor var_1192_cast_fp16 = matmul(transpose_x = var_1192_transpose_x_0, transpose_y = var_1192_transpose_y_0, x = attn_11_cast_fp16, y = v_7_cast_fp16)[name = string("op_1192_cast_fp16")]; + tensor var_1193_perm_0 = const()[name = string("op_1193_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1194 = const()[name = string("op_1194"), val = tensor([2, 512, 256])]; + tensor var_1193_cast_fp16 = transpose(perm = var_1193_perm_0, x = var_1192_cast_fp16)[name = string("transpose_65")]; + tensor input_193_cast_fp16 = reshape(shape = var_1194, x = var_1193_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13919104))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13984704))))[name = string("main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13984832)))]; + tensor linear_19_cast_fp16 = linear(bias = main_blocks_1_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_193_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor out_31_perm_0 = const()[name = string("out_31_perm_0"), val = tensor([0, 2, 1])]; + tensor out_31_cast_fp16 = transpose(perm = out_31_perm_0, x = linear_19_cast_fp16)[name = string("transpose_64")]; + tensor var_1201_cast_fp16 = mul(x = out_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1201_cast_fp16")]; + tensor x_101_cast_fp16 = add(x = x_m_7_cast_fp16, y = var_1201_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_195_perm_0 = const()[name = string("input_195_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1208_axes_0 = const()[name = string("op_1208_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13985920)))]; + tensor main_blocks_1_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13987008)))]; + tensor input_195_cast_fp16 = transpose(perm = input_195_perm_0, x = x_101_cast_fp16)[name = string("transpose_63")]; + tensor var_1208_cast_fp16 = layer_norm(axes = var_1208_axes_0, beta = main_blocks_1_style_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_style_norm_norm_weight_to_fp16, x = input_195_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_perm_0 = const()[name = string("op_1209_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1209_cast_fp16 = transpose(perm = var_1209_perm_0, x = var_1208_cast_fp16)[name = string("transpose_62")]; + tensor x_103_cast_fp16 = mul(x = var_1209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_103_cast_fp16")]; + int32 var_1228 = const()[name = string("op_1228"), val = int32(-1)]; + tensor x_105_cast_fp16 = mul(x = x_103_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_1263_cast_fp16 = mul(x = x_105_cast_fp16, y = last_1_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor x_last_25_axes_0 = const()[name = string("x_last_25_axes_0"), val = tensor([-1])]; + bool x_last_25_keep_dims_0 = const()[name = string("x_last_25_keep_dims_0"), val = bool(true)]; + tensor x_last_25_cast_fp16 = reduce_sum(axes = x_last_25_axes_0, keep_dims = x_last_25_keep_dims_0, x = var_1263_cast_fp16)[name = string("x_last_25_cast_fp16")]; + tensor var_1266_cast_fp16 = mul(x = x_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1266_cast_fp16")]; + tensor var_1268_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_25_cast_fp16)[name = string("op_1268_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1266_cast_fp16, y = var_1268_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_201_mode_0 = const()[name = string("input_201_mode_0"), val = string("replicate")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_201_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")]; + string h_75_pad_type_0 = const()[name = string("h_75_pad_type_0"), val = string("valid")]; + int32 h_75_groups_0 = const()[name = string("h_75_groups_0"), val = int32(512)]; + tensor h_75_strides_0 = const()[name = string("h_75_strides_0"), val = tensor([1])]; + tensor h_75_pad_0 = const()[name = string("h_75_pad_0"), val = tensor([0, 0])]; + tensor h_75_dilations_0 = const()[name = string("h_75_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13988096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13989440))))[name = string("main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13989568)))]; + tensor h_75_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_75_dilations_0, groups = h_75_groups_0, pad = h_75_pad_0, pad_type = h_75_pad_type_0, strides = h_75_strides_0, weight = main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_201_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = h_75_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_203_perm_0 = const()[name = string("input_203_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1286_axes_0 = const()[name = string("op_1286_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13990656)))]; + tensor main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13991744)))]; + fp16 var_1233_to_fp16 = const()[name = string("op_1233_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_203_cast_fp16 = transpose(perm = input_203_perm_0, x = x_107_cast_fp16)[name = string("transpose_61")]; + tensor var_1286_cast_fp16 = layer_norm(axes = var_1286_axes_0, beta = main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16, x = input_203_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string h_77_pad_type_0 = const()[name = string("h_77_pad_type_0"), val = string("valid")]; + tensor h_77_strides_0 = const()[name = string("h_77_strides_0"), val = tensor([1])]; + tensor h_77_pad_0 = const()[name = string("h_77_pad_0"), val = tensor([0, 0])]; + tensor h_77_dilations_0 = const()[name = string("h_77_dilations_0"), val = tensor([1])]; + int32 h_77_groups_0 = const()[name = string("h_77_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13992832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14517184))))[name = string("main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14517312)))]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = var_1286_cast_fp16)[name = string("transpose_60")]; + tensor h_77_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_77_dilations_0, groups = h_77_groups_0, pad = h_77_pad_0, pad_type = h_77_pad_type_0, strides = h_77_strides_0, weight = main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_205_cast_fp16)[name = string("h_77_cast_fp16")]; + string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")]; + tensor input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = h_77_cast_fp16)[name = string("input_207_cast_fp16")]; + string h_79_pad_type_0 = const()[name = string("h_79_pad_type_0"), val = string("valid")]; + tensor h_79_strides_0 = const()[name = string("h_79_strides_0"), val = tensor([1])]; + tensor h_79_pad_0 = const()[name = string("h_79_pad_0"), val = tensor([0, 0])]; + tensor h_79_dilations_0 = const()[name = string("h_79_dilations_0"), val = tensor([1])]; + int32 h_79_groups_0 = const()[name = string("h_79_groups_0"), val = int32(1)]; + tensor op_1303_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14521472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15045824))))[name = string("op_1303_weight_0_to_fp16_palettized")]; + tensor var_1303_bias_0_to_fp16 = const()[name = string("op_1303_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15045952)))]; + tensor var_1303_cast_fp16 = conv(bias = var_1303_bias_0_to_fp16, dilations = h_79_dilations_0, groups = h_79_groups_0, pad = h_79_pad_0, pad_type = h_79_pad_type_0, strides = h_79_strides_0, weight = op_1303_weight_0_to_fp16_palettized, x = input_207_cast_fp16)[name = string("op_1303_cast_fp16")]; + tensor out_33_cast_fp16 = add(x = x_105_cast_fp16, y = var_1303_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor x_109_cast_fp16 = mul(x = out_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor x_111_cast_fp16 = mul(x = x_109_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_1317_cast_fp16 = mul(x = x_111_cast_fp16, y = last_1_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor x_last_27_axes_0 = const()[name = string("x_last_27_axes_0"), val = tensor([-1])]; + bool x_last_27_keep_dims_0 = const()[name = string("x_last_27_keep_dims_0"), val = bool(true)]; + tensor x_last_27_cast_fp16 = reduce_sum(axes = x_last_27_axes_0, keep_dims = x_last_27_keep_dims_0, x = var_1317_cast_fp16)[name = string("x_last_27_cast_fp16")]; + tensor var_1320_cast_fp16 = mul(x = x_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1322_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_27_cast_fp16)[name = string("op_1322_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1320_cast_fp16, y = var_1322_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("replicate")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor input_213_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = input_213_mode_0, pad = input_213_pad_0, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + string h_81_pad_type_0 = const()[name = string("h_81_pad_type_0"), val = string("valid")]; + tensor h_81_dilations_0 = const()[name = string("h_81_dilations_0"), val = tensor([2])]; + int32 h_81_groups_0 = const()[name = string("h_81_groups_0"), val = int32(512)]; + tensor h_81_strides_0 = const()[name = string("h_81_strides_0"), val = tensor([1])]; + tensor h_81_pad_0 = const()[name = string("h_81_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15047040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15048384))))[name = string("main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15048512)))]; + tensor h_81_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_81_dilations_0, groups = h_81_groups_0, pad = h_81_pad_0, pad_type = h_81_pad_type_0, strides = h_81_strides_0, weight = main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_213_cast_fp16)[name = string("h_81_cast_fp16")]; + tensor x_113_cast_fp16 = mul(x = h_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor input_215_perm_0 = const()[name = string("input_215_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1340_axes_0 = const()[name = string("op_1340_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15049600)))]; + tensor main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15050688)))]; + tensor input_215_cast_fp16 = transpose(perm = input_215_perm_0, x = x_113_cast_fp16)[name = string("transpose_59")]; + tensor var_1340_cast_fp16 = layer_norm(axes = var_1340_axes_0, beta = main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16, x = input_215_cast_fp16)[name = string("op_1340_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + string h_83_pad_type_0 = const()[name = string("h_83_pad_type_0"), val = string("valid")]; + tensor h_83_strides_0 = const()[name = string("h_83_strides_0"), val = tensor([1])]; + tensor h_83_pad_0 = const()[name = string("h_83_pad_0"), val = tensor([0, 0])]; + tensor h_83_dilations_0 = const()[name = string("h_83_dilations_0"), val = tensor([1])]; + int32 h_83_groups_0 = const()[name = string("h_83_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15051776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15576128))))[name = string("main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15576256)))]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = var_1340_cast_fp16)[name = string("transpose_58")]; + tensor h_83_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_83_dilations_0, groups = h_83_groups_0, pad = h_83_pad_0, pad_type = h_83_pad_type_0, strides = h_83_strides_0, weight = main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = string("h_83_cast_fp16")]; + string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")]; + tensor input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = h_83_cast_fp16)[name = string("input_219_cast_fp16")]; + string h_85_pad_type_0 = const()[name = string("h_85_pad_type_0"), val = string("valid")]; + tensor h_85_strides_0 = const()[name = string("h_85_strides_0"), val = tensor([1])]; + tensor h_85_pad_0 = const()[name = string("h_85_pad_0"), val = tensor([0, 0])]; + tensor h_85_dilations_0 = const()[name = string("h_85_dilations_0"), val = tensor([1])]; + int32 h_85_groups_0 = const()[name = string("h_85_groups_0"), val = int32(1)]; + tensor op_1357_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15580416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16104768))))[name = string("op_1357_weight_0_to_fp16_palettized")]; + tensor var_1357_bias_0_to_fp16 = const()[name = string("op_1357_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16104896)))]; + tensor var_1357_cast_fp16 = conv(bias = var_1357_bias_0_to_fp16, dilations = h_85_dilations_0, groups = h_85_groups_0, pad = h_85_pad_0, pad_type = h_85_pad_type_0, strides = h_85_strides_0, weight = op_1357_weight_0_to_fp16_palettized, x = input_219_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor out_35_cast_fp16 = add(x = x_111_cast_fp16, y = var_1357_cast_fp16)[name = string("out_35_cast_fp16")]; + tensor x_115_cast_fp16 = mul(x = out_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_cast_fp16 = mul(x = x_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1371_cast_fp16 = mul(x = x_117_cast_fp16, y = last_1_cast_fp16)[name = string("op_1371_cast_fp16")]; + tensor x_last_29_axes_0 = const()[name = string("x_last_29_axes_0"), val = tensor([-1])]; + bool x_last_29_keep_dims_0 = const()[name = string("x_last_29_keep_dims_0"), val = bool(true)]; + tensor x_last_29_cast_fp16 = reduce_sum(axes = x_last_29_axes_0, keep_dims = x_last_29_keep_dims_0, x = var_1371_cast_fp16)[name = string("x_last_29_cast_fp16")]; + tensor var_1374_cast_fp16 = mul(x = x_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_29_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1376_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_225_mode_0 = const()[name = string("input_225_mode_0"), val = string("replicate")]; + fp16 const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = fp16(0x0p+0)]; + tensor input_225_cast_fp16 = pad(constant_val = const_48_to_fp16, mode = input_225_mode_0, pad = input_225_pad_0, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")]; + string h_87_pad_type_0 = const()[name = string("h_87_pad_type_0"), val = string("valid")]; + tensor h_87_dilations_0 = const()[name = string("h_87_dilations_0"), val = tensor([4])]; + int32 h_87_groups_0 = const()[name = string("h_87_groups_0"), val = int32(512)]; + tensor h_87_strides_0 = const()[name = string("h_87_strides_0"), val = tensor([1])]; + tensor h_87_pad_0 = const()[name = string("h_87_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16105984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16107328))))[name = string("main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16107456)))]; + tensor h_87_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_87_dilations_0, groups = h_87_groups_0, pad = h_87_pad_0, pad_type = h_87_pad_type_0, strides = h_87_strides_0, weight = main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor x_119_cast_fp16 = mul(x = h_87_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_227_perm_0 = const()[name = string("input_227_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1394_axes_0 = const()[name = string("op_1394_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16108544)))]; + tensor main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16109632)))]; + tensor input_227_cast_fp16 = transpose(perm = input_227_perm_0, x = x_119_cast_fp16)[name = string("transpose_57")]; + tensor var_1394_cast_fp16 = layer_norm(axes = var_1394_axes_0, beta = main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16, x = input_227_cast_fp16)[name = string("op_1394_cast_fp16")]; + tensor input_229_perm_0 = const()[name = string("input_229_perm_0"), val = tensor([0, 2, 1])]; + string h_89_pad_type_0 = const()[name = string("h_89_pad_type_0"), val = string("valid")]; + tensor h_89_strides_0 = const()[name = string("h_89_strides_0"), val = tensor([1])]; + tensor h_89_pad_0 = const()[name = string("h_89_pad_0"), val = tensor([0, 0])]; + tensor h_89_dilations_0 = const()[name = string("h_89_dilations_0"), val = tensor([1])]; + int32 h_89_groups_0 = const()[name = string("h_89_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16110720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16635072))))[name = string("main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16635200)))]; + tensor input_229_cast_fp16 = transpose(perm = input_229_perm_0, x = var_1394_cast_fp16)[name = string("transpose_56")]; + tensor h_89_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_89_dilations_0, groups = h_89_groups_0, pad = h_89_pad_0, pad_type = h_89_pad_type_0, strides = h_89_strides_0, weight = main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = string("h_89_cast_fp16")]; + string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")]; + tensor input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = h_89_cast_fp16)[name = string("input_231_cast_fp16")]; + string h_91_pad_type_0 = const()[name = string("h_91_pad_type_0"), val = string("valid")]; + tensor h_91_strides_0 = const()[name = string("h_91_strides_0"), val = tensor([1])]; + tensor h_91_pad_0 = const()[name = string("h_91_pad_0"), val = tensor([0, 0])]; + tensor h_91_dilations_0 = const()[name = string("h_91_dilations_0"), val = tensor([1])]; + int32 h_91_groups_0 = const()[name = string("h_91_groups_0"), val = int32(1)]; + tensor op_1411_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16639360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17163712))))[name = string("op_1411_weight_0_to_fp16_palettized")]; + tensor var_1411_bias_0_to_fp16 = const()[name = string("op_1411_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17163840)))]; + tensor var_1411_cast_fp16 = conv(bias = var_1411_bias_0_to_fp16, dilations = h_91_dilations_0, groups = h_91_groups_0, pad = h_91_pad_0, pad_type = h_91_pad_type_0, strides = h_91_strides_0, weight = op_1411_weight_0_to_fp16_palettized, x = input_231_cast_fp16)[name = string("op_1411_cast_fp16")]; + tensor out_37_cast_fp16 = add(x = x_117_cast_fp16, y = var_1411_cast_fp16)[name = string("out_37_cast_fp16")]; + tensor x_121_cast_fp16 = mul(x = out_37_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_121_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1425_cast_fp16 = mul(x = x_123_cast_fp16, y = last_1_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor x_last_31_axes_0 = const()[name = string("x_last_31_axes_0"), val = tensor([-1])]; + bool x_last_31_keep_dims_0 = const()[name = string("x_last_31_keep_dims_0"), val = bool(true)]; + tensor x_last_31_cast_fp16 = reduce_sum(axes = x_last_31_axes_0, keep_dims = x_last_31_keep_dims_0, x = var_1425_cast_fp16)[name = string("x_last_31_cast_fp16")]; + tensor var_1428_cast_fp16 = mul(x = x_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_31_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = var_1428_cast_fp16, y = var_1430_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("replicate")]; + fp16 const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = fp16(0x0p+0)]; + tensor input_237_cast_fp16 = pad(constant_val = const_50_to_fp16, mode = input_237_mode_0, pad = input_237_pad_0, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + string h_93_pad_type_0 = const()[name = string("h_93_pad_type_0"), val = string("valid")]; + tensor h_93_dilations_0 = const()[name = string("h_93_dilations_0"), val = tensor([8])]; + int32 h_93_groups_0 = const()[name = string("h_93_groups_0"), val = int32(512)]; + tensor h_93_strides_0 = const()[name = string("h_93_strides_0"), val = tensor([1])]; + tensor h_93_pad_0 = const()[name = string("h_93_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17164928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17166272))))[name = string("main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17166400)))]; + tensor h_93_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_93_dilations_0, groups = h_93_groups_0, pad = h_93_pad_0, pad_type = h_93_pad_type_0, strides = h_93_strides_0, weight = main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = string("h_93_cast_fp16")]; + tensor x_125_cast_fp16 = mul(x = h_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_239_perm_0 = const()[name = string("input_239_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1448_axes_0 = const()[name = string("op_1448_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17167488)))]; + tensor main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17168576)))]; + tensor input_239_cast_fp16 = transpose(perm = input_239_perm_0, x = x_125_cast_fp16)[name = string("transpose_55")]; + tensor var_1448_cast_fp16 = layer_norm(axes = var_1448_axes_0, beta = main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16, x = input_239_cast_fp16)[name = string("op_1448_cast_fp16")]; + tensor input_241_perm_0 = const()[name = string("input_241_perm_0"), val = tensor([0, 2, 1])]; + string h_95_pad_type_0 = const()[name = string("h_95_pad_type_0"), val = string("valid")]; + tensor h_95_strides_0 = const()[name = string("h_95_strides_0"), val = tensor([1])]; + tensor h_95_pad_0 = const()[name = string("h_95_pad_0"), val = tensor([0, 0])]; + tensor h_95_dilations_0 = const()[name = string("h_95_dilations_0"), val = tensor([1])]; + int32 h_95_groups_0 = const()[name = string("h_95_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17169664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17694016))))[name = string("main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17694144)))]; + tensor input_241_cast_fp16 = transpose(perm = input_241_perm_0, x = var_1448_cast_fp16)[name = string("transpose_54")]; + tensor h_95_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_95_dilations_0, groups = h_95_groups_0, pad = h_95_pad_0, pad_type = h_95_pad_type_0, strides = h_95_strides_0, weight = main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_241_cast_fp16)[name = string("h_95_cast_fp16")]; + string input_243_mode_0 = const()[name = string("input_243_mode_0"), val = string("EXACT")]; + tensor input_243_cast_fp16 = gelu(mode = input_243_mode_0, x = h_95_cast_fp16)[name = string("input_243_cast_fp16")]; + string h_97_pad_type_0 = const()[name = string("h_97_pad_type_0"), val = string("valid")]; + tensor h_97_strides_0 = const()[name = string("h_97_strides_0"), val = tensor([1])]; + tensor h_97_pad_0 = const()[name = string("h_97_pad_0"), val = tensor([0, 0])]; + tensor h_97_dilations_0 = const()[name = string("h_97_dilations_0"), val = tensor([1])]; + int32 h_97_groups_0 = const()[name = string("h_97_groups_0"), val = int32(1)]; + tensor op_1465_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17698304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18222656))))[name = string("op_1465_weight_0_to_fp16_palettized")]; + tensor var_1465_bias_0_to_fp16 = const()[name = string("op_1465_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18222784)))]; + tensor var_1465_cast_fp16 = conv(bias = var_1465_bias_0_to_fp16, dilations = h_97_dilations_0, groups = h_97_groups_0, pad = h_97_pad_0, pad_type = h_97_pad_type_0, strides = h_97_strides_0, weight = op_1465_weight_0_to_fp16_palettized, x = input_243_cast_fp16)[name = string("op_1465_cast_fp16")]; + tensor out_39_cast_fp16 = add(x = x_123_cast_fp16, y = var_1465_cast_fp16)[name = string("out_39_cast_fp16")]; + tensor x_127_cast_fp16 = mul(x = out_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18223872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18240320))))[name = string("main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18240448)))]; + tensor linear_20_cast_fp16 = linear(bias = main_blocks_2_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor t_9_axes_0 = const()[name = string("t_9_axes_0"), val = tensor([-1])]; + tensor t_9_cast_fp16 = expand_dims(axes = t_9_axes_0, x = linear_20_cast_fp16)[name = string("t_9_cast_fp16")]; + tensor var_1475_cast_fp16 = add(x = x_127_cast_fp16, y = t_9_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = var_1475_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor x_131_cast_fp16 = mul(x = x_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor var_1488_cast_fp16 = mul(x = x_131_cast_fp16, y = last_1_cast_fp16)[name = string("op_1488_cast_fp16")]; + tensor x_last_33_axes_0 = const()[name = string("x_last_33_axes_0"), val = tensor([-1])]; + bool x_last_33_keep_dims_0 = const()[name = string("x_last_33_keep_dims_0"), val = bool(true)]; + tensor x_last_33_cast_fp16 = reduce_sum(axes = x_last_33_axes_0, keep_dims = x_last_33_keep_dims_0, x = var_1488_cast_fp16)[name = string("x_last_33_cast_fp16")]; + tensor var_1491_cast_fp16 = mul(x = x_131_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1491_cast_fp16")]; + tensor var_1493_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_33_cast_fp16)[name = string("op_1493_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = var_1491_cast_fp16, y = var_1493_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_251_mode_0 = const()[name = string("input_251_mode_0"), val = string("replicate")]; + fp16 const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = fp16(0x0p+0)]; + tensor input_251_cast_fp16 = pad(constant_val = const_52_to_fp16, mode = input_251_mode_0, pad = input_251_pad_0, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + string h_99_pad_type_0 = const()[name = string("h_99_pad_type_0"), val = string("valid")]; + int32 h_99_groups_0 = const()[name = string("h_99_groups_0"), val = int32(512)]; + tensor h_99_strides_0 = const()[name = string("h_99_strides_0"), val = tensor([1])]; + tensor h_99_pad_0 = const()[name = string("h_99_pad_0"), val = tensor([0, 0])]; + tensor h_99_dilations_0 = const()[name = string("h_99_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18241536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18242880))))[name = string("main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18243008)))]; + tensor h_99_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_99_dilations_0, groups = h_99_groups_0, pad = h_99_pad_0, pad_type = h_99_pad_type_0, strides = h_99_strides_0, weight = main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor x_133_cast_fp16 = mul(x = h_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_253_perm_0 = const()[name = string("input_253_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1511_axes_0 = const()[name = string("op_1511_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18244096)))]; + tensor main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18245184)))]; + tensor input_253_cast_fp16 = transpose(perm = input_253_perm_0, x = x_133_cast_fp16)[name = string("transpose_53")]; + tensor var_1511_cast_fp16 = layer_norm(axes = var_1511_axes_0, beta = main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16, x = input_253_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor input_255_perm_0 = const()[name = string("input_255_perm_0"), val = tensor([0, 2, 1])]; + string h_101_pad_type_0 = const()[name = string("h_101_pad_type_0"), val = string("valid")]; + tensor h_101_strides_0 = const()[name = string("h_101_strides_0"), val = tensor([1])]; + tensor h_101_pad_0 = const()[name = string("h_101_pad_0"), val = tensor([0, 0])]; + tensor h_101_dilations_0 = const()[name = string("h_101_dilations_0"), val = tensor([1])]; + int32 h_101_groups_0 = const()[name = string("h_101_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18246272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18770624))))[name = string("main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18770752)))]; + tensor input_255_cast_fp16 = transpose(perm = input_255_perm_0, x = var_1511_cast_fp16)[name = string("transpose_52")]; + tensor h_101_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_101_dilations_0, groups = h_101_groups_0, pad = h_101_pad_0, pad_type = h_101_pad_type_0, strides = h_101_strides_0, weight = main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_255_cast_fp16)[name = string("h_101_cast_fp16")]; + string input_257_mode_0 = const()[name = string("input_257_mode_0"), val = string("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = h_101_cast_fp16)[name = string("input_257_cast_fp16")]; + string h_103_pad_type_0 = const()[name = string("h_103_pad_type_0"), val = string("valid")]; + tensor h_103_strides_0 = const()[name = string("h_103_strides_0"), val = tensor([1])]; + tensor h_103_pad_0 = const()[name = string("h_103_pad_0"), val = tensor([0, 0])]; + tensor h_103_dilations_0 = const()[name = string("h_103_dilations_0"), val = tensor([1])]; + int32 h_103_groups_0 = const()[name = string("h_103_groups_0"), val = int32(1)]; + tensor op_1528_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18774912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19299264))))[name = string("op_1528_weight_0_to_fp16_palettized")]; + tensor var_1528_bias_0_to_fp16 = const()[name = string("op_1528_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19299392)))]; + tensor var_1528_cast_fp16 = conv(bias = var_1528_bias_0_to_fp16, dilations = h_103_dilations_0, groups = h_103_groups_0, pad = h_103_pad_0, pad_type = h_103_pad_type_0, strides = h_103_strides_0, weight = op_1528_weight_0_to_fp16_palettized, x = input_257_cast_fp16)[name = string("op_1528_cast_fp16")]; + tensor out_41_cast_fp16 = add(x = x_131_cast_fp16, y = var_1528_cast_fp16)[name = string("out_41_cast_fp16")]; + tensor x_135_cast_fp16 = mul(x = out_41_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor x_m_9_cast_fp16 = mul(x = x_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_9_cast_fp16")]; + tensor input_259_perm_0 = const()[name = string("input_259_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19300480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19431616))))[name = string("main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19431744)))]; + tensor input_259_cast_fp16 = transpose(perm = input_259_perm_0, x = x_135_cast_fp16)[name = string("transpose_51")]; + tensor linear_21_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_259_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_1546 = const()[name = string("op_1546"), val = tensor([2, 512, 8, 64])]; + tensor var_1547_cast_fp16 = reshape(shape = var_1546, x = linear_21_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor x_137_perm_0 = const()[name = string("x_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19432832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19498432))))[name = string("main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19498560)))]; + tensor linear_22_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_1554 = const()[name = string("op_1554"), val = tensor([2, 128, 8, 64])]; + tensor var_1555_cast_fp16 = reshape(shape = var_1554, x = linear_22_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_139_perm_0 = const()[name = string("x_139_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19499648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19565248))))[name = string("main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19565376)))]; + tensor linear_23_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_1562 = const()[name = string("op_1562"), val = tensor([2, 128, 8, 64])]; + tensor var_1563_cast_fp16 = reshape(shape = var_1562, x = linear_23_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_9_begin_0 = const()[name = string("x_a_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_9_end_0 = const()[name = string("x_a_9_end_0"), val = tensor([2, 8, 512, 32])]; + tensor x_a_9_end_mask_0 = const()[name = string("x_a_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_137_cast_fp16 = transpose(perm = x_137_perm_0, x = var_1547_cast_fp16)[name = string("transpose_50")]; + tensor x_a_9_cast_fp16 = slice_by_index(begin = x_a_9_begin_0, end = x_a_9_end_0, end_mask = x_a_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_a_9_cast_fp16")]; + tensor x_b_9_begin_0 = const()[name = string("x_b_9_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_9_end_0 = const()[name = string("x_b_9_end_0"), val = tensor([2, 8, 512, 64])]; + tensor x_b_9_end_mask_0 = const()[name = string("x_b_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_9_cast_fp16 = slice_by_index(begin = x_b_9_begin_0, end = x_b_9_end_0, end_mask = x_b_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_b_9_cast_fp16")]; + tensor var_1591_cast_fp16 = mul(x = x_a_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1591_cast_fp16")]; + tensor var_1592_cast_fp16 = mul(x = x_b_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor rot_a_9_cast_fp16 = sub(x = var_1591_cast_fp16, y = var_1592_cast_fp16)[name = string("rot_a_9_cast_fp16")]; + tensor var_1594_cast_fp16 = mul(x = x_a_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595_cast_fp16 = mul(x = x_b_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1595_cast_fp16")]; + tensor rot_b_9_cast_fp16 = add(x = var_1594_cast_fp16, y = var_1595_cast_fp16)[name = string("rot_b_9_cast_fp16")]; + bool q_9_interleave_0 = const()[name = string("q_9_interleave_0"), val = bool(false)]; + tensor q_9_cast_fp16 = concat(axis = var_1228, interleave = q_9_interleave_0, values = (rot_a_9_cast_fp16, rot_b_9_cast_fp16))[name = string("q_9_cast_fp16")]; + tensor x_a_11_begin_0 = const()[name = string("x_a_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_11_end_0 = const()[name = string("x_a_11_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_11_end_mask_0 = const()[name = string("x_a_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_139_cast_fp16 = transpose(perm = x_139_perm_0, x = var_1555_cast_fp16)[name = string("transpose_49")]; + tensor x_a_11_cast_fp16 = slice_by_index(begin = x_a_11_begin_0, end = x_a_11_end_0, end_mask = x_a_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_a_11_cast_fp16")]; + tensor x_b_11_begin_0 = const()[name = string("x_b_11_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_11_end_0 = const()[name = string("x_b_11_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_11_end_mask_0 = const()[name = string("x_b_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_11_cast_fp16 = slice_by_index(begin = x_b_11_begin_0, end = x_b_11_end_0, end_mask = x_b_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_b_11_cast_fp16")]; + tensor var_1609_cast_fp16 = mul(x = x_a_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor var_1610_cast_fp16 = mul(x = x_b_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor rot_a_11_cast_fp16 = sub(x = var_1609_cast_fp16, y = var_1610_cast_fp16)[name = string("rot_a_11_cast_fp16")]; + tensor var_1612_cast_fp16 = mul(x = x_a_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1612_cast_fp16")]; + tensor var_1613_cast_fp16 = mul(x = x_b_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1613_cast_fp16")]; + tensor rot_b_11_cast_fp16 = add(x = var_1612_cast_fp16, y = var_1613_cast_fp16)[name = string("rot_b_11_cast_fp16")]; + bool k_9_interleave_0 = const()[name = string("k_9_interleave_0"), val = bool(false)]; + tensor k_9_cast_fp16 = concat(axis = var_1228, interleave = k_9_interleave_0, values = (rot_a_11_cast_fp16, rot_b_11_cast_fp16))[name = string("k_9_cast_fp16")]; + bool var_1618_transpose_x_1 = const()[name = string("op_1618_transpose_x_1"), val = bool(false)]; + bool var_1618_transpose_y_1 = const()[name = string("op_1618_transpose_y_1"), val = bool(true)]; + tensor var_1618_cast_fp16 = matmul(transpose_x = var_1618_transpose_x_1, transpose_y = var_1618_transpose_y_1, x = q_9_cast_fp16, y = k_9_cast_fp16)[name = string("op_1618_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_1618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor input_265_cast_fp16 = sub(x = _inversed_scores_5_cast_fp16, y = var_530_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1228, x = input_265_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool var_1627_transpose_x_0 = const()[name = string("op_1627_transpose_x_0"), val = bool(false)]; + bool var_1627_transpose_y_0 = const()[name = string("op_1627_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1563_cast_fp16)[name = string("transpose_48")]; + tensor var_1627_cast_fp16 = matmul(transpose_x = var_1627_transpose_x_0, transpose_y = var_1627_transpose_y_0, x = attn_13_cast_fp16, y = v_9_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor var_1628_perm_0 = const()[name = string("op_1628_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1629 = const()[name = string("op_1629"), val = tensor([2, 512, 512])]; + tensor var_1628_cast_fp16 = transpose(perm = var_1628_perm_0, x = var_1627_cast_fp16)[name = string("transpose_47")]; + tensor input_267_cast_fp16 = reshape(shape = var_1629, x = var_1628_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19566464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19697600))))[name = string("main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19697728)))]; + tensor linear_24_cast_fp16 = linear(bias = main_blocks_2_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor out_43_perm_0 = const()[name = string("out_43_perm_0"), val = tensor([0, 2, 1])]; + tensor out_43_cast_fp16 = transpose(perm = out_43_perm_0, x = linear_24_cast_fp16)[name = string("transpose_46")]; + tensor var_1636_cast_fp16 = mul(x = out_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1636_cast_fp16")]; + tensor x_141_cast_fp16 = add(x = x_m_9_cast_fp16, y = var_1636_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1643_axes_0 = const()[name = string("op_1643_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19698816)))]; + tensor main_blocks_2_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19699904)))]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_141_cast_fp16)[name = string("transpose_45")]; + tensor var_1643_cast_fp16 = layer_norm(axes = var_1643_axes_0, beta = main_blocks_2_text_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_text_norm_norm_weight_to_fp16, x = input_269_cast_fp16)[name = string("op_1643_cast_fp16")]; + tensor var_1644_perm_0 = const()[name = string("op_1644_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1644_cast_fp16 = transpose(perm = var_1644_perm_0, x = var_1643_cast_fp16)[name = string("transpose_44")]; + tensor x_143_cast_fp16 = mul(x = var_1644_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor x_145_cast_fp16 = mul(x = x_143_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1657_cast_fp16 = mul(x = x_145_cast_fp16, y = last_1_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor x_last_35_axes_0 = const()[name = string("x_last_35_axes_0"), val = tensor([-1])]; + bool x_last_35_keep_dims_0 = const()[name = string("x_last_35_keep_dims_0"), val = bool(true)]; + tensor x_last_35_cast_fp16 = reduce_sum(axes = x_last_35_axes_0, keep_dims = x_last_35_keep_dims_0, x = var_1657_cast_fp16)[name = string("x_last_35_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = x_145_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1662_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_35_cast_fp16)[name = string("op_1662_cast_fp16")]; + tensor input_273_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1662_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("replicate")]; + fp16 const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = fp16(0x0p+0)]; + tensor input_275_cast_fp16 = pad(constant_val = const_59_to_fp16, mode = input_275_mode_0, pad = input_275_pad_0, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")]; + string h_105_pad_type_0 = const()[name = string("h_105_pad_type_0"), val = string("valid")]; + int32 h_105_groups_0 = const()[name = string("h_105_groups_0"), val = int32(512)]; + tensor h_105_strides_0 = const()[name = string("h_105_strides_0"), val = tensor([1])]; + tensor h_105_pad_0 = const()[name = string("h_105_pad_0"), val = tensor([0, 0])]; + tensor h_105_dilations_0 = const()[name = string("h_105_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19700992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19702336))))[name = string("main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19702464)))]; + tensor h_105_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_105_dilations_0, groups = h_105_groups_0, pad = h_105_pad_0, pad_type = h_105_pad_type_0, strides = h_105_strides_0, weight = main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_275_cast_fp16)[name = string("h_105_cast_fp16")]; + tensor x_147_cast_fp16 = mul(x = h_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_277_perm_0 = const()[name = string("input_277_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1680_axes_0 = const()[name = string("op_1680_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19703552)))]; + tensor main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19704640)))]; + tensor input_277_cast_fp16 = transpose(perm = input_277_perm_0, x = x_147_cast_fp16)[name = string("transpose_43")]; + tensor var_1680_cast_fp16 = layer_norm(axes = var_1680_axes_0, beta = main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16, x = input_277_cast_fp16)[name = string("op_1680_cast_fp16")]; + tensor input_279_perm_0 = const()[name = string("input_279_perm_0"), val = tensor([0, 2, 1])]; + string h_107_pad_type_0 = const()[name = string("h_107_pad_type_0"), val = string("valid")]; + tensor h_107_strides_0 = const()[name = string("h_107_strides_0"), val = tensor([1])]; + tensor h_107_pad_0 = const()[name = string("h_107_pad_0"), val = tensor([0, 0])]; + tensor h_107_dilations_0 = const()[name = string("h_107_dilations_0"), val = tensor([1])]; + int32 h_107_groups_0 = const()[name = string("h_107_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19705728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20230080))))[name = string("main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20230208)))]; + tensor input_279_cast_fp16 = transpose(perm = input_279_perm_0, x = var_1680_cast_fp16)[name = string("transpose_42")]; + tensor h_107_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_107_dilations_0, groups = h_107_groups_0, pad = h_107_pad_0, pad_type = h_107_pad_type_0, strides = h_107_strides_0, weight = main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_279_cast_fp16)[name = string("h_107_cast_fp16")]; + string input_281_mode_0 = const()[name = string("input_281_mode_0"), val = string("EXACT")]; + tensor input_281_cast_fp16 = gelu(mode = input_281_mode_0, x = h_107_cast_fp16)[name = string("input_281_cast_fp16")]; + string h_109_pad_type_0 = const()[name = string("h_109_pad_type_0"), val = string("valid")]; + tensor h_109_strides_0 = const()[name = string("h_109_strides_0"), val = tensor([1])]; + tensor h_109_pad_0 = const()[name = string("h_109_pad_0"), val = tensor([0, 0])]; + tensor h_109_dilations_0 = const()[name = string("h_109_dilations_0"), val = tensor([1])]; + int32 h_109_groups_0 = const()[name = string("h_109_groups_0"), val = int32(1)]; + tensor op_1697_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20234368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20758720))))[name = string("op_1697_weight_0_to_fp16_palettized")]; + tensor var_1697_bias_0_to_fp16 = const()[name = string("op_1697_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20758848)))]; + tensor var_1697_cast_fp16 = conv(bias = var_1697_bias_0_to_fp16, dilations = h_109_dilations_0, groups = h_109_groups_0, pad = h_109_pad_0, pad_type = h_109_pad_type_0, strides = h_109_strides_0, weight = op_1697_weight_0_to_fp16_palettized, x = input_281_cast_fp16)[name = string("op_1697_cast_fp16")]; + tensor out_45_cast_fp16 = add(x = x_145_cast_fp16, y = var_1697_cast_fp16)[name = string("out_45_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = out_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor x_m_11_cast_fp16 = mul(x = x_149_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_11_cast_fp16")]; + tensor input_283_perm_0 = const()[name = string("input_283_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20759936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20825536))))[name = string("main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20825664)))]; + tensor input_283_cast_fp16 = transpose(perm = input_283_perm_0, x = x_149_cast_fp16)[name = string("transpose_41")]; + tensor linear_25_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_283_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_1713 = const()[name = string("op_1713"), val = tensor([2, 512, 2, 128])]; + tensor var_1714_cast_fp16 = reshape(shape = var_1713, x = linear_25_cast_fp16)[name = string("op_1714_cast_fp16")]; + tensor q_11_perm_0 = const()[name = string("q_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20826240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20859072))))[name = string("main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20859200)))]; + tensor linear_27_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1727 = const()[name = string("op_1727"), val = tensor([2, 50, 2, 128])]; + tensor var_1728_cast_fp16 = reshape(shape = var_1727, x = linear_27_cast_fp16)[name = string("op_1728_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1732_transpose_x_0 = const()[name = string("op_1732_transpose_x_0"), val = bool(false)]; + bool var_1732_transpose_y_0 = const()[name = string("op_1732_transpose_y_0"), val = bool(false)]; + tensor op_1731_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20859776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20872640))))[name = string("op_1731_to_fp16_palettized")]; + tensor q_11_cast_fp16 = transpose(perm = q_11_perm_0, x = var_1714_cast_fp16)[name = string("transpose_40")]; + tensor var_1732_cast_fp16 = matmul(transpose_x = var_1732_transpose_x_0, transpose_y = var_1732_transpose_y_0, x = q_11_cast_fp16, y = op_1731_to_fp16_palettized)[name = string("op_1732_cast_fp16")]; + fp16 _inversed_input_285_y_0_to_fp16 = const()[name = string("_inversed_input_285_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_285_cast_fp16 = mul(x = var_1732_cast_fp16, y = _inversed_input_285_y_0_to_fp16)[name = string("_inversed_input_285_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1228, x = _inversed_input_285_cast_fp16)[name = string("attn_15_cast_fp16")]; + tensor attn_17_cast_fp16 = mul(x = attn_15_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool var_1739_transpose_x_0 = const()[name = string("op_1739_transpose_x_0"), val = bool(false)]; + bool var_1739_transpose_y_0 = const()[name = string("op_1739_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_1728_cast_fp16)[name = string("transpose_39")]; + tensor var_1739_cast_fp16 = matmul(transpose_x = var_1739_transpose_x_0, transpose_y = var_1739_transpose_y_0, x = attn_17_cast_fp16, y = v_11_cast_fp16)[name = string("op_1739_cast_fp16")]; + tensor var_1740_perm_0 = const()[name = string("op_1740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1741 = const()[name = string("op_1741"), val = tensor([2, 512, 256])]; + tensor var_1740_cast_fp16 = transpose(perm = var_1740_perm_0, x = var_1739_cast_fp16)[name = string("transpose_38")]; + tensor input_287_cast_fp16 = reshape(shape = var_1741, x = var_1740_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20872768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20938368))))[name = string("main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20938496)))]; + tensor linear_28_cast_fp16 = linear(bias = main_blocks_2_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor out_47_perm_0 = const()[name = string("out_47_perm_0"), val = tensor([0, 2, 1])]; + tensor out_47_cast_fp16 = transpose(perm = out_47_perm_0, x = linear_28_cast_fp16)[name = string("transpose_37")]; + tensor var_1748_cast_fp16 = mul(x = out_47_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1748_cast_fp16")]; + tensor x_151_cast_fp16 = add(x = x_m_11_cast_fp16, y = var_1748_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_289_perm_0 = const()[name = string("input_289_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1755_axes_0 = const()[name = string("op_1755_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20939584)))]; + tensor main_blocks_2_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20940672)))]; + tensor input_289_cast_fp16 = transpose(perm = input_289_perm_0, x = x_151_cast_fp16)[name = string("transpose_36")]; + tensor var_1755_cast_fp16 = layer_norm(axes = var_1755_axes_0, beta = main_blocks_2_style_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_style_norm_norm_weight_to_fp16, x = input_289_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor var_1756_perm_0 = const()[name = string("op_1756_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1756_cast_fp16 = transpose(perm = var_1756_perm_0, x = var_1755_cast_fp16)[name = string("transpose_35")]; + tensor x_153_cast_fp16 = mul(x = var_1756_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_153_cast_fp16")]; + int32 var_1775 = const()[name = string("op_1775"), val = int32(-1)]; + tensor x_155_cast_fp16 = mul(x = x_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor var_1810_cast_fp16 = mul(x = x_155_cast_fp16, y = last_1_cast_fp16)[name = string("op_1810_cast_fp16")]; + tensor x_last_37_axes_0 = const()[name = string("x_last_37_axes_0"), val = tensor([-1])]; + bool x_last_37_keep_dims_0 = const()[name = string("x_last_37_keep_dims_0"), val = bool(true)]; + tensor x_last_37_cast_fp16 = reduce_sum(axes = x_last_37_axes_0, keep_dims = x_last_37_keep_dims_0, x = var_1810_cast_fp16)[name = string("x_last_37_cast_fp16")]; + tensor var_1813_cast_fp16 = mul(x = x_155_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1815_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_37_cast_fp16)[name = string("op_1815_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = var_1813_cast_fp16, y = var_1815_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_295_mode_0 = const()[name = string("input_295_mode_0"), val = string("replicate")]; + fp16 const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = fp16(0x0p+0)]; + tensor input_295_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")]; + string h_111_pad_type_0 = const()[name = string("h_111_pad_type_0"), val = string("valid")]; + int32 h_111_groups_0 = const()[name = string("h_111_groups_0"), val = int32(512)]; + tensor h_111_strides_0 = const()[name = string("h_111_strides_0"), val = tensor([1])]; + tensor h_111_pad_0 = const()[name = string("h_111_pad_0"), val = tensor([0, 0])]; + tensor h_111_dilations_0 = const()[name = string("h_111_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20941760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20943104))))[name = string("main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20943232)))]; + tensor h_111_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_111_dilations_0, groups = h_111_groups_0, pad = h_111_pad_0, pad_type = h_111_pad_type_0, strides = h_111_strides_0, weight = main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_295_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor x_157_cast_fp16 = mul(x = h_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor input_297_perm_0 = const()[name = string("input_297_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1833_axes_0 = const()[name = string("op_1833_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20944320)))]; + tensor main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20945408)))]; + fp16 var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_297_cast_fp16 = transpose(perm = input_297_perm_0, x = x_157_cast_fp16)[name = string("transpose_34")]; + tensor var_1833_cast_fp16 = layer_norm(axes = var_1833_axes_0, beta = main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16, x = input_297_cast_fp16)[name = string("op_1833_cast_fp16")]; + tensor input_299_perm_0 = const()[name = string("input_299_perm_0"), val = tensor([0, 2, 1])]; + string h_113_pad_type_0 = const()[name = string("h_113_pad_type_0"), val = string("valid")]; + tensor h_113_strides_0 = const()[name = string("h_113_strides_0"), val = tensor([1])]; + tensor h_113_pad_0 = const()[name = string("h_113_pad_0"), val = tensor([0, 0])]; + tensor h_113_dilations_0 = const()[name = string("h_113_dilations_0"), val = tensor([1])]; + int32 h_113_groups_0 = const()[name = string("h_113_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20946496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21470848))))[name = string("main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21470976)))]; + tensor input_299_cast_fp16 = transpose(perm = input_299_perm_0, x = var_1833_cast_fp16)[name = string("transpose_33")]; + tensor h_113_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_113_dilations_0, groups = h_113_groups_0, pad = h_113_pad_0, pad_type = h_113_pad_type_0, strides = h_113_strides_0, weight = main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("h_113_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = h_113_cast_fp16)[name = string("input_301_cast_fp16")]; + string h_115_pad_type_0 = const()[name = string("h_115_pad_type_0"), val = string("valid")]; + tensor h_115_strides_0 = const()[name = string("h_115_strides_0"), val = tensor([1])]; + tensor h_115_pad_0 = const()[name = string("h_115_pad_0"), val = tensor([0, 0])]; + tensor h_115_dilations_0 = const()[name = string("h_115_dilations_0"), val = tensor([1])]; + int32 h_115_groups_0 = const()[name = string("h_115_groups_0"), val = int32(1)]; + tensor op_1850_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21475136))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21999488))))[name = string("op_1850_weight_0_to_fp16_palettized")]; + tensor var_1850_bias_0_to_fp16 = const()[name = string("op_1850_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21999616)))]; + tensor var_1850_cast_fp16 = conv(bias = var_1850_bias_0_to_fp16, dilations = h_115_dilations_0, groups = h_115_groups_0, pad = h_115_pad_0, pad_type = h_115_pad_type_0, strides = h_115_strides_0, weight = op_1850_weight_0_to_fp16_palettized, x = input_301_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor out_49_cast_fp16 = add(x = x_155_cast_fp16, y = var_1850_cast_fp16)[name = string("out_49_cast_fp16")]; + tensor x_159_cast_fp16 = mul(x = out_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor x_161_cast_fp16 = mul(x = x_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor var_1864_cast_fp16 = mul(x = x_161_cast_fp16, y = last_1_cast_fp16)[name = string("op_1864_cast_fp16")]; + tensor x_last_39_axes_0 = const()[name = string("x_last_39_axes_0"), val = tensor([-1])]; + bool x_last_39_keep_dims_0 = const()[name = string("x_last_39_keep_dims_0"), val = bool(true)]; + tensor x_last_39_cast_fp16 = reduce_sum(axes = x_last_39_axes_0, keep_dims = x_last_39_keep_dims_0, x = var_1864_cast_fp16)[name = string("x_last_39_cast_fp16")]; + tensor var_1867_cast_fp16 = mul(x = x_161_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_39_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor input_305_cast_fp16 = add(x = var_1867_cast_fp16, y = var_1869_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("replicate")]; + fp16 const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_66_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string h_117_pad_type_0 = const()[name = string("h_117_pad_type_0"), val = string("valid")]; + tensor h_117_dilations_0 = const()[name = string("h_117_dilations_0"), val = tensor([2])]; + int32 h_117_groups_0 = const()[name = string("h_117_groups_0"), val = int32(512)]; + tensor h_117_strides_0 = const()[name = string("h_117_strides_0"), val = tensor([1])]; + tensor h_117_pad_0 = const()[name = string("h_117_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22000704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22002048))))[name = string("main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22002176)))]; + tensor h_117_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_117_dilations_0, groups = h_117_groups_0, pad = h_117_pad_0, pad_type = h_117_pad_type_0, strides = h_117_strides_0, weight = main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_307_cast_fp16)[name = string("h_117_cast_fp16")]; + tensor x_163_cast_fp16 = mul(x = h_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1887_axes_0 = const()[name = string("op_1887_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22003264)))]; + tensor main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22004352)))]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_163_cast_fp16)[name = string("transpose_32")]; + tensor var_1887_cast_fp16 = layer_norm(axes = var_1887_axes_0, beta = main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16, x = input_309_cast_fp16)[name = string("op_1887_cast_fp16")]; + tensor input_311_perm_0 = const()[name = string("input_311_perm_0"), val = tensor([0, 2, 1])]; + string h_119_pad_type_0 = const()[name = string("h_119_pad_type_0"), val = string("valid")]; + tensor h_119_strides_0 = const()[name = string("h_119_strides_0"), val = tensor([1])]; + tensor h_119_pad_0 = const()[name = string("h_119_pad_0"), val = tensor([0, 0])]; + tensor h_119_dilations_0 = const()[name = string("h_119_dilations_0"), val = tensor([1])]; + int32 h_119_groups_0 = const()[name = string("h_119_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22005440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22529792))))[name = string("main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22529920)))]; + tensor input_311_cast_fp16 = transpose(perm = input_311_perm_0, x = var_1887_cast_fp16)[name = string("transpose_31")]; + tensor h_119_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_119_dilations_0, groups = h_119_groups_0, pad = h_119_pad_0, pad_type = h_119_pad_type_0, strides = h_119_strides_0, weight = main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_311_cast_fp16)[name = string("h_119_cast_fp16")]; + string input_313_mode_0 = const()[name = string("input_313_mode_0"), val = string("EXACT")]; + tensor input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = h_119_cast_fp16)[name = string("input_313_cast_fp16")]; + string h_121_pad_type_0 = const()[name = string("h_121_pad_type_0"), val = string("valid")]; + tensor h_121_strides_0 = const()[name = string("h_121_strides_0"), val = tensor([1])]; + tensor h_121_pad_0 = const()[name = string("h_121_pad_0"), val = tensor([0, 0])]; + tensor h_121_dilations_0 = const()[name = string("h_121_dilations_0"), val = tensor([1])]; + int32 h_121_groups_0 = const()[name = string("h_121_groups_0"), val = int32(1)]; + tensor op_1904_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22534080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23058432))))[name = string("op_1904_weight_0_to_fp16_palettized")]; + tensor var_1904_bias_0_to_fp16 = const()[name = string("op_1904_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23058560)))]; + tensor var_1904_cast_fp16 = conv(bias = var_1904_bias_0_to_fp16, dilations = h_121_dilations_0, groups = h_121_groups_0, pad = h_121_pad_0, pad_type = h_121_pad_type_0, strides = h_121_strides_0, weight = op_1904_weight_0_to_fp16_palettized, x = input_313_cast_fp16)[name = string("op_1904_cast_fp16")]; + tensor out_51_cast_fp16 = add(x = x_161_cast_fp16, y = var_1904_cast_fp16)[name = string("out_51_cast_fp16")]; + tensor x_165_cast_fp16 = mul(x = out_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor x_167_cast_fp16 = mul(x = x_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1918_cast_fp16 = mul(x = x_167_cast_fp16, y = last_1_cast_fp16)[name = string("op_1918_cast_fp16")]; + tensor x_last_41_axes_0 = const()[name = string("x_last_41_axes_0"), val = tensor([-1])]; + bool x_last_41_keep_dims_0 = const()[name = string("x_last_41_keep_dims_0"), val = bool(true)]; + tensor x_last_41_cast_fp16 = reduce_sum(axes = x_last_41_axes_0, keep_dims = x_last_41_keep_dims_0, x = var_1918_cast_fp16)[name = string("x_last_41_cast_fp16")]; + tensor var_1921_cast_fp16 = mul(x = x_167_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1921_cast_fp16")]; + tensor var_1923_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_41_cast_fp16)[name = string("op_1923_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = var_1921_cast_fp16, y = var_1923_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("replicate")]; + fp16 const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = fp16(0x0p+0)]; + tensor input_319_cast_fp16 = pad(constant_val = const_68_to_fp16, mode = input_319_mode_0, pad = input_319_pad_0, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string h_123_pad_type_0 = const()[name = string("h_123_pad_type_0"), val = string("valid")]; + tensor h_123_dilations_0 = const()[name = string("h_123_dilations_0"), val = tensor([4])]; + int32 h_123_groups_0 = const()[name = string("h_123_groups_0"), val = int32(512)]; + tensor h_123_strides_0 = const()[name = string("h_123_strides_0"), val = tensor([1])]; + tensor h_123_pad_0 = const()[name = string("h_123_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23059648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23060992))))[name = string("main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23061120)))]; + tensor h_123_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_123_dilations_0, groups = h_123_groups_0, pad = h_123_pad_0, pad_type = h_123_pad_type_0, strides = h_123_strides_0, weight = main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_319_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor x_169_cast_fp16 = mul(x = h_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1941_axes_0 = const()[name = string("op_1941_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23062208)))]; + tensor main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23063296)))]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_169_cast_fp16)[name = string("transpose_30")]; + tensor var_1941_cast_fp16 = layer_norm(axes = var_1941_axes_0, beta = main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16, x = input_321_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor([0, 2, 1])]; + string h_125_pad_type_0 = const()[name = string("h_125_pad_type_0"), val = string("valid")]; + tensor h_125_strides_0 = const()[name = string("h_125_strides_0"), val = tensor([1])]; + tensor h_125_pad_0 = const()[name = string("h_125_pad_0"), val = tensor([0, 0])]; + tensor h_125_dilations_0 = const()[name = string("h_125_dilations_0"), val = tensor([1])]; + int32 h_125_groups_0 = const()[name = string("h_125_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23064384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23588736))))[name = string("main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23588864)))]; + tensor input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = var_1941_cast_fp16)[name = string("transpose_29")]; + tensor h_125_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_125_dilations_0, groups = h_125_groups_0, pad = h_125_pad_0, pad_type = h_125_pad_type_0, strides = h_125_strides_0, weight = main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = string("h_125_cast_fp16")]; + string input_325_mode_0 = const()[name = string("input_325_mode_0"), val = string("EXACT")]; + tensor input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = h_125_cast_fp16)[name = string("input_325_cast_fp16")]; + string h_127_pad_type_0 = const()[name = string("h_127_pad_type_0"), val = string("valid")]; + tensor h_127_strides_0 = const()[name = string("h_127_strides_0"), val = tensor([1])]; + tensor h_127_pad_0 = const()[name = string("h_127_pad_0"), val = tensor([0, 0])]; + tensor h_127_dilations_0 = const()[name = string("h_127_dilations_0"), val = tensor([1])]; + int32 h_127_groups_0 = const()[name = string("h_127_groups_0"), val = int32(1)]; + tensor op_1958_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23593024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24117376))))[name = string("op_1958_weight_0_to_fp16_palettized")]; + tensor var_1958_bias_0_to_fp16 = const()[name = string("op_1958_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24117504)))]; + tensor var_1958_cast_fp16 = conv(bias = var_1958_bias_0_to_fp16, dilations = h_127_dilations_0, groups = h_127_groups_0, pad = h_127_pad_0, pad_type = h_127_pad_type_0, strides = h_127_strides_0, weight = op_1958_weight_0_to_fp16_palettized, x = input_325_cast_fp16)[name = string("op_1958_cast_fp16")]; + tensor out_53_cast_fp16 = add(x = x_167_cast_fp16, y = var_1958_cast_fp16)[name = string("out_53_cast_fp16")]; + tensor x_171_cast_fp16 = mul(x = out_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_171_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor var_1972_cast_fp16 = mul(x = x_173_cast_fp16, y = last_1_cast_fp16)[name = string("op_1972_cast_fp16")]; + tensor x_last_43_axes_0 = const()[name = string("x_last_43_axes_0"), val = tensor([-1])]; + bool x_last_43_keep_dims_0 = const()[name = string("x_last_43_keep_dims_0"), val = bool(true)]; + tensor x_last_43_cast_fp16 = reduce_sum(axes = x_last_43_axes_0, keep_dims = x_last_43_keep_dims_0, x = var_1972_cast_fp16)[name = string("x_last_43_cast_fp16")]; + tensor var_1975_cast_fp16 = mul(x = x_173_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1975_cast_fp16")]; + tensor var_1977_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_43_cast_fp16)[name = string("op_1977_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1977_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("replicate")]; + fp16 const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = fp16(0x0p+0)]; + tensor input_331_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = input_331_mode_0, pad = input_331_pad_0, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + string h_129_pad_type_0 = const()[name = string("h_129_pad_type_0"), val = string("valid")]; + tensor h_129_dilations_0 = const()[name = string("h_129_dilations_0"), val = tensor([8])]; + int32 h_129_groups_0 = const()[name = string("h_129_groups_0"), val = int32(512)]; + tensor h_129_strides_0 = const()[name = string("h_129_strides_0"), val = tensor([1])]; + tensor h_129_pad_0 = const()[name = string("h_129_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24118592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24119936))))[name = string("main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24120064)))]; + tensor h_129_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_129_dilations_0, groups = h_129_groups_0, pad = h_129_pad_0, pad_type = h_129_pad_type_0, strides = h_129_strides_0, weight = main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = string("h_129_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = h_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_333_perm_0 = const()[name = string("input_333_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1995_axes_0 = const()[name = string("op_1995_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24121152)))]; + tensor main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24122240)))]; + tensor input_333_cast_fp16 = transpose(perm = input_333_perm_0, x = x_175_cast_fp16)[name = string("transpose_28")]; + tensor var_1995_cast_fp16 = layer_norm(axes = var_1995_axes_0, beta = main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16, x = input_333_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor input_335_perm_0 = const()[name = string("input_335_perm_0"), val = tensor([0, 2, 1])]; + string h_131_pad_type_0 = const()[name = string("h_131_pad_type_0"), val = string("valid")]; + tensor h_131_strides_0 = const()[name = string("h_131_strides_0"), val = tensor([1])]; + tensor h_131_pad_0 = const()[name = string("h_131_pad_0"), val = tensor([0, 0])]; + tensor h_131_dilations_0 = const()[name = string("h_131_dilations_0"), val = tensor([1])]; + int32 h_131_groups_0 = const()[name = string("h_131_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24123328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24647680))))[name = string("main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24647808)))]; + tensor input_335_cast_fp16 = transpose(perm = input_335_perm_0, x = var_1995_cast_fp16)[name = string("transpose_27")]; + tensor h_131_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_131_dilations_0, groups = h_131_groups_0, pad = h_131_pad_0, pad_type = h_131_pad_type_0, strides = h_131_strides_0, weight = main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_335_cast_fp16)[name = string("h_131_cast_fp16")]; + string input_337_mode_0 = const()[name = string("input_337_mode_0"), val = string("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = h_131_cast_fp16)[name = string("input_337_cast_fp16")]; + string h_133_pad_type_0 = const()[name = string("h_133_pad_type_0"), val = string("valid")]; + tensor h_133_strides_0 = const()[name = string("h_133_strides_0"), val = tensor([1])]; + tensor h_133_pad_0 = const()[name = string("h_133_pad_0"), val = tensor([0, 0])]; + tensor h_133_dilations_0 = const()[name = string("h_133_dilations_0"), val = tensor([1])]; + int32 h_133_groups_0 = const()[name = string("h_133_groups_0"), val = int32(1)]; + tensor op_2012_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24651968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25176320))))[name = string("op_2012_weight_0_to_fp16_palettized")]; + tensor var_2012_bias_0_to_fp16 = const()[name = string("op_2012_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25176448)))]; + tensor var_2012_cast_fp16 = conv(bias = var_2012_bias_0_to_fp16, dilations = h_133_dilations_0, groups = h_133_groups_0, pad = h_133_pad_0, pad_type = h_133_pad_type_0, strides = h_133_strides_0, weight = op_2012_weight_0_to_fp16_palettized, x = input_337_cast_fp16)[name = string("op_2012_cast_fp16")]; + tensor out_55_cast_fp16 = add(x = x_173_cast_fp16, y = var_2012_cast_fp16)[name = string("out_55_cast_fp16")]; + tensor x_177_cast_fp16 = mul(x = out_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25177536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25193984))))[name = string("main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25194112)))]; + tensor linear_29_cast_fp16 = linear(bias = main_blocks_3_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor t_axes_0 = const()[name = string("t_axes_0"), val = tensor([-1])]; + tensor t_cast_fp16 = expand_dims(axes = t_axes_0, x = linear_29_cast_fp16)[name = string("t_cast_fp16")]; + tensor var_2022_cast_fp16 = add(x = x_177_cast_fp16, y = t_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor x_179_cast_fp16 = mul(x = var_2022_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor x_181_cast_fp16 = mul(x = x_179_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor var_2035_cast_fp16 = mul(x = x_181_cast_fp16, y = last_1_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor x_last_45_axes_0 = const()[name = string("x_last_45_axes_0"), val = tensor([-1])]; + bool x_last_45_keep_dims_0 = const()[name = string("x_last_45_keep_dims_0"), val = bool(true)]; + tensor x_last_45_cast_fp16 = reduce_sum(axes = x_last_45_axes_0, keep_dims = x_last_45_keep_dims_0, x = var_2035_cast_fp16)[name = string("x_last_45_cast_fp16")]; + tensor var_2038_cast_fp16 = mul(x = x_181_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2038_cast_fp16")]; + tensor var_2040_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_45_cast_fp16)[name = string("op_2040_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = var_2038_cast_fp16, y = var_2040_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_345_mode_0 = const()[name = string("input_345_mode_0"), val = string("replicate")]; + fp16 const_72_to_fp16 = const()[name = string("const_72_to_fp16"), val = fp16(0x0p+0)]; + tensor input_345_cast_fp16 = pad(constant_val = const_72_to_fp16, mode = input_345_mode_0, pad = input_345_pad_0, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + string h_135_pad_type_0 = const()[name = string("h_135_pad_type_0"), val = string("valid")]; + int32 h_135_groups_0 = const()[name = string("h_135_groups_0"), val = int32(512)]; + tensor h_135_strides_0 = const()[name = string("h_135_strides_0"), val = tensor([1])]; + tensor h_135_pad_0 = const()[name = string("h_135_pad_0"), val = tensor([0, 0])]; + tensor h_135_dilations_0 = const()[name = string("h_135_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25195200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25196544))))[name = string("main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25196672)))]; + tensor h_135_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_135_dilations_0, groups = h_135_groups_0, pad = h_135_pad_0, pad_type = h_135_pad_type_0, strides = h_135_strides_0, weight = main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_345_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor x_183_cast_fp16 = mul(x = h_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_183_cast_fp16")]; + tensor input_347_perm_0 = const()[name = string("input_347_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2058_axes_0 = const()[name = string("op_2058_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25197760)))]; + tensor main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25198848)))]; + tensor input_347_cast_fp16 = transpose(perm = input_347_perm_0, x = x_183_cast_fp16)[name = string("transpose_26")]; + tensor var_2058_cast_fp16 = layer_norm(axes = var_2058_axes_0, beta = main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16, x = input_347_cast_fp16)[name = string("op_2058_cast_fp16")]; + tensor input_349_perm_0 = const()[name = string("input_349_perm_0"), val = tensor([0, 2, 1])]; + string h_137_pad_type_0 = const()[name = string("h_137_pad_type_0"), val = string("valid")]; + tensor h_137_strides_0 = const()[name = string("h_137_strides_0"), val = tensor([1])]; + tensor h_137_pad_0 = const()[name = string("h_137_pad_0"), val = tensor([0, 0])]; + tensor h_137_dilations_0 = const()[name = string("h_137_dilations_0"), val = tensor([1])]; + int32 h_137_groups_0 = const()[name = string("h_137_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25199936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25724288))))[name = string("main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25724416)))]; + tensor input_349_cast_fp16 = transpose(perm = input_349_perm_0, x = var_2058_cast_fp16)[name = string("transpose_25")]; + tensor h_137_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_137_dilations_0, groups = h_137_groups_0, pad = h_137_pad_0, pad_type = h_137_pad_type_0, strides = h_137_strides_0, weight = main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_349_cast_fp16)[name = string("h_137_cast_fp16")]; + string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")]; + tensor input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = h_137_cast_fp16)[name = string("input_351_cast_fp16")]; + string h_139_pad_type_0 = const()[name = string("h_139_pad_type_0"), val = string("valid")]; + tensor h_139_strides_0 = const()[name = string("h_139_strides_0"), val = tensor([1])]; + tensor h_139_pad_0 = const()[name = string("h_139_pad_0"), val = tensor([0, 0])]; + tensor h_139_dilations_0 = const()[name = string("h_139_dilations_0"), val = tensor([1])]; + int32 h_139_groups_0 = const()[name = string("h_139_groups_0"), val = int32(1)]; + tensor op_2075_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25728576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26252928))))[name = string("op_2075_weight_0_to_fp16_palettized")]; + tensor var_2075_bias_0_to_fp16 = const()[name = string("op_2075_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26253056)))]; + tensor var_2075_cast_fp16 = conv(bias = var_2075_bias_0_to_fp16, dilations = h_139_dilations_0, groups = h_139_groups_0, pad = h_139_pad_0, pad_type = h_139_pad_type_0, strides = h_139_strides_0, weight = op_2075_weight_0_to_fp16_palettized, x = input_351_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor out_57_cast_fp16 = add(x = x_181_cast_fp16, y = var_2075_cast_fp16)[name = string("out_57_cast_fp16")]; + tensor x_185_cast_fp16 = mul(x = out_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor x_m_13_cast_fp16 = mul(x = x_185_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_13_cast_fp16")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26254144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26385280))))[name = string("main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26385408)))]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_185_cast_fp16)[name = string("transpose_24")]; + tensor linear_30_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_2093 = const()[name = string("op_2093"), val = tensor([2, 512, 8, 64])]; + tensor var_2094_cast_fp16 = reshape(shape = var_2093, x = linear_30_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor x_187_perm_0 = const()[name = string("x_187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26386496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26452096))))[name = string("main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26452224)))]; + tensor linear_31_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_2101 = const()[name = string("op_2101"), val = tensor([2, 128, 8, 64])]; + tensor var_2102_cast_fp16 = reshape(shape = var_2101, x = linear_31_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor x_189_perm_0 = const()[name = string("x_189_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26453312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26518912))))[name = string("main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26519040)))]; + tensor linear_32_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_2109 = const()[name = string("op_2109"), val = tensor([2, 128, 8, 64])]; + tensor var_2110_cast_fp16 = reshape(shape = var_2109, x = linear_32_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_13_begin_0 = const()[name = string("x_a_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_13_end_0 = const()[name = string("x_a_13_end_0"), val = tensor([2, 8, 512, 32])]; + tensor x_a_13_end_mask_0 = const()[name = string("x_a_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_187_cast_fp16 = transpose(perm = x_187_perm_0, x = var_2094_cast_fp16)[name = string("transpose_23")]; + tensor x_a_13_cast_fp16 = slice_by_index(begin = x_a_13_begin_0, end = x_a_13_end_0, end_mask = x_a_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_a_13_cast_fp16")]; + tensor x_b_13_begin_0 = const()[name = string("x_b_13_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_13_end_0 = const()[name = string("x_b_13_end_0"), val = tensor([2, 8, 512, 64])]; + tensor x_b_13_end_mask_0 = const()[name = string("x_b_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_13_cast_fp16 = slice_by_index(begin = x_b_13_begin_0, end = x_b_13_end_0, end_mask = x_b_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_b_13_cast_fp16")]; + tensor var_2138_cast_fp16 = mul(x = x_a_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2138_cast_fp16")]; + tensor var_2139_cast_fp16 = mul(x = x_b_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2139_cast_fp16")]; + tensor rot_a_13_cast_fp16 = sub(x = var_2138_cast_fp16, y = var_2139_cast_fp16)[name = string("rot_a_13_cast_fp16")]; + tensor var_2141_cast_fp16 = mul(x = x_a_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2141_cast_fp16")]; + tensor var_2142_cast_fp16 = mul(x = x_b_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2142_cast_fp16")]; + tensor rot_b_13_cast_fp16 = add(x = var_2141_cast_fp16, y = var_2142_cast_fp16)[name = string("rot_b_13_cast_fp16")]; + bool q_13_interleave_0 = const()[name = string("q_13_interleave_0"), val = bool(false)]; + tensor q_13_cast_fp16 = concat(axis = var_1775, interleave = q_13_interleave_0, values = (rot_a_13_cast_fp16, rot_b_13_cast_fp16))[name = string("q_13_cast_fp16")]; + tensor x_a_begin_0 = const()[name = string("x_a_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_end_0 = const()[name = string("x_a_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_end_mask_0 = const()[name = string("x_a_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_189_cast_fp16 = transpose(perm = x_189_perm_0, x = var_2102_cast_fp16)[name = string("transpose_22")]; + tensor x_a_cast_fp16 = slice_by_index(begin = x_a_begin_0, end = x_a_end_0, end_mask = x_a_end_mask_0, x = x_189_cast_fp16)[name = string("x_a_cast_fp16")]; + tensor x_b_begin_0 = const()[name = string("x_b_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_end_0 = const()[name = string("x_b_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_end_mask_0 = const()[name = string("x_b_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_cast_fp16 = slice_by_index(begin = x_b_begin_0, end = x_b_end_0, end_mask = x_b_end_mask_0, x = x_189_cast_fp16)[name = string("x_b_cast_fp16")]; + tensor var_2156_cast_fp16 = mul(x = x_a_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2156_cast_fp16")]; + tensor var_2157_cast_fp16 = mul(x = x_b_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2157_cast_fp16")]; + tensor rot_a_cast_fp16 = sub(x = var_2156_cast_fp16, y = var_2157_cast_fp16)[name = string("rot_a_cast_fp16")]; + tensor var_2159_cast_fp16 = mul(x = x_a_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2159_cast_fp16")]; + tensor var_2160_cast_fp16 = mul(x = x_b_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2160_cast_fp16")]; + tensor rot_b_cast_fp16 = add(x = var_2159_cast_fp16, y = var_2160_cast_fp16)[name = string("rot_b_cast_fp16")]; + bool k_13_interleave_0 = const()[name = string("k_13_interleave_0"), val = bool(false)]; + tensor k_13_cast_fp16 = concat(axis = var_1775, interleave = k_13_interleave_0, values = (rot_a_cast_fp16, rot_b_cast_fp16))[name = string("k_13_cast_fp16")]; + bool var_2165_transpose_x_1 = const()[name = string("op_2165_transpose_x_1"), val = bool(false)]; + bool var_2165_transpose_y_1 = const()[name = string("op_2165_transpose_y_1"), val = bool(true)]; + tensor var_2165_cast_fp16 = matmul(transpose_x = var_2165_transpose_x_1, transpose_y = var_2165_transpose_y_1, x = q_13_cast_fp16, y = k_13_cast_fp16)[name = string("op_2165_cast_fp16")]; + fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_cast_fp16 = mul(x = var_2165_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")]; + tensor input_359_cast_fp16 = sub(x = _inversed_scores_cast_fp16, y = var_530_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_1775, x = input_359_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool var_2174_transpose_x_0 = const()[name = string("op_2174_transpose_x_0"), val = bool(false)]; + bool var_2174_transpose_y_0 = const()[name = string("op_2174_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2110_cast_fp16)[name = string("transpose_21")]; + tensor var_2174_cast_fp16 = matmul(transpose_x = var_2174_transpose_x_0, transpose_y = var_2174_transpose_y_0, x = attn_19_cast_fp16, y = v_13_cast_fp16)[name = string("op_2174_cast_fp16")]; + tensor var_2175_perm_0 = const()[name = string("op_2175_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2176 = const()[name = string("op_2176"), val = tensor([2, 512, 512])]; + tensor var_2175_cast_fp16 = transpose(perm = var_2175_perm_0, x = var_2174_cast_fp16)[name = string("transpose_20")]; + tensor input_361_cast_fp16 = reshape(shape = var_2176, x = var_2175_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26520128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26651264))))[name = string("main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26651392)))]; + tensor linear_33_cast_fp16 = linear(bias = main_blocks_3_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor out_59_perm_0 = const()[name = string("out_59_perm_0"), val = tensor([0, 2, 1])]; + tensor out_59_cast_fp16 = transpose(perm = out_59_perm_0, x = linear_33_cast_fp16)[name = string("transpose_19")]; + tensor var_2183_cast_fp16 = mul(x = out_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_m_13_cast_fp16, y = var_2183_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor input_363_perm_0 = const()[name = string("input_363_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2190_axes_0 = const()[name = string("op_2190_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26652480)))]; + tensor main_blocks_3_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26653568)))]; + tensor input_363_cast_fp16 = transpose(perm = input_363_perm_0, x = x_191_cast_fp16)[name = string("transpose_18")]; + tensor var_2190_cast_fp16 = layer_norm(axes = var_2190_axes_0, beta = main_blocks_3_text_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_text_norm_norm_weight_to_fp16, x = input_363_cast_fp16)[name = string("op_2190_cast_fp16")]; + tensor var_2191_perm_0 = const()[name = string("op_2191_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2191_perm_0, x = var_2190_cast_fp16)[name = string("transpose_17")]; + tensor x_193_cast_fp16 = mul(x = var_2191_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_193_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2204_cast_fp16 = mul(x = x_195_cast_fp16, y = last_1_cast_fp16)[name = string("op_2204_cast_fp16")]; + tensor x_last_47_axes_0 = const()[name = string("x_last_47_axes_0"), val = tensor([-1])]; + bool x_last_47_keep_dims_0 = const()[name = string("x_last_47_keep_dims_0"), val = bool(true)]; + tensor x_last_47_cast_fp16 = reduce_sum(axes = x_last_47_axes_0, keep_dims = x_last_47_keep_dims_0, x = var_2204_cast_fp16)[name = string("x_last_47_cast_fp16")]; + tensor var_2207_cast_fp16 = mul(x = x_195_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2207_cast_fp16")]; + tensor var_2209_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_47_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor input_367_cast_fp16 = add(x = var_2207_cast_fp16, y = var_2209_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_369_mode_0 = const()[name = string("input_369_mode_0"), val = string("replicate")]; + fp16 const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = fp16(0x0p+0)]; + tensor input_369_cast_fp16 = pad(constant_val = const_79_to_fp16, mode = input_369_mode_0, pad = input_369_pad_0, x = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + string h_141_pad_type_0 = const()[name = string("h_141_pad_type_0"), val = string("valid")]; + int32 h_141_groups_0 = const()[name = string("h_141_groups_0"), val = int32(512)]; + tensor h_141_strides_0 = const()[name = string("h_141_strides_0"), val = tensor([1])]; + tensor h_141_pad_0 = const()[name = string("h_141_pad_0"), val = tensor([0, 0])]; + tensor h_141_dilations_0 = const()[name = string("h_141_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26654656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26656000))))[name = string("main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26656128)))]; + tensor h_141_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_141_dilations_0, groups = h_141_groups_0, pad = h_141_pad_0, pad_type = h_141_pad_type_0, strides = h_141_strides_0, weight = main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_369_cast_fp16)[name = string("h_141_cast_fp16")]; + tensor x_197_cast_fp16 = mul(x = h_141_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_371_perm_0 = const()[name = string("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2227_axes_0 = const()[name = string("op_2227_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26657216)))]; + tensor main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26658304)))]; + tensor input_371_cast_fp16 = transpose(perm = input_371_perm_0, x = x_197_cast_fp16)[name = string("transpose_16")]; + tensor var_2227_cast_fp16 = layer_norm(axes = var_2227_axes_0, beta = main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16, x = input_371_cast_fp16)[name = string("op_2227_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + string h_143_pad_type_0 = const()[name = string("h_143_pad_type_0"), val = string("valid")]; + tensor h_143_strides_0 = const()[name = string("h_143_strides_0"), val = tensor([1])]; + tensor h_143_pad_0 = const()[name = string("h_143_pad_0"), val = tensor([0, 0])]; + tensor h_143_dilations_0 = const()[name = string("h_143_dilations_0"), val = tensor([1])]; + int32 h_143_groups_0 = const()[name = string("h_143_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26659392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27183744))))[name = string("main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27183872)))]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = var_2227_cast_fp16)[name = string("transpose_15")]; + tensor h_143_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_143_dilations_0, groups = h_143_groups_0, pad = h_143_pad_0, pad_type = h_143_pad_type_0, strides = h_143_strides_0, weight = main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = string("h_143_cast_fp16")]; + string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")]; + tensor input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = h_143_cast_fp16)[name = string("input_375_cast_fp16")]; + string h_145_pad_type_0 = const()[name = string("h_145_pad_type_0"), val = string("valid")]; + tensor h_145_strides_0 = const()[name = string("h_145_strides_0"), val = tensor([1])]; + tensor h_145_pad_0 = const()[name = string("h_145_pad_0"), val = tensor([0, 0])]; + tensor h_145_dilations_0 = const()[name = string("h_145_dilations_0"), val = tensor([1])]; + int32 h_145_groups_0 = const()[name = string("h_145_groups_0"), val = int32(1)]; + tensor op_2244_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27188032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27712384))))[name = string("op_2244_weight_0_to_fp16_palettized")]; + tensor var_2244_bias_0_to_fp16 = const()[name = string("op_2244_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27712512)))]; + tensor var_2244_cast_fp16 = conv(bias = var_2244_bias_0_to_fp16, dilations = h_145_dilations_0, groups = h_145_groups_0, pad = h_145_pad_0, pad_type = h_145_pad_type_0, strides = h_145_strides_0, weight = op_2244_weight_0_to_fp16_palettized, x = input_375_cast_fp16)[name = string("op_2244_cast_fp16")]; + tensor out_61_cast_fp16 = add(x = x_195_cast_fp16, y = var_2244_cast_fp16)[name = string("out_61_cast_fp16")]; + tensor x_199_cast_fp16 = mul(x = out_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor x_m_cast_fp16 = mul(x = x_199_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_cast_fp16")]; + tensor input_377_perm_0 = const()[name = string("input_377_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27713600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27779200))))[name = string("main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27779328)))]; + tensor input_377_cast_fp16 = transpose(perm = input_377_perm_0, x = x_199_cast_fp16)[name = string("transpose_14")]; + tensor linear_34_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_2260 = const()[name = string("op_2260"), val = tensor([2, 512, 2, 128])]; + tensor var_2261_cast_fp16 = reshape(shape = var_2260, x = linear_34_cast_fp16)[name = string("op_2261_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27779904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27812736))))[name = string("main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27812864)))]; + tensor linear_36_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_2274 = const()[name = string("op_2274"), val = tensor([2, 50, 2, 128])]; + tensor var_2275_cast_fp16 = reshape(shape = var_2274, x = linear_36_cast_fp16)[name = string("op_2275_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_2279_transpose_x_0 = const()[name = string("op_2279_transpose_x_0"), val = bool(false)]; + bool var_2279_transpose_y_0 = const()[name = string("op_2279_transpose_y_0"), val = bool(false)]; + tensor op_2278_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27813440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27826304))))[name = string("op_2278_to_fp16_palettized")]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_2261_cast_fp16)[name = string("transpose_13")]; + tensor var_2279_cast_fp16 = matmul(transpose_x = var_2279_transpose_x_0, transpose_y = var_2279_transpose_y_0, x = q_cast_fp16, y = op_2278_to_fp16_palettized)[name = string("op_2279_cast_fp16")]; + fp16 _inversed_input_379_y_0_to_fp16 = const()[name = string("_inversed_input_379_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_379_cast_fp16 = mul(x = var_2279_cast_fp16, y = _inversed_input_379_y_0_to_fp16)[name = string("_inversed_input_379_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_1775, x = _inversed_input_379_cast_fp16)[name = string("attn_21_cast_fp16")]; + tensor attn_cast_fp16 = mul(x = attn_21_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_cast_fp16")]; + bool var_2286_transpose_x_0 = const()[name = string("op_2286_transpose_x_0"), val = bool(false)]; + bool var_2286_transpose_y_0 = const()[name = string("op_2286_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_2275_cast_fp16)[name = string("transpose_12")]; + tensor var_2286_cast_fp16 = matmul(transpose_x = var_2286_transpose_x_0, transpose_y = var_2286_transpose_y_0, x = attn_cast_fp16, y = v_15_cast_fp16)[name = string("op_2286_cast_fp16")]; + tensor var_2287_perm_0 = const()[name = string("op_2287_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2288 = const()[name = string("op_2288"), val = tensor([2, 512, 256])]; + tensor var_2287_cast_fp16 = transpose(perm = var_2287_perm_0, x = var_2286_cast_fp16)[name = string("transpose_11")]; + tensor input_381_cast_fp16 = reshape(shape = var_2288, x = var_2287_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27826432))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27892032))))[name = string("main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27892160)))]; + tensor linear_37_cast_fp16 = linear(bias = main_blocks_3_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor out_63_perm_0 = const()[name = string("out_63_perm_0"), val = tensor([0, 2, 1])]; + tensor out_63_cast_fp16 = transpose(perm = out_63_perm_0, x = linear_37_cast_fp16)[name = string("transpose_10")]; + tensor var_2295_cast_fp16 = mul(x = out_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2295_cast_fp16")]; + tensor x_201_cast_fp16 = add(x = x_m_cast_fp16, y = var_2295_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_383_perm_0 = const()[name = string("input_383_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2302_axes_0 = const()[name = string("op_2302_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27893248)))]; + tensor main_blocks_3_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894336)))]; + tensor input_383_cast_fp16 = transpose(perm = input_383_perm_0, x = x_201_cast_fp16)[name = string("transpose_9")]; + tensor var_2302_cast_fp16 = layer_norm(axes = var_2302_axes_0, beta = main_blocks_3_style_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_style_norm_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_perm_0 = const()[name = string("op_2303_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2303_cast_fp16 = transpose(perm = var_2303_perm_0, x = var_2302_cast_fp16)[name = string("transpose_8")]; + tensor x_203_cast_fp16 = mul(x = var_2303_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor x_205_cast_fp16 = mul(x = x_203_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor var_2330_cast_fp16 = mul(x = x_205_cast_fp16, y = last_1_cast_fp16)[name = string("op_2330_cast_fp16")]; + tensor x_last_49_axes_0 = const()[name = string("x_last_49_axes_0"), val = tensor([-1])]; + bool x_last_49_keep_dims_0 = const()[name = string("x_last_49_keep_dims_0"), val = bool(true)]; + tensor x_last_49_cast_fp16 = reduce_sum(axes = x_last_49_axes_0, keep_dims = x_last_49_keep_dims_0, x = var_2330_cast_fp16)[name = string("x_last_49_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = x_205_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor var_2335_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_49_cast_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2335_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_pad_0 = const()[name = string("input_389_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("replicate")]; + fp16 const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = fp16(0x0p+0)]; + tensor input_389_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = input_389_mode_0, pad = input_389_pad_0, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + string h_147_pad_type_0 = const()[name = string("h_147_pad_type_0"), val = string("valid")]; + int32 h_147_groups_0 = const()[name = string("h_147_groups_0"), val = int32(512)]; + tensor h_147_strides_0 = const()[name = string("h_147_strides_0"), val = tensor([1])]; + tensor h_147_pad_0 = const()[name = string("h_147_pad_0"), val = tensor([0, 0])]; + tensor h_147_dilations_0 = const()[name = string("h_147_dilations_0"), val = tensor([1])]; + tensor last_convnext_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27895424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896768))))[name = string("last_convnext_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896896)))]; + tensor h_147_cast_fp16 = conv(bias = last_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_147_dilations_0, groups = h_147_groups_0, pad = h_147_pad_0, pad_type = h_147_pad_type_0, strides = h_147_strides_0, weight = last_convnext_0_dwconv__conv_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor x_207_cast_fp16 = mul(x = h_147_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_391_perm_0 = const()[name = string("input_391_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2353_axes_0 = const()[name = string("op_2353_axes_0"), val = tensor([-1])]; + tensor last_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27897984)))]; + tensor last_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27899072)))]; + fp16 var_2307_to_fp16 = const()[name = string("op_2307_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_391_cast_fp16 = transpose(perm = input_391_perm_0, x = x_207_cast_fp16)[name = string("transpose_7")]; + tensor var_2353_cast_fp16 = layer_norm(axes = var_2353_axes_0, beta = last_convnext_0_norm_norm_bias_to_fp16, epsilon = var_2307_to_fp16, gamma = last_convnext_0_norm_norm_weight_to_fp16, x = input_391_cast_fp16)[name = string("op_2353_cast_fp16")]; + tensor input_393_perm_0 = const()[name = string("input_393_perm_0"), val = tensor([0, 2, 1])]; + string h_149_pad_type_0 = const()[name = string("h_149_pad_type_0"), val = string("valid")]; + tensor h_149_strides_0 = const()[name = string("h_149_strides_0"), val = tensor([1])]; + tensor h_149_pad_0 = const()[name = string("h_149_pad_0"), val = tensor([0, 0])]; + tensor h_149_dilations_0 = const()[name = string("h_149_dilations_0"), val = tensor([1])]; + int32 h_149_groups_0 = const()[name = string("h_149_groups_0"), val = int32(1)]; + tensor last_convnext_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27900160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28424512))))[name = string("last_convnext_0_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28424640)))]; + tensor input_393_cast_fp16 = transpose(perm = input_393_perm_0, x = var_2353_cast_fp16)[name = string("transpose_6")]; + tensor h_149_cast_fp16 = conv(bias = last_convnext_0_pwconv1_bias_to_fp16, dilations = h_149_dilations_0, groups = h_149_groups_0, pad = h_149_pad_0, pad_type = h_149_pad_type_0, strides = h_149_strides_0, weight = last_convnext_0_pwconv1_weight_to_fp16_palettized, x = input_393_cast_fp16)[name = string("h_149_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = h_149_cast_fp16)[name = string("input_395_cast_fp16")]; + string h_151_pad_type_0 = const()[name = string("h_151_pad_type_0"), val = string("valid")]; + tensor h_151_strides_0 = const()[name = string("h_151_strides_0"), val = tensor([1])]; + tensor h_151_pad_0 = const()[name = string("h_151_pad_0"), val = tensor([0, 0])]; + tensor h_151_dilations_0 = const()[name = string("h_151_dilations_0"), val = tensor([1])]; + int32 h_151_groups_0 = const()[name = string("h_151_groups_0"), val = int32(1)]; + tensor op_2370_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28428800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28953152))))[name = string("op_2370_weight_0_to_fp16_palettized")]; + tensor var_2370_bias_0_to_fp16 = const()[name = string("op_2370_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28953280)))]; + tensor var_2370_cast_fp16 = conv(bias = var_2370_bias_0_to_fp16, dilations = h_151_dilations_0, groups = h_151_groups_0, pad = h_151_pad_0, pad_type = h_151_pad_type_0, strides = h_151_strides_0, weight = op_2370_weight_0_to_fp16_palettized, x = input_395_cast_fp16)[name = string("op_2370_cast_fp16")]; + tensor out_65_cast_fp16 = add(x = x_205_cast_fp16, y = var_2370_cast_fp16)[name = string("out_65_cast_fp16")]; + tensor x_209_cast_fp16 = mul(x = out_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor x_211_cast_fp16 = mul(x = x_209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_2398_cast_fp16 = mul(x = x_211_cast_fp16, y = last_1_cast_fp16)[name = string("op_2398_cast_fp16")]; + tensor x_last_51_axes_0 = const()[name = string("x_last_51_axes_0"), val = tensor([-1])]; + bool x_last_51_keep_dims_0 = const()[name = string("x_last_51_keep_dims_0"), val = bool(true)]; + tensor x_last_51_cast_fp16 = reduce_sum(axes = x_last_51_axes_0, keep_dims = x_last_51_keep_dims_0, x = var_2398_cast_fp16)[name = string("x_last_51_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = x_211_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_51_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor input_399_cast_fp16 = add(x = var_2401_cast_fp16, y = var_2403_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor input_401_pad_0 = const()[name = string("input_401_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_401_mode_0 = const()[name = string("input_401_mode_0"), val = string("replicate")]; + fp16 const_86_to_fp16 = const()[name = string("const_86_to_fp16"), val = fp16(0x0p+0)]; + tensor input_401_cast_fp16 = pad(constant_val = const_86_to_fp16, mode = input_401_mode_0, pad = input_401_pad_0, x = input_399_cast_fp16)[name = string("input_401_cast_fp16")]; + string h_153_pad_type_0 = const()[name = string("h_153_pad_type_0"), val = string("valid")]; + int32 h_153_groups_0 = const()[name = string("h_153_groups_0"), val = int32(512)]; + tensor h_153_strides_0 = const()[name = string("h_153_strides_0"), val = tensor([1])]; + tensor h_153_pad_0 = const()[name = string("h_153_pad_0"), val = tensor([0, 0])]; + tensor h_153_dilations_0 = const()[name = string("h_153_dilations_0"), val = tensor([1])]; + tensor last_convnext_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28954368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28955712))))[name = string("last_convnext_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28955840)))]; + tensor h_153_cast_fp16 = conv(bias = last_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_153_dilations_0, groups = h_153_groups_0, pad = h_153_pad_0, pad_type = h_153_pad_type_0, strides = h_153_strides_0, weight = last_convnext_1_dwconv__conv_weight_to_fp16_palettized, x = input_401_cast_fp16)[name = string("h_153_cast_fp16")]; + tensor x_213_cast_fp16 = mul(x = h_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_213_cast_fp16")]; + tensor input_403_perm_0 = const()[name = string("input_403_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2421_axes_0 = const()[name = string("op_2421_axes_0"), val = tensor([-1])]; + tensor last_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28956928)))]; + tensor last_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28958016)))]; + fp16 var_2375_to_fp16 = const()[name = string("op_2375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_403_cast_fp16 = transpose(perm = input_403_perm_0, x = x_213_cast_fp16)[name = string("transpose_5")]; + tensor var_2421_cast_fp16 = layer_norm(axes = var_2421_axes_0, beta = last_convnext_1_norm_norm_bias_to_fp16, epsilon = var_2375_to_fp16, gamma = last_convnext_1_norm_norm_weight_to_fp16, x = input_403_cast_fp16)[name = string("op_2421_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string h_155_pad_type_0 = const()[name = string("h_155_pad_type_0"), val = string("valid")]; + tensor h_155_strides_0 = const()[name = string("h_155_strides_0"), val = tensor([1])]; + tensor h_155_pad_0 = const()[name = string("h_155_pad_0"), val = tensor([0, 0])]; + tensor h_155_dilations_0 = const()[name = string("h_155_dilations_0"), val = tensor([1])]; + int32 h_155_groups_0 = const()[name = string("h_155_groups_0"), val = int32(1)]; + tensor last_convnext_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28959104))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29483456))))[name = string("last_convnext_1_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29483584)))]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = var_2421_cast_fp16)[name = string("transpose_4")]; + tensor h_155_cast_fp16 = conv(bias = last_convnext_1_pwconv1_bias_to_fp16, dilations = h_155_dilations_0, groups = h_155_groups_0, pad = h_155_pad_0, pad_type = h_155_pad_type_0, strides = h_155_strides_0, weight = last_convnext_1_pwconv1_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = string("h_155_cast_fp16")]; + string input_407_mode_0 = const()[name = string("input_407_mode_0"), val = string("EXACT")]; + tensor input_407_cast_fp16 = gelu(mode = input_407_mode_0, x = h_155_cast_fp16)[name = string("input_407_cast_fp16")]; + string h_157_pad_type_0 = const()[name = string("h_157_pad_type_0"), val = string("valid")]; + tensor h_157_strides_0 = const()[name = string("h_157_strides_0"), val = tensor([1])]; + tensor h_157_pad_0 = const()[name = string("h_157_pad_0"), val = tensor([0, 0])]; + tensor h_157_dilations_0 = const()[name = string("h_157_dilations_0"), val = tensor([1])]; + int32 h_157_groups_0 = const()[name = string("h_157_groups_0"), val = int32(1)]; + tensor op_2438_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29487744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30012096))))[name = string("op_2438_weight_0_to_fp16_palettized")]; + tensor var_2438_bias_0_to_fp16 = const()[name = string("op_2438_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30012224)))]; + tensor var_2438_cast_fp16 = conv(bias = var_2438_bias_0_to_fp16, dilations = h_157_dilations_0, groups = h_157_groups_0, pad = h_157_pad_0, pad_type = h_157_pad_type_0, strides = h_157_strides_0, weight = op_2438_weight_0_to_fp16_palettized, x = input_407_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor out_67_cast_fp16 = add(x = x_211_cast_fp16, y = var_2438_cast_fp16)[name = string("out_67_cast_fp16")]; + tensor x_215_cast_fp16 = mul(x = out_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_215_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2466_cast_fp16 = mul(x = x_217_cast_fp16, y = last_1_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor x_last_53_axes_0 = const()[name = string("x_last_53_axes_0"), val = tensor([-1])]; + bool x_last_53_keep_dims_0 = const()[name = string("x_last_53_keep_dims_0"), val = bool(true)]; + tensor x_last_53_cast_fp16 = reduce_sum(axes = x_last_53_axes_0, keep_dims = x_last_53_keep_dims_0, x = var_2466_cast_fp16)[name = string("x_last_53_cast_fp16")]; + tensor var_2469_cast_fp16 = mul(x = x_217_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor var_2471_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_53_cast_fp16)[name = string("op_2471_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = var_2469_cast_fp16, y = var_2471_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("replicate")]; + fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)]; + tensor input_413_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = input_413_mode_0, pad = input_413_pad_0, x = input_411_cast_fp16)[name = string("input_413_cast_fp16")]; + string h_159_pad_type_0 = const()[name = string("h_159_pad_type_0"), val = string("valid")]; + int32 h_159_groups_0 = const()[name = string("h_159_groups_0"), val = int32(512)]; + tensor h_159_strides_0 = const()[name = string("h_159_strides_0"), val = tensor([1])]; + tensor h_159_pad_0 = const()[name = string("h_159_pad_0"), val = tensor([0, 0])]; + tensor h_159_dilations_0 = const()[name = string("h_159_dilations_0"), val = tensor([1])]; + tensor last_convnext_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30013312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30014656))))[name = string("last_convnext_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30014784)))]; + tensor h_159_cast_fp16 = conv(bias = last_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_159_dilations_0, groups = h_159_groups_0, pad = h_159_pad_0, pad_type = h_159_pad_type_0, strides = h_159_strides_0, weight = last_convnext_2_dwconv__conv_weight_to_fp16_palettized, x = input_413_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor x_219_cast_fp16 = mul(x = h_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_415_perm_0 = const()[name = string("input_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2489_axes_0 = const()[name = string("op_2489_axes_0"), val = tensor([-1])]; + tensor last_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30015872)))]; + tensor last_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30016960)))]; + fp16 var_2443_to_fp16 = const()[name = string("op_2443_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_415_cast_fp16 = transpose(perm = input_415_perm_0, x = x_219_cast_fp16)[name = string("transpose_3")]; + tensor var_2489_cast_fp16 = layer_norm(axes = var_2489_axes_0, beta = last_convnext_2_norm_norm_bias_to_fp16, epsilon = var_2443_to_fp16, gamma = last_convnext_2_norm_norm_weight_to_fp16, x = input_415_cast_fp16)[name = string("op_2489_cast_fp16")]; + tensor input_417_perm_0 = const()[name = string("input_417_perm_0"), val = tensor([0, 2, 1])]; + string h_161_pad_type_0 = const()[name = string("h_161_pad_type_0"), val = string("valid")]; + tensor h_161_strides_0 = const()[name = string("h_161_strides_0"), val = tensor([1])]; + tensor h_161_pad_0 = const()[name = string("h_161_pad_0"), val = tensor([0, 0])]; + tensor h_161_dilations_0 = const()[name = string("h_161_dilations_0"), val = tensor([1])]; + int32 h_161_groups_0 = const()[name = string("h_161_groups_0"), val = int32(1)]; + tensor last_convnext_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30018048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30542400))))[name = string("last_convnext_2_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30542528)))]; + tensor input_417_cast_fp16 = transpose(perm = input_417_perm_0, x = var_2489_cast_fp16)[name = string("transpose_2")]; + tensor h_161_cast_fp16 = conv(bias = last_convnext_2_pwconv1_bias_to_fp16, dilations = h_161_dilations_0, groups = h_161_groups_0, pad = h_161_pad_0, pad_type = h_161_pad_type_0, strides = h_161_strides_0, weight = last_convnext_2_pwconv1_weight_to_fp16_palettized, x = input_417_cast_fp16)[name = string("h_161_cast_fp16")]; + string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("EXACT")]; + tensor input_419_cast_fp16 = gelu(mode = input_419_mode_0, x = h_161_cast_fp16)[name = string("input_419_cast_fp16")]; + string h_163_pad_type_0 = const()[name = string("h_163_pad_type_0"), val = string("valid")]; + tensor h_163_strides_0 = const()[name = string("h_163_strides_0"), val = tensor([1])]; + tensor h_163_pad_0 = const()[name = string("h_163_pad_0"), val = tensor([0, 0])]; + tensor h_163_dilations_0 = const()[name = string("h_163_dilations_0"), val = tensor([1])]; + int32 h_163_groups_0 = const()[name = string("h_163_groups_0"), val = int32(1)]; + tensor op_2506_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30546688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31071040))))[name = string("op_2506_weight_0_to_fp16_palettized")]; + tensor var_2506_bias_0_to_fp16 = const()[name = string("op_2506_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31071168)))]; + tensor var_2506_cast_fp16 = conv(bias = var_2506_bias_0_to_fp16, dilations = h_163_dilations_0, groups = h_163_groups_0, pad = h_163_pad_0, pad_type = h_163_pad_type_0, strides = h_163_strides_0, weight = op_2506_weight_0_to_fp16_palettized, x = input_419_cast_fp16)[name = string("op_2506_cast_fp16")]; + tensor out_69_cast_fp16 = add(x = x_217_cast_fp16, y = var_2506_cast_fp16)[name = string("out_69_cast_fp16")]; + tensor x_221_cast_fp16 = mul(x = out_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor x_223_cast_fp16 = mul(x = x_221_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_2534_cast_fp16 = mul(x = x_223_cast_fp16, y = last_1_cast_fp16)[name = string("op_2534_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_2534_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_2537_cast_fp16 = mul(x = x_223_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2537_cast_fp16")]; + tensor var_2539_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_cast_fp16)[name = string("op_2539_cast_fp16")]; + tensor input_423_cast_fp16 = add(x = var_2537_cast_fp16, y = var_2539_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_425_mode_0 = const()[name = string("input_425_mode_0"), val = string("replicate")]; + fp16 const_90_to_fp16 = const()[name = string("const_90_to_fp16"), val = fp16(0x0p+0)]; + tensor input_425_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = input_425_mode_0, pad = input_425_pad_0, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")]; + string h_165_pad_type_0 = const()[name = string("h_165_pad_type_0"), val = string("valid")]; + int32 h_165_groups_0 = const()[name = string("h_165_groups_0"), val = int32(512)]; + tensor h_165_strides_0 = const()[name = string("h_165_strides_0"), val = tensor([1])]; + tensor h_165_pad_0 = const()[name = string("h_165_pad_0"), val = tensor([0, 0])]; + tensor h_165_dilations_0 = const()[name = string("h_165_dilations_0"), val = tensor([1])]; + tensor last_convnext_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31072256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31073600))))[name = string("last_convnext_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31073728)))]; + tensor h_165_cast_fp16 = conv(bias = last_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_165_dilations_0, groups = h_165_groups_0, pad = h_165_pad_0, pad_type = h_165_pad_type_0, strides = h_165_strides_0, weight = last_convnext_3_dwconv__conv_weight_to_fp16_palettized, x = input_425_cast_fp16)[name = string("h_165_cast_fp16")]; + tensor x_cast_fp16 = mul(x = h_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2557_axes_0 = const()[name = string("op_2557_axes_0"), val = tensor([-1])]; + tensor last_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31074816)))]; + tensor last_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31075904)))]; + fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_cast_fp16)[name = string("transpose_1")]; + tensor var_2557_cast_fp16 = layer_norm(axes = var_2557_axes_0, beta = last_convnext_3_norm_norm_bias_to_fp16, epsilon = var_2511_to_fp16, gamma = last_convnext_3_norm_norm_weight_to_fp16, x = input_427_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor input_429_perm_0 = const()[name = string("input_429_perm_0"), val = tensor([0, 2, 1])]; + string h_167_pad_type_0 = const()[name = string("h_167_pad_type_0"), val = string("valid")]; + tensor h_167_strides_0 = const()[name = string("h_167_strides_0"), val = tensor([1])]; + tensor h_167_pad_0 = const()[name = string("h_167_pad_0"), val = tensor([0, 0])]; + tensor h_167_dilations_0 = const()[name = string("h_167_dilations_0"), val = tensor([1])]; + int32 h_167_groups_0 = const()[name = string("h_167_groups_0"), val = int32(1)]; + tensor last_convnext_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31076992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31601344))))[name = string("last_convnext_3_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31601472)))]; + tensor input_429_cast_fp16 = transpose(perm = input_429_perm_0, x = var_2557_cast_fp16)[name = string("transpose_0")]; + tensor h_167_cast_fp16 = conv(bias = last_convnext_3_pwconv1_bias_to_fp16, dilations = h_167_dilations_0, groups = h_167_groups_0, pad = h_167_pad_0, pad_type = h_167_pad_type_0, strides = h_167_strides_0, weight = last_convnext_3_pwconv1_weight_to_fp16_palettized, x = input_429_cast_fp16)[name = string("h_167_cast_fp16")]; + string input_431_mode_0 = const()[name = string("input_431_mode_0"), val = string("EXACT")]; + tensor input_431_cast_fp16 = gelu(mode = input_431_mode_0, x = h_167_cast_fp16)[name = string("input_431_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor op_2574_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31605632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32129984))))[name = string("op_2574_weight_0_to_fp16_palettized")]; + tensor var_2574_bias_0_to_fp16 = const()[name = string("op_2574_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32130112)))]; + tensor var_2574_cast_fp16 = conv(bias = var_2574_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = op_2574_weight_0_to_fp16_palettized, x = input_431_cast_fp16)[name = string("op_2574_cast_fp16")]; + tensor out_cast_fp16 = add(x = x_223_cast_fp16, y = var_2574_cast_fp16)[name = string("out_cast_fp16")]; + tensor input_cast_fp16 = mul(x = out_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("input_cast_fp16")]; + string var_2587_pad_type_0 = const()[name = string("op_2587_pad_type_0"), val = string("valid")]; + tensor var_2587_strides_0 = const()[name = string("op_2587_strides_0"), val = tensor([1])]; + tensor var_2587_pad_0 = const()[name = string("op_2587_pad_0"), val = tensor([0, 0])]; + tensor var_2587_dilations_0 = const()[name = string("op_2587_dilations_0"), val = tensor([1])]; + int32 var_2587_groups_0 = const()[name = string("op_2587_groups_0"), val = int32(1)]; + tensor proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32131200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32168128))))[name = string("proj_out_weight_to_fp16_palettized")]; + tensor var_2587_cast_fp16 = conv(dilations = var_2587_dilations_0, groups = var_2587_groups_0, pad = var_2587_pad_0, pad_type = var_2587_pad_type_0, strides = var_2587_strides_0, weight = proj_out_weight_to_fp16_palettized, x = input_cast_fp16)[name = string("op_2587_cast_fp16")]; + tensor v_cast_fp16 = mul(x = var_2587_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("v_cast_fp16")]; + tensor cond_begin_0 = const()[name = string("cond_begin_0"), val = tensor([0, 0, 0])]; + tensor cond_end_0 = const()[name = string("cond_end_0"), val = tensor([1, 144, 512])]; + tensor cond_end_mask_0 = const()[name = string("cond_end_mask_0"), val = tensor([false, true, true])]; + tensor cond_cast_fp16 = slice_by_index(begin = cond_begin_0, end = cond_end_0, end_mask = cond_end_mask_0, x = v_cast_fp16)[name = string("cond_cast_fp16")]; + tensor uncond_begin_0 = const()[name = string("uncond_begin_0"), val = tensor([1, 0, 0])]; + tensor uncond_end_0 = const()[name = string("uncond_end_0"), val = tensor([2, 144, 512])]; + tensor uncond_end_mask_0 = const()[name = string("uncond_end_mask_0"), val = tensor([true, true, true])]; + tensor uncond_cast_fp16 = slice_by_index(begin = uncond_begin_0, end = uncond_end_0, end_mask = uncond_end_mask_0, x = v_cast_fp16)[name = string("uncond_cast_fp16")]; + fp32 var_2597_epsilon_0 = const()[name = string("op_2597_epsilon_0"), val = fp32(0x1.a36e2ep-14)]; + tensor var_2597_cast_fp16 = inverse(epsilon = var_2597_epsilon_0, x = total_step_to_fp16)[name = string("op_2597_cast_fp16")]; + tensor var_2603 = const()[name = string("op_2603"), val = tensor([-1, 1, 1])]; + tensor step_cast_fp16 = reshape(shape = var_2603, x = var_2597_cast_fp16)[name = string("step_cast_fp16")]; + fp16 var_2605_to_fp16 = const()[name = string("op_2605_to_fp16"), val = fp16(0x1p+2)]; + tensor var_2606_cast_fp16 = mul(x = cond_cast_fp16, y = var_2605_to_fp16)[name = string("op_2606_cast_fp16")]; + fp16 var_2607_to_fp16 = const()[name = string("op_2607_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_2608_cast_fp16 = mul(x = uncond_cast_fp16, y = var_2607_to_fp16)[name = string("op_2608_cast_fp16")]; + tensor var_2610_cast_fp16 = sub(x = var_2606_cast_fp16, y = var_2608_cast_fp16)[name = string("op_2610_cast_fp16")]; + tensor var_2611_cast_fp16 = mul(x = step_cast_fp16, y = var_2610_cast_fp16)[name = string("op_2611_cast_fp16")]; + tensor var_2613_cast_fp16 = add(x = noisy_latent_to_fp16, y = var_2611_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor var_2614_cast_fp16 = mul(x = var_2613_cast_fp16, y = latent_mask_to_fp16)[name = string("op_2614_cast_fp16")]; + string var_2614_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2614_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor denoised_latent = cast(dtype = var_2614_cast_fp16_to_fp32_dtype_0, x = var_2614_cast_fp16)[name = string("cast_0")]; + } -> (denoised_latent); +} \ No newline at end of file diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L512_int4.mlmodelc/weights/weight.bin b/VectorEstimatorVariants/VectorEstimator_v2_L512_int4.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..98991e441bd318b1eb1929dacc6c2b6be567d462 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L512_int4.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75621d8dcab2fb486fcf7424bf44f472c84f55fdabe5b9b9a5fa781447c7a860 +size 32168224 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L512_int4.mlpackage/Data/com.apple.CoreML/model.mlmodel 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sha256:e13b27d31d2c0b3e8875500d5b4dfe254ad4c447e854a0e053045eba86c1b656 +size 553 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L512_int6.mlmodelc/model.mil b/VectorEstimatorVariants/VectorEstimator_v2_L512_int6.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..a685f98811bbf9d8ca2c3b1fc0cc4763e7762447 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L512_int6.mlmodelc/model.mil @@ -0,0 +1,2102 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor current_step, tensor latent_mask, tensor noisy_latent, tensor style_ttl, tensor text_emb, tensor text_mask, tensor total_step) { + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + bool text_emb_interleave_0 = const()[name = string("text_emb_interleave_0"), val = bool(false)]; + string text_emb_to_fp16_dtype_0 = const()[name = string("text_emb_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_uncond_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24704))))[name = string("text_uncond_to_fp16_palettized")]; + tensor text_emb_to_fp16 = cast(dtype = text_emb_to_fp16_dtype_0, x = text_emb)[name = string("cast_7")]; + tensor text_emb_cast_fp16 = concat(axis = var_39, interleave = text_emb_interleave_0, values = (text_emb_to_fp16, text_uncond_to_fp16_palettized))[name = string("text_emb_cast_fp16")]; + bool input_95_interleave_0 = const()[name = string("input_95_interleave_0"), val = bool(false)]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor uncond_masker_style_value_special_token_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34560))))[name = string("uncond_masker_style_value_special_token_to_fp16_palettized")]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_6")]; + tensor input_95_cast_fp16 = concat(axis = var_39, interleave = input_95_interleave_0, values = (style_ttl_to_fp16, uncond_masker_style_value_special_token_to_fp16_palettized))[name = string("input_95_cast_fp16")]; + int32 var_64 = const()[name = string("op_64"), val = int32(0)]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + string noisy_latent_to_fp16_dtype_0 = const()[name = string("noisy_latent_to_fp16_dtype_0"), val = string("fp16")]; + tensor noisy_latent_to_fp16 = cast(dtype = noisy_latent_to_fp16_dtype_0, x = noisy_latent)[name = string("cast_5")]; + tensor input_5_cast_fp16 = concat(axis = var_64, interleave = input_5_interleave_0, values = (noisy_latent_to_fp16, noisy_latent_to_fp16))[name = string("input_5_cast_fp16")]; + int32 var_67 = const()[name = string("op_67"), val = int32(0)]; + bool latent_mask_b_interleave_0 = const()[name = string("latent_mask_b_interleave_0"), val = bool(false)]; + string latent_mask_to_fp16_dtype_0 = const()[name = string("latent_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor latent_mask_to_fp16 = cast(dtype = latent_mask_to_fp16_dtype_0, x = latent_mask)[name = string("cast_4")]; + tensor latent_mask_b_cast_fp16 = concat(axis = var_67, interleave = latent_mask_b_interleave_0, values = (latent_mask_to_fp16, latent_mask_to_fp16))[name = string("latent_mask_b_cast_fp16")]; + int32 var_70 = const()[name = string("op_70"), val = int32(0)]; + bool text_mask_interleave_0 = const()[name = string("text_mask_interleave_0"), val = bool(false)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_3")]; + tensor text_mask_cast_fp16 = concat(axis = var_70, interleave = text_mask_interleave_0, values = (text_mask_to_fp16, text_mask_to_fp16))[name = string("text_mask_cast_fp16")]; + string current_step_to_fp16_dtype_0 = const()[name = string("current_step_to_fp16_dtype_0"), val = string("fp16")]; + string total_step_to_fp16_dtype_0 = const()[name = string("total_step_to_fp16_dtype_0"), val = string("fp16")]; + tensor current_step_to_fp16 = cast(dtype = current_step_to_fp16_dtype_0, x = current_step)[name = string("cast_1")]; + tensor total_step_to_fp16 = cast(dtype = total_step_to_fp16_dtype_0, x = total_step)[name = string("cast_2")]; + tensor t_1_cast_fp16 = real_div(x = current_step_to_fp16, y = total_step_to_fp16)[name = string("t_1_cast_fp16")]; + int32 var_74 = const()[name = string("op_74"), val = int32(0)]; + bool t_3_interleave_0 = const()[name = string("t_3_interleave_0"), val = bool(false)]; + tensor t_3_cast_fp16 = concat(axis = var_74, interleave = t_3_interleave_0, values = (t_1_cast_fp16, t_1_cast_fp16))[name = string("t_3_cast_fp16")]; + int32 var_78 = const()[name = string("op_78"), val = int32(-1)]; + tensor var_85 = const()[name = string("op_85"), val = tensor([-1, 1])]; + tensor var_86_cast_fp16 = reshape(shape = var_85, x = t_3_cast_fp16)[name = string("op_86_cast_fp16")]; + fp16 time_encoder_time_scale_to_fp16 = const()[name = string("time_encoder_time_scale_to_fp16"), val = fp16(0x1.f4p+9)]; + tensor t_scaled_cast_fp16 = mul(x = var_86_cast_fp16, y = time_encoder_time_scale_to_fp16)[name = string("t_scaled_cast_fp16")]; + tensor time_encoder_omegas_to_fp16 = const()[name = string("time_encoder_omegas_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34752)))]; + tensor phase_cast_fp16 = mul(x = t_scaled_cast_fp16, y = time_encoder_omegas_to_fp16)[name = string("phase_cast_fp16")]; + tensor var_89_cast_fp16 = sin(x = phase_cast_fp16)[name = string("op_89_cast_fp16")]; + tensor var_90_cast_fp16 = cos(x = phase_cast_fp16)[name = string("op_90_cast_fp16")]; + bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)]; + tensor input_1_cast_fp16 = concat(axis = var_78, interleave = input_1_interleave_0, values = (var_89_cast_fp16, var_90_cast_fp16))[name = string("input_1_cast_fp16")]; + tensor time_encoder_mlp_0_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47232))))[name = string("time_encoder_mlp_0_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_0_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47424)))]; + tensor linear_0_cast_fp16 = linear(bias = time_encoder_mlp_0_linear_bias_to_fp16, weight = time_encoder_mlp_0_linear_weight_to_fp16_palettized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_97_cast_fp16 = softplus(x = linear_0_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = tanh(x = var_97_cast_fp16)[name = string("op_98_cast_fp16")]; + tensor input_3_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_98_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor time_encoder_mlp_2_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60352))))[name = string("time_encoder_mlp_2_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_2_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60544)))]; + tensor linear_1_cast_fp16 = linear(bias = time_encoder_mlp_2_linear_bias_to_fp16, weight = time_encoder_mlp_2_linear_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor time_emb_axes_0 = const()[name = string("time_emb_axes_0"), val = tensor([-1])]; + tensor time_emb_cast_fp16 = expand_dims(axes = time_emb_axes_0, x = linear_1_cast_fp16)[name = string("time_emb_cast_fp16")]; + string var_115_pad_type_0 = const()[name = string("op_115_pad_type_0"), val = string("valid")]; + tensor var_115_strides_0 = const()[name = string("op_115_strides_0"), val = tensor([1])]; + tensor var_115_pad_0 = const()[name = string("op_115_pad_0"), val = tensor([0, 0])]; + tensor var_115_dilations_0 = const()[name = string("op_115_dilations_0"), val = tensor([1])]; + int32 var_115_groups_0 = const()[name = string("op_115_groups_0"), val = int32(1)]; + tensor proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116096))))[name = string("proj_in_weight_to_fp16_palettized")]; + tensor var_115_cast_fp16 = conv(dilations = var_115_dilations_0, groups = var_115_groups_0, pad = var_115_pad_0, pad_type = var_115_pad_type_0, strides = var_115_strides_0, weight = proj_in_weight_to_fp16_palettized, x = input_5_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = var_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_3_cast_fp16")]; + int32 var_134 = const()[name = string("op_134"), val = int32(-1)]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([0, 0, 1])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 512])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([true, true, true])]; + tensor input_7_cast_fp16 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, x = latent_mask_b_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_166_pad_0 = const()[name = string("op_166_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_166_mode_0 = const()[name = string("op_166_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_166_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_166_mode_0, pad = var_166_pad_0, x = input_7_cast_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_133_to_fp16 = const()[name = string("op_133_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = sub(x = var_133_to_fp16, y = var_166_cast_fp16)[name = string("op_167_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = latent_mask_b_cast_fp16, y = var_167_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_169_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_172_cast_fp16 = mul(x = x_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor var_173_cast_fp16 = sub(x = var_133_to_fp16, y = latent_mask_b_cast_fp16)[name = string("op_173_cast_fp16")]; + tensor var_174_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_174_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_174_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("replicate")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor input_11_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_11_mode_0, pad = input_11_pad_0, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(512)]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118272))))[name = string("main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118464)))]; + tensor h_3_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, 2, 1])]; + tensor var_192_axes_0 = const()[name = string("op_192_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119552)))]; + tensor main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120640)))]; + fp16 var_139_to_fp16 = const()[name = string("op_139_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_7_cast_fp16)[name = string("transpose_117")]; + tensor var_192_cast_fp16 = layer_norm(axes = var_192_axes_0, beta = main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16, x = input_13_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, 2, 1])]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908224))))[name = string("main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908416)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = var_192_cast_fp16)[name = string("transpose_116")]; + tensor h_5_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = string("h_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = h_5_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)]; + tensor op_209_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1699072))))[name = string("op_209_weight_0_to_fp16_palettized")]; + tensor var_209_bias_0_to_fp16 = const()[name = string("op_209_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1699264)))]; + tensor var_209_cast_fp16 = conv(bias = var_209_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = op_209_weight_0_to_fp16_palettized, x = input_17_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_209_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_223_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_223_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_226_cast_fp16 = mul(x = x_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_226_cast_fp16")]; + tensor var_228_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_228_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_226_cast_fp16, y = var_228_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")]; + fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)]; + tensor input_23_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([2])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(512)]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1700352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1702336))))[name = string("main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1702528)))]; + tensor h_9_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor var_246_axes_0 = const()[name = string("op_246_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1703616)))]; + tensor main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1704704)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_13_cast_fp16)[name = string("transpose_115")]; + tensor var_246_cast_fp16 = layer_norm(axes = var_246_axes_0, beta = main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_246_cast_fp16")]; + tensor input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor([0, 2, 1])]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1705792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2492288))))[name = string("main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2492480)))]; + tensor input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_246_cast_fp16)[name = string("transpose_114")]; + tensor h_11_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_27_cast_fp16)[name = string("h_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_11_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)]; + tensor op_263_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2496640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3283136))))[name = string("op_263_weight_0_to_fp16_palettized")]; + tensor var_263_bias_0_to_fp16 = const()[name = string("op_263_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3283328)))]; + tensor var_263_cast_fp16 = conv(bias = var_263_bias_0_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = op_263_weight_0_to_fp16_palettized, x = input_29_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_263_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_277_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_277_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_277_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_280_cast_fp16 = mul(x = x_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_280_cast_fp16")]; + tensor var_282_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_280_cast_fp16, y = var_282_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("replicate")]; + fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)]; + tensor input_35_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = input_35_mode_0, pad = input_35_pad_0, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([4])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(512)]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3284416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3286400))))[name = string("main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3286592)))]; + tensor h_15_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor var_300_axes_0 = const()[name = string("op_300_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3287680)))]; + tensor main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3288768)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_19_cast_fp16)[name = string("transpose_113")]; + tensor var_300_cast_fp16 = layer_norm(axes = var_300_axes_0, beta = main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16, x = input_37_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor([0, 2, 1])]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3289856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4076352))))[name = string("main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4076544)))]; + tensor input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_300_cast_fp16)[name = string("transpose_112")]; + tensor h_17_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = string("h_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = h_17_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)]; + tensor op_317_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4080704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4867200))))[name = string("op_317_weight_0_to_fp16_palettized")]; + tensor var_317_bias_0_to_fp16 = const()[name = string("op_317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4867392)))]; + tensor var_317_cast_fp16 = conv(bias = var_317_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = op_317_weight_0_to_fp16_palettized, x = input_41_cast_fp16)[name = string("op_317_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_317_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor var_331_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_331_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_331_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = x_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor var_336_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_336_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_334_cast_fp16, y = var_336_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")]; + fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([8])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(512)]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4868480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4870464))))[name = string("main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4870656)))]; + tensor h_21_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4871744)))]; + tensor main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4872832)))]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_25_cast_fp16)[name = string("transpose_111")]; + tensor var_354_cast_fp16 = layer_norm(axes = var_354_axes_0, beta = main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_354_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4873920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5660416))))[name = string("main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5660608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_354_cast_fp16)[name = string("transpose_110")]; + tensor h_23_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("h_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_23_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)]; + tensor op_371_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5664768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6451264))))[name = string("op_371_weight_0_to_fp16_palettized")]; + tensor var_371_bias_0_to_fp16 = const()[name = string("op_371_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6451456)))]; + tensor var_371_cast_fp16 = conv(bias = var_371_bias_0_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = op_371_weight_0_to_fp16_palettized, x = input_53_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_371_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor input_55_cast_fp16 = squeeze(axes = input_55_axes_0, x = time_emb_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6452544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6477184))))[name = string("main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6477376)))]; + tensor linear_2_cast_fp16 = linear(bias = main_blocks_0_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor t_5_axes_0 = const()[name = string("t_5_axes_0"), val = tensor([-1])]; + tensor t_5_cast_fp16 = expand_dims(axes = t_5_axes_0, x = linear_2_cast_fp16)[name = string("t_5_cast_fp16")]; + tensor var_381_cast_fp16 = add(x = x_27_cast_fp16, y = t_5_cast_fp16)[name = string("op_381_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_381_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_394_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_394_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_394_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_397_cast_fp16 = mul(x = x_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = var_397_cast_fp16, y = var_399_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("replicate")]; + fp16 const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = fp16(0x0p+0)]; + tensor input_61_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = input_61_mode_0, pad = input_61_pad_0, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(512)]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6478464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6480448))))[name = string("main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6480640)))]; + tensor h_27_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417_axes_0 = const()[name = string("op_417_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6481728)))]; + tensor main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6482816)))]; + tensor input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_33_cast_fp16)[name = string("transpose_109")]; + tensor var_417_cast_fp16 = layer_norm(axes = var_417_axes_0, beta = main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16, x = input_63_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6483904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7270400))))[name = string("main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7270592)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = var_417_cast_fp16)[name = string("transpose_108")]; + tensor h_29_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_65_cast_fp16)[name = string("h_29_cast_fp16")]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("EXACT")]; + tensor input_67_cast_fp16 = gelu(mode = input_67_mode_0, x = h_29_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)]; + tensor op_434_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7274752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8061248))))[name = string("op_434_weight_0_to_fp16_palettized")]; + tensor var_434_bias_0_to_fp16 = const()[name = string("op_434_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8061440)))]; + tensor var_434_cast_fp16 = conv(bias = var_434_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = op_434_weight_0_to_fp16_palettized, x = input_67_cast_fp16)[name = string("op_434_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_434_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_m_1_cast_fp16 = mul(x = x_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_1_cast_fp16")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8062528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8259200))))[name = string("main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8259392)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_35_cast_fp16)[name = string("transpose_107")]; + tensor linear_3_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([2, 512, 8, 64])]; + tensor var_453_cast_fp16 = reshape(shape = var_452, x = linear_3_cast_fp16)[name = string("op_453_cast_fp16")]; + tensor x_37_perm_0 = const()[name = string("x_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8260480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8358848))))[name = string("main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8359040)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = text_emb_cast_fp16)[name = string("transpose_106")]; + tensor linear_4_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([2, 128, 8, 64])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = linear_4_cast_fp16)[name = string("op_461_cast_fp16")]; + tensor x_39_perm_0 = const()[name = string("x_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8360128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8458496))))[name = string("main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8458688)))]; + tensor linear_5_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_468 = const()[name = string("op_468"), val = tensor([2, 128, 8, 64])]; + tensor var_469_cast_fp16 = reshape(shape = var_468, x = linear_5_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor divisor_1_axes_0 = const()[name = string("divisor_1_axes_0"), val = tensor([1, 2])]; + bool divisor_1_keep_dims_0 = const()[name = string("divisor_1_keep_dims_0"), val = bool(false)]; + tensor divisor_1_cast_fp16 = reduce_sum(axes = divisor_1_axes_0, keep_dims = divisor_1_keep_dims_0, x = latent_mask_b_cast_fp16)[name = string("divisor_1_cast_fp16")]; + tensor divisor_3_axes_0 = const()[name = string("divisor_3_axes_0"), val = tensor([1, 2])]; + bool divisor_3_keep_dims_0 = const()[name = string("divisor_3_keep_dims_0"), val = bool(false)]; + tensor divisor_3_cast_fp16 = reduce_sum(axes = divisor_3_axes_0, keep_dims = divisor_3_keep_dims_0, x = text_mask_cast_fp16)[name = string("divisor_3_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([-1, 1, 1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = divisor_1_cast_fp16)[name = string("op_488_cast_fp16")]; + tensor const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8459776)))]; + tensor scaled_1_cast_fp16 = real_div(x = const_16_to_fp16, y = var_488_cast_fp16)[name = string("scaled_1_cast_fp16")]; + tensor main_blocks_0_text_attn_theta_to_fp16 = const()[name = string("main_blocks_0_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8461888)))]; + tensor angles_1_cast_fp16 = mul(x = scaled_1_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_1_cast_fp16")]; + tensor var_491_cast_fp16 = cos(x = angles_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = var_491_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor var_493_cast_fp16 = sin(x = angles_1_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = var_493_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor x_a_1_begin_0 = const()[name = string("x_a_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_1_end_0 = const()[name = string("x_a_1_end_0"), val = tensor([2, 8, 512, 32])]; + tensor x_a_1_end_mask_0 = const()[name = string("x_a_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_37_cast_fp16 = transpose(perm = x_37_perm_0, x = var_453_cast_fp16)[name = string("transpose_105")]; + tensor x_a_1_cast_fp16 = slice_by_index(begin = x_a_1_begin_0, end = x_a_1_end_0, end_mask = x_a_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_a_1_cast_fp16")]; + tensor x_b_1_begin_0 = const()[name = string("x_b_1_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_1_end_0 = const()[name = string("x_b_1_end_0"), val = tensor([2, 8, 512, 64])]; + tensor x_b_1_end_mask_0 = const()[name = string("x_b_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_1_cast_fp16 = slice_by_index(begin = x_b_1_begin_0, end = x_b_1_end_0, end_mask = x_b_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_b_1_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = x_a_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor var_498_cast_fp16 = mul(x = x_b_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_498_cast_fp16")]; + tensor rot_a_1_cast_fp16 = sub(x = var_497_cast_fp16, y = var_498_cast_fp16)[name = string("rot_a_1_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = x_a_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = x_b_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor rot_b_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("rot_b_1_cast_fp16")]; + bool q_1_interleave_0 = const()[name = string("q_1_interleave_0"), val = bool(false)]; + tensor q_1_cast_fp16 = concat(axis = var_134, interleave = q_1_interleave_0, values = (rot_a_1_cast_fp16, rot_b_1_cast_fp16))[name = string("q_1_cast_fp16")]; + tensor var_505 = const()[name = string("op_505"), val = tensor([-1, 1, 1])]; + tensor var_506_cast_fp16 = reshape(shape = var_505, x = divisor_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8462016)))]; + tensor scaled_3_cast_fp16 = real_div(x = const_17_to_fp16, y = var_506_cast_fp16)[name = string("scaled_3_cast_fp16")]; + tensor angles_3_cast_fp16 = mul(x = scaled_3_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_3_cast_fp16")]; + tensor var_509_cast_fp16 = cos(x = angles_3_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor cos_3_axes_0 = const()[name = string("cos_3_axes_0"), val = tensor([1])]; + tensor cos_3_cast_fp16 = expand_dims(axes = cos_3_axes_0, x = var_509_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor var_511_cast_fp16 = sin(x = angles_3_cast_fp16)[name = string("op_511_cast_fp16")]; + tensor sin_3_axes_0 = const()[name = string("sin_3_axes_0"), val = tensor([1])]; + tensor sin_3_cast_fp16 = expand_dims(axes = sin_3_axes_0, x = var_511_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor x_a_3_begin_0 = const()[name = string("x_a_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_3_end_0 = const()[name = string("x_a_3_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_3_end_mask_0 = const()[name = string("x_a_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_39_cast_fp16 = transpose(perm = x_39_perm_0, x = var_461_cast_fp16)[name = string("transpose_104")]; + tensor x_a_3_cast_fp16 = slice_by_index(begin = x_a_3_begin_0, end = x_a_3_end_0, end_mask = x_a_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_a_3_cast_fp16")]; + tensor x_b_3_begin_0 = const()[name = string("x_b_3_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_3_end_0 = const()[name = string("x_b_3_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_3_end_mask_0 = const()[name = string("x_b_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_3_cast_fp16 = slice_by_index(begin = x_b_3_begin_0, end = x_b_3_end_0, end_mask = x_b_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_b_3_cast_fp16")]; + tensor var_515_cast_fp16 = mul(x = x_a_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_515_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = x_b_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor rot_a_3_cast_fp16 = sub(x = var_515_cast_fp16, y = var_516_cast_fp16)[name = string("rot_a_3_cast_fp16")]; + tensor var_518_cast_fp16 = mul(x = x_a_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = mul(x = x_b_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor rot_b_3_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = string("rot_b_3_cast_fp16")]; + bool k_1_interleave_0 = const()[name = string("k_1_interleave_0"), val = bool(false)]; + tensor k_1_cast_fp16 = concat(axis = var_134, interleave = k_1_interleave_0, values = (rot_a_3_cast_fp16, rot_b_3_cast_fp16))[name = string("k_1_cast_fp16")]; + bool var_524_transpose_x_1 = const()[name = string("op_524_transpose_x_1"), val = bool(false)]; + bool var_524_transpose_y_1 = const()[name = string("op_524_transpose_y_1"), val = bool(true)]; + tensor var_524_cast_fp16 = matmul(transpose_x = var_524_transpose_x_1, transpose_y = var_524_transpose_y_1, x = q_1_cast_fp16, y = k_1_cast_fp16)[name = string("op_524_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_524_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([2])]; + tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = text_mask_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528_cast_fp16 = sub(x = var_133_to_fp16, y = var_527_cast_fp16)[name = string("op_528_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_530_cast_fp16 = mul(x = var_528_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_75_cast_fp16 = sub(x = _inversed_scores_1_cast_fp16, y = var_530_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_134, x = input_75_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool var_533_transpose_x_0 = const()[name = string("op_533_transpose_x_0"), val = bool(false)]; + bool var_533_transpose_y_0 = const()[name = string("op_533_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_469_cast_fp16)[name = string("transpose_103")]; + tensor var_533_cast_fp16 = matmul(transpose_x = var_533_transpose_x_0, transpose_y = var_533_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("op_533_cast_fp16")]; + tensor var_534_perm_0 = const()[name = string("op_534_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_535 = const()[name = string("op_535"), val = tensor([2, 512, 512])]; + tensor var_534_cast_fp16 = transpose(perm = var_534_perm_0, x = var_533_cast_fp16)[name = string("transpose_102")]; + tensor input_77_cast_fp16 = reshape(shape = var_535, x = var_534_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8462592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8659264))))[name = string("main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8659456)))]; + tensor linear_6_cast_fp16 = linear(bias = main_blocks_0_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor out_11_perm_0 = const()[name = string("out_11_perm_0"), val = tensor([0, 2, 1])]; + tensor out_11_cast_fp16 = transpose(perm = out_11_perm_0, x = linear_6_cast_fp16)[name = string("transpose_101")]; + tensor var_542_cast_fp16 = mul(x = out_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_542_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = x_m_1_cast_fp16, y = var_542_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor var_549_axes_0 = const()[name = string("op_549_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8660544)))]; + tensor main_blocks_0_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8661632)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_41_cast_fp16)[name = string("transpose_100")]; + tensor var_549_cast_fp16 = layer_norm(axes = var_549_axes_0, beta = main_blocks_0_text_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_text_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor var_550_perm_0 = const()[name = string("op_550_perm_0"), val = tensor([0, 2, 1])]; + tensor var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = var_549_cast_fp16)[name = string("transpose_99")]; + tensor x_43_cast_fp16 = mul(x = var_550_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = x_45_cast_fp16, y = last_1_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor x_last_11_axes_0 = const()[name = string("x_last_11_axes_0"), val = tensor([-1])]; + bool x_last_11_keep_dims_0 = const()[name = string("x_last_11_keep_dims_0"), val = bool(true)]; + tensor x_last_11_cast_fp16 = reduce_sum(axes = x_last_11_axes_0, keep_dims = x_last_11_keep_dims_0, x = var_563_cast_fp16)[name = string("x_last_11_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = x_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor var_568_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_11_cast_fp16)[name = string("op_568_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = var_566_cast_fp16, y = var_568_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_85_mode_0 = const()[name = string("input_85_mode_0"), val = string("replicate")]; + fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)]; + tensor input_85_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(512)]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8662720))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8664704))))[name = string("main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8664896)))]; + tensor h_33_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("h_33_cast_fp16")]; + tensor x_47_cast_fp16 = mul(x = h_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8665984)))]; + tensor main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8667072)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_47_cast_fp16)[name = string("transpose_98")]; + tensor var_586_cast_fp16 = layer_norm(axes = var_586_axes_0, beta = main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_586_cast_fp16")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8668160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9454656))))[name = string("main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9454848)))]; + tensor input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = var_586_cast_fp16)[name = string("transpose_97")]; + tensor h_35_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_89_cast_fp16)[name = string("h_35_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = h_35_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")]; + tensor h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor([1])]; + tensor h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor([0, 0])]; + tensor h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor([1])]; + int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)]; + tensor op_603_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9459008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10245504))))[name = string("op_603_weight_0_to_fp16_palettized")]; + tensor var_603_bias_0_to_fp16 = const()[name = string("op_603_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10245696)))]; + tensor var_603_cast_fp16 = conv(bias = var_603_bias_0_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = op_603_weight_0_to_fp16_palettized, x = input_91_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor out_13_cast_fp16 = add(x = x_45_cast_fp16, y = var_603_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_49_cast_fp16 = mul(x = out_13_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_m_3_cast_fp16 = mul(x = x_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_3_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10246784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10345152))))[name = string("main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10345344)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_49_cast_fp16)[name = string("transpose_96")]; + tensor linear_7_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([2, 512, 2, 128])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = linear_7_cast_fp16)[name = string("op_620_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10345920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10395136))))[name = string("main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10395328)))]; + tensor linear_9_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_633 = const()[name = string("op_633"), val = tensor([2, 50, 2, 128])]; + tensor var_634_cast_fp16 = reshape(shape = var_633, x = linear_9_cast_fp16)[name = string("op_634_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_638_transpose_x_0 = const()[name = string("op_638_transpose_x_0"), val = bool(false)]; + bool var_638_transpose_y_0 = const()[name = string("op_638_transpose_y_0"), val = bool(false)]; + tensor op_637_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10395904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10415168))))[name = string("op_637_to_fp16_palettized")]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_620_cast_fp16)[name = string("transpose_95")]; + tensor var_638_cast_fp16 = matmul(transpose_x = var_638_transpose_x_0, transpose_y = var_638_transpose_y_0, x = q_3_cast_fp16, y = op_637_to_fp16_palettized)[name = string("op_638_cast_fp16")]; + fp16 _inversed_input_97_y_0_to_fp16 = const()[name = string("_inversed_input_97_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_97_cast_fp16 = mul(x = var_638_cast_fp16, y = _inversed_input_97_y_0_to_fp16)[name = string("_inversed_input_97_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_134, x = _inversed_input_97_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_642_perm_0 = const()[name = string("op_642_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_1_axes_0 = const()[name = string("mask_1_axes_0"), val = tensor([1])]; + tensor var_642_cast_fp16 = transpose(perm = var_642_perm_0, x = latent_mask_b_cast_fp16)[name = string("transpose_94")]; + tensor mask_1_cast_fp16 = expand_dims(axes = mask_1_axes_0, x = var_642_cast_fp16)[name = string("mask_1_cast_fp16")]; + tensor attn_5_cast_fp16 = mul(x = attn_3_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool var_645_transpose_x_0 = const()[name = string("op_645_transpose_x_0"), val = bool(false)]; + bool var_645_transpose_y_0 = const()[name = string("op_645_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_634_cast_fp16)[name = string("transpose_93")]; + tensor var_645_cast_fp16 = matmul(transpose_x = var_645_transpose_x_0, transpose_y = var_645_transpose_y_0, x = attn_5_cast_fp16, y = v_3_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_646_perm_0 = const()[name = string("op_646_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_647 = const()[name = string("op_647"), val = tensor([2, 512, 256])]; + tensor var_646_cast_fp16 = transpose(perm = var_646_perm_0, x = var_645_cast_fp16)[name = string("transpose_92")]; + tensor input_99_cast_fp16 = reshape(shape = var_647, x = var_646_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10415360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10513728))))[name = string("main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10513920)))]; + tensor linear_10_cast_fp16 = linear(bias = main_blocks_0_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor out_15_perm_0 = const()[name = string("out_15_perm_0"), val = tensor([0, 2, 1])]; + tensor out_15_cast_fp16 = transpose(perm = out_15_perm_0, x = linear_10_cast_fp16)[name = string("transpose_91")]; + tensor var_654_cast_fp16 = mul(x = out_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor x_51_cast_fp16 = add(x = x_m_3_cast_fp16, y = var_654_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10515008)))]; + tensor main_blocks_0_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10516096)))]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_51_cast_fp16)[name = string("transpose_90")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = main_blocks_0_style_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_style_norm_norm_weight_to_fp16, x = input_101_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_662_perm_0 = const()[name = string("op_662_perm_0"), val = tensor([0, 2, 1])]; + tensor var_662_cast_fp16 = transpose(perm = var_662_perm_0, x = var_661_cast_fp16)[name = string("transpose_89")]; + tensor x_53_cast_fp16 = mul(x = var_662_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_53_cast_fp16")]; + int32 var_681 = const()[name = string("op_681"), val = int32(-1)]; + tensor x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = x_55_cast_fp16, y = last_1_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor x_last_13_axes_0 = const()[name = string("x_last_13_axes_0"), val = tensor([-1])]; + bool x_last_13_keep_dims_0 = const()[name = string("x_last_13_keep_dims_0"), val = bool(true)]; + tensor x_last_13_cast_fp16 = reduce_sum(axes = x_last_13_axes_0, keep_dims = x_last_13_keep_dims_0, x = var_716_cast_fp16)[name = string("x_last_13_cast_fp16")]; + tensor var_719_cast_fp16 = mul(x = x_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_721_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_13_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_719_cast_fp16, y = var_721_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("replicate")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(512)]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10517184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10519168))))[name = string("main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10519360)))]; + tensor h_39_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor x_57_cast_fp16 = mul(x = h_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_739_axes_0 = const()[name = string("op_739_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10520448)))]; + tensor main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10521536)))]; + fp16 var_686_to_fp16 = const()[name = string("op_686_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_57_cast_fp16)[name = string("transpose_88")]; + tensor var_739_cast_fp16 = layer_norm(axes = var_739_axes_0, beta = main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_739_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + string h_41_pad_type_0 = const()[name = string("h_41_pad_type_0"), val = string("valid")]; + tensor h_41_strides_0 = const()[name = string("h_41_strides_0"), val = tensor([1])]; + tensor h_41_pad_0 = const()[name = string("h_41_pad_0"), val = tensor([0, 0])]; + tensor h_41_dilations_0 = const()[name = string("h_41_dilations_0"), val = tensor([1])]; + int32 h_41_groups_0 = const()[name = string("h_41_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10522624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11309120))))[name = string("main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11309312)))]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = var_739_cast_fp16)[name = string("transpose_87")]; + tensor h_41_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_41_dilations_0, groups = h_41_groups_0, pad = h_41_pad_0, pad_type = h_41_pad_type_0, strides = h_41_strides_0, weight = main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = h_41_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor op_756_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11313472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12099968))))[name = string("op_756_weight_0_to_fp16_palettized")]; + tensor var_756_bias_0_to_fp16 = const()[name = string("op_756_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12100160)))]; + tensor var_756_cast_fp16 = conv(bias = var_756_bias_0_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = op_756_weight_0_to_fp16_palettized, x = input_113_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor out_17_cast_fp16 = add(x = x_55_cast_fp16, y = var_756_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_59_cast_fp16 = mul(x = out_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor x_61_cast_fp16 = mul(x = x_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_770_cast_fp16 = mul(x = x_61_cast_fp16, y = last_1_cast_fp16)[name = string("op_770_cast_fp16")]; + tensor x_last_15_axes_0 = const()[name = string("x_last_15_axes_0"), val = tensor([-1])]; + bool x_last_15_keep_dims_0 = const()[name = string("x_last_15_keep_dims_0"), val = bool(true)]; + tensor x_last_15_cast_fp16 = reduce_sum(axes = x_last_15_axes_0, keep_dims = x_last_15_keep_dims_0, x = var_770_cast_fp16)[name = string("x_last_15_cast_fp16")]; + tensor var_773_cast_fp16 = mul(x = x_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor var_775_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_15_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = var_773_cast_fp16, y = var_775_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("replicate")]; + fp16 const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = fp16(0x0p+0)]; + tensor input_119_cast_fp16 = pad(constant_val = const_26_to_fp16, mode = input_119_mode_0, pad = input_119_pad_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")]; + string h_45_pad_type_0 = const()[name = string("h_45_pad_type_0"), val = string("valid")]; + tensor h_45_dilations_0 = const()[name = string("h_45_dilations_0"), val = tensor([2])]; + int32 h_45_groups_0 = const()[name = string("h_45_groups_0"), val = int32(512)]; + tensor h_45_strides_0 = const()[name = string("h_45_strides_0"), val = tensor([1])]; + tensor h_45_pad_0 = const()[name = string("h_45_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12101248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12103232))))[name = string("main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12103424)))]; + tensor h_45_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_45_dilations_0, groups = h_45_groups_0, pad = h_45_pad_0, pad_type = h_45_pad_type_0, strides = h_45_strides_0, weight = main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = h_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12104512)))]; + tensor main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12105600)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_63_cast_fp16)[name = string("transpose_86")]; + tensor var_793_cast_fp16 = layer_norm(axes = var_793_axes_0, beta = main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor input_123_perm_0 = const()[name = string("input_123_perm_0"), val = tensor([0, 2, 1])]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12106688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12893184))))[name = string("main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12893376)))]; + tensor input_123_cast_fp16 = transpose(perm = input_123_perm_0, x = var_793_cast_fp16)[name = string("transpose_85")]; + tensor h_47_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = string("h_47_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = h_47_cast_fp16)[name = string("input_125_cast_fp16")]; + string h_49_pad_type_0 = const()[name = string("h_49_pad_type_0"), val = string("valid")]; + tensor h_49_strides_0 = const()[name = string("h_49_strides_0"), val = tensor([1])]; + tensor h_49_pad_0 = const()[name = string("h_49_pad_0"), val = tensor([0, 0])]; + tensor h_49_dilations_0 = const()[name = string("h_49_dilations_0"), val = tensor([1])]; + int32 h_49_groups_0 = const()[name = string("h_49_groups_0"), val = int32(1)]; + tensor op_810_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12897536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13684032))))[name = string("op_810_weight_0_to_fp16_palettized")]; + tensor var_810_bias_0_to_fp16 = const()[name = string("op_810_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13684224)))]; + tensor var_810_cast_fp16 = conv(bias = var_810_bias_0_to_fp16, dilations = h_49_dilations_0, groups = h_49_groups_0, pad = h_49_pad_0, pad_type = h_49_pad_type_0, strides = h_49_strides_0, weight = op_810_weight_0_to_fp16_palettized, x = input_125_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = x_61_cast_fp16, y = var_810_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor x_65_cast_fp16 = mul(x = out_19_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = x_67_cast_fp16, y = last_1_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_last_17_axes_0 = const()[name = string("x_last_17_axes_0"), val = tensor([-1])]; + bool x_last_17_keep_dims_0 = const()[name = string("x_last_17_keep_dims_0"), val = bool(true)]; + tensor x_last_17_cast_fp16 = reduce_sum(axes = x_last_17_axes_0, keep_dims = x_last_17_keep_dims_0, x = var_824_cast_fp16)[name = string("x_last_17_cast_fp16")]; + tensor var_827_cast_fp16 = mul(x = x_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_829_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_17_cast_fp16)[name = string("op_829_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = var_827_cast_fp16, y = var_829_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("replicate")]; + fp16 const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = fp16(0x0p+0)]; + tensor input_131_cast_fp16 = pad(constant_val = const_28_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + string h_51_pad_type_0 = const()[name = string("h_51_pad_type_0"), val = string("valid")]; + tensor h_51_dilations_0 = const()[name = string("h_51_dilations_0"), val = tensor([4])]; + int32 h_51_groups_0 = const()[name = string("h_51_groups_0"), val = int32(512)]; + tensor h_51_strides_0 = const()[name = string("h_51_strides_0"), val = tensor([1])]; + tensor h_51_pad_0 = const()[name = string("h_51_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13685312))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13687296))))[name = string("main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13687488)))]; + tensor h_51_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_51_dilations_0, groups = h_51_groups_0, pad = h_51_pad_0, pad_type = h_51_pad_type_0, strides = h_51_strides_0, weight = main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor x_69_cast_fp16 = mul(x = h_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_847_axes_0 = const()[name = string("op_847_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13688576)))]; + tensor main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13689664)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_69_cast_fp16)[name = string("transpose_84")]; + tensor var_847_cast_fp16 = layer_norm(axes = var_847_axes_0, beta = main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_847_cast_fp16")]; + tensor input_135_perm_0 = const()[name = string("input_135_perm_0"), val = tensor([0, 2, 1])]; + string h_53_pad_type_0 = const()[name = string("h_53_pad_type_0"), val = string("valid")]; + tensor h_53_strides_0 = const()[name = string("h_53_strides_0"), val = tensor([1])]; + tensor h_53_pad_0 = const()[name = string("h_53_pad_0"), val = tensor([0, 0])]; + tensor h_53_dilations_0 = const()[name = string("h_53_dilations_0"), val = tensor([1])]; + int32 h_53_groups_0 = const()[name = string("h_53_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13690752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14477248))))[name = string("main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14477440)))]; + tensor input_135_cast_fp16 = transpose(perm = input_135_perm_0, x = var_847_cast_fp16)[name = string("transpose_83")]; + tensor h_53_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_53_dilations_0, groups = h_53_groups_0, pad = h_53_pad_0, pad_type = h_53_pad_type_0, strides = h_53_strides_0, weight = main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = string("h_53_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = h_53_cast_fp16)[name = string("input_137_cast_fp16")]; + string h_55_pad_type_0 = const()[name = string("h_55_pad_type_0"), val = string("valid")]; + tensor h_55_strides_0 = const()[name = string("h_55_strides_0"), val = tensor([1])]; + tensor h_55_pad_0 = const()[name = string("h_55_pad_0"), val = tensor([0, 0])]; + tensor h_55_dilations_0 = const()[name = string("h_55_dilations_0"), val = tensor([1])]; + int32 h_55_groups_0 = const()[name = string("h_55_groups_0"), val = int32(1)]; + tensor op_864_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14481600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15268096))))[name = string("op_864_weight_0_to_fp16_palettized")]; + tensor var_864_bias_0_to_fp16 = const()[name = string("op_864_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15268288)))]; + tensor var_864_cast_fp16 = conv(bias = var_864_bias_0_to_fp16, dilations = h_55_dilations_0, groups = h_55_groups_0, pad = h_55_pad_0, pad_type = h_55_pad_type_0, strides = h_55_strides_0, weight = op_864_weight_0_to_fp16_palettized, x = input_137_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor out_21_cast_fp16 = add(x = x_67_cast_fp16, y = var_864_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = out_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor x_73_cast_fp16 = mul(x = x_71_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_878_cast_fp16 = mul(x = x_73_cast_fp16, y = last_1_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor x_last_19_axes_0 = const()[name = string("x_last_19_axes_0"), val = tensor([-1])]; + bool x_last_19_keep_dims_0 = const()[name = string("x_last_19_keep_dims_0"), val = bool(true)]; + tensor x_last_19_cast_fp16 = reduce_sum(axes = x_last_19_axes_0, keep_dims = x_last_19_keep_dims_0, x = var_878_cast_fp16)[name = string("x_last_19_cast_fp16")]; + tensor var_881_cast_fp16 = mul(x = x_73_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_881_cast_fp16")]; + tensor var_883_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_19_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = var_881_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("replicate")]; + fp16 const_30_to_fp16 = const()[name = string("const_30_to_fp16"), val = fp16(0x0p+0)]; + tensor input_143_cast_fp16 = pad(constant_val = const_30_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + string h_57_pad_type_0 = const()[name = string("h_57_pad_type_0"), val = string("valid")]; + tensor h_57_dilations_0 = const()[name = string("h_57_dilations_0"), val = tensor([8])]; + int32 h_57_groups_0 = const()[name = string("h_57_groups_0"), val = int32(512)]; + tensor h_57_strides_0 = const()[name = string("h_57_strides_0"), val = tensor([1])]; + tensor h_57_pad_0 = const()[name = string("h_57_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15269376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15271360))))[name = string("main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15271552)))]; + tensor h_57_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_57_dilations_0, groups = h_57_groups_0, pad = h_57_pad_0, pad_type = h_57_pad_type_0, strides = h_57_strides_0, weight = main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = string("h_57_cast_fp16")]; + tensor x_75_cast_fp16 = mul(x = h_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_901_axes_0 = const()[name = string("op_901_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15272640)))]; + tensor main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15273728)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_75_cast_fp16)[name = string("transpose_82")]; + tensor var_901_cast_fp16 = layer_norm(axes = var_901_axes_0, beta = main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor input_147_perm_0 = const()[name = string("input_147_perm_0"), val = tensor([0, 2, 1])]; + string h_59_pad_type_0 = const()[name = string("h_59_pad_type_0"), val = string("valid")]; + tensor h_59_strides_0 = const()[name = string("h_59_strides_0"), val = tensor([1])]; + tensor h_59_pad_0 = const()[name = string("h_59_pad_0"), val = tensor([0, 0])]; + tensor h_59_dilations_0 = const()[name = string("h_59_dilations_0"), val = tensor([1])]; + int32 h_59_groups_0 = const()[name = string("h_59_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15274816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16061312))))[name = string("main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16061504)))]; + tensor input_147_cast_fp16 = transpose(perm = input_147_perm_0, x = var_901_cast_fp16)[name = string("transpose_81")]; + tensor h_59_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_59_dilations_0, groups = h_59_groups_0, pad = h_59_pad_0, pad_type = h_59_pad_type_0, strides = h_59_strides_0, weight = main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("h_59_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = h_59_cast_fp16)[name = string("input_149_cast_fp16")]; + string h_61_pad_type_0 = const()[name = string("h_61_pad_type_0"), val = string("valid")]; + tensor h_61_strides_0 = const()[name = string("h_61_strides_0"), val = tensor([1])]; + tensor h_61_pad_0 = const()[name = string("h_61_pad_0"), val = tensor([0, 0])]; + tensor h_61_dilations_0 = const()[name = string("h_61_dilations_0"), val = tensor([1])]; + int32 h_61_groups_0 = const()[name = string("h_61_groups_0"), val = int32(1)]; + tensor op_918_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16065664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16852160))))[name = string("op_918_weight_0_to_fp16_palettized")]; + tensor var_918_bias_0_to_fp16 = const()[name = string("op_918_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16852352)))]; + tensor var_918_cast_fp16 = conv(bias = var_918_bias_0_to_fp16, dilations = h_61_dilations_0, groups = h_61_groups_0, pad = h_61_pad_0, pad_type = h_61_pad_type_0, strides = h_61_strides_0, weight = op_918_weight_0_to_fp16_palettized, x = input_149_cast_fp16)[name = string("op_918_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = x_73_cast_fp16, y = var_918_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor x_77_cast_fp16 = mul(x = out_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16853440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16878080))))[name = string("main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16878272)))]; + tensor linear_11_cast_fp16 = linear(bias = main_blocks_1_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor t_7_axes_0 = const()[name = string("t_7_axes_0"), val = tensor([-1])]; + tensor t_7_cast_fp16 = expand_dims(axes = t_7_axes_0, x = linear_11_cast_fp16)[name = string("t_7_cast_fp16")]; + tensor var_928_cast_fp16 = add(x = x_77_cast_fp16, y = t_7_cast_fp16)[name = string("op_928_cast_fp16")]; + tensor x_79_cast_fp16 = mul(x = var_928_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor x_81_cast_fp16 = mul(x = x_79_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = x_81_cast_fp16, y = last_1_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor x_last_21_axes_0 = const()[name = string("x_last_21_axes_0"), val = tensor([-1])]; + bool x_last_21_keep_dims_0 = const()[name = string("x_last_21_keep_dims_0"), val = bool(true)]; + tensor x_last_21_cast_fp16 = reduce_sum(axes = x_last_21_axes_0, keep_dims = x_last_21_keep_dims_0, x = var_941_cast_fp16)[name = string("x_last_21_cast_fp16")]; + tensor var_944_cast_fp16 = mul(x = x_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor var_946_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_21_cast_fp16)[name = string("op_946_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = var_944_cast_fp16, y = var_946_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("replicate")]; + fp16 const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = fp16(0x0p+0)]; + tensor input_157_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string h_63_pad_type_0 = const()[name = string("h_63_pad_type_0"), val = string("valid")]; + int32 h_63_groups_0 = const()[name = string("h_63_groups_0"), val = int32(512)]; + tensor h_63_strides_0 = const()[name = string("h_63_strides_0"), val = tensor([1])]; + tensor h_63_pad_0 = const()[name = string("h_63_pad_0"), val = tensor([0, 0])]; + tensor h_63_dilations_0 = const()[name = string("h_63_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16879360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16881344))))[name = string("main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16881536)))]; + tensor h_63_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_63_dilations_0, groups = h_63_groups_0, pad = h_63_pad_0, pad_type = h_63_pad_type_0, strides = h_63_strides_0, weight = main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor x_83_cast_fp16 = mul(x = h_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor var_964_axes_0 = const()[name = string("op_964_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16882624)))]; + tensor main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16883712)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_83_cast_fp16)[name = string("transpose_80")]; + tensor var_964_cast_fp16 = layer_norm(axes = var_964_axes_0, beta = main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + string h_65_pad_type_0 = const()[name = string("h_65_pad_type_0"), val = string("valid")]; + tensor h_65_strides_0 = const()[name = string("h_65_strides_0"), val = tensor([1])]; + tensor h_65_pad_0 = const()[name = string("h_65_pad_0"), val = tensor([0, 0])]; + tensor h_65_dilations_0 = const()[name = string("h_65_dilations_0"), val = tensor([1])]; + int32 h_65_groups_0 = const()[name = string("h_65_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16884800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17671296))))[name = string("main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17671488)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = var_964_cast_fp16)[name = string("transpose_79")]; + tensor h_65_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_65_dilations_0, groups = h_65_groups_0, pad = h_65_pad_0, pad_type = h_65_pad_type_0, strides = h_65_strides_0, weight = main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = string("h_65_cast_fp16")]; + string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("EXACT")]; + tensor input_163_cast_fp16 = gelu(mode = input_163_mode_0, x = h_65_cast_fp16)[name = string("input_163_cast_fp16")]; + string h_67_pad_type_0 = const()[name = string("h_67_pad_type_0"), val = string("valid")]; + tensor h_67_strides_0 = const()[name = string("h_67_strides_0"), val = tensor([1])]; + tensor h_67_pad_0 = const()[name = string("h_67_pad_0"), val = tensor([0, 0])]; + tensor h_67_dilations_0 = const()[name = string("h_67_dilations_0"), val = tensor([1])]; + int32 h_67_groups_0 = const()[name = string("h_67_groups_0"), val = int32(1)]; + tensor op_981_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17675648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18462144))))[name = string("op_981_weight_0_to_fp16_palettized")]; + tensor var_981_bias_0_to_fp16 = const()[name = string("op_981_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18462336)))]; + tensor var_981_cast_fp16 = conv(bias = var_981_bias_0_to_fp16, dilations = h_67_dilations_0, groups = h_67_groups_0, pad = h_67_pad_0, pad_type = h_67_pad_type_0, strides = h_67_strides_0, weight = op_981_weight_0_to_fp16_palettized, x = input_163_cast_fp16)[name = string("op_981_cast_fp16")]; + tensor out_25_cast_fp16 = add(x = x_81_cast_fp16, y = var_981_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = out_25_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor x_m_5_cast_fp16 = mul(x = x_85_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_5_cast_fp16")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18463424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18660096))))[name = string("main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18660288)))]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_85_cast_fp16)[name = string("transpose_78")]; + tensor linear_12_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_999 = const()[name = string("op_999"), val = tensor([2, 512, 8, 64])]; + tensor var_1000_cast_fp16 = reshape(shape = var_999, x = linear_12_cast_fp16)[name = string("op_1000_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18661376))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18759744))))[name = string("main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18759936)))]; + tensor linear_13_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_1007 = const()[name = string("op_1007"), val = tensor([2, 128, 8, 64])]; + tensor var_1008_cast_fp16 = reshape(shape = var_1007, x = linear_13_cast_fp16)[name = string("op_1008_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18761024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18859392))))[name = string("main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18859584)))]; + tensor linear_14_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_1015 = const()[name = string("op_1015"), val = tensor([2, 128, 8, 64])]; + tensor var_1016_cast_fp16 = reshape(shape = var_1015, x = linear_14_cast_fp16)[name = string("op_1016_cast_fp16")]; + tensor v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_5_begin_0 = const()[name = string("x_a_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_5_end_0 = const()[name = string("x_a_5_end_0"), val = tensor([2, 8, 512, 32])]; + tensor x_a_5_end_mask_0 = const()[name = string("x_a_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_1000_cast_fp16)[name = string("transpose_77")]; + tensor x_a_5_cast_fp16 = slice_by_index(begin = x_a_5_begin_0, end = x_a_5_end_0, end_mask = x_a_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_a_5_cast_fp16")]; + tensor x_b_5_begin_0 = const()[name = string("x_b_5_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_5_end_0 = const()[name = string("x_b_5_end_0"), val = tensor([2, 8, 512, 64])]; + tensor x_b_5_end_mask_0 = const()[name = string("x_b_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_5_cast_fp16 = slice_by_index(begin = x_b_5_begin_0, end = x_b_5_end_0, end_mask = x_b_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_b_5_cast_fp16")]; + tensor var_1044_cast_fp16 = mul(x = x_a_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1044_cast_fp16")]; + tensor var_1045_cast_fp16 = mul(x = x_b_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor rot_a_5_cast_fp16 = sub(x = var_1044_cast_fp16, y = var_1045_cast_fp16)[name = string("rot_a_5_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_a_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_cast_fp16 = mul(x = x_b_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1048_cast_fp16")]; + tensor rot_b_5_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("rot_b_5_cast_fp16")]; + bool q_5_interleave_0 = const()[name = string("q_5_interleave_0"), val = bool(false)]; + tensor q_5_cast_fp16 = concat(axis = var_681, interleave = q_5_interleave_0, values = (rot_a_5_cast_fp16, rot_b_5_cast_fp16))[name = string("q_5_cast_fp16")]; + tensor x_a_7_begin_0 = const()[name = string("x_a_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_7_end_0 = const()[name = string("x_a_7_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_7_end_mask_0 = const()[name = string("x_a_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_1008_cast_fp16)[name = string("transpose_76")]; + tensor x_a_7_cast_fp16 = slice_by_index(begin = x_a_7_begin_0, end = x_a_7_end_0, end_mask = x_a_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_a_7_cast_fp16")]; + tensor x_b_7_begin_0 = const()[name = string("x_b_7_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_7_end_0 = const()[name = string("x_b_7_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_7_end_mask_0 = const()[name = string("x_b_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_7_cast_fp16 = slice_by_index(begin = x_b_7_begin_0, end = x_b_7_end_0, end_mask = x_b_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_b_7_cast_fp16")]; + tensor var_1062_cast_fp16 = mul(x = x_a_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = x_b_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor rot_a_7_cast_fp16 = sub(x = var_1062_cast_fp16, y = var_1063_cast_fp16)[name = string("rot_a_7_cast_fp16")]; + tensor var_1065_cast_fp16 = mul(x = x_a_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1065_cast_fp16")]; + tensor var_1066_cast_fp16 = mul(x = x_b_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1066_cast_fp16")]; + tensor rot_b_7_cast_fp16 = add(x = var_1065_cast_fp16, y = var_1066_cast_fp16)[name = string("rot_b_7_cast_fp16")]; + bool k_5_interleave_0 = const()[name = string("k_5_interleave_0"), val = bool(false)]; + tensor k_5_cast_fp16 = concat(axis = var_681, interleave = k_5_interleave_0, values = (rot_a_7_cast_fp16, rot_b_7_cast_fp16))[name = string("k_5_cast_fp16")]; + bool var_1071_transpose_x_1 = const()[name = string("op_1071_transpose_x_1"), val = bool(false)]; + bool var_1071_transpose_y_1 = const()[name = string("op_1071_transpose_y_1"), val = bool(true)]; + tensor var_1071_cast_fp16 = matmul(transpose_x = var_1071_transpose_x_1, transpose_y = var_1071_transpose_y_1, x = q_5_cast_fp16, y = k_5_cast_fp16)[name = string("op_1071_cast_fp16")]; + fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_3_cast_fp16 = mul(x = var_1071_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")]; + tensor input_171_cast_fp16 = sub(x = _inversed_scores_3_cast_fp16, y = var_530_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_681, x = input_171_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool var_1080_transpose_x_0 = const()[name = string("op_1080_transpose_x_0"), val = bool(false)]; + bool var_1080_transpose_y_0 = const()[name = string("op_1080_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1016_cast_fp16)[name = string("transpose_75")]; + tensor var_1080_cast_fp16 = matmul(transpose_x = var_1080_transpose_x_0, transpose_y = var_1080_transpose_y_0, x = attn_7_cast_fp16, y = v_5_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_perm_0 = const()[name = string("op_1081_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1082 = const()[name = string("op_1082"), val = tensor([2, 512, 512])]; + tensor var_1081_cast_fp16 = transpose(perm = var_1081_perm_0, x = var_1080_cast_fp16)[name = string("transpose_74")]; + tensor input_173_cast_fp16 = reshape(shape = var_1082, x = var_1081_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18860672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19057344))))[name = string("main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19057536)))]; + tensor linear_15_cast_fp16 = linear(bias = main_blocks_1_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor out_27_perm_0 = const()[name = string("out_27_perm_0"), val = tensor([0, 2, 1])]; + tensor out_27_cast_fp16 = transpose(perm = out_27_perm_0, x = linear_15_cast_fp16)[name = string("transpose_73")]; + tensor var_1089_cast_fp16 = mul(x = out_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor x_91_cast_fp16 = add(x = x_m_5_cast_fp16, y = var_1089_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_175_perm_0 = const()[name = string("input_175_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1096_axes_0 = const()[name = string("op_1096_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19058624)))]; + tensor main_blocks_1_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19059712)))]; + tensor input_175_cast_fp16 = transpose(perm = input_175_perm_0, x = x_91_cast_fp16)[name = string("transpose_72")]; + tensor var_1096_cast_fp16 = layer_norm(axes = var_1096_axes_0, beta = main_blocks_1_text_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_text_norm_norm_weight_to_fp16, x = input_175_cast_fp16)[name = string("op_1096_cast_fp16")]; + tensor var_1097_perm_0 = const()[name = string("op_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1097_cast_fp16 = transpose(perm = var_1097_perm_0, x = var_1096_cast_fp16)[name = string("transpose_71")]; + tensor x_93_cast_fp16 = mul(x = var_1097_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor x_95_cast_fp16 = mul(x = x_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_1110_cast_fp16 = mul(x = x_95_cast_fp16, y = last_1_cast_fp16)[name = string("op_1110_cast_fp16")]; + tensor x_last_23_axes_0 = const()[name = string("x_last_23_axes_0"), val = tensor([-1])]; + bool x_last_23_keep_dims_0 = const()[name = string("x_last_23_keep_dims_0"), val = bool(true)]; + tensor x_last_23_cast_fp16 = reduce_sum(axes = x_last_23_axes_0, keep_dims = x_last_23_keep_dims_0, x = var_1110_cast_fp16)[name = string("x_last_23_cast_fp16")]; + tensor var_1113_cast_fp16 = mul(x = x_95_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1115_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_23_cast_fp16)[name = string("op_1115_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = var_1113_cast_fp16, y = var_1115_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("replicate")]; + fp16 const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = fp16(0x0p+0)]; + tensor input_181_cast_fp16 = pad(constant_val = const_39_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + string h_69_pad_type_0 = const()[name = string("h_69_pad_type_0"), val = string("valid")]; + int32 h_69_groups_0 = const()[name = string("h_69_groups_0"), val = int32(512)]; + tensor h_69_strides_0 = const()[name = string("h_69_strides_0"), val = tensor([1])]; + tensor h_69_pad_0 = const()[name = string("h_69_pad_0"), val = tensor([0, 0])]; + tensor h_69_dilations_0 = const()[name = string("h_69_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19060800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19062784))))[name = string("main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19062976)))]; + tensor h_69_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_69_dilations_0, groups = h_69_groups_0, pad = h_69_pad_0, pad_type = h_69_pad_type_0, strides = h_69_strides_0, weight = main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = string("h_69_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = h_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_183_perm_0 = const()[name = string("input_183_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1133_axes_0 = const()[name = string("op_1133_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19064064)))]; + tensor main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065152)))]; + tensor input_183_cast_fp16 = transpose(perm = input_183_perm_0, x = x_97_cast_fp16)[name = string("transpose_70")]; + tensor var_1133_cast_fp16 = layer_norm(axes = var_1133_axes_0, beta = main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16, x = input_183_cast_fp16)[name = string("op_1133_cast_fp16")]; + tensor input_185_perm_0 = const()[name = string("input_185_perm_0"), val = tensor([0, 2, 1])]; + string h_71_pad_type_0 = const()[name = string("h_71_pad_type_0"), val = string("valid")]; + tensor h_71_strides_0 = const()[name = string("h_71_strides_0"), val = tensor([1])]; + tensor h_71_pad_0 = const()[name = string("h_71_pad_0"), val = tensor([0, 0])]; + tensor h_71_dilations_0 = const()[name = string("h_71_dilations_0"), val = tensor([1])]; + int32 h_71_groups_0 = const()[name = string("h_71_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19066240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19852736))))[name = string("main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19852928)))]; + tensor input_185_cast_fp16 = transpose(perm = input_185_perm_0, x = var_1133_cast_fp16)[name = string("transpose_69")]; + tensor h_71_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_71_dilations_0, groups = h_71_groups_0, pad = h_71_pad_0, pad_type = h_71_pad_type_0, strides = h_71_strides_0, weight = main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = string("h_71_cast_fp16")]; + string input_187_mode_0 = const()[name = string("input_187_mode_0"), val = string("EXACT")]; + tensor input_187_cast_fp16 = gelu(mode = input_187_mode_0, x = h_71_cast_fp16)[name = string("input_187_cast_fp16")]; + string h_73_pad_type_0 = const()[name = string("h_73_pad_type_0"), val = string("valid")]; + tensor h_73_strides_0 = const()[name = string("h_73_strides_0"), val = tensor([1])]; + tensor h_73_pad_0 = const()[name = string("h_73_pad_0"), val = tensor([0, 0])]; + tensor h_73_dilations_0 = const()[name = string("h_73_dilations_0"), val = tensor([1])]; + int32 h_73_groups_0 = const()[name = string("h_73_groups_0"), val = int32(1)]; + tensor op_1150_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19857088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20643584))))[name = string("op_1150_weight_0_to_fp16_palettized")]; + tensor var_1150_bias_0_to_fp16 = const()[name = string("op_1150_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20643776)))]; + tensor var_1150_cast_fp16 = conv(bias = var_1150_bias_0_to_fp16, dilations = h_73_dilations_0, groups = h_73_groups_0, pad = h_73_pad_0, pad_type = h_73_pad_type_0, strides = h_73_strides_0, weight = op_1150_weight_0_to_fp16_palettized, x = input_187_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor out_29_cast_fp16 = add(x = x_95_cast_fp16, y = var_1150_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor x_99_cast_fp16 = mul(x = out_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor x_m_7_cast_fp16 = mul(x = x_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_7_cast_fp16")]; + tensor input_189_perm_0 = const()[name = string("input_189_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20644864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20743232))))[name = string("main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20743424)))]; + tensor input_189_cast_fp16 = transpose(perm = input_189_perm_0, x = x_99_cast_fp16)[name = string("transpose_68")]; + tensor linear_16_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_1166 = const()[name = string("op_1166"), val = tensor([2, 512, 2, 128])]; + tensor var_1167_cast_fp16 = reshape(shape = var_1166, x = linear_16_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20744000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20793216))))[name = string("main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20793408)))]; + tensor linear_18_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_1180 = const()[name = string("op_1180"), val = tensor([2, 50, 2, 128])]; + tensor var_1181_cast_fp16 = reshape(shape = var_1180, x = linear_18_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1185_transpose_x_0 = const()[name = string("op_1185_transpose_x_0"), val = bool(false)]; + bool var_1185_transpose_y_0 = const()[name = string("op_1185_transpose_y_0"), val = bool(false)]; + tensor op_1184_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20793984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20813248))))[name = string("op_1184_to_fp16_palettized")]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1167_cast_fp16)[name = string("transpose_67")]; + tensor var_1185_cast_fp16 = matmul(transpose_x = var_1185_transpose_x_0, transpose_y = var_1185_transpose_y_0, x = q_7_cast_fp16, y = op_1184_to_fp16_palettized)[name = string("op_1185_cast_fp16")]; + fp16 _inversed_input_191_y_0_to_fp16 = const()[name = string("_inversed_input_191_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_191_cast_fp16 = mul(x = var_1185_cast_fp16, y = _inversed_input_191_y_0_to_fp16)[name = string("_inversed_input_191_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_681, x = _inversed_input_191_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor attn_11_cast_fp16 = mul(x = attn_9_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool var_1192_transpose_x_0 = const()[name = string("op_1192_transpose_x_0"), val = bool(false)]; + bool var_1192_transpose_y_0 = const()[name = string("op_1192_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1181_cast_fp16)[name = string("transpose_66")]; + tensor var_1192_cast_fp16 = matmul(transpose_x = var_1192_transpose_x_0, transpose_y = var_1192_transpose_y_0, x = attn_11_cast_fp16, y = v_7_cast_fp16)[name = string("op_1192_cast_fp16")]; + tensor var_1193_perm_0 = const()[name = string("op_1193_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1194 = const()[name = string("op_1194"), val = tensor([2, 512, 256])]; + tensor var_1193_cast_fp16 = transpose(perm = var_1193_perm_0, x = var_1192_cast_fp16)[name = string("transpose_65")]; + tensor input_193_cast_fp16 = reshape(shape = var_1194, x = var_1193_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20813440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20911808))))[name = string("main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20912000)))]; + tensor linear_19_cast_fp16 = linear(bias = main_blocks_1_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_193_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor out_31_perm_0 = const()[name = string("out_31_perm_0"), val = tensor([0, 2, 1])]; + tensor out_31_cast_fp16 = transpose(perm = out_31_perm_0, x = linear_19_cast_fp16)[name = string("transpose_64")]; + tensor var_1201_cast_fp16 = mul(x = out_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1201_cast_fp16")]; + tensor x_101_cast_fp16 = add(x = x_m_7_cast_fp16, y = var_1201_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_195_perm_0 = const()[name = string("input_195_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1208_axes_0 = const()[name = string("op_1208_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20913088)))]; + tensor main_blocks_1_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20914176)))]; + tensor input_195_cast_fp16 = transpose(perm = input_195_perm_0, x = x_101_cast_fp16)[name = string("transpose_63")]; + tensor var_1208_cast_fp16 = layer_norm(axes = var_1208_axes_0, beta = main_blocks_1_style_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_style_norm_norm_weight_to_fp16, x = input_195_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_perm_0 = const()[name = string("op_1209_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1209_cast_fp16 = transpose(perm = var_1209_perm_0, x = var_1208_cast_fp16)[name = string("transpose_62")]; + tensor x_103_cast_fp16 = mul(x = var_1209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_103_cast_fp16")]; + int32 var_1228 = const()[name = string("op_1228"), val = int32(-1)]; + tensor x_105_cast_fp16 = mul(x = x_103_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_1263_cast_fp16 = mul(x = x_105_cast_fp16, y = last_1_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor x_last_25_axes_0 = const()[name = string("x_last_25_axes_0"), val = tensor([-1])]; + bool x_last_25_keep_dims_0 = const()[name = string("x_last_25_keep_dims_0"), val = bool(true)]; + tensor x_last_25_cast_fp16 = reduce_sum(axes = x_last_25_axes_0, keep_dims = x_last_25_keep_dims_0, x = var_1263_cast_fp16)[name = string("x_last_25_cast_fp16")]; + tensor var_1266_cast_fp16 = mul(x = x_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1266_cast_fp16")]; + tensor var_1268_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_25_cast_fp16)[name = string("op_1268_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1266_cast_fp16, y = var_1268_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_201_mode_0 = const()[name = string("input_201_mode_0"), val = string("replicate")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_201_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")]; + string h_75_pad_type_0 = const()[name = string("h_75_pad_type_0"), val = string("valid")]; + int32 h_75_groups_0 = const()[name = string("h_75_groups_0"), val = int32(512)]; + tensor h_75_strides_0 = const()[name = string("h_75_strides_0"), val = tensor([1])]; + tensor h_75_pad_0 = const()[name = string("h_75_pad_0"), val = tensor([0, 0])]; + tensor h_75_dilations_0 = const()[name = string("h_75_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20915264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20917248))))[name = string("main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20917440)))]; + tensor h_75_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_75_dilations_0, groups = h_75_groups_0, pad = h_75_pad_0, pad_type = h_75_pad_type_0, strides = h_75_strides_0, weight = main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_201_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = h_75_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_203_perm_0 = const()[name = string("input_203_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1286_axes_0 = const()[name = string("op_1286_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20918528)))]; + tensor main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20919616)))]; + fp16 var_1233_to_fp16 = const()[name = string("op_1233_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_203_cast_fp16 = transpose(perm = input_203_perm_0, x = x_107_cast_fp16)[name = string("transpose_61")]; + tensor var_1286_cast_fp16 = layer_norm(axes = var_1286_axes_0, beta = main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16, x = input_203_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string h_77_pad_type_0 = const()[name = string("h_77_pad_type_0"), val = string("valid")]; + tensor h_77_strides_0 = const()[name = string("h_77_strides_0"), val = tensor([1])]; + tensor h_77_pad_0 = const()[name = string("h_77_pad_0"), val = tensor([0, 0])]; + tensor h_77_dilations_0 = const()[name = string("h_77_dilations_0"), val = tensor([1])]; + int32 h_77_groups_0 = const()[name = string("h_77_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20920704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21707200))))[name = string("main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21707392)))]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = var_1286_cast_fp16)[name = string("transpose_60")]; + tensor h_77_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_77_dilations_0, groups = h_77_groups_0, pad = h_77_pad_0, pad_type = h_77_pad_type_0, strides = h_77_strides_0, weight = main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_205_cast_fp16)[name = string("h_77_cast_fp16")]; + string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")]; + tensor input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = h_77_cast_fp16)[name = string("input_207_cast_fp16")]; + string h_79_pad_type_0 = const()[name = string("h_79_pad_type_0"), val = string("valid")]; + tensor h_79_strides_0 = const()[name = string("h_79_strides_0"), val = tensor([1])]; + tensor h_79_pad_0 = const()[name = string("h_79_pad_0"), val = tensor([0, 0])]; + tensor h_79_dilations_0 = const()[name = string("h_79_dilations_0"), val = tensor([1])]; + int32 h_79_groups_0 = const()[name = string("h_79_groups_0"), val = int32(1)]; + tensor op_1303_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21711552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22498048))))[name = string("op_1303_weight_0_to_fp16_palettized")]; + tensor var_1303_bias_0_to_fp16 = const()[name = string("op_1303_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22498240)))]; + tensor var_1303_cast_fp16 = conv(bias = var_1303_bias_0_to_fp16, dilations = h_79_dilations_0, groups = h_79_groups_0, pad = h_79_pad_0, pad_type = h_79_pad_type_0, strides = h_79_strides_0, weight = op_1303_weight_0_to_fp16_palettized, x = input_207_cast_fp16)[name = string("op_1303_cast_fp16")]; + tensor out_33_cast_fp16 = add(x = x_105_cast_fp16, y = var_1303_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor x_109_cast_fp16 = mul(x = out_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor x_111_cast_fp16 = mul(x = x_109_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_1317_cast_fp16 = mul(x = x_111_cast_fp16, y = last_1_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor x_last_27_axes_0 = const()[name = string("x_last_27_axes_0"), val = tensor([-1])]; + bool x_last_27_keep_dims_0 = const()[name = string("x_last_27_keep_dims_0"), val = bool(true)]; + tensor x_last_27_cast_fp16 = reduce_sum(axes = x_last_27_axes_0, keep_dims = x_last_27_keep_dims_0, x = var_1317_cast_fp16)[name = string("x_last_27_cast_fp16")]; + tensor var_1320_cast_fp16 = mul(x = x_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1322_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_27_cast_fp16)[name = string("op_1322_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1320_cast_fp16, y = var_1322_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("replicate")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor input_213_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = input_213_mode_0, pad = input_213_pad_0, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + string h_81_pad_type_0 = const()[name = string("h_81_pad_type_0"), val = string("valid")]; + tensor h_81_dilations_0 = const()[name = string("h_81_dilations_0"), val = tensor([2])]; + int32 h_81_groups_0 = const()[name = string("h_81_groups_0"), val = int32(512)]; + tensor h_81_strides_0 = const()[name = string("h_81_strides_0"), val = tensor([1])]; + tensor h_81_pad_0 = const()[name = string("h_81_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22499328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22501312))))[name = string("main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22501504)))]; + tensor h_81_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_81_dilations_0, groups = h_81_groups_0, pad = h_81_pad_0, pad_type = h_81_pad_type_0, strides = h_81_strides_0, weight = main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_213_cast_fp16)[name = string("h_81_cast_fp16")]; + tensor x_113_cast_fp16 = mul(x = h_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor input_215_perm_0 = const()[name = string("input_215_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1340_axes_0 = const()[name = string("op_1340_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22502592)))]; + tensor main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22503680)))]; + tensor input_215_cast_fp16 = transpose(perm = input_215_perm_0, x = x_113_cast_fp16)[name = string("transpose_59")]; + tensor var_1340_cast_fp16 = layer_norm(axes = var_1340_axes_0, beta = main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16, x = input_215_cast_fp16)[name = string("op_1340_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + string h_83_pad_type_0 = const()[name = string("h_83_pad_type_0"), val = string("valid")]; + tensor h_83_strides_0 = const()[name = string("h_83_strides_0"), val = tensor([1])]; + tensor h_83_pad_0 = const()[name = string("h_83_pad_0"), val = tensor([0, 0])]; + tensor h_83_dilations_0 = const()[name = string("h_83_dilations_0"), val = tensor([1])]; + int32 h_83_groups_0 = const()[name = string("h_83_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22504768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23291264))))[name = string("main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23291456)))]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = var_1340_cast_fp16)[name = string("transpose_58")]; + tensor h_83_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_83_dilations_0, groups = h_83_groups_0, pad = h_83_pad_0, pad_type = h_83_pad_type_0, strides = h_83_strides_0, weight = main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = string("h_83_cast_fp16")]; + string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")]; + tensor input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = h_83_cast_fp16)[name = string("input_219_cast_fp16")]; + string h_85_pad_type_0 = const()[name = string("h_85_pad_type_0"), val = string("valid")]; + tensor h_85_strides_0 = const()[name = string("h_85_strides_0"), val = tensor([1])]; + tensor h_85_pad_0 = const()[name = string("h_85_pad_0"), val = tensor([0, 0])]; + tensor h_85_dilations_0 = const()[name = string("h_85_dilations_0"), val = tensor([1])]; + int32 h_85_groups_0 = const()[name = string("h_85_groups_0"), val = int32(1)]; + tensor op_1357_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23295616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24082112))))[name = string("op_1357_weight_0_to_fp16_palettized")]; + tensor var_1357_bias_0_to_fp16 = const()[name = string("op_1357_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24082304)))]; + tensor var_1357_cast_fp16 = conv(bias = var_1357_bias_0_to_fp16, dilations = h_85_dilations_0, groups = h_85_groups_0, pad = h_85_pad_0, pad_type = h_85_pad_type_0, strides = h_85_strides_0, weight = op_1357_weight_0_to_fp16_palettized, x = input_219_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor out_35_cast_fp16 = add(x = x_111_cast_fp16, y = var_1357_cast_fp16)[name = string("out_35_cast_fp16")]; + tensor x_115_cast_fp16 = mul(x = out_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_cast_fp16 = mul(x = x_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1371_cast_fp16 = mul(x = x_117_cast_fp16, y = last_1_cast_fp16)[name = string("op_1371_cast_fp16")]; + tensor x_last_29_axes_0 = const()[name = string("x_last_29_axes_0"), val = tensor([-1])]; + bool x_last_29_keep_dims_0 = const()[name = string("x_last_29_keep_dims_0"), val = bool(true)]; + tensor x_last_29_cast_fp16 = reduce_sum(axes = x_last_29_axes_0, keep_dims = x_last_29_keep_dims_0, x = var_1371_cast_fp16)[name = string("x_last_29_cast_fp16")]; + tensor var_1374_cast_fp16 = mul(x = x_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_29_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1376_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_225_mode_0 = const()[name = string("input_225_mode_0"), val = string("replicate")]; + fp16 const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = fp16(0x0p+0)]; + tensor input_225_cast_fp16 = pad(constant_val = const_48_to_fp16, mode = input_225_mode_0, pad = input_225_pad_0, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")]; + string h_87_pad_type_0 = const()[name = string("h_87_pad_type_0"), val = string("valid")]; + tensor h_87_dilations_0 = const()[name = string("h_87_dilations_0"), val = tensor([4])]; + int32 h_87_groups_0 = const()[name = string("h_87_groups_0"), val = int32(512)]; + tensor h_87_strides_0 = const()[name = string("h_87_strides_0"), val = tensor([1])]; + tensor h_87_pad_0 = const()[name = string("h_87_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24083392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24085376))))[name = string("main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24085568)))]; + tensor h_87_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_87_dilations_0, groups = h_87_groups_0, pad = h_87_pad_0, pad_type = h_87_pad_type_0, strides = h_87_strides_0, weight = main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor x_119_cast_fp16 = mul(x = h_87_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_227_perm_0 = const()[name = string("input_227_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1394_axes_0 = const()[name = string("op_1394_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24086656)))]; + tensor main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24087744)))]; + tensor input_227_cast_fp16 = transpose(perm = input_227_perm_0, x = x_119_cast_fp16)[name = string("transpose_57")]; + tensor var_1394_cast_fp16 = layer_norm(axes = var_1394_axes_0, beta = main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16, x = input_227_cast_fp16)[name = string("op_1394_cast_fp16")]; + tensor input_229_perm_0 = const()[name = string("input_229_perm_0"), val = tensor([0, 2, 1])]; + string h_89_pad_type_0 = const()[name = string("h_89_pad_type_0"), val = string("valid")]; + tensor h_89_strides_0 = const()[name = string("h_89_strides_0"), val = tensor([1])]; + tensor h_89_pad_0 = const()[name = string("h_89_pad_0"), val = tensor([0, 0])]; + tensor h_89_dilations_0 = const()[name = string("h_89_dilations_0"), val = tensor([1])]; + int32 h_89_groups_0 = const()[name = string("h_89_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24088832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24875328))))[name = string("main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24875520)))]; + tensor input_229_cast_fp16 = transpose(perm = input_229_perm_0, x = var_1394_cast_fp16)[name = string("transpose_56")]; + tensor h_89_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_89_dilations_0, groups = h_89_groups_0, pad = h_89_pad_0, pad_type = h_89_pad_type_0, strides = h_89_strides_0, weight = main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = string("h_89_cast_fp16")]; + string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")]; + tensor input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = h_89_cast_fp16)[name = string("input_231_cast_fp16")]; + string h_91_pad_type_0 = const()[name = string("h_91_pad_type_0"), val = string("valid")]; + tensor h_91_strides_0 = const()[name = string("h_91_strides_0"), val = tensor([1])]; + tensor h_91_pad_0 = const()[name = string("h_91_pad_0"), val = tensor([0, 0])]; + tensor h_91_dilations_0 = const()[name = string("h_91_dilations_0"), val = tensor([1])]; + int32 h_91_groups_0 = const()[name = string("h_91_groups_0"), val = int32(1)]; + tensor op_1411_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24879680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25666176))))[name = string("op_1411_weight_0_to_fp16_palettized")]; + tensor var_1411_bias_0_to_fp16 = const()[name = string("op_1411_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25666368)))]; + tensor var_1411_cast_fp16 = conv(bias = var_1411_bias_0_to_fp16, dilations = h_91_dilations_0, groups = h_91_groups_0, pad = h_91_pad_0, pad_type = h_91_pad_type_0, strides = h_91_strides_0, weight = op_1411_weight_0_to_fp16_palettized, x = input_231_cast_fp16)[name = string("op_1411_cast_fp16")]; + tensor out_37_cast_fp16 = add(x = x_117_cast_fp16, y = var_1411_cast_fp16)[name = string("out_37_cast_fp16")]; + tensor x_121_cast_fp16 = mul(x = out_37_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_121_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1425_cast_fp16 = mul(x = x_123_cast_fp16, y = last_1_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor x_last_31_axes_0 = const()[name = string("x_last_31_axes_0"), val = tensor([-1])]; + bool x_last_31_keep_dims_0 = const()[name = string("x_last_31_keep_dims_0"), val = bool(true)]; + tensor x_last_31_cast_fp16 = reduce_sum(axes = x_last_31_axes_0, keep_dims = x_last_31_keep_dims_0, x = var_1425_cast_fp16)[name = string("x_last_31_cast_fp16")]; + tensor var_1428_cast_fp16 = mul(x = x_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_31_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = var_1428_cast_fp16, y = var_1430_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("replicate")]; + fp16 const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = fp16(0x0p+0)]; + tensor input_237_cast_fp16 = pad(constant_val = const_50_to_fp16, mode = input_237_mode_0, pad = input_237_pad_0, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + string h_93_pad_type_0 = const()[name = string("h_93_pad_type_0"), val = string("valid")]; + tensor h_93_dilations_0 = const()[name = string("h_93_dilations_0"), val = tensor([8])]; + int32 h_93_groups_0 = const()[name = string("h_93_groups_0"), val = int32(512)]; + tensor h_93_strides_0 = const()[name = string("h_93_strides_0"), val = tensor([1])]; + tensor h_93_pad_0 = const()[name = string("h_93_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25667456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25669440))))[name = string("main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25669632)))]; + tensor h_93_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_93_dilations_0, groups = h_93_groups_0, pad = h_93_pad_0, pad_type = h_93_pad_type_0, strides = h_93_strides_0, weight = main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = string("h_93_cast_fp16")]; + tensor x_125_cast_fp16 = mul(x = h_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_239_perm_0 = const()[name = string("input_239_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1448_axes_0 = const()[name = string("op_1448_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25670720)))]; + tensor main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25671808)))]; + tensor input_239_cast_fp16 = transpose(perm = input_239_perm_0, x = x_125_cast_fp16)[name = string("transpose_55")]; + tensor var_1448_cast_fp16 = layer_norm(axes = var_1448_axes_0, beta = main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16, x = input_239_cast_fp16)[name = string("op_1448_cast_fp16")]; + tensor input_241_perm_0 = const()[name = string("input_241_perm_0"), val = tensor([0, 2, 1])]; + string h_95_pad_type_0 = const()[name = string("h_95_pad_type_0"), val = string("valid")]; + tensor h_95_strides_0 = const()[name = string("h_95_strides_0"), val = tensor([1])]; + tensor h_95_pad_0 = const()[name = string("h_95_pad_0"), val = tensor([0, 0])]; + tensor h_95_dilations_0 = const()[name = string("h_95_dilations_0"), val = tensor([1])]; + int32 h_95_groups_0 = const()[name = string("h_95_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25672896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26459392))))[name = string("main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26459584)))]; + tensor input_241_cast_fp16 = transpose(perm = input_241_perm_0, x = var_1448_cast_fp16)[name = string("transpose_54")]; + tensor h_95_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_95_dilations_0, groups = h_95_groups_0, pad = h_95_pad_0, pad_type = h_95_pad_type_0, strides = h_95_strides_0, weight = main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_241_cast_fp16)[name = string("h_95_cast_fp16")]; + string input_243_mode_0 = const()[name = string("input_243_mode_0"), val = string("EXACT")]; + tensor input_243_cast_fp16 = gelu(mode = input_243_mode_0, x = h_95_cast_fp16)[name = string("input_243_cast_fp16")]; + string h_97_pad_type_0 = const()[name = string("h_97_pad_type_0"), val = string("valid")]; + tensor h_97_strides_0 = const()[name = string("h_97_strides_0"), val = tensor([1])]; + tensor h_97_pad_0 = const()[name = string("h_97_pad_0"), val = tensor([0, 0])]; + tensor h_97_dilations_0 = const()[name = string("h_97_dilations_0"), val = tensor([1])]; + int32 h_97_groups_0 = const()[name = string("h_97_groups_0"), val = int32(1)]; + tensor op_1465_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26463744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27250240))))[name = string("op_1465_weight_0_to_fp16_palettized")]; + tensor var_1465_bias_0_to_fp16 = const()[name = string("op_1465_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27250432)))]; + tensor var_1465_cast_fp16 = conv(bias = var_1465_bias_0_to_fp16, dilations = h_97_dilations_0, groups = h_97_groups_0, pad = h_97_pad_0, pad_type = h_97_pad_type_0, strides = h_97_strides_0, weight = op_1465_weight_0_to_fp16_palettized, x = input_243_cast_fp16)[name = string("op_1465_cast_fp16")]; + tensor out_39_cast_fp16 = add(x = x_123_cast_fp16, y = var_1465_cast_fp16)[name = string("out_39_cast_fp16")]; + tensor x_127_cast_fp16 = mul(x = out_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27251520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27276160))))[name = string("main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27276352)))]; + tensor linear_20_cast_fp16 = linear(bias = main_blocks_2_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor t_9_axes_0 = const()[name = string("t_9_axes_0"), val = tensor([-1])]; + tensor t_9_cast_fp16 = expand_dims(axes = t_9_axes_0, x = linear_20_cast_fp16)[name = string("t_9_cast_fp16")]; + tensor var_1475_cast_fp16 = add(x = x_127_cast_fp16, y = t_9_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = var_1475_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor x_131_cast_fp16 = mul(x = x_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor var_1488_cast_fp16 = mul(x = x_131_cast_fp16, y = last_1_cast_fp16)[name = string("op_1488_cast_fp16")]; + tensor x_last_33_axes_0 = const()[name = string("x_last_33_axes_0"), val = tensor([-1])]; + bool x_last_33_keep_dims_0 = const()[name = string("x_last_33_keep_dims_0"), val = bool(true)]; + tensor x_last_33_cast_fp16 = reduce_sum(axes = x_last_33_axes_0, keep_dims = x_last_33_keep_dims_0, x = var_1488_cast_fp16)[name = string("x_last_33_cast_fp16")]; + tensor var_1491_cast_fp16 = mul(x = x_131_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1491_cast_fp16")]; + tensor var_1493_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_33_cast_fp16)[name = string("op_1493_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = var_1491_cast_fp16, y = var_1493_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_251_mode_0 = const()[name = string("input_251_mode_0"), val = string("replicate")]; + fp16 const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = fp16(0x0p+0)]; + tensor input_251_cast_fp16 = pad(constant_val = const_52_to_fp16, mode = input_251_mode_0, pad = input_251_pad_0, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + string h_99_pad_type_0 = const()[name = string("h_99_pad_type_0"), val = string("valid")]; + int32 h_99_groups_0 = const()[name = string("h_99_groups_0"), val = int32(512)]; + tensor h_99_strides_0 = const()[name = string("h_99_strides_0"), val = tensor([1])]; + tensor h_99_pad_0 = const()[name = string("h_99_pad_0"), val = tensor([0, 0])]; + tensor h_99_dilations_0 = const()[name = string("h_99_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27277440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27279424))))[name = string("main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27279616)))]; + tensor h_99_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_99_dilations_0, groups = h_99_groups_0, pad = h_99_pad_0, pad_type = h_99_pad_type_0, strides = h_99_strides_0, weight = main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor x_133_cast_fp16 = mul(x = h_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_253_perm_0 = const()[name = string("input_253_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1511_axes_0 = const()[name = string("op_1511_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27280704)))]; + tensor main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27281792)))]; + tensor input_253_cast_fp16 = transpose(perm = input_253_perm_0, x = x_133_cast_fp16)[name = string("transpose_53")]; + tensor var_1511_cast_fp16 = layer_norm(axes = var_1511_axes_0, beta = main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16, x = input_253_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor input_255_perm_0 = const()[name = string("input_255_perm_0"), val = tensor([0, 2, 1])]; + string h_101_pad_type_0 = const()[name = string("h_101_pad_type_0"), val = string("valid")]; + tensor h_101_strides_0 = const()[name = string("h_101_strides_0"), val = tensor([1])]; + tensor h_101_pad_0 = const()[name = string("h_101_pad_0"), val = tensor([0, 0])]; + tensor h_101_dilations_0 = const()[name = string("h_101_dilations_0"), val = tensor([1])]; + int32 h_101_groups_0 = const()[name = string("h_101_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27282880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28069376))))[name = string("main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28069568)))]; + tensor input_255_cast_fp16 = transpose(perm = input_255_perm_0, x = var_1511_cast_fp16)[name = string("transpose_52")]; + tensor h_101_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_101_dilations_0, groups = h_101_groups_0, pad = h_101_pad_0, pad_type = h_101_pad_type_0, strides = h_101_strides_0, weight = main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_255_cast_fp16)[name = string("h_101_cast_fp16")]; + string input_257_mode_0 = const()[name = string("input_257_mode_0"), val = string("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = h_101_cast_fp16)[name = string("input_257_cast_fp16")]; + string h_103_pad_type_0 = const()[name = string("h_103_pad_type_0"), val = string("valid")]; + tensor h_103_strides_0 = const()[name = string("h_103_strides_0"), val = tensor([1])]; + tensor h_103_pad_0 = const()[name = string("h_103_pad_0"), val = tensor([0, 0])]; + tensor h_103_dilations_0 = const()[name = string("h_103_dilations_0"), val = tensor([1])]; + int32 h_103_groups_0 = const()[name = string("h_103_groups_0"), val = int32(1)]; + tensor op_1528_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28073728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28860224))))[name = string("op_1528_weight_0_to_fp16_palettized")]; + tensor var_1528_bias_0_to_fp16 = const()[name = string("op_1528_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28860416)))]; + tensor var_1528_cast_fp16 = conv(bias = var_1528_bias_0_to_fp16, dilations = h_103_dilations_0, groups = h_103_groups_0, pad = h_103_pad_0, pad_type = h_103_pad_type_0, strides = h_103_strides_0, weight = op_1528_weight_0_to_fp16_palettized, x = input_257_cast_fp16)[name = string("op_1528_cast_fp16")]; + tensor out_41_cast_fp16 = add(x = x_131_cast_fp16, y = var_1528_cast_fp16)[name = string("out_41_cast_fp16")]; + tensor x_135_cast_fp16 = mul(x = out_41_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor x_m_9_cast_fp16 = mul(x = x_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_9_cast_fp16")]; + tensor input_259_perm_0 = const()[name = string("input_259_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28861504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29058176))))[name = string("main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29058368)))]; + tensor input_259_cast_fp16 = transpose(perm = input_259_perm_0, x = x_135_cast_fp16)[name = string("transpose_51")]; + tensor linear_21_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_259_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_1546 = const()[name = string("op_1546"), val = tensor([2, 512, 8, 64])]; + tensor var_1547_cast_fp16 = reshape(shape = var_1546, x = linear_21_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor x_137_perm_0 = const()[name = string("x_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29059456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29157824))))[name = string("main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29158016)))]; + tensor linear_22_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_1554 = const()[name = string("op_1554"), val = tensor([2, 128, 8, 64])]; + tensor var_1555_cast_fp16 = reshape(shape = var_1554, x = linear_22_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_139_perm_0 = const()[name = string("x_139_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29159104))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29257472))))[name = string("main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29257664)))]; + tensor linear_23_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_1562 = const()[name = string("op_1562"), val = tensor([2, 128, 8, 64])]; + tensor var_1563_cast_fp16 = reshape(shape = var_1562, x = linear_23_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_9_begin_0 = const()[name = string("x_a_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_9_end_0 = const()[name = string("x_a_9_end_0"), val = tensor([2, 8, 512, 32])]; + tensor x_a_9_end_mask_0 = const()[name = string("x_a_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_137_cast_fp16 = transpose(perm = x_137_perm_0, x = var_1547_cast_fp16)[name = string("transpose_50")]; + tensor x_a_9_cast_fp16 = slice_by_index(begin = x_a_9_begin_0, end = x_a_9_end_0, end_mask = x_a_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_a_9_cast_fp16")]; + tensor x_b_9_begin_0 = const()[name = string("x_b_9_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_9_end_0 = const()[name = string("x_b_9_end_0"), val = tensor([2, 8, 512, 64])]; + tensor x_b_9_end_mask_0 = const()[name = string("x_b_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_9_cast_fp16 = slice_by_index(begin = x_b_9_begin_0, end = x_b_9_end_0, end_mask = x_b_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_b_9_cast_fp16")]; + tensor var_1591_cast_fp16 = mul(x = x_a_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1591_cast_fp16")]; + tensor var_1592_cast_fp16 = mul(x = x_b_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor rot_a_9_cast_fp16 = sub(x = var_1591_cast_fp16, y = var_1592_cast_fp16)[name = string("rot_a_9_cast_fp16")]; + tensor var_1594_cast_fp16 = mul(x = x_a_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595_cast_fp16 = mul(x = x_b_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1595_cast_fp16")]; + tensor rot_b_9_cast_fp16 = add(x = var_1594_cast_fp16, y = var_1595_cast_fp16)[name = string("rot_b_9_cast_fp16")]; + bool q_9_interleave_0 = const()[name = string("q_9_interleave_0"), val = bool(false)]; + tensor q_9_cast_fp16 = concat(axis = var_1228, interleave = q_9_interleave_0, values = (rot_a_9_cast_fp16, rot_b_9_cast_fp16))[name = string("q_9_cast_fp16")]; + tensor x_a_11_begin_0 = const()[name = string("x_a_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_11_end_0 = const()[name = string("x_a_11_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_11_end_mask_0 = const()[name = string("x_a_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_139_cast_fp16 = transpose(perm = x_139_perm_0, x = var_1555_cast_fp16)[name = string("transpose_49")]; + tensor x_a_11_cast_fp16 = slice_by_index(begin = x_a_11_begin_0, end = x_a_11_end_0, end_mask = x_a_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_a_11_cast_fp16")]; + tensor x_b_11_begin_0 = const()[name = string("x_b_11_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_11_end_0 = const()[name = string("x_b_11_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_11_end_mask_0 = const()[name = string("x_b_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_11_cast_fp16 = slice_by_index(begin = x_b_11_begin_0, end = x_b_11_end_0, end_mask = x_b_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_b_11_cast_fp16")]; + tensor var_1609_cast_fp16 = mul(x = x_a_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor var_1610_cast_fp16 = mul(x = x_b_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor rot_a_11_cast_fp16 = sub(x = var_1609_cast_fp16, y = var_1610_cast_fp16)[name = string("rot_a_11_cast_fp16")]; + tensor var_1612_cast_fp16 = mul(x = x_a_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1612_cast_fp16")]; + tensor var_1613_cast_fp16 = mul(x = x_b_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1613_cast_fp16")]; + tensor rot_b_11_cast_fp16 = add(x = var_1612_cast_fp16, y = var_1613_cast_fp16)[name = string("rot_b_11_cast_fp16")]; + bool k_9_interleave_0 = const()[name = string("k_9_interleave_0"), val = bool(false)]; + tensor k_9_cast_fp16 = concat(axis = var_1228, interleave = k_9_interleave_0, values = (rot_a_11_cast_fp16, rot_b_11_cast_fp16))[name = string("k_9_cast_fp16")]; + bool var_1618_transpose_x_1 = const()[name = string("op_1618_transpose_x_1"), val = bool(false)]; + bool var_1618_transpose_y_1 = const()[name = string("op_1618_transpose_y_1"), val = bool(true)]; + tensor var_1618_cast_fp16 = matmul(transpose_x = var_1618_transpose_x_1, transpose_y = var_1618_transpose_y_1, x = q_9_cast_fp16, y = k_9_cast_fp16)[name = string("op_1618_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_1618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor input_265_cast_fp16 = sub(x = _inversed_scores_5_cast_fp16, y = var_530_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1228, x = input_265_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool var_1627_transpose_x_0 = const()[name = string("op_1627_transpose_x_0"), val = bool(false)]; + bool var_1627_transpose_y_0 = const()[name = string("op_1627_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1563_cast_fp16)[name = string("transpose_48")]; + tensor var_1627_cast_fp16 = matmul(transpose_x = var_1627_transpose_x_0, transpose_y = var_1627_transpose_y_0, x = attn_13_cast_fp16, y = v_9_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor var_1628_perm_0 = const()[name = string("op_1628_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1629 = const()[name = string("op_1629"), val = tensor([2, 512, 512])]; + tensor var_1628_cast_fp16 = transpose(perm = var_1628_perm_0, x = var_1627_cast_fp16)[name = string("transpose_47")]; + tensor input_267_cast_fp16 = reshape(shape = var_1629, x = var_1628_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29258752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29455424))))[name = string("main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29455616)))]; + tensor linear_24_cast_fp16 = linear(bias = main_blocks_2_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor out_43_perm_0 = const()[name = string("out_43_perm_0"), val = tensor([0, 2, 1])]; + tensor out_43_cast_fp16 = transpose(perm = out_43_perm_0, x = linear_24_cast_fp16)[name = string("transpose_46")]; + tensor var_1636_cast_fp16 = mul(x = out_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1636_cast_fp16")]; + tensor x_141_cast_fp16 = add(x = x_m_9_cast_fp16, y = var_1636_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1643_axes_0 = const()[name = string("op_1643_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29456704)))]; + tensor main_blocks_2_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29457792)))]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_141_cast_fp16)[name = string("transpose_45")]; + tensor var_1643_cast_fp16 = layer_norm(axes = var_1643_axes_0, beta = main_blocks_2_text_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_text_norm_norm_weight_to_fp16, x = input_269_cast_fp16)[name = string("op_1643_cast_fp16")]; + tensor var_1644_perm_0 = const()[name = string("op_1644_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1644_cast_fp16 = transpose(perm = var_1644_perm_0, x = var_1643_cast_fp16)[name = string("transpose_44")]; + tensor x_143_cast_fp16 = mul(x = var_1644_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor x_145_cast_fp16 = mul(x = x_143_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1657_cast_fp16 = mul(x = x_145_cast_fp16, y = last_1_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor x_last_35_axes_0 = const()[name = string("x_last_35_axes_0"), val = tensor([-1])]; + bool x_last_35_keep_dims_0 = const()[name = string("x_last_35_keep_dims_0"), val = bool(true)]; + tensor x_last_35_cast_fp16 = reduce_sum(axes = x_last_35_axes_0, keep_dims = x_last_35_keep_dims_0, x = var_1657_cast_fp16)[name = string("x_last_35_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = x_145_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1662_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_35_cast_fp16)[name = string("op_1662_cast_fp16")]; + tensor input_273_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1662_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("replicate")]; + fp16 const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = fp16(0x0p+0)]; + tensor input_275_cast_fp16 = pad(constant_val = const_59_to_fp16, mode = input_275_mode_0, pad = input_275_pad_0, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")]; + string h_105_pad_type_0 = const()[name = string("h_105_pad_type_0"), val = string("valid")]; + int32 h_105_groups_0 = const()[name = string("h_105_groups_0"), val = int32(512)]; + tensor h_105_strides_0 = const()[name = string("h_105_strides_0"), val = tensor([1])]; + tensor h_105_pad_0 = const()[name = string("h_105_pad_0"), val = tensor([0, 0])]; + tensor h_105_dilations_0 = const()[name = string("h_105_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29458880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29460864))))[name = string("main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29461056)))]; + tensor h_105_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_105_dilations_0, groups = h_105_groups_0, pad = h_105_pad_0, pad_type = h_105_pad_type_0, strides = h_105_strides_0, weight = main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_275_cast_fp16)[name = string("h_105_cast_fp16")]; + tensor x_147_cast_fp16 = mul(x = h_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_277_perm_0 = const()[name = string("input_277_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1680_axes_0 = const()[name = string("op_1680_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29462144)))]; + tensor main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29463232)))]; + tensor input_277_cast_fp16 = transpose(perm = input_277_perm_0, x = x_147_cast_fp16)[name = string("transpose_43")]; + tensor var_1680_cast_fp16 = layer_norm(axes = var_1680_axes_0, beta = main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16, x = input_277_cast_fp16)[name = string("op_1680_cast_fp16")]; + tensor input_279_perm_0 = const()[name = string("input_279_perm_0"), val = tensor([0, 2, 1])]; + string h_107_pad_type_0 = const()[name = string("h_107_pad_type_0"), val = string("valid")]; + tensor h_107_strides_0 = const()[name = string("h_107_strides_0"), val = tensor([1])]; + tensor h_107_pad_0 = const()[name = string("h_107_pad_0"), val = tensor([0, 0])]; + tensor h_107_dilations_0 = const()[name = string("h_107_dilations_0"), val = tensor([1])]; + int32 h_107_groups_0 = const()[name = string("h_107_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29464320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30250816))))[name = string("main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30251008)))]; + tensor input_279_cast_fp16 = transpose(perm = input_279_perm_0, x = var_1680_cast_fp16)[name = string("transpose_42")]; + tensor h_107_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_107_dilations_0, groups = h_107_groups_0, pad = h_107_pad_0, pad_type = h_107_pad_type_0, strides = h_107_strides_0, weight = main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_279_cast_fp16)[name = string("h_107_cast_fp16")]; + string input_281_mode_0 = const()[name = string("input_281_mode_0"), val = string("EXACT")]; + tensor input_281_cast_fp16 = gelu(mode = input_281_mode_0, x = h_107_cast_fp16)[name = string("input_281_cast_fp16")]; + string h_109_pad_type_0 = const()[name = string("h_109_pad_type_0"), val = string("valid")]; + tensor h_109_strides_0 = const()[name = string("h_109_strides_0"), val = tensor([1])]; + tensor h_109_pad_0 = const()[name = string("h_109_pad_0"), val = tensor([0, 0])]; + tensor h_109_dilations_0 = const()[name = string("h_109_dilations_0"), val = tensor([1])]; + int32 h_109_groups_0 = const()[name = string("h_109_groups_0"), val = int32(1)]; + tensor op_1697_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30255168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31041664))))[name = string("op_1697_weight_0_to_fp16_palettized")]; + tensor var_1697_bias_0_to_fp16 = const()[name = string("op_1697_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31041856)))]; + tensor var_1697_cast_fp16 = conv(bias = var_1697_bias_0_to_fp16, dilations = h_109_dilations_0, groups = h_109_groups_0, pad = h_109_pad_0, pad_type = h_109_pad_type_0, strides = h_109_strides_0, weight = op_1697_weight_0_to_fp16_palettized, x = input_281_cast_fp16)[name = string("op_1697_cast_fp16")]; + tensor out_45_cast_fp16 = add(x = x_145_cast_fp16, y = var_1697_cast_fp16)[name = string("out_45_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = out_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor x_m_11_cast_fp16 = mul(x = x_149_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_11_cast_fp16")]; + tensor input_283_perm_0 = const()[name = string("input_283_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31042944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31141312))))[name = string("main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31141504)))]; + tensor input_283_cast_fp16 = transpose(perm = input_283_perm_0, x = x_149_cast_fp16)[name = string("transpose_41")]; + tensor linear_25_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_283_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_1713 = const()[name = string("op_1713"), val = tensor([2, 512, 2, 128])]; + tensor var_1714_cast_fp16 = reshape(shape = var_1713, x = linear_25_cast_fp16)[name = string("op_1714_cast_fp16")]; + tensor q_11_perm_0 = const()[name = string("q_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31142080))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31191296))))[name = string("main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31191488)))]; + tensor linear_27_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1727 = const()[name = string("op_1727"), val = tensor([2, 50, 2, 128])]; + tensor var_1728_cast_fp16 = reshape(shape = var_1727, x = linear_27_cast_fp16)[name = string("op_1728_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1732_transpose_x_0 = const()[name = string("op_1732_transpose_x_0"), val = bool(false)]; + bool var_1732_transpose_y_0 = const()[name = string("op_1732_transpose_y_0"), val = bool(false)]; + tensor op_1731_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31192064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31211328))))[name = string("op_1731_to_fp16_palettized")]; + tensor q_11_cast_fp16 = transpose(perm = q_11_perm_0, x = var_1714_cast_fp16)[name = string("transpose_40")]; + tensor var_1732_cast_fp16 = matmul(transpose_x = var_1732_transpose_x_0, transpose_y = var_1732_transpose_y_0, x = q_11_cast_fp16, y = op_1731_to_fp16_palettized)[name = string("op_1732_cast_fp16")]; + fp16 _inversed_input_285_y_0_to_fp16 = const()[name = string("_inversed_input_285_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_285_cast_fp16 = mul(x = var_1732_cast_fp16, y = _inversed_input_285_y_0_to_fp16)[name = string("_inversed_input_285_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1228, x = _inversed_input_285_cast_fp16)[name = string("attn_15_cast_fp16")]; + tensor attn_17_cast_fp16 = mul(x = attn_15_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool var_1739_transpose_x_0 = const()[name = string("op_1739_transpose_x_0"), val = bool(false)]; + bool var_1739_transpose_y_0 = const()[name = string("op_1739_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_1728_cast_fp16)[name = string("transpose_39")]; + tensor var_1739_cast_fp16 = matmul(transpose_x = var_1739_transpose_x_0, transpose_y = var_1739_transpose_y_0, x = attn_17_cast_fp16, y = v_11_cast_fp16)[name = string("op_1739_cast_fp16")]; + tensor var_1740_perm_0 = const()[name = string("op_1740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1741 = const()[name = string("op_1741"), val = tensor([2, 512, 256])]; + tensor var_1740_cast_fp16 = transpose(perm = var_1740_perm_0, x = var_1739_cast_fp16)[name = string("transpose_38")]; + tensor input_287_cast_fp16 = reshape(shape = var_1741, x = var_1740_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31211520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31309888))))[name = string("main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31310080)))]; + tensor linear_28_cast_fp16 = linear(bias = main_blocks_2_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor out_47_perm_0 = const()[name = string("out_47_perm_0"), val = tensor([0, 2, 1])]; + tensor out_47_cast_fp16 = transpose(perm = out_47_perm_0, x = linear_28_cast_fp16)[name = string("transpose_37")]; + tensor var_1748_cast_fp16 = mul(x = out_47_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1748_cast_fp16")]; + tensor x_151_cast_fp16 = add(x = x_m_11_cast_fp16, y = var_1748_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_289_perm_0 = const()[name = string("input_289_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1755_axes_0 = const()[name = string("op_1755_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31311168)))]; + tensor main_blocks_2_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31312256)))]; + tensor input_289_cast_fp16 = transpose(perm = input_289_perm_0, x = x_151_cast_fp16)[name = string("transpose_36")]; + tensor var_1755_cast_fp16 = layer_norm(axes = var_1755_axes_0, beta = main_blocks_2_style_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_style_norm_norm_weight_to_fp16, x = input_289_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor var_1756_perm_0 = const()[name = string("op_1756_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1756_cast_fp16 = transpose(perm = var_1756_perm_0, x = var_1755_cast_fp16)[name = string("transpose_35")]; + tensor x_153_cast_fp16 = mul(x = var_1756_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_153_cast_fp16")]; + int32 var_1775 = const()[name = string("op_1775"), val = int32(-1)]; + tensor x_155_cast_fp16 = mul(x = x_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor var_1810_cast_fp16 = mul(x = x_155_cast_fp16, y = last_1_cast_fp16)[name = string("op_1810_cast_fp16")]; + tensor x_last_37_axes_0 = const()[name = string("x_last_37_axes_0"), val = tensor([-1])]; + bool x_last_37_keep_dims_0 = const()[name = string("x_last_37_keep_dims_0"), val = bool(true)]; + tensor x_last_37_cast_fp16 = reduce_sum(axes = x_last_37_axes_0, keep_dims = x_last_37_keep_dims_0, x = var_1810_cast_fp16)[name = string("x_last_37_cast_fp16")]; + tensor var_1813_cast_fp16 = mul(x = x_155_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1815_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_37_cast_fp16)[name = string("op_1815_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = var_1813_cast_fp16, y = var_1815_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_295_mode_0 = const()[name = string("input_295_mode_0"), val = string("replicate")]; + fp16 const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = fp16(0x0p+0)]; + tensor input_295_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")]; + string h_111_pad_type_0 = const()[name = string("h_111_pad_type_0"), val = string("valid")]; + int32 h_111_groups_0 = const()[name = string("h_111_groups_0"), val = int32(512)]; + tensor h_111_strides_0 = const()[name = string("h_111_strides_0"), val = tensor([1])]; + tensor h_111_pad_0 = const()[name = string("h_111_pad_0"), val = tensor([0, 0])]; + tensor h_111_dilations_0 = const()[name = string("h_111_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31313344))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31315328))))[name = string("main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31315520)))]; + tensor h_111_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_111_dilations_0, groups = h_111_groups_0, pad = h_111_pad_0, pad_type = h_111_pad_type_0, strides = h_111_strides_0, weight = main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_295_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor x_157_cast_fp16 = mul(x = h_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor input_297_perm_0 = const()[name = string("input_297_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1833_axes_0 = const()[name = string("op_1833_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31316608)))]; + tensor main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31317696)))]; + fp16 var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_297_cast_fp16 = transpose(perm = input_297_perm_0, x = x_157_cast_fp16)[name = string("transpose_34")]; + tensor var_1833_cast_fp16 = layer_norm(axes = var_1833_axes_0, beta = main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16, x = input_297_cast_fp16)[name = string("op_1833_cast_fp16")]; + tensor input_299_perm_0 = const()[name = string("input_299_perm_0"), val = tensor([0, 2, 1])]; + string h_113_pad_type_0 = const()[name = string("h_113_pad_type_0"), val = string("valid")]; + tensor h_113_strides_0 = const()[name = string("h_113_strides_0"), val = tensor([1])]; + tensor h_113_pad_0 = const()[name = string("h_113_pad_0"), val = tensor([0, 0])]; + tensor h_113_dilations_0 = const()[name = string("h_113_dilations_0"), val = tensor([1])]; + int32 h_113_groups_0 = const()[name = string("h_113_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31318784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32105280))))[name = string("main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32105472)))]; + tensor input_299_cast_fp16 = transpose(perm = input_299_perm_0, x = var_1833_cast_fp16)[name = string("transpose_33")]; + tensor h_113_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_113_dilations_0, groups = h_113_groups_0, pad = h_113_pad_0, pad_type = h_113_pad_type_0, strides = h_113_strides_0, weight = main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("h_113_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = h_113_cast_fp16)[name = string("input_301_cast_fp16")]; + string h_115_pad_type_0 = const()[name = string("h_115_pad_type_0"), val = string("valid")]; + tensor h_115_strides_0 = const()[name = string("h_115_strides_0"), val = tensor([1])]; + tensor h_115_pad_0 = const()[name = string("h_115_pad_0"), val = tensor([0, 0])]; + tensor h_115_dilations_0 = const()[name = string("h_115_dilations_0"), val = tensor([1])]; + int32 h_115_groups_0 = const()[name = string("h_115_groups_0"), val = int32(1)]; + tensor op_1850_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32109632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32896128))))[name = string("op_1850_weight_0_to_fp16_palettized")]; + tensor var_1850_bias_0_to_fp16 = const()[name = string("op_1850_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32896320)))]; + tensor var_1850_cast_fp16 = conv(bias = var_1850_bias_0_to_fp16, dilations = h_115_dilations_0, groups = h_115_groups_0, pad = h_115_pad_0, pad_type = h_115_pad_type_0, strides = h_115_strides_0, weight = op_1850_weight_0_to_fp16_palettized, x = input_301_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor out_49_cast_fp16 = add(x = x_155_cast_fp16, y = var_1850_cast_fp16)[name = string("out_49_cast_fp16")]; + tensor x_159_cast_fp16 = mul(x = out_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor x_161_cast_fp16 = mul(x = x_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor var_1864_cast_fp16 = mul(x = x_161_cast_fp16, y = last_1_cast_fp16)[name = string("op_1864_cast_fp16")]; + tensor x_last_39_axes_0 = const()[name = string("x_last_39_axes_0"), val = tensor([-1])]; + bool x_last_39_keep_dims_0 = const()[name = string("x_last_39_keep_dims_0"), val = bool(true)]; + tensor x_last_39_cast_fp16 = reduce_sum(axes = x_last_39_axes_0, keep_dims = x_last_39_keep_dims_0, x = var_1864_cast_fp16)[name = string("x_last_39_cast_fp16")]; + tensor var_1867_cast_fp16 = mul(x = x_161_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_39_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor input_305_cast_fp16 = add(x = var_1867_cast_fp16, y = var_1869_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("replicate")]; + fp16 const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_66_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string h_117_pad_type_0 = const()[name = string("h_117_pad_type_0"), val = string("valid")]; + tensor h_117_dilations_0 = const()[name = string("h_117_dilations_0"), val = tensor([2])]; + int32 h_117_groups_0 = const()[name = string("h_117_groups_0"), val = int32(512)]; + tensor h_117_strides_0 = const()[name = string("h_117_strides_0"), val = tensor([1])]; + tensor h_117_pad_0 = const()[name = string("h_117_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32897408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32899392))))[name = string("main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32899584)))]; + tensor h_117_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_117_dilations_0, groups = h_117_groups_0, pad = h_117_pad_0, pad_type = h_117_pad_type_0, strides = h_117_strides_0, weight = main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_307_cast_fp16)[name = string("h_117_cast_fp16")]; + tensor x_163_cast_fp16 = mul(x = h_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1887_axes_0 = const()[name = string("op_1887_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32900672)))]; + tensor main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32901760)))]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_163_cast_fp16)[name = string("transpose_32")]; + tensor var_1887_cast_fp16 = layer_norm(axes = var_1887_axes_0, beta = main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16, x = input_309_cast_fp16)[name = string("op_1887_cast_fp16")]; + tensor input_311_perm_0 = const()[name = string("input_311_perm_0"), val = tensor([0, 2, 1])]; + string h_119_pad_type_0 = const()[name = string("h_119_pad_type_0"), val = string("valid")]; + tensor h_119_strides_0 = const()[name = string("h_119_strides_0"), val = tensor([1])]; + tensor h_119_pad_0 = const()[name = string("h_119_pad_0"), val = tensor([0, 0])]; + tensor h_119_dilations_0 = const()[name = string("h_119_dilations_0"), val = tensor([1])]; + int32 h_119_groups_0 = const()[name = string("h_119_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32902848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33689344))))[name = string("main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33689536)))]; + tensor input_311_cast_fp16 = transpose(perm = input_311_perm_0, x = var_1887_cast_fp16)[name = string("transpose_31")]; + tensor h_119_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_119_dilations_0, groups = h_119_groups_0, pad = h_119_pad_0, pad_type = h_119_pad_type_0, strides = h_119_strides_0, weight = main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_311_cast_fp16)[name = string("h_119_cast_fp16")]; + string input_313_mode_0 = const()[name = string("input_313_mode_0"), val = string("EXACT")]; + tensor input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = h_119_cast_fp16)[name = string("input_313_cast_fp16")]; + string h_121_pad_type_0 = const()[name = string("h_121_pad_type_0"), val = string("valid")]; + tensor h_121_strides_0 = const()[name = string("h_121_strides_0"), val = tensor([1])]; + tensor h_121_pad_0 = const()[name = string("h_121_pad_0"), val = tensor([0, 0])]; + tensor h_121_dilations_0 = const()[name = string("h_121_dilations_0"), val = tensor([1])]; + int32 h_121_groups_0 = const()[name = string("h_121_groups_0"), val = int32(1)]; + tensor op_1904_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33693696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34480192))))[name = string("op_1904_weight_0_to_fp16_palettized")]; + tensor var_1904_bias_0_to_fp16 = const()[name = string("op_1904_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34480384)))]; + tensor var_1904_cast_fp16 = conv(bias = var_1904_bias_0_to_fp16, dilations = h_121_dilations_0, groups = h_121_groups_0, pad = h_121_pad_0, pad_type = h_121_pad_type_0, strides = h_121_strides_0, weight = op_1904_weight_0_to_fp16_palettized, x = input_313_cast_fp16)[name = string("op_1904_cast_fp16")]; + tensor out_51_cast_fp16 = add(x = x_161_cast_fp16, y = var_1904_cast_fp16)[name = string("out_51_cast_fp16")]; + tensor x_165_cast_fp16 = mul(x = out_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor x_167_cast_fp16 = mul(x = x_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1918_cast_fp16 = mul(x = x_167_cast_fp16, y = last_1_cast_fp16)[name = string("op_1918_cast_fp16")]; + tensor x_last_41_axes_0 = const()[name = string("x_last_41_axes_0"), val = tensor([-1])]; + bool x_last_41_keep_dims_0 = const()[name = string("x_last_41_keep_dims_0"), val = bool(true)]; + tensor x_last_41_cast_fp16 = reduce_sum(axes = x_last_41_axes_0, keep_dims = x_last_41_keep_dims_0, x = var_1918_cast_fp16)[name = string("x_last_41_cast_fp16")]; + tensor var_1921_cast_fp16 = mul(x = x_167_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1921_cast_fp16")]; + tensor var_1923_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_41_cast_fp16)[name = string("op_1923_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = var_1921_cast_fp16, y = var_1923_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("replicate")]; + fp16 const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = fp16(0x0p+0)]; + tensor input_319_cast_fp16 = pad(constant_val = const_68_to_fp16, mode = input_319_mode_0, pad = input_319_pad_0, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string h_123_pad_type_0 = const()[name = string("h_123_pad_type_0"), val = string("valid")]; + tensor h_123_dilations_0 = const()[name = string("h_123_dilations_0"), val = tensor([4])]; + int32 h_123_groups_0 = const()[name = string("h_123_groups_0"), val = int32(512)]; + tensor h_123_strides_0 = const()[name = string("h_123_strides_0"), val = tensor([1])]; + tensor h_123_pad_0 = const()[name = string("h_123_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34481472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34483456))))[name = string("main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34483648)))]; + tensor h_123_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_123_dilations_0, groups = h_123_groups_0, pad = h_123_pad_0, pad_type = h_123_pad_type_0, strides = h_123_strides_0, weight = main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_319_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor x_169_cast_fp16 = mul(x = h_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1941_axes_0 = const()[name = string("op_1941_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34484736)))]; + tensor main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34485824)))]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_169_cast_fp16)[name = string("transpose_30")]; + tensor var_1941_cast_fp16 = layer_norm(axes = var_1941_axes_0, beta = main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16, x = input_321_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor([0, 2, 1])]; + string h_125_pad_type_0 = const()[name = string("h_125_pad_type_0"), val = string("valid")]; + tensor h_125_strides_0 = const()[name = string("h_125_strides_0"), val = tensor([1])]; + tensor h_125_pad_0 = const()[name = string("h_125_pad_0"), val = tensor([0, 0])]; + tensor h_125_dilations_0 = const()[name = string("h_125_dilations_0"), val = tensor([1])]; + int32 h_125_groups_0 = const()[name = string("h_125_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34486912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35273408))))[name = string("main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35273600)))]; + tensor input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = var_1941_cast_fp16)[name = string("transpose_29")]; + tensor h_125_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_125_dilations_0, groups = h_125_groups_0, pad = h_125_pad_0, pad_type = h_125_pad_type_0, strides = h_125_strides_0, weight = main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = string("h_125_cast_fp16")]; + string input_325_mode_0 = const()[name = string("input_325_mode_0"), val = string("EXACT")]; + tensor input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = h_125_cast_fp16)[name = string("input_325_cast_fp16")]; + string h_127_pad_type_0 = const()[name = string("h_127_pad_type_0"), val = string("valid")]; + tensor h_127_strides_0 = const()[name = string("h_127_strides_0"), val = tensor([1])]; + tensor h_127_pad_0 = const()[name = string("h_127_pad_0"), val = tensor([0, 0])]; + tensor h_127_dilations_0 = const()[name = string("h_127_dilations_0"), val = tensor([1])]; + int32 h_127_groups_0 = const()[name = string("h_127_groups_0"), val = int32(1)]; + tensor op_1958_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35277760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36064256))))[name = string("op_1958_weight_0_to_fp16_palettized")]; + tensor var_1958_bias_0_to_fp16 = const()[name = string("op_1958_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36064448)))]; + tensor var_1958_cast_fp16 = conv(bias = var_1958_bias_0_to_fp16, dilations = h_127_dilations_0, groups = h_127_groups_0, pad = h_127_pad_0, pad_type = h_127_pad_type_0, strides = h_127_strides_0, weight = op_1958_weight_0_to_fp16_palettized, x = input_325_cast_fp16)[name = string("op_1958_cast_fp16")]; + tensor out_53_cast_fp16 = add(x = x_167_cast_fp16, y = var_1958_cast_fp16)[name = string("out_53_cast_fp16")]; + tensor x_171_cast_fp16 = mul(x = out_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_171_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor var_1972_cast_fp16 = mul(x = x_173_cast_fp16, y = last_1_cast_fp16)[name = string("op_1972_cast_fp16")]; + tensor x_last_43_axes_0 = const()[name = string("x_last_43_axes_0"), val = tensor([-1])]; + bool x_last_43_keep_dims_0 = const()[name = string("x_last_43_keep_dims_0"), val = bool(true)]; + tensor x_last_43_cast_fp16 = reduce_sum(axes = x_last_43_axes_0, keep_dims = x_last_43_keep_dims_0, x = var_1972_cast_fp16)[name = string("x_last_43_cast_fp16")]; + tensor var_1975_cast_fp16 = mul(x = x_173_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1975_cast_fp16")]; + tensor var_1977_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_43_cast_fp16)[name = string("op_1977_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1977_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("replicate")]; + fp16 const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = fp16(0x0p+0)]; + tensor input_331_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = input_331_mode_0, pad = input_331_pad_0, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + string h_129_pad_type_0 = const()[name = string("h_129_pad_type_0"), val = string("valid")]; + tensor h_129_dilations_0 = const()[name = string("h_129_dilations_0"), val = tensor([8])]; + int32 h_129_groups_0 = const()[name = string("h_129_groups_0"), val = int32(512)]; + tensor h_129_strides_0 = const()[name = string("h_129_strides_0"), val = tensor([1])]; + tensor h_129_pad_0 = const()[name = string("h_129_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36065536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36067520))))[name = string("main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36067712)))]; + tensor h_129_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_129_dilations_0, groups = h_129_groups_0, pad = h_129_pad_0, pad_type = h_129_pad_type_0, strides = h_129_strides_0, weight = main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = string("h_129_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = h_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_333_perm_0 = const()[name = string("input_333_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1995_axes_0 = const()[name = string("op_1995_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36068800)))]; + tensor main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36069888)))]; + tensor input_333_cast_fp16 = transpose(perm = input_333_perm_0, x = x_175_cast_fp16)[name = string("transpose_28")]; + tensor var_1995_cast_fp16 = layer_norm(axes = var_1995_axes_0, beta = main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16, x = input_333_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor input_335_perm_0 = const()[name = string("input_335_perm_0"), val = tensor([0, 2, 1])]; + string h_131_pad_type_0 = const()[name = string("h_131_pad_type_0"), val = string("valid")]; + tensor h_131_strides_0 = const()[name = string("h_131_strides_0"), val = tensor([1])]; + tensor h_131_pad_0 = const()[name = string("h_131_pad_0"), val = tensor([0, 0])]; + tensor h_131_dilations_0 = const()[name = string("h_131_dilations_0"), val = tensor([1])]; + int32 h_131_groups_0 = const()[name = string("h_131_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36070976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36857472))))[name = string("main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36857664)))]; + tensor input_335_cast_fp16 = transpose(perm = input_335_perm_0, x = var_1995_cast_fp16)[name = string("transpose_27")]; + tensor h_131_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_131_dilations_0, groups = h_131_groups_0, pad = h_131_pad_0, pad_type = h_131_pad_type_0, strides = h_131_strides_0, weight = main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_335_cast_fp16)[name = string("h_131_cast_fp16")]; + string input_337_mode_0 = const()[name = string("input_337_mode_0"), val = string("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = h_131_cast_fp16)[name = string("input_337_cast_fp16")]; + string h_133_pad_type_0 = const()[name = string("h_133_pad_type_0"), val = string("valid")]; + tensor h_133_strides_0 = const()[name = string("h_133_strides_0"), val = tensor([1])]; + tensor h_133_pad_0 = const()[name = string("h_133_pad_0"), val = tensor([0, 0])]; + tensor h_133_dilations_0 = const()[name = string("h_133_dilations_0"), val = tensor([1])]; + int32 h_133_groups_0 = const()[name = string("h_133_groups_0"), val = int32(1)]; + tensor op_2012_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36861824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37648320))))[name = string("op_2012_weight_0_to_fp16_palettized")]; + tensor var_2012_bias_0_to_fp16 = const()[name = string("op_2012_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37648512)))]; + tensor var_2012_cast_fp16 = conv(bias = var_2012_bias_0_to_fp16, dilations = h_133_dilations_0, groups = h_133_groups_0, pad = h_133_pad_0, pad_type = h_133_pad_type_0, strides = h_133_strides_0, weight = op_2012_weight_0_to_fp16_palettized, x = input_337_cast_fp16)[name = string("op_2012_cast_fp16")]; + tensor out_55_cast_fp16 = add(x = x_173_cast_fp16, y = var_2012_cast_fp16)[name = string("out_55_cast_fp16")]; + tensor x_177_cast_fp16 = mul(x = out_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37649600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37674240))))[name = string("main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37674432)))]; + tensor linear_29_cast_fp16 = linear(bias = main_blocks_3_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor t_axes_0 = const()[name = string("t_axes_0"), val = tensor([-1])]; + tensor t_cast_fp16 = expand_dims(axes = t_axes_0, x = linear_29_cast_fp16)[name = string("t_cast_fp16")]; + tensor var_2022_cast_fp16 = add(x = x_177_cast_fp16, y = t_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor x_179_cast_fp16 = mul(x = var_2022_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor x_181_cast_fp16 = mul(x = x_179_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor var_2035_cast_fp16 = mul(x = x_181_cast_fp16, y = last_1_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor x_last_45_axes_0 = const()[name = string("x_last_45_axes_0"), val = tensor([-1])]; + bool x_last_45_keep_dims_0 = const()[name = string("x_last_45_keep_dims_0"), val = bool(true)]; + tensor x_last_45_cast_fp16 = reduce_sum(axes = x_last_45_axes_0, keep_dims = x_last_45_keep_dims_0, x = var_2035_cast_fp16)[name = string("x_last_45_cast_fp16")]; + tensor var_2038_cast_fp16 = mul(x = x_181_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2038_cast_fp16")]; + tensor var_2040_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_45_cast_fp16)[name = string("op_2040_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = var_2038_cast_fp16, y = var_2040_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_345_mode_0 = const()[name = string("input_345_mode_0"), val = string("replicate")]; + fp16 const_72_to_fp16 = const()[name = string("const_72_to_fp16"), val = fp16(0x0p+0)]; + tensor input_345_cast_fp16 = pad(constant_val = const_72_to_fp16, mode = input_345_mode_0, pad = input_345_pad_0, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + string h_135_pad_type_0 = const()[name = string("h_135_pad_type_0"), val = string("valid")]; + int32 h_135_groups_0 = const()[name = string("h_135_groups_0"), val = int32(512)]; + tensor h_135_strides_0 = const()[name = string("h_135_strides_0"), val = tensor([1])]; + tensor h_135_pad_0 = const()[name = string("h_135_pad_0"), val = tensor([0, 0])]; + tensor h_135_dilations_0 = const()[name = string("h_135_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37675520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37677504))))[name = string("main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37677696)))]; + tensor h_135_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_135_dilations_0, groups = h_135_groups_0, pad = h_135_pad_0, pad_type = h_135_pad_type_0, strides = h_135_strides_0, weight = main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_345_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor x_183_cast_fp16 = mul(x = h_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_183_cast_fp16")]; + tensor input_347_perm_0 = const()[name = string("input_347_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2058_axes_0 = const()[name = string("op_2058_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37678784)))]; + tensor main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37679872)))]; + tensor input_347_cast_fp16 = transpose(perm = input_347_perm_0, x = x_183_cast_fp16)[name = string("transpose_26")]; + tensor var_2058_cast_fp16 = layer_norm(axes = var_2058_axes_0, beta = main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16, x = input_347_cast_fp16)[name = string("op_2058_cast_fp16")]; + tensor input_349_perm_0 = const()[name = string("input_349_perm_0"), val = tensor([0, 2, 1])]; + string h_137_pad_type_0 = const()[name = string("h_137_pad_type_0"), val = string("valid")]; + tensor h_137_strides_0 = const()[name = string("h_137_strides_0"), val = tensor([1])]; + tensor h_137_pad_0 = const()[name = string("h_137_pad_0"), val = tensor([0, 0])]; + tensor h_137_dilations_0 = const()[name = string("h_137_dilations_0"), val = tensor([1])]; + int32 h_137_groups_0 = const()[name = string("h_137_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37680960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38467456))))[name = string("main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38467648)))]; + tensor input_349_cast_fp16 = transpose(perm = input_349_perm_0, x = var_2058_cast_fp16)[name = string("transpose_25")]; + tensor h_137_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_137_dilations_0, groups = h_137_groups_0, pad = h_137_pad_0, pad_type = h_137_pad_type_0, strides = h_137_strides_0, weight = main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_349_cast_fp16)[name = string("h_137_cast_fp16")]; + string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")]; + tensor input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = h_137_cast_fp16)[name = string("input_351_cast_fp16")]; + string h_139_pad_type_0 = const()[name = string("h_139_pad_type_0"), val = string("valid")]; + tensor h_139_strides_0 = const()[name = string("h_139_strides_0"), val = tensor([1])]; + tensor h_139_pad_0 = const()[name = string("h_139_pad_0"), val = tensor([0, 0])]; + tensor h_139_dilations_0 = const()[name = string("h_139_dilations_0"), val = tensor([1])]; + int32 h_139_groups_0 = const()[name = string("h_139_groups_0"), val = int32(1)]; + tensor op_2075_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38471808))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39258304))))[name = string("op_2075_weight_0_to_fp16_palettized")]; + tensor var_2075_bias_0_to_fp16 = const()[name = string("op_2075_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39258496)))]; + tensor var_2075_cast_fp16 = conv(bias = var_2075_bias_0_to_fp16, dilations = h_139_dilations_0, groups = h_139_groups_0, pad = h_139_pad_0, pad_type = h_139_pad_type_0, strides = h_139_strides_0, weight = op_2075_weight_0_to_fp16_palettized, x = input_351_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor out_57_cast_fp16 = add(x = x_181_cast_fp16, y = var_2075_cast_fp16)[name = string("out_57_cast_fp16")]; + tensor x_185_cast_fp16 = mul(x = out_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor x_m_13_cast_fp16 = mul(x = x_185_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_13_cast_fp16")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39259584))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456256))))[name = string("main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456448)))]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_185_cast_fp16)[name = string("transpose_24")]; + tensor linear_30_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_2093 = const()[name = string("op_2093"), val = tensor([2, 512, 8, 64])]; + tensor var_2094_cast_fp16 = reshape(shape = var_2093, x = linear_30_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor x_187_perm_0 = const()[name = string("x_187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39457536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39555904))))[name = string("main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39556096)))]; + tensor linear_31_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_2101 = const()[name = string("op_2101"), val = tensor([2, 128, 8, 64])]; + tensor var_2102_cast_fp16 = reshape(shape = var_2101, x = linear_31_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor x_189_perm_0 = const()[name = string("x_189_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39557184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39655552))))[name = string("main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39655744)))]; + tensor linear_32_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_2109 = const()[name = string("op_2109"), val = tensor([2, 128, 8, 64])]; + tensor var_2110_cast_fp16 = reshape(shape = var_2109, x = linear_32_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_13_begin_0 = const()[name = string("x_a_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_13_end_0 = const()[name = string("x_a_13_end_0"), val = tensor([2, 8, 512, 32])]; + tensor x_a_13_end_mask_0 = const()[name = string("x_a_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_187_cast_fp16 = transpose(perm = x_187_perm_0, x = var_2094_cast_fp16)[name = string("transpose_23")]; + tensor x_a_13_cast_fp16 = slice_by_index(begin = x_a_13_begin_0, end = x_a_13_end_0, end_mask = x_a_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_a_13_cast_fp16")]; + tensor x_b_13_begin_0 = const()[name = string("x_b_13_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_13_end_0 = const()[name = string("x_b_13_end_0"), val = tensor([2, 8, 512, 64])]; + tensor x_b_13_end_mask_0 = const()[name = string("x_b_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_13_cast_fp16 = slice_by_index(begin = x_b_13_begin_0, end = x_b_13_end_0, end_mask = x_b_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_b_13_cast_fp16")]; + tensor var_2138_cast_fp16 = mul(x = x_a_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2138_cast_fp16")]; + tensor var_2139_cast_fp16 = mul(x = x_b_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2139_cast_fp16")]; + tensor rot_a_13_cast_fp16 = sub(x = var_2138_cast_fp16, y = var_2139_cast_fp16)[name = string("rot_a_13_cast_fp16")]; + tensor var_2141_cast_fp16 = mul(x = x_a_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2141_cast_fp16")]; + tensor var_2142_cast_fp16 = mul(x = x_b_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2142_cast_fp16")]; + tensor rot_b_13_cast_fp16 = add(x = var_2141_cast_fp16, y = var_2142_cast_fp16)[name = string("rot_b_13_cast_fp16")]; + bool q_13_interleave_0 = const()[name = string("q_13_interleave_0"), val = bool(false)]; + tensor q_13_cast_fp16 = concat(axis = var_1775, interleave = q_13_interleave_0, values = (rot_a_13_cast_fp16, rot_b_13_cast_fp16))[name = string("q_13_cast_fp16")]; + tensor x_a_begin_0 = const()[name = string("x_a_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_end_0 = const()[name = string("x_a_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_end_mask_0 = const()[name = string("x_a_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_189_cast_fp16 = transpose(perm = x_189_perm_0, x = var_2102_cast_fp16)[name = string("transpose_22")]; + tensor x_a_cast_fp16 = slice_by_index(begin = x_a_begin_0, end = x_a_end_0, end_mask = x_a_end_mask_0, x = x_189_cast_fp16)[name = string("x_a_cast_fp16")]; + tensor x_b_begin_0 = const()[name = string("x_b_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_end_0 = const()[name = string("x_b_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_end_mask_0 = const()[name = string("x_b_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_cast_fp16 = slice_by_index(begin = x_b_begin_0, end = x_b_end_0, end_mask = x_b_end_mask_0, x = x_189_cast_fp16)[name = string("x_b_cast_fp16")]; + tensor var_2156_cast_fp16 = mul(x = x_a_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2156_cast_fp16")]; + tensor var_2157_cast_fp16 = mul(x = x_b_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2157_cast_fp16")]; + tensor rot_a_cast_fp16 = sub(x = var_2156_cast_fp16, y = var_2157_cast_fp16)[name = string("rot_a_cast_fp16")]; + tensor var_2159_cast_fp16 = mul(x = x_a_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2159_cast_fp16")]; + tensor var_2160_cast_fp16 = mul(x = x_b_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2160_cast_fp16")]; + tensor rot_b_cast_fp16 = add(x = var_2159_cast_fp16, y = var_2160_cast_fp16)[name = string("rot_b_cast_fp16")]; + bool k_13_interleave_0 = const()[name = string("k_13_interleave_0"), val = bool(false)]; + tensor k_13_cast_fp16 = concat(axis = var_1775, interleave = k_13_interleave_0, values = (rot_a_cast_fp16, rot_b_cast_fp16))[name = string("k_13_cast_fp16")]; + bool var_2165_transpose_x_1 = const()[name = string("op_2165_transpose_x_1"), val = bool(false)]; + bool var_2165_transpose_y_1 = const()[name = string("op_2165_transpose_y_1"), val = bool(true)]; + tensor var_2165_cast_fp16 = matmul(transpose_x = var_2165_transpose_x_1, transpose_y = var_2165_transpose_y_1, x = q_13_cast_fp16, y = k_13_cast_fp16)[name = string("op_2165_cast_fp16")]; + fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_cast_fp16 = mul(x = var_2165_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")]; + tensor input_359_cast_fp16 = sub(x = _inversed_scores_cast_fp16, y = var_530_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_1775, x = input_359_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool var_2174_transpose_x_0 = const()[name = string("op_2174_transpose_x_0"), val = bool(false)]; + bool var_2174_transpose_y_0 = const()[name = string("op_2174_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2110_cast_fp16)[name = string("transpose_21")]; + tensor var_2174_cast_fp16 = matmul(transpose_x = var_2174_transpose_x_0, transpose_y = var_2174_transpose_y_0, x = attn_19_cast_fp16, y = v_13_cast_fp16)[name = string("op_2174_cast_fp16")]; + tensor var_2175_perm_0 = const()[name = string("op_2175_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2176 = const()[name = string("op_2176"), val = tensor([2, 512, 512])]; + tensor var_2175_cast_fp16 = transpose(perm = var_2175_perm_0, x = var_2174_cast_fp16)[name = string("transpose_20")]; + tensor input_361_cast_fp16 = reshape(shape = var_2176, x = var_2175_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39656832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39853504))))[name = string("main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39853696)))]; + tensor linear_33_cast_fp16 = linear(bias = main_blocks_3_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor out_59_perm_0 = const()[name = string("out_59_perm_0"), val = tensor([0, 2, 1])]; + tensor out_59_cast_fp16 = transpose(perm = out_59_perm_0, x = linear_33_cast_fp16)[name = string("transpose_19")]; + tensor var_2183_cast_fp16 = mul(x = out_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_m_13_cast_fp16, y = var_2183_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor input_363_perm_0 = const()[name = string("input_363_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2190_axes_0 = const()[name = string("op_2190_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39854784)))]; + tensor main_blocks_3_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39855872)))]; + tensor input_363_cast_fp16 = transpose(perm = input_363_perm_0, x = x_191_cast_fp16)[name = string("transpose_18")]; + tensor var_2190_cast_fp16 = layer_norm(axes = var_2190_axes_0, beta = main_blocks_3_text_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_text_norm_norm_weight_to_fp16, x = input_363_cast_fp16)[name = string("op_2190_cast_fp16")]; + tensor var_2191_perm_0 = const()[name = string("op_2191_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2191_perm_0, x = var_2190_cast_fp16)[name = string("transpose_17")]; + tensor x_193_cast_fp16 = mul(x = var_2191_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_193_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2204_cast_fp16 = mul(x = x_195_cast_fp16, y = last_1_cast_fp16)[name = string("op_2204_cast_fp16")]; + tensor x_last_47_axes_0 = const()[name = string("x_last_47_axes_0"), val = tensor([-1])]; + bool x_last_47_keep_dims_0 = const()[name = string("x_last_47_keep_dims_0"), val = bool(true)]; + tensor x_last_47_cast_fp16 = reduce_sum(axes = x_last_47_axes_0, keep_dims = x_last_47_keep_dims_0, x = var_2204_cast_fp16)[name = string("x_last_47_cast_fp16")]; + tensor var_2207_cast_fp16 = mul(x = x_195_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2207_cast_fp16")]; + tensor var_2209_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_47_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor input_367_cast_fp16 = add(x = var_2207_cast_fp16, y = var_2209_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_369_mode_0 = const()[name = string("input_369_mode_0"), val = string("replicate")]; + fp16 const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = fp16(0x0p+0)]; + tensor input_369_cast_fp16 = pad(constant_val = const_79_to_fp16, mode = input_369_mode_0, pad = input_369_pad_0, x = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + string h_141_pad_type_0 = const()[name = string("h_141_pad_type_0"), val = string("valid")]; + int32 h_141_groups_0 = const()[name = string("h_141_groups_0"), val = int32(512)]; + tensor h_141_strides_0 = const()[name = string("h_141_strides_0"), val = tensor([1])]; + tensor h_141_pad_0 = const()[name = string("h_141_pad_0"), val = tensor([0, 0])]; + tensor h_141_dilations_0 = const()[name = string("h_141_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39856960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39858944))))[name = string("main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39859136)))]; + tensor h_141_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_141_dilations_0, groups = h_141_groups_0, pad = h_141_pad_0, pad_type = h_141_pad_type_0, strides = h_141_strides_0, weight = main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_369_cast_fp16)[name = string("h_141_cast_fp16")]; + tensor x_197_cast_fp16 = mul(x = h_141_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_371_perm_0 = const()[name = string("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2227_axes_0 = const()[name = string("op_2227_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39860224)))]; + tensor main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39861312)))]; + tensor input_371_cast_fp16 = transpose(perm = input_371_perm_0, x = x_197_cast_fp16)[name = string("transpose_16")]; + tensor var_2227_cast_fp16 = layer_norm(axes = var_2227_axes_0, beta = main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16, x = input_371_cast_fp16)[name = string("op_2227_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + string h_143_pad_type_0 = const()[name = string("h_143_pad_type_0"), val = string("valid")]; + tensor h_143_strides_0 = const()[name = string("h_143_strides_0"), val = tensor([1])]; + tensor h_143_pad_0 = const()[name = string("h_143_pad_0"), val = tensor([0, 0])]; + tensor h_143_dilations_0 = const()[name = string("h_143_dilations_0"), val = tensor([1])]; + int32 h_143_groups_0 = const()[name = string("h_143_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39862400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40648896))))[name = string("main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40649088)))]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = var_2227_cast_fp16)[name = string("transpose_15")]; + tensor h_143_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_143_dilations_0, groups = h_143_groups_0, pad = h_143_pad_0, pad_type = h_143_pad_type_0, strides = h_143_strides_0, weight = main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = string("h_143_cast_fp16")]; + string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")]; + tensor input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = h_143_cast_fp16)[name = string("input_375_cast_fp16")]; + string h_145_pad_type_0 = const()[name = string("h_145_pad_type_0"), val = string("valid")]; + tensor h_145_strides_0 = const()[name = string("h_145_strides_0"), val = tensor([1])]; + tensor h_145_pad_0 = const()[name = string("h_145_pad_0"), val = tensor([0, 0])]; + tensor h_145_dilations_0 = const()[name = string("h_145_dilations_0"), val = tensor([1])]; + int32 h_145_groups_0 = const()[name = string("h_145_groups_0"), val = int32(1)]; + tensor op_2244_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40653248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41439744))))[name = string("op_2244_weight_0_to_fp16_palettized")]; + tensor var_2244_bias_0_to_fp16 = const()[name = string("op_2244_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41439936)))]; + tensor var_2244_cast_fp16 = conv(bias = var_2244_bias_0_to_fp16, dilations = h_145_dilations_0, groups = h_145_groups_0, pad = h_145_pad_0, pad_type = h_145_pad_type_0, strides = h_145_strides_0, weight = op_2244_weight_0_to_fp16_palettized, x = input_375_cast_fp16)[name = string("op_2244_cast_fp16")]; + tensor out_61_cast_fp16 = add(x = x_195_cast_fp16, y = var_2244_cast_fp16)[name = string("out_61_cast_fp16")]; + tensor x_199_cast_fp16 = mul(x = out_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor x_m_cast_fp16 = mul(x = x_199_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_cast_fp16")]; + tensor input_377_perm_0 = const()[name = string("input_377_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41441024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41539392))))[name = string("main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41539584)))]; + tensor input_377_cast_fp16 = transpose(perm = input_377_perm_0, x = x_199_cast_fp16)[name = string("transpose_14")]; + tensor linear_34_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_2260 = const()[name = string("op_2260"), val = tensor([2, 512, 2, 128])]; + tensor var_2261_cast_fp16 = reshape(shape = var_2260, x = linear_34_cast_fp16)[name = string("op_2261_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41540160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41589376))))[name = string("main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41589568)))]; + tensor linear_36_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_2274 = const()[name = string("op_2274"), val = tensor([2, 50, 2, 128])]; + tensor var_2275_cast_fp16 = reshape(shape = var_2274, x = linear_36_cast_fp16)[name = string("op_2275_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_2279_transpose_x_0 = const()[name = string("op_2279_transpose_x_0"), val = bool(false)]; + bool var_2279_transpose_y_0 = const()[name = string("op_2279_transpose_y_0"), val = bool(false)]; + tensor op_2278_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41590144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41609408))))[name = string("op_2278_to_fp16_palettized")]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_2261_cast_fp16)[name = string("transpose_13")]; + tensor var_2279_cast_fp16 = matmul(transpose_x = var_2279_transpose_x_0, transpose_y = var_2279_transpose_y_0, x = q_cast_fp16, y = op_2278_to_fp16_palettized)[name = string("op_2279_cast_fp16")]; + fp16 _inversed_input_379_y_0_to_fp16 = const()[name = string("_inversed_input_379_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_379_cast_fp16 = mul(x = var_2279_cast_fp16, y = _inversed_input_379_y_0_to_fp16)[name = string("_inversed_input_379_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_1775, x = _inversed_input_379_cast_fp16)[name = string("attn_21_cast_fp16")]; + tensor attn_cast_fp16 = mul(x = attn_21_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_cast_fp16")]; + bool var_2286_transpose_x_0 = const()[name = string("op_2286_transpose_x_0"), val = bool(false)]; + bool var_2286_transpose_y_0 = const()[name = string("op_2286_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_2275_cast_fp16)[name = string("transpose_12")]; + tensor var_2286_cast_fp16 = matmul(transpose_x = var_2286_transpose_x_0, transpose_y = var_2286_transpose_y_0, x = attn_cast_fp16, y = v_15_cast_fp16)[name = string("op_2286_cast_fp16")]; + tensor var_2287_perm_0 = const()[name = string("op_2287_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2288 = const()[name = string("op_2288"), val = tensor([2, 512, 256])]; + tensor var_2287_cast_fp16 = transpose(perm = var_2287_perm_0, x = var_2286_cast_fp16)[name = string("transpose_11")]; + tensor input_381_cast_fp16 = reshape(shape = var_2288, x = var_2287_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41609600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41707968))))[name = string("main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41708160)))]; + tensor linear_37_cast_fp16 = linear(bias = main_blocks_3_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor out_63_perm_0 = const()[name = string("out_63_perm_0"), val = tensor([0, 2, 1])]; + tensor out_63_cast_fp16 = transpose(perm = out_63_perm_0, x = linear_37_cast_fp16)[name = string("transpose_10")]; + tensor var_2295_cast_fp16 = mul(x = out_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2295_cast_fp16")]; + tensor x_201_cast_fp16 = add(x = x_m_cast_fp16, y = var_2295_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_383_perm_0 = const()[name = string("input_383_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2302_axes_0 = const()[name = string("op_2302_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41709248)))]; + tensor main_blocks_3_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41710336)))]; + tensor input_383_cast_fp16 = transpose(perm = input_383_perm_0, x = x_201_cast_fp16)[name = string("transpose_9")]; + tensor var_2302_cast_fp16 = layer_norm(axes = var_2302_axes_0, beta = main_blocks_3_style_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_style_norm_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_perm_0 = const()[name = string("op_2303_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2303_cast_fp16 = transpose(perm = var_2303_perm_0, x = var_2302_cast_fp16)[name = string("transpose_8")]; + tensor x_203_cast_fp16 = mul(x = var_2303_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor x_205_cast_fp16 = mul(x = x_203_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor var_2330_cast_fp16 = mul(x = x_205_cast_fp16, y = last_1_cast_fp16)[name = string("op_2330_cast_fp16")]; + tensor x_last_49_axes_0 = const()[name = string("x_last_49_axes_0"), val = tensor([-1])]; + bool x_last_49_keep_dims_0 = const()[name = string("x_last_49_keep_dims_0"), val = bool(true)]; + tensor x_last_49_cast_fp16 = reduce_sum(axes = x_last_49_axes_0, keep_dims = x_last_49_keep_dims_0, x = var_2330_cast_fp16)[name = string("x_last_49_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = x_205_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor var_2335_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_49_cast_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2335_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_pad_0 = const()[name = string("input_389_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("replicate")]; + fp16 const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = fp16(0x0p+0)]; + tensor input_389_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = input_389_mode_0, pad = input_389_pad_0, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + string h_147_pad_type_0 = const()[name = string("h_147_pad_type_0"), val = string("valid")]; + int32 h_147_groups_0 = const()[name = string("h_147_groups_0"), val = int32(512)]; + tensor h_147_strides_0 = const()[name = string("h_147_strides_0"), val = tensor([1])]; + tensor h_147_pad_0 = const()[name = string("h_147_pad_0"), val = tensor([0, 0])]; + tensor h_147_dilations_0 = const()[name = string("h_147_dilations_0"), val = tensor([1])]; + tensor last_convnext_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41711424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41713408))))[name = string("last_convnext_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41713600)))]; + tensor h_147_cast_fp16 = conv(bias = last_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_147_dilations_0, groups = h_147_groups_0, pad = h_147_pad_0, pad_type = h_147_pad_type_0, strides = h_147_strides_0, weight = last_convnext_0_dwconv__conv_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor x_207_cast_fp16 = mul(x = h_147_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_391_perm_0 = const()[name = string("input_391_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2353_axes_0 = const()[name = string("op_2353_axes_0"), val = tensor([-1])]; + tensor last_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41714688)))]; + tensor last_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41715776)))]; + fp16 var_2307_to_fp16 = const()[name = string("op_2307_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_391_cast_fp16 = transpose(perm = input_391_perm_0, x = x_207_cast_fp16)[name = string("transpose_7")]; + tensor var_2353_cast_fp16 = layer_norm(axes = var_2353_axes_0, beta = last_convnext_0_norm_norm_bias_to_fp16, epsilon = var_2307_to_fp16, gamma = last_convnext_0_norm_norm_weight_to_fp16, x = input_391_cast_fp16)[name = string("op_2353_cast_fp16")]; + tensor input_393_perm_0 = const()[name = string("input_393_perm_0"), val = tensor([0, 2, 1])]; + string h_149_pad_type_0 = const()[name = string("h_149_pad_type_0"), val = string("valid")]; + tensor h_149_strides_0 = const()[name = string("h_149_strides_0"), val = tensor([1])]; + tensor h_149_pad_0 = const()[name = string("h_149_pad_0"), val = tensor([0, 0])]; + tensor h_149_dilations_0 = const()[name = string("h_149_dilations_0"), val = tensor([1])]; + int32 h_149_groups_0 = const()[name = string("h_149_groups_0"), val = int32(1)]; + tensor last_convnext_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41716864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42503360))))[name = string("last_convnext_0_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42503552)))]; + tensor input_393_cast_fp16 = transpose(perm = input_393_perm_0, x = var_2353_cast_fp16)[name = string("transpose_6")]; + tensor h_149_cast_fp16 = conv(bias = last_convnext_0_pwconv1_bias_to_fp16, dilations = h_149_dilations_0, groups = h_149_groups_0, pad = h_149_pad_0, pad_type = h_149_pad_type_0, strides = h_149_strides_0, weight = last_convnext_0_pwconv1_weight_to_fp16_palettized, x = input_393_cast_fp16)[name = string("h_149_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = h_149_cast_fp16)[name = string("input_395_cast_fp16")]; + string h_151_pad_type_0 = const()[name = string("h_151_pad_type_0"), val = string("valid")]; + tensor h_151_strides_0 = const()[name = string("h_151_strides_0"), val = tensor([1])]; + tensor h_151_pad_0 = const()[name = string("h_151_pad_0"), val = tensor([0, 0])]; + tensor h_151_dilations_0 = const()[name = string("h_151_dilations_0"), val = tensor([1])]; + int32 h_151_groups_0 = const()[name = string("h_151_groups_0"), val = int32(1)]; + tensor op_2370_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42507712))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43294208))))[name = string("op_2370_weight_0_to_fp16_palettized")]; + tensor var_2370_bias_0_to_fp16 = const()[name = string("op_2370_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43294400)))]; + tensor var_2370_cast_fp16 = conv(bias = var_2370_bias_0_to_fp16, dilations = h_151_dilations_0, groups = h_151_groups_0, pad = h_151_pad_0, pad_type = h_151_pad_type_0, strides = h_151_strides_0, weight = op_2370_weight_0_to_fp16_palettized, x = input_395_cast_fp16)[name = string("op_2370_cast_fp16")]; + tensor out_65_cast_fp16 = add(x = x_205_cast_fp16, y = var_2370_cast_fp16)[name = string("out_65_cast_fp16")]; + tensor x_209_cast_fp16 = mul(x = out_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor x_211_cast_fp16 = mul(x = x_209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_2398_cast_fp16 = mul(x = x_211_cast_fp16, y = last_1_cast_fp16)[name = string("op_2398_cast_fp16")]; + tensor x_last_51_axes_0 = const()[name = string("x_last_51_axes_0"), val = tensor([-1])]; + bool x_last_51_keep_dims_0 = const()[name = string("x_last_51_keep_dims_0"), val = bool(true)]; + tensor x_last_51_cast_fp16 = reduce_sum(axes = x_last_51_axes_0, keep_dims = x_last_51_keep_dims_0, x = var_2398_cast_fp16)[name = string("x_last_51_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = x_211_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_51_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor input_399_cast_fp16 = add(x = var_2401_cast_fp16, y = var_2403_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor input_401_pad_0 = const()[name = string("input_401_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_401_mode_0 = const()[name = string("input_401_mode_0"), val = string("replicate")]; + fp16 const_86_to_fp16 = const()[name = string("const_86_to_fp16"), val = fp16(0x0p+0)]; + tensor input_401_cast_fp16 = pad(constant_val = const_86_to_fp16, mode = input_401_mode_0, pad = input_401_pad_0, x = input_399_cast_fp16)[name = string("input_401_cast_fp16")]; + string h_153_pad_type_0 = const()[name = string("h_153_pad_type_0"), val = string("valid")]; + int32 h_153_groups_0 = const()[name = string("h_153_groups_0"), val = int32(512)]; + tensor h_153_strides_0 = const()[name = string("h_153_strides_0"), val = tensor([1])]; + tensor h_153_pad_0 = const()[name = string("h_153_pad_0"), val = tensor([0, 0])]; + tensor h_153_dilations_0 = const()[name = string("h_153_dilations_0"), val = tensor([1])]; + tensor last_convnext_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43295488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43297472))))[name = string("last_convnext_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43297664)))]; + tensor h_153_cast_fp16 = conv(bias = last_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_153_dilations_0, groups = h_153_groups_0, pad = h_153_pad_0, pad_type = h_153_pad_type_0, strides = h_153_strides_0, weight = last_convnext_1_dwconv__conv_weight_to_fp16_palettized, x = input_401_cast_fp16)[name = string("h_153_cast_fp16")]; + tensor x_213_cast_fp16 = mul(x = h_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_213_cast_fp16")]; + tensor input_403_perm_0 = const()[name = string("input_403_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2421_axes_0 = const()[name = string("op_2421_axes_0"), val = tensor([-1])]; + tensor last_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43298752)))]; + tensor last_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43299840)))]; + fp16 var_2375_to_fp16 = const()[name = string("op_2375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_403_cast_fp16 = transpose(perm = input_403_perm_0, x = x_213_cast_fp16)[name = string("transpose_5")]; + tensor var_2421_cast_fp16 = layer_norm(axes = var_2421_axes_0, beta = last_convnext_1_norm_norm_bias_to_fp16, epsilon = var_2375_to_fp16, gamma = last_convnext_1_norm_norm_weight_to_fp16, x = input_403_cast_fp16)[name = string("op_2421_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string h_155_pad_type_0 = const()[name = string("h_155_pad_type_0"), val = string("valid")]; + tensor h_155_strides_0 = const()[name = string("h_155_strides_0"), val = tensor([1])]; + tensor h_155_pad_0 = const()[name = string("h_155_pad_0"), val = tensor([0, 0])]; + tensor h_155_dilations_0 = const()[name = string("h_155_dilations_0"), val = tensor([1])]; + int32 h_155_groups_0 = const()[name = string("h_155_groups_0"), val = int32(1)]; + tensor last_convnext_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43300928))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44087424))))[name = string("last_convnext_1_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44087616)))]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = var_2421_cast_fp16)[name = string("transpose_4")]; + tensor h_155_cast_fp16 = conv(bias = last_convnext_1_pwconv1_bias_to_fp16, dilations = h_155_dilations_0, groups = h_155_groups_0, pad = h_155_pad_0, pad_type = h_155_pad_type_0, strides = h_155_strides_0, weight = last_convnext_1_pwconv1_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = string("h_155_cast_fp16")]; + string input_407_mode_0 = const()[name = string("input_407_mode_0"), val = string("EXACT")]; + tensor input_407_cast_fp16 = gelu(mode = input_407_mode_0, x = h_155_cast_fp16)[name = string("input_407_cast_fp16")]; + string h_157_pad_type_0 = const()[name = string("h_157_pad_type_0"), val = string("valid")]; + tensor h_157_strides_0 = const()[name = string("h_157_strides_0"), val = tensor([1])]; + tensor h_157_pad_0 = const()[name = string("h_157_pad_0"), val = tensor([0, 0])]; + tensor h_157_dilations_0 = const()[name = string("h_157_dilations_0"), val = tensor([1])]; + int32 h_157_groups_0 = const()[name = string("h_157_groups_0"), val = int32(1)]; + tensor op_2438_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44091776))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44878272))))[name = string("op_2438_weight_0_to_fp16_palettized")]; + tensor var_2438_bias_0_to_fp16 = const()[name = string("op_2438_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44878464)))]; + tensor var_2438_cast_fp16 = conv(bias = var_2438_bias_0_to_fp16, dilations = h_157_dilations_0, groups = h_157_groups_0, pad = h_157_pad_0, pad_type = h_157_pad_type_0, strides = h_157_strides_0, weight = op_2438_weight_0_to_fp16_palettized, x = input_407_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor out_67_cast_fp16 = add(x = x_211_cast_fp16, y = var_2438_cast_fp16)[name = string("out_67_cast_fp16")]; + tensor x_215_cast_fp16 = mul(x = out_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_215_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2466_cast_fp16 = mul(x = x_217_cast_fp16, y = last_1_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor x_last_53_axes_0 = const()[name = string("x_last_53_axes_0"), val = tensor([-1])]; + bool x_last_53_keep_dims_0 = const()[name = string("x_last_53_keep_dims_0"), val = bool(true)]; + tensor x_last_53_cast_fp16 = reduce_sum(axes = x_last_53_axes_0, keep_dims = x_last_53_keep_dims_0, x = var_2466_cast_fp16)[name = string("x_last_53_cast_fp16")]; + tensor var_2469_cast_fp16 = mul(x = x_217_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor var_2471_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_53_cast_fp16)[name = string("op_2471_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = var_2469_cast_fp16, y = var_2471_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("replicate")]; + fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)]; + tensor input_413_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = input_413_mode_0, pad = input_413_pad_0, x = input_411_cast_fp16)[name = string("input_413_cast_fp16")]; + string h_159_pad_type_0 = const()[name = string("h_159_pad_type_0"), val = string("valid")]; + int32 h_159_groups_0 = const()[name = string("h_159_groups_0"), val = int32(512)]; + tensor h_159_strides_0 = const()[name = string("h_159_strides_0"), val = tensor([1])]; + tensor h_159_pad_0 = const()[name = string("h_159_pad_0"), val = tensor([0, 0])]; + tensor h_159_dilations_0 = const()[name = string("h_159_dilations_0"), val = tensor([1])]; + tensor last_convnext_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44879552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44881536))))[name = string("last_convnext_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44881728)))]; + tensor h_159_cast_fp16 = conv(bias = last_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_159_dilations_0, groups = h_159_groups_0, pad = h_159_pad_0, pad_type = h_159_pad_type_0, strides = h_159_strides_0, weight = last_convnext_2_dwconv__conv_weight_to_fp16_palettized, x = input_413_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor x_219_cast_fp16 = mul(x = h_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_415_perm_0 = const()[name = string("input_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2489_axes_0 = const()[name = string("op_2489_axes_0"), val = tensor([-1])]; + tensor last_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44882816)))]; + tensor last_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44883904)))]; + fp16 var_2443_to_fp16 = const()[name = string("op_2443_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_415_cast_fp16 = transpose(perm = input_415_perm_0, x = x_219_cast_fp16)[name = string("transpose_3")]; + tensor var_2489_cast_fp16 = layer_norm(axes = var_2489_axes_0, beta = last_convnext_2_norm_norm_bias_to_fp16, epsilon = var_2443_to_fp16, gamma = last_convnext_2_norm_norm_weight_to_fp16, x = input_415_cast_fp16)[name = string("op_2489_cast_fp16")]; + tensor input_417_perm_0 = const()[name = string("input_417_perm_0"), val = tensor([0, 2, 1])]; + string h_161_pad_type_0 = const()[name = string("h_161_pad_type_0"), val = string("valid")]; + tensor h_161_strides_0 = const()[name = string("h_161_strides_0"), val = tensor([1])]; + tensor h_161_pad_0 = const()[name = string("h_161_pad_0"), val = tensor([0, 0])]; + tensor h_161_dilations_0 = const()[name = string("h_161_dilations_0"), val = tensor([1])]; + int32 h_161_groups_0 = const()[name = string("h_161_groups_0"), val = int32(1)]; + tensor last_convnext_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44884992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45671488))))[name = string("last_convnext_2_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45671680)))]; + tensor input_417_cast_fp16 = transpose(perm = input_417_perm_0, x = var_2489_cast_fp16)[name = string("transpose_2")]; + tensor h_161_cast_fp16 = conv(bias = last_convnext_2_pwconv1_bias_to_fp16, dilations = h_161_dilations_0, groups = h_161_groups_0, pad = h_161_pad_0, pad_type = h_161_pad_type_0, strides = h_161_strides_0, weight = last_convnext_2_pwconv1_weight_to_fp16_palettized, x = input_417_cast_fp16)[name = string("h_161_cast_fp16")]; + string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("EXACT")]; + tensor input_419_cast_fp16 = gelu(mode = input_419_mode_0, x = h_161_cast_fp16)[name = string("input_419_cast_fp16")]; + string h_163_pad_type_0 = const()[name = string("h_163_pad_type_0"), val = string("valid")]; + tensor h_163_strides_0 = const()[name = string("h_163_strides_0"), val = tensor([1])]; + tensor h_163_pad_0 = const()[name = string("h_163_pad_0"), val = tensor([0, 0])]; + tensor h_163_dilations_0 = const()[name = string("h_163_dilations_0"), val = tensor([1])]; + int32 h_163_groups_0 = const()[name = string("h_163_groups_0"), val = int32(1)]; + tensor op_2506_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45675840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46462336))))[name = string("op_2506_weight_0_to_fp16_palettized")]; + tensor var_2506_bias_0_to_fp16 = const()[name = string("op_2506_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46462528)))]; + tensor var_2506_cast_fp16 = conv(bias = var_2506_bias_0_to_fp16, dilations = h_163_dilations_0, groups = h_163_groups_0, pad = h_163_pad_0, pad_type = h_163_pad_type_0, strides = h_163_strides_0, weight = op_2506_weight_0_to_fp16_palettized, x = input_419_cast_fp16)[name = string("op_2506_cast_fp16")]; + tensor out_69_cast_fp16 = add(x = x_217_cast_fp16, y = var_2506_cast_fp16)[name = string("out_69_cast_fp16")]; + tensor x_221_cast_fp16 = mul(x = out_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor x_223_cast_fp16 = mul(x = x_221_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_2534_cast_fp16 = mul(x = x_223_cast_fp16, y = last_1_cast_fp16)[name = string("op_2534_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_2534_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_2537_cast_fp16 = mul(x = x_223_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2537_cast_fp16")]; + tensor var_2539_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_cast_fp16)[name = string("op_2539_cast_fp16")]; + tensor input_423_cast_fp16 = add(x = var_2537_cast_fp16, y = var_2539_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_425_mode_0 = const()[name = string("input_425_mode_0"), val = string("replicate")]; + fp16 const_90_to_fp16 = const()[name = string("const_90_to_fp16"), val = fp16(0x0p+0)]; + tensor input_425_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = input_425_mode_0, pad = input_425_pad_0, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")]; + string h_165_pad_type_0 = const()[name = string("h_165_pad_type_0"), val = string("valid")]; + int32 h_165_groups_0 = const()[name = string("h_165_groups_0"), val = int32(512)]; + tensor h_165_strides_0 = const()[name = string("h_165_strides_0"), val = tensor([1])]; + tensor h_165_pad_0 = const()[name = string("h_165_pad_0"), val = tensor([0, 0])]; + tensor h_165_dilations_0 = const()[name = string("h_165_dilations_0"), val = tensor([1])]; + tensor last_convnext_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46463616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46465600))))[name = string("last_convnext_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46465792)))]; + tensor h_165_cast_fp16 = conv(bias = last_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_165_dilations_0, groups = h_165_groups_0, pad = h_165_pad_0, pad_type = h_165_pad_type_0, strides = h_165_strides_0, weight = last_convnext_3_dwconv__conv_weight_to_fp16_palettized, x = input_425_cast_fp16)[name = string("h_165_cast_fp16")]; + tensor x_cast_fp16 = mul(x = h_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2557_axes_0 = const()[name = string("op_2557_axes_0"), val = tensor([-1])]; + tensor last_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46466880)))]; + tensor last_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46467968)))]; + fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_cast_fp16)[name = string("transpose_1")]; + tensor var_2557_cast_fp16 = layer_norm(axes = var_2557_axes_0, beta = last_convnext_3_norm_norm_bias_to_fp16, epsilon = var_2511_to_fp16, gamma = last_convnext_3_norm_norm_weight_to_fp16, x = input_427_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor input_429_perm_0 = const()[name = string("input_429_perm_0"), val = tensor([0, 2, 1])]; + string h_167_pad_type_0 = const()[name = string("h_167_pad_type_0"), val = string("valid")]; + tensor h_167_strides_0 = const()[name = string("h_167_strides_0"), val = tensor([1])]; + tensor h_167_pad_0 = const()[name = string("h_167_pad_0"), val = tensor([0, 0])]; + tensor h_167_dilations_0 = const()[name = string("h_167_dilations_0"), val = tensor([1])]; + int32 h_167_groups_0 = const()[name = string("h_167_groups_0"), val = int32(1)]; + tensor last_convnext_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46469056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47255552))))[name = string("last_convnext_3_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47255744)))]; + tensor input_429_cast_fp16 = transpose(perm = input_429_perm_0, x = var_2557_cast_fp16)[name = string("transpose_0")]; + tensor h_167_cast_fp16 = conv(bias = last_convnext_3_pwconv1_bias_to_fp16, dilations = h_167_dilations_0, groups = h_167_groups_0, pad = h_167_pad_0, pad_type = h_167_pad_type_0, strides = h_167_strides_0, weight = last_convnext_3_pwconv1_weight_to_fp16_palettized, x = input_429_cast_fp16)[name = string("h_167_cast_fp16")]; + string input_431_mode_0 = const()[name = string("input_431_mode_0"), val = string("EXACT")]; + tensor input_431_cast_fp16 = gelu(mode = input_431_mode_0, x = h_167_cast_fp16)[name = string("input_431_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor op_2574_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47259904))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48046400))))[name = string("op_2574_weight_0_to_fp16_palettized")]; + tensor var_2574_bias_0_to_fp16 = const()[name = string("op_2574_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48046592)))]; + tensor var_2574_cast_fp16 = conv(bias = var_2574_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = op_2574_weight_0_to_fp16_palettized, x = input_431_cast_fp16)[name = string("op_2574_cast_fp16")]; + tensor out_cast_fp16 = add(x = x_223_cast_fp16, y = var_2574_cast_fp16)[name = string("out_cast_fp16")]; + tensor input_cast_fp16 = mul(x = out_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("input_cast_fp16")]; + string var_2587_pad_type_0 = const()[name = string("op_2587_pad_type_0"), val = string("valid")]; + tensor var_2587_strides_0 = const()[name = string("op_2587_strides_0"), val = tensor([1])]; + tensor var_2587_pad_0 = const()[name = string("op_2587_pad_0"), val = tensor([0, 0])]; + tensor var_2587_dilations_0 = const()[name = string("op_2587_dilations_0"), val = tensor([1])]; + int32 var_2587_groups_0 = const()[name = string("op_2587_groups_0"), val = int32(1)]; + tensor proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48047680))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48103040))))[name = string("proj_out_weight_to_fp16_palettized")]; + tensor var_2587_cast_fp16 = conv(dilations = var_2587_dilations_0, groups = var_2587_groups_0, pad = var_2587_pad_0, pad_type = var_2587_pad_type_0, strides = var_2587_strides_0, weight = proj_out_weight_to_fp16_palettized, x = input_cast_fp16)[name = string("op_2587_cast_fp16")]; + tensor v_cast_fp16 = mul(x = var_2587_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("v_cast_fp16")]; + tensor cond_begin_0 = const()[name = string("cond_begin_0"), val = tensor([0, 0, 0])]; + tensor cond_end_0 = const()[name = string("cond_end_0"), val = tensor([1, 144, 512])]; + tensor cond_end_mask_0 = const()[name = string("cond_end_mask_0"), val = tensor([false, true, true])]; + tensor cond_cast_fp16 = slice_by_index(begin = cond_begin_0, end = cond_end_0, end_mask = cond_end_mask_0, x = v_cast_fp16)[name = string("cond_cast_fp16")]; + tensor uncond_begin_0 = const()[name = string("uncond_begin_0"), val = tensor([1, 0, 0])]; + tensor uncond_end_0 = const()[name = string("uncond_end_0"), val = tensor([2, 144, 512])]; + tensor uncond_end_mask_0 = const()[name = string("uncond_end_mask_0"), val = tensor([true, true, true])]; + tensor uncond_cast_fp16 = slice_by_index(begin = uncond_begin_0, end = uncond_end_0, end_mask = uncond_end_mask_0, x = v_cast_fp16)[name = string("uncond_cast_fp16")]; + fp32 var_2597_epsilon_0 = const()[name = string("op_2597_epsilon_0"), val = fp32(0x1.a36e2ep-14)]; + tensor var_2597_cast_fp16 = inverse(epsilon = var_2597_epsilon_0, x = total_step_to_fp16)[name = string("op_2597_cast_fp16")]; + tensor var_2603 = const()[name = string("op_2603"), val = tensor([-1, 1, 1])]; + tensor step_cast_fp16 = reshape(shape = var_2603, x = var_2597_cast_fp16)[name = string("step_cast_fp16")]; + fp16 var_2605_to_fp16 = const()[name = string("op_2605_to_fp16"), val = fp16(0x1p+2)]; + tensor var_2606_cast_fp16 = mul(x = cond_cast_fp16, y = var_2605_to_fp16)[name = string("op_2606_cast_fp16")]; + fp16 var_2607_to_fp16 = const()[name = string("op_2607_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_2608_cast_fp16 = mul(x = uncond_cast_fp16, y = var_2607_to_fp16)[name = string("op_2608_cast_fp16")]; + tensor var_2610_cast_fp16 = sub(x = var_2606_cast_fp16, y = var_2608_cast_fp16)[name = string("op_2610_cast_fp16")]; + tensor var_2611_cast_fp16 = mul(x = step_cast_fp16, y = var_2610_cast_fp16)[name = string("op_2611_cast_fp16")]; + tensor var_2613_cast_fp16 = add(x = noisy_latent_to_fp16, y = var_2611_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor var_2614_cast_fp16 = mul(x = var_2613_cast_fp16, y = latent_mask_to_fp16)[name = string("op_2614_cast_fp16")]; + string var_2614_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2614_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor denoised_latent = cast(dtype = var_2614_cast_fp16_to_fp32_dtype_0, x = var_2614_cast_fp16)[name = string("cast_0")]; + } -> (denoised_latent); +} \ No newline at end of file diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L512_int6.mlmodelc/weights/weight.bin b/VectorEstimatorVariants/VectorEstimator_v2_L512_int6.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..b3347919d29b19f7f267de52e2bc872675191d49 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L512_int6.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:005d553c70f2b19969c8968eb7aceb411de7d83cc8a7778e78435d9b39ce2ff0 +size 48103232 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L512_int6.mlpackage/Data/com.apple.CoreML/model.mlmodel b/VectorEstimatorVariants/VectorEstimator_v2_L512_int6.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 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a/VectorEstimatorVariants/VectorEstimator_v2_L512_int8.mlmodelc/model.mil b/VectorEstimatorVariants/VectorEstimator_v2_L512_int8.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..a44efe5fbb53cdca9622e6cae99324f8d9b8b0e7 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L512_int8.mlmodelc/model.mil @@ -0,0 +1,2102 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor current_step, tensor latent_mask, tensor noisy_latent, tensor style_ttl, tensor text_emb, tensor text_mask, tensor total_step) { + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + bool text_emb_interleave_0 = const()[name = string("text_emb_interleave_0"), val = bool(false)]; + string text_emb_to_fp16_dtype_0 = const()[name = string("text_emb_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_uncond_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor([[[0x1.9b8p-10]]]))[name = string("text_uncond_to_fp16_quantized")]; + tensor text_emb_to_fp16 = cast(dtype = text_emb_to_fp16_dtype_0, x = text_emb)[name = string("cast_7")]; + tensor text_emb_cast_fp16 = concat(axis = var_39, interleave = text_emb_interleave_0, values = (text_emb_to_fp16, text_uncond_to_fp16_quantized))[name = string("text_emb_cast_fp16")]; + bool input_95_interleave_0 = const()[name = string("input_95_interleave_0"), val = bool(false)]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor uncond_masker_style_value_special_token_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32896))), scale = tensor([[[0x1.bb4p-9]]]))[name = string("uncond_masker_style_value_special_token_to_fp16_quantized")]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_6")]; + tensor input_95_cast_fp16 = concat(axis = var_39, interleave = input_95_interleave_0, values = (style_ttl_to_fp16, uncond_masker_style_value_special_token_to_fp16_quantized))[name = string("input_95_cast_fp16")]; + int32 var_64 = const()[name = string("op_64"), val = int32(0)]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + string noisy_latent_to_fp16_dtype_0 = const()[name = string("noisy_latent_to_fp16_dtype_0"), val = string("fp16")]; + tensor noisy_latent_to_fp16 = cast(dtype = noisy_latent_to_fp16_dtype_0, x = noisy_latent)[name = string("cast_5")]; + tensor input_5_cast_fp16 = concat(axis = var_64, interleave = input_5_interleave_0, values = (noisy_latent_to_fp16, noisy_latent_to_fp16))[name = string("input_5_cast_fp16")]; + int32 var_67 = const()[name = string("op_67"), val = int32(0)]; + bool latent_mask_b_interleave_0 = const()[name = string("latent_mask_b_interleave_0"), val = bool(false)]; + string latent_mask_to_fp16_dtype_0 = const()[name = string("latent_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor latent_mask_to_fp16 = cast(dtype = latent_mask_to_fp16_dtype_0, x = latent_mask)[name = string("cast_4")]; + tensor latent_mask_b_cast_fp16 = concat(axis = var_67, interleave = latent_mask_b_interleave_0, values = (latent_mask_to_fp16, latent_mask_to_fp16))[name = string("latent_mask_b_cast_fp16")]; + int32 var_70 = const()[name = string("op_70"), val = int32(0)]; + bool text_mask_interleave_0 = const()[name = string("text_mask_interleave_0"), val = bool(false)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_3")]; + tensor text_mask_cast_fp16 = concat(axis = var_70, interleave = text_mask_interleave_0, values = (text_mask_to_fp16, text_mask_to_fp16))[name = string("text_mask_cast_fp16")]; + string current_step_to_fp16_dtype_0 = const()[name = string("current_step_to_fp16_dtype_0"), val = string("fp16")]; + string total_step_to_fp16_dtype_0 = const()[name = string("total_step_to_fp16_dtype_0"), val = string("fp16")]; + tensor current_step_to_fp16 = cast(dtype = current_step_to_fp16_dtype_0, x = current_step)[name = string("cast_1")]; + tensor total_step_to_fp16 = cast(dtype = total_step_to_fp16_dtype_0, x = total_step)[name = string("cast_2")]; + tensor t_1_cast_fp16 = real_div(x = current_step_to_fp16, y = total_step_to_fp16)[name = string("t_1_cast_fp16")]; + int32 var_74 = const()[name = string("op_74"), val = int32(0)]; + bool t_3_interleave_0 = const()[name = string("t_3_interleave_0"), val = bool(false)]; + tensor t_3_cast_fp16 = concat(axis = var_74, interleave = t_3_interleave_0, values = (t_1_cast_fp16, t_1_cast_fp16))[name = string("t_3_cast_fp16")]; + int32 var_78 = const()[name = string("op_78"), val = int32(-1)]; + tensor var_85 = const()[name = string("op_85"), val = tensor([-1, 1])]; + tensor var_86_cast_fp16 = reshape(shape = var_85, x = t_3_cast_fp16)[name = string("op_86_cast_fp16")]; + fp16 time_encoder_time_scale_to_fp16 = const()[name = string("time_encoder_time_scale_to_fp16"), val = fp16(0x1.f4p+9)]; + tensor t_scaled_cast_fp16 = mul(x = var_86_cast_fp16, y = time_encoder_time_scale_to_fp16)[name = string("t_scaled_cast_fp16")]; + tensor time_encoder_omegas_to_fp16 = const()[name = string("time_encoder_omegas_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45760)))]; + tensor phase_cast_fp16 = mul(x = t_scaled_cast_fp16, y = time_encoder_omegas_to_fp16)[name = string("phase_cast_fp16")]; + tensor var_89_cast_fp16 = sin(x = phase_cast_fp16)[name = string("op_89_cast_fp16")]; + tensor var_90_cast_fp16 = cos(x = phase_cast_fp16)[name = string("op_90_cast_fp16")]; + bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)]; + tensor input_1_cast_fp16 = concat(axis = var_78, interleave = input_1_interleave_0, values = (var_89_cast_fp16, var_90_cast_fp16))[name = string("input_1_cast_fp16")]; + tensor time_encoder_mlp_0_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62336))))[name = string("time_encoder_mlp_0_linear_weight_to_fp16_quantized")]; + tensor time_encoder_mlp_0_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62912)))]; + tensor linear_0_cast_fp16 = linear(bias = time_encoder_mlp_0_linear_bias_to_fp16, weight = time_encoder_mlp_0_linear_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_97_cast_fp16 = softplus(x = linear_0_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = tanh(x = var_97_cast_fp16)[name = string("op_98_cast_fp16")]; + tensor input_3_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_98_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor time_encoder_mlp_2_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79936))))[name = string("time_encoder_mlp_2_linear_weight_to_fp16_quantized")]; + tensor time_encoder_mlp_2_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80128)))]; + tensor linear_1_cast_fp16 = linear(bias = time_encoder_mlp_2_linear_bias_to_fp16, weight = time_encoder_mlp_2_linear_weight_to_fp16_quantized, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor time_emb_axes_0 = const()[name = string("time_emb_axes_0"), val = tensor([-1])]; + tensor time_emb_cast_fp16 = expand_dims(axes = time_emb_axes_0, x = linear_1_cast_fp16)[name = string("time_emb_cast_fp16")]; + string var_115_pad_type_0 = const()[name = string("op_115_pad_type_0"), val = string("valid")]; + tensor var_115_strides_0 = const()[name = string("op_115_strides_0"), val = tensor([1])]; + tensor var_115_pad_0 = const()[name = string("op_115_pad_0"), val = tensor([0, 0])]; + tensor var_115_dilations_0 = const()[name = string("op_115_dilations_0"), val = tensor([1])]; + int32 var_115_groups_0 = const()[name = string("op_115_groups_0"), val = int32(1)]; + tensor proj_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154112))))[name = string("proj_in_weight_to_fp16_quantized")]; + tensor var_115_cast_fp16 = conv(dilations = var_115_dilations_0, groups = var_115_groups_0, pad = var_115_pad_0, pad_type = var_115_pad_type_0, strides = var_115_strides_0, weight = proj_in_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = var_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_3_cast_fp16")]; + int32 var_134 = const()[name = string("op_134"), val = int32(-1)]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([0, 0, 1])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 512])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([true, true, true])]; + tensor input_7_cast_fp16 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, x = latent_mask_b_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_166_pad_0 = const()[name = string("op_166_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_166_mode_0 = const()[name = string("op_166_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_166_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_166_mode_0, pad = var_166_pad_0, x = input_7_cast_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_133_to_fp16 = const()[name = string("op_133_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = sub(x = var_133_to_fp16, y = var_166_cast_fp16)[name = string("op_167_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = latent_mask_b_cast_fp16, y = var_167_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_169_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_172_cast_fp16 = mul(x = x_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor var_173_cast_fp16 = sub(x = var_133_to_fp16, y = latent_mask_b_cast_fp16)[name = string("op_173_cast_fp16")]; + tensor var_174_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_174_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_174_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("replicate")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor input_11_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_11_mode_0, pad = input_11_pad_0, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(512)]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157824))))[name = string("main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158912)))]; + tensor h_3_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, 2, 1])]; + tensor var_192_axes_0 = const()[name = string("op_192_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160000)))]; + tensor main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161088)))]; + fp16 var_139_to_fp16 = const()[name = string("op_139_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_7_cast_fp16)[name = string("transpose_117")]; + tensor var_192_cast_fp16 = layer_norm(axes = var_192_axes_0, beta = main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16, x = input_13_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, 2, 1])]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1210816))))[name = string("main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214976)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = var_192_cast_fp16)[name = string("transpose_116")]; + tensor h_5_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("h_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = h_5_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)]; + tensor op_209_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1219136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2267776))))[name = string("op_209_weight_0_to_fp16_quantized")]; + tensor var_209_bias_0_to_fp16 = const()[name = string("op_209_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2268864)))]; + tensor var_209_cast_fp16 = conv(bias = var_209_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = op_209_weight_0_to_fp16_quantized, x = input_17_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_209_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_223_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_223_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_226_cast_fp16 = mul(x = x_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_226_cast_fp16")]; + tensor var_228_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_228_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_226_cast_fp16, y = var_228_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")]; + fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)]; + tensor input_23_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([2])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(512)]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2269952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2272576))))[name = string("main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2273664)))]; + tensor h_9_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor var_246_axes_0 = const()[name = string("op_246_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2274752)))]; + tensor main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2275840)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_13_cast_fp16)[name = string("transpose_115")]; + tensor var_246_cast_fp16 = layer_norm(axes = var_246_axes_0, beta = main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_246_cast_fp16")]; + tensor input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor([0, 2, 1])]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2276928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3325568))))[name = string("main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3329728)))]; + tensor input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_246_cast_fp16)[name = string("transpose_114")]; + tensor h_11_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("h_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_11_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)]; + tensor op_263_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3333888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4382528))))[name = string("op_263_weight_0_to_fp16_quantized")]; + tensor var_263_bias_0_to_fp16 = const()[name = string("op_263_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4383616)))]; + tensor var_263_cast_fp16 = conv(bias = var_263_bias_0_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = op_263_weight_0_to_fp16_quantized, x = input_29_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_263_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_277_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_277_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_277_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_280_cast_fp16 = mul(x = x_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_280_cast_fp16")]; + tensor var_282_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_280_cast_fp16, y = var_282_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("replicate")]; + fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)]; + tensor input_35_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = input_35_mode_0, pad = input_35_pad_0, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([4])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(512)]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4384704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4387328))))[name = string("main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4388416)))]; + tensor h_15_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor var_300_axes_0 = const()[name = string("op_300_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4389504)))]; + tensor main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4390592)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_19_cast_fp16)[name = string("transpose_113")]; + tensor var_300_cast_fp16 = layer_norm(axes = var_300_axes_0, beta = main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16, x = input_37_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor([0, 2, 1])]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4391680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5440320))))[name = string("main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5444480)))]; + tensor input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_300_cast_fp16)[name = string("transpose_112")]; + tensor h_17_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("h_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = h_17_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)]; + tensor op_317_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5448640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6497280))))[name = string("op_317_weight_0_to_fp16_quantized")]; + tensor var_317_bias_0_to_fp16 = const()[name = string("op_317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6498368)))]; + tensor var_317_cast_fp16 = conv(bias = var_317_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = op_317_weight_0_to_fp16_quantized, x = input_41_cast_fp16)[name = string("op_317_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_317_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor var_331_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_331_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_331_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = x_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor var_336_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_336_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_334_cast_fp16, y = var_336_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")]; + fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([8])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(512)]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6499456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6502080))))[name = string("main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6503168)))]; + tensor h_21_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6504256)))]; + tensor main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6505344)))]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_25_cast_fp16)[name = string("transpose_111")]; + tensor var_354_cast_fp16 = layer_norm(axes = var_354_axes_0, beta = main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_354_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6506432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7555072))))[name = string("main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7559232)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_354_cast_fp16)[name = string("transpose_110")]; + tensor h_23_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("h_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_23_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)]; + tensor op_371_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7563392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8612032))))[name = string("op_371_weight_0_to_fp16_quantized")]; + tensor var_371_bias_0_to_fp16 = const()[name = string("op_371_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8613120)))]; + tensor var_371_cast_fp16 = conv(bias = var_371_bias_0_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = op_371_weight_0_to_fp16_quantized, x = input_53_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_371_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor input_55_cast_fp16 = squeeze(axes = input_55_axes_0, x = time_emb_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor main_blocks_0_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8614208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8647040))))[name = string("main_blocks_0_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8648128)))]; + tensor linear_2_cast_fp16 = linear(bias = main_blocks_0_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_0_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor t_5_axes_0 = const()[name = string("t_5_axes_0"), val = tensor([-1])]; + tensor t_5_cast_fp16 = expand_dims(axes = t_5_axes_0, x = linear_2_cast_fp16)[name = string("t_5_cast_fp16")]; + tensor var_381_cast_fp16 = add(x = x_27_cast_fp16, y = t_5_cast_fp16)[name = string("op_381_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_381_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_394_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_394_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_394_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_397_cast_fp16 = mul(x = x_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = var_397_cast_fp16, y = var_399_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("replicate")]; + fp16 const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = fp16(0x0p+0)]; + tensor input_61_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = input_61_mode_0, pad = input_61_pad_0, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(512)]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8649216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8651840))))[name = string("main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8652928)))]; + tensor h_27_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_61_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417_axes_0 = const()[name = string("op_417_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8654016)))]; + tensor main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8655104)))]; + tensor input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_33_cast_fp16)[name = string("transpose_109")]; + tensor var_417_cast_fp16 = layer_norm(axes = var_417_axes_0, beta = main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16, x = input_63_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8656192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9704832))))[name = string("main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9708992)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = var_417_cast_fp16)[name = string("transpose_108")]; + tensor h_29_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("h_29_cast_fp16")]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("EXACT")]; + tensor input_67_cast_fp16 = gelu(mode = input_67_mode_0, x = h_29_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)]; + tensor op_434_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9713152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10761792))))[name = string("op_434_weight_0_to_fp16_quantized")]; + tensor var_434_bias_0_to_fp16 = const()[name = string("op_434_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10762880)))]; + tensor var_434_cast_fp16 = conv(bias = var_434_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = op_434_weight_0_to_fp16_quantized, x = input_67_cast_fp16)[name = string("op_434_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_434_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_m_1_cast_fp16 = mul(x = x_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_1_cast_fp16")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10763968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11026176))))[name = string("main_blocks_0_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11027264)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_35_cast_fp16)[name = string("transpose_107")]; + tensor linear_3_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([2, 512, 8, 64])]; + tensor var_453_cast_fp16 = reshape(shape = var_452, x = linear_3_cast_fp16)[name = string("op_453_cast_fp16")]; + tensor x_37_perm_0 = const()[name = string("x_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11028352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11159488))))[name = string("main_blocks_0_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11160576)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = text_emb_cast_fp16)[name = string("transpose_106")]; + tensor linear_4_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([2, 128, 8, 64])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = linear_4_cast_fp16)[name = string("op_461_cast_fp16")]; + tensor x_39_perm_0 = const()[name = string("x_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11161664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11292800))))[name = string("main_blocks_0_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11293888)))]; + tensor linear_5_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_468 = const()[name = string("op_468"), val = tensor([2, 128, 8, 64])]; + tensor var_469_cast_fp16 = reshape(shape = var_468, x = linear_5_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor divisor_1_axes_0 = const()[name = string("divisor_1_axes_0"), val = tensor([1, 2])]; + bool divisor_1_keep_dims_0 = const()[name = string("divisor_1_keep_dims_0"), val = bool(false)]; + tensor divisor_1_cast_fp16 = reduce_sum(axes = divisor_1_axes_0, keep_dims = divisor_1_keep_dims_0, x = latent_mask_b_cast_fp16)[name = string("divisor_1_cast_fp16")]; + tensor divisor_3_axes_0 = const()[name = string("divisor_3_axes_0"), val = tensor([1, 2])]; + bool divisor_3_keep_dims_0 = const()[name = string("divisor_3_keep_dims_0"), val = bool(false)]; + tensor divisor_3_cast_fp16 = reduce_sum(axes = divisor_3_axes_0, keep_dims = divisor_3_keep_dims_0, x = text_mask_cast_fp16)[name = string("divisor_3_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([-1, 1, 1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = divisor_1_cast_fp16)[name = string("op_488_cast_fp16")]; + tensor const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11294976)))]; + tensor scaled_1_cast_fp16 = real_div(x = const_16_to_fp16, y = var_488_cast_fp16)[name = string("scaled_1_cast_fp16")]; + tensor main_blocks_0_text_attn_theta_to_fp16 = const()[name = string("main_blocks_0_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11297088)))]; + tensor angles_1_cast_fp16 = mul(x = scaled_1_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_1_cast_fp16")]; + tensor var_491_cast_fp16 = cos(x = angles_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = var_491_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor var_493_cast_fp16 = sin(x = angles_1_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = var_493_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor x_a_1_begin_0 = const()[name = string("x_a_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_1_end_0 = const()[name = string("x_a_1_end_0"), val = tensor([2, 8, 512, 32])]; + tensor x_a_1_end_mask_0 = const()[name = string("x_a_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_37_cast_fp16 = transpose(perm = x_37_perm_0, x = var_453_cast_fp16)[name = string("transpose_105")]; + tensor x_a_1_cast_fp16 = slice_by_index(begin = x_a_1_begin_0, end = x_a_1_end_0, end_mask = x_a_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_a_1_cast_fp16")]; + tensor x_b_1_begin_0 = const()[name = string("x_b_1_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_1_end_0 = const()[name = string("x_b_1_end_0"), val = tensor([2, 8, 512, 64])]; + tensor x_b_1_end_mask_0 = const()[name = string("x_b_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_1_cast_fp16 = slice_by_index(begin = x_b_1_begin_0, end = x_b_1_end_0, end_mask = x_b_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_b_1_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = x_a_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor var_498_cast_fp16 = mul(x = x_b_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_498_cast_fp16")]; + tensor rot_a_1_cast_fp16 = sub(x = var_497_cast_fp16, y = var_498_cast_fp16)[name = string("rot_a_1_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = x_a_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = x_b_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor rot_b_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("rot_b_1_cast_fp16")]; + bool q_1_interleave_0 = const()[name = string("q_1_interleave_0"), val = bool(false)]; + tensor q_1_cast_fp16 = concat(axis = var_134, interleave = q_1_interleave_0, values = (rot_a_1_cast_fp16, rot_b_1_cast_fp16))[name = string("q_1_cast_fp16")]; + tensor var_505 = const()[name = string("op_505"), val = tensor([-1, 1, 1])]; + tensor var_506_cast_fp16 = reshape(shape = var_505, x = divisor_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor const_17_to_fp16 = const()[name = string("const_17_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11297216)))]; + tensor scaled_3_cast_fp16 = real_div(x = const_17_to_fp16, y = var_506_cast_fp16)[name = string("scaled_3_cast_fp16")]; + tensor angles_3_cast_fp16 = mul(x = scaled_3_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_3_cast_fp16")]; + tensor var_509_cast_fp16 = cos(x = angles_3_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor cos_3_axes_0 = const()[name = string("cos_3_axes_0"), val = tensor([1])]; + tensor cos_3_cast_fp16 = expand_dims(axes = cos_3_axes_0, x = var_509_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor var_511_cast_fp16 = sin(x = angles_3_cast_fp16)[name = string("op_511_cast_fp16")]; + tensor sin_3_axes_0 = const()[name = string("sin_3_axes_0"), val = tensor([1])]; + tensor sin_3_cast_fp16 = expand_dims(axes = sin_3_axes_0, x = var_511_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor x_a_3_begin_0 = const()[name = string("x_a_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_3_end_0 = const()[name = string("x_a_3_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_3_end_mask_0 = const()[name = string("x_a_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_39_cast_fp16 = transpose(perm = x_39_perm_0, x = var_461_cast_fp16)[name = string("transpose_104")]; + tensor x_a_3_cast_fp16 = slice_by_index(begin = x_a_3_begin_0, end = x_a_3_end_0, end_mask = x_a_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_a_3_cast_fp16")]; + tensor x_b_3_begin_0 = const()[name = string("x_b_3_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_3_end_0 = const()[name = string("x_b_3_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_3_end_mask_0 = const()[name = string("x_b_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_3_cast_fp16 = slice_by_index(begin = x_b_3_begin_0, end = x_b_3_end_0, end_mask = x_b_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_b_3_cast_fp16")]; + tensor var_515_cast_fp16 = mul(x = x_a_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_515_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = x_b_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor rot_a_3_cast_fp16 = sub(x = var_515_cast_fp16, y = var_516_cast_fp16)[name = string("rot_a_3_cast_fp16")]; + tensor var_518_cast_fp16 = mul(x = x_a_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = mul(x = x_b_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor rot_b_3_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = string("rot_b_3_cast_fp16")]; + bool k_1_interleave_0 = const()[name = string("k_1_interleave_0"), val = bool(false)]; + tensor k_1_cast_fp16 = concat(axis = var_134, interleave = k_1_interleave_0, values = (rot_a_3_cast_fp16, rot_b_3_cast_fp16))[name = string("k_1_cast_fp16")]; + bool var_524_transpose_x_1 = const()[name = string("op_524_transpose_x_1"), val = bool(false)]; + bool var_524_transpose_y_1 = const()[name = string("op_524_transpose_y_1"), val = bool(true)]; + tensor var_524_cast_fp16 = matmul(transpose_x = var_524_transpose_x_1, transpose_y = var_524_transpose_y_1, x = q_1_cast_fp16, y = k_1_cast_fp16)[name = string("op_524_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_524_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([2])]; + tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = text_mask_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528_cast_fp16 = sub(x = var_133_to_fp16, y = var_527_cast_fp16)[name = string("op_528_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_530_cast_fp16 = mul(x = var_528_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_75_cast_fp16 = sub(x = _inversed_scores_1_cast_fp16, y = var_530_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_134, x = input_75_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool var_533_transpose_x_0 = const()[name = string("op_533_transpose_x_0"), val = bool(false)]; + bool var_533_transpose_y_0 = const()[name = string("op_533_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_469_cast_fp16)[name = string("transpose_103")]; + tensor var_533_cast_fp16 = matmul(transpose_x = var_533_transpose_x_0, transpose_y = var_533_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("op_533_cast_fp16")]; + tensor var_534_perm_0 = const()[name = string("op_534_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_535 = const()[name = string("op_535"), val = tensor([2, 512, 512])]; + tensor var_534_cast_fp16 = transpose(perm = var_534_perm_0, x = var_533_cast_fp16)[name = string("transpose_102")]; + tensor input_77_cast_fp16 = reshape(shape = var_535, x = var_534_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11297792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11560000))))[name = string("main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11561088)))]; + tensor linear_6_cast_fp16 = linear(bias = main_blocks_0_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor out_11_perm_0 = const()[name = string("out_11_perm_0"), val = tensor([0, 2, 1])]; + tensor out_11_cast_fp16 = transpose(perm = out_11_perm_0, x = linear_6_cast_fp16)[name = string("transpose_101")]; + tensor var_542_cast_fp16 = mul(x = out_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_542_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = x_m_1_cast_fp16, y = var_542_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor var_549_axes_0 = const()[name = string("op_549_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11562176)))]; + tensor main_blocks_0_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11563264)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_41_cast_fp16)[name = string("transpose_100")]; + tensor var_549_cast_fp16 = layer_norm(axes = var_549_axes_0, beta = main_blocks_0_text_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_text_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor var_550_perm_0 = const()[name = string("op_550_perm_0"), val = tensor([0, 2, 1])]; + tensor var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = var_549_cast_fp16)[name = string("transpose_99")]; + tensor x_43_cast_fp16 = mul(x = var_550_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = x_45_cast_fp16, y = last_1_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor x_last_11_axes_0 = const()[name = string("x_last_11_axes_0"), val = tensor([-1])]; + bool x_last_11_keep_dims_0 = const()[name = string("x_last_11_keep_dims_0"), val = bool(true)]; + tensor x_last_11_cast_fp16 = reduce_sum(axes = x_last_11_axes_0, keep_dims = x_last_11_keep_dims_0, x = var_563_cast_fp16)[name = string("x_last_11_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = x_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor var_568_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_11_cast_fp16)[name = string("op_568_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = var_566_cast_fp16, y = var_568_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_85_mode_0 = const()[name = string("input_85_mode_0"), val = string("replicate")]; + fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)]; + tensor input_85_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(512)]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11564352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11566976))))[name = string("main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11568064)))]; + tensor h_33_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("h_33_cast_fp16")]; + tensor x_47_cast_fp16 = mul(x = h_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11569152)))]; + tensor main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11570240)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_47_cast_fp16)[name = string("transpose_98")]; + tensor var_586_cast_fp16 = layer_norm(axes = var_586_axes_0, beta = main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_586_cast_fp16")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11571328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12619968))))[name = string("main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12624128)))]; + tensor input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = var_586_cast_fp16)[name = string("transpose_97")]; + tensor h_35_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("h_35_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = h_35_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")]; + tensor h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor([1])]; + tensor h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor([0, 0])]; + tensor h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor([1])]; + int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)]; + tensor op_603_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12628288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13676928))))[name = string("op_603_weight_0_to_fp16_quantized")]; + tensor var_603_bias_0_to_fp16 = const()[name = string("op_603_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13678016)))]; + tensor var_603_cast_fp16 = conv(bias = var_603_bias_0_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = op_603_weight_0_to_fp16_quantized, x = input_91_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor out_13_cast_fp16 = add(x = x_45_cast_fp16, y = var_603_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_49_cast_fp16 = mul(x = out_13_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_m_3_cast_fp16 = mul(x = x_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_3_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13679104))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13810240))))[name = string("main_blocks_0_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13810816)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_49_cast_fp16)[name = string("transpose_96")]; + tensor linear_7_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([2, 512, 2, 128])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = linear_7_cast_fp16)[name = string("op_620_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_0_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13811392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13876992))))[name = string("main_blocks_0_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13877568)))]; + tensor linear_9_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_633 = const()[name = string("op_633"), val = tensor([2, 50, 2, 128])]; + tensor var_634_cast_fp16 = reshape(shape = var_633, x = linear_9_cast_fp16)[name = string("op_634_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_638_transpose_x_0 = const()[name = string("op_638_transpose_x_0"), val = bool(false)]; + bool var_638_transpose_y_0 = const()[name = string("op_638_transpose_y_0"), val = bool(false)]; + tensor op_637_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13878144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13903808))))[name = string("op_637_to_fp16_quantized")]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_620_cast_fp16)[name = string("transpose_95")]; + tensor var_638_cast_fp16 = matmul(transpose_x = var_638_transpose_x_0, transpose_y = var_638_transpose_y_0, x = q_3_cast_fp16, y = op_637_to_fp16_quantized)[name = string("op_638_cast_fp16")]; + fp16 _inversed_input_97_y_0_to_fp16 = const()[name = string("_inversed_input_97_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_97_cast_fp16 = mul(x = var_638_cast_fp16, y = _inversed_input_97_y_0_to_fp16)[name = string("_inversed_input_97_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_134, x = _inversed_input_97_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_642_perm_0 = const()[name = string("op_642_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_1_axes_0 = const()[name = string("mask_1_axes_0"), val = tensor([1])]; + tensor var_642_cast_fp16 = transpose(perm = var_642_perm_0, x = latent_mask_b_cast_fp16)[name = string("transpose_94")]; + tensor mask_1_cast_fp16 = expand_dims(axes = mask_1_axes_0, x = var_642_cast_fp16)[name = string("mask_1_cast_fp16")]; + tensor attn_5_cast_fp16 = mul(x = attn_3_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool var_645_transpose_x_0 = const()[name = string("op_645_transpose_x_0"), val = bool(false)]; + bool var_645_transpose_y_0 = const()[name = string("op_645_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_634_cast_fp16)[name = string("transpose_93")]; + tensor var_645_cast_fp16 = matmul(transpose_x = var_645_transpose_x_0, transpose_y = var_645_transpose_y_0, x = attn_5_cast_fp16, y = v_3_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_646_perm_0 = const()[name = string("op_646_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_647 = const()[name = string("op_647"), val = tensor([2, 512, 256])]; + tensor var_646_cast_fp16 = transpose(perm = var_646_perm_0, x = var_645_cast_fp16)[name = string("transpose_92")]; + tensor input_99_cast_fp16 = reshape(shape = var_647, x = var_646_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13904000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14035136))))[name = string("main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14036224)))]; + tensor linear_10_cast_fp16 = linear(bias = main_blocks_0_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor out_15_perm_0 = const()[name = string("out_15_perm_0"), val = tensor([0, 2, 1])]; + tensor out_15_cast_fp16 = transpose(perm = out_15_perm_0, x = linear_10_cast_fp16)[name = string("transpose_91")]; + tensor var_654_cast_fp16 = mul(x = out_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor x_51_cast_fp16 = add(x = x_m_3_cast_fp16, y = var_654_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14037312)))]; + tensor main_blocks_0_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14038400)))]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_51_cast_fp16)[name = string("transpose_90")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = main_blocks_0_style_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_style_norm_norm_weight_to_fp16, x = input_101_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_662_perm_0 = const()[name = string("op_662_perm_0"), val = tensor([0, 2, 1])]; + tensor var_662_cast_fp16 = transpose(perm = var_662_perm_0, x = var_661_cast_fp16)[name = string("transpose_89")]; + tensor x_53_cast_fp16 = mul(x = var_662_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_53_cast_fp16")]; + int32 var_681 = const()[name = string("op_681"), val = int32(-1)]; + tensor x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = x_55_cast_fp16, y = last_1_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor x_last_13_axes_0 = const()[name = string("x_last_13_axes_0"), val = tensor([-1])]; + bool x_last_13_keep_dims_0 = const()[name = string("x_last_13_keep_dims_0"), val = bool(true)]; + tensor x_last_13_cast_fp16 = reduce_sum(axes = x_last_13_axes_0, keep_dims = x_last_13_keep_dims_0, x = var_716_cast_fp16)[name = string("x_last_13_cast_fp16")]; + tensor var_719_cast_fp16 = mul(x = x_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_721_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_13_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_719_cast_fp16, y = var_721_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("replicate")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(512)]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14039488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14042112))))[name = string("main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14043200)))]; + tensor h_39_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor x_57_cast_fp16 = mul(x = h_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_739_axes_0 = const()[name = string("op_739_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14044288)))]; + tensor main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14045376)))]; + fp16 var_686_to_fp16 = const()[name = string("op_686_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_57_cast_fp16)[name = string("transpose_88")]; + tensor var_739_cast_fp16 = layer_norm(axes = var_739_axes_0, beta = main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_739_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + string h_41_pad_type_0 = const()[name = string("h_41_pad_type_0"), val = string("valid")]; + tensor h_41_strides_0 = const()[name = string("h_41_strides_0"), val = tensor([1])]; + tensor h_41_pad_0 = const()[name = string("h_41_pad_0"), val = tensor([0, 0])]; + tensor h_41_dilations_0 = const()[name = string("h_41_dilations_0"), val = tensor([1])]; + int32 h_41_groups_0 = const()[name = string("h_41_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14046464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15095104))))[name = string("main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15099264)))]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = var_739_cast_fp16)[name = string("transpose_87")]; + tensor h_41_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_41_dilations_0, groups = h_41_groups_0, pad = h_41_pad_0, pad_type = h_41_pad_type_0, strides = h_41_strides_0, weight = main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = h_41_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor op_756_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15103424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16152064))))[name = string("op_756_weight_0_to_fp16_quantized")]; + tensor var_756_bias_0_to_fp16 = const()[name = string("op_756_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16153152)))]; + tensor var_756_cast_fp16 = conv(bias = var_756_bias_0_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = op_756_weight_0_to_fp16_quantized, x = input_113_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor out_17_cast_fp16 = add(x = x_55_cast_fp16, y = var_756_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_59_cast_fp16 = mul(x = out_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor x_61_cast_fp16 = mul(x = x_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_770_cast_fp16 = mul(x = x_61_cast_fp16, y = last_1_cast_fp16)[name = string("op_770_cast_fp16")]; + tensor x_last_15_axes_0 = const()[name = string("x_last_15_axes_0"), val = tensor([-1])]; + bool x_last_15_keep_dims_0 = const()[name = string("x_last_15_keep_dims_0"), val = bool(true)]; + tensor x_last_15_cast_fp16 = reduce_sum(axes = x_last_15_axes_0, keep_dims = x_last_15_keep_dims_0, x = var_770_cast_fp16)[name = string("x_last_15_cast_fp16")]; + tensor var_773_cast_fp16 = mul(x = x_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor var_775_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_15_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = var_773_cast_fp16, y = var_775_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("replicate")]; + fp16 const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = fp16(0x0p+0)]; + tensor input_119_cast_fp16 = pad(constant_val = const_26_to_fp16, mode = input_119_mode_0, pad = input_119_pad_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")]; + string h_45_pad_type_0 = const()[name = string("h_45_pad_type_0"), val = string("valid")]; + tensor h_45_dilations_0 = const()[name = string("h_45_dilations_0"), val = tensor([2])]; + int32 h_45_groups_0 = const()[name = string("h_45_groups_0"), val = int32(512)]; + tensor h_45_strides_0 = const()[name = string("h_45_strides_0"), val = tensor([1])]; + tensor h_45_pad_0 = const()[name = string("h_45_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16154240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16156864))))[name = string("main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16157952)))]; + tensor h_45_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_45_dilations_0, groups = h_45_groups_0, pad = h_45_pad_0, pad_type = h_45_pad_type_0, strides = h_45_strides_0, weight = main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = h_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16159040)))]; + tensor main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16160128)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_63_cast_fp16)[name = string("transpose_86")]; + tensor var_793_cast_fp16 = layer_norm(axes = var_793_axes_0, beta = main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor input_123_perm_0 = const()[name = string("input_123_perm_0"), val = tensor([0, 2, 1])]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16161216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17209856))))[name = string("main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17214016)))]; + tensor input_123_cast_fp16 = transpose(perm = input_123_perm_0, x = var_793_cast_fp16)[name = string("transpose_85")]; + tensor h_47_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_123_cast_fp16)[name = string("h_47_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = h_47_cast_fp16)[name = string("input_125_cast_fp16")]; + string h_49_pad_type_0 = const()[name = string("h_49_pad_type_0"), val = string("valid")]; + tensor h_49_strides_0 = const()[name = string("h_49_strides_0"), val = tensor([1])]; + tensor h_49_pad_0 = const()[name = string("h_49_pad_0"), val = tensor([0, 0])]; + tensor h_49_dilations_0 = const()[name = string("h_49_dilations_0"), val = tensor([1])]; + int32 h_49_groups_0 = const()[name = string("h_49_groups_0"), val = int32(1)]; + tensor op_810_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17218176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18266816))))[name = string("op_810_weight_0_to_fp16_quantized")]; + tensor var_810_bias_0_to_fp16 = const()[name = string("op_810_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18267904)))]; + tensor var_810_cast_fp16 = conv(bias = var_810_bias_0_to_fp16, dilations = h_49_dilations_0, groups = h_49_groups_0, pad = h_49_pad_0, pad_type = h_49_pad_type_0, strides = h_49_strides_0, weight = op_810_weight_0_to_fp16_quantized, x = input_125_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = x_61_cast_fp16, y = var_810_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor x_65_cast_fp16 = mul(x = out_19_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = x_67_cast_fp16, y = last_1_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_last_17_axes_0 = const()[name = string("x_last_17_axes_0"), val = tensor([-1])]; + bool x_last_17_keep_dims_0 = const()[name = string("x_last_17_keep_dims_0"), val = bool(true)]; + tensor x_last_17_cast_fp16 = reduce_sum(axes = x_last_17_axes_0, keep_dims = x_last_17_keep_dims_0, x = var_824_cast_fp16)[name = string("x_last_17_cast_fp16")]; + tensor var_827_cast_fp16 = mul(x = x_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_829_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_17_cast_fp16)[name = string("op_829_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = var_827_cast_fp16, y = var_829_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("replicate")]; + fp16 const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = fp16(0x0p+0)]; + tensor input_131_cast_fp16 = pad(constant_val = const_28_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + string h_51_pad_type_0 = const()[name = string("h_51_pad_type_0"), val = string("valid")]; + tensor h_51_dilations_0 = const()[name = string("h_51_dilations_0"), val = tensor([4])]; + int32 h_51_groups_0 = const()[name = string("h_51_groups_0"), val = int32(512)]; + tensor h_51_strides_0 = const()[name = string("h_51_strides_0"), val = tensor([1])]; + tensor h_51_pad_0 = const()[name = string("h_51_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18268992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18271616))))[name = string("main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18272704)))]; + tensor h_51_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_51_dilations_0, groups = h_51_groups_0, pad = h_51_pad_0, pad_type = h_51_pad_type_0, strides = h_51_strides_0, weight = main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor x_69_cast_fp16 = mul(x = h_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_847_axes_0 = const()[name = string("op_847_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18273792)))]; + tensor main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18274880)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_69_cast_fp16)[name = string("transpose_84")]; + tensor var_847_cast_fp16 = layer_norm(axes = var_847_axes_0, beta = main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_847_cast_fp16")]; + tensor input_135_perm_0 = const()[name = string("input_135_perm_0"), val = tensor([0, 2, 1])]; + string h_53_pad_type_0 = const()[name = string("h_53_pad_type_0"), val = string("valid")]; + tensor h_53_strides_0 = const()[name = string("h_53_strides_0"), val = tensor([1])]; + tensor h_53_pad_0 = const()[name = string("h_53_pad_0"), val = tensor([0, 0])]; + tensor h_53_dilations_0 = const()[name = string("h_53_dilations_0"), val = tensor([1])]; + int32 h_53_groups_0 = const()[name = string("h_53_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18275968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19324608))))[name = string("main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19328768)))]; + tensor input_135_cast_fp16 = transpose(perm = input_135_perm_0, x = var_847_cast_fp16)[name = string("transpose_83")]; + tensor h_53_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_53_dilations_0, groups = h_53_groups_0, pad = h_53_pad_0, pad_type = h_53_pad_type_0, strides = h_53_strides_0, weight = main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("h_53_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = h_53_cast_fp16)[name = string("input_137_cast_fp16")]; + string h_55_pad_type_0 = const()[name = string("h_55_pad_type_0"), val = string("valid")]; + tensor h_55_strides_0 = const()[name = string("h_55_strides_0"), val = tensor([1])]; + tensor h_55_pad_0 = const()[name = string("h_55_pad_0"), val = tensor([0, 0])]; + tensor h_55_dilations_0 = const()[name = string("h_55_dilations_0"), val = tensor([1])]; + int32 h_55_groups_0 = const()[name = string("h_55_groups_0"), val = int32(1)]; + tensor op_864_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19332928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20381568))))[name = string("op_864_weight_0_to_fp16_quantized")]; + tensor var_864_bias_0_to_fp16 = const()[name = string("op_864_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20382656)))]; + tensor var_864_cast_fp16 = conv(bias = var_864_bias_0_to_fp16, dilations = h_55_dilations_0, groups = h_55_groups_0, pad = h_55_pad_0, pad_type = h_55_pad_type_0, strides = h_55_strides_0, weight = op_864_weight_0_to_fp16_quantized, x = input_137_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor out_21_cast_fp16 = add(x = x_67_cast_fp16, y = var_864_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = out_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor x_73_cast_fp16 = mul(x = x_71_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_878_cast_fp16 = mul(x = x_73_cast_fp16, y = last_1_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor x_last_19_axes_0 = const()[name = string("x_last_19_axes_0"), val = tensor([-1])]; + bool x_last_19_keep_dims_0 = const()[name = string("x_last_19_keep_dims_0"), val = bool(true)]; + tensor x_last_19_cast_fp16 = reduce_sum(axes = x_last_19_axes_0, keep_dims = x_last_19_keep_dims_0, x = var_878_cast_fp16)[name = string("x_last_19_cast_fp16")]; + tensor var_881_cast_fp16 = mul(x = x_73_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_881_cast_fp16")]; + tensor var_883_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_19_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = var_881_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("replicate")]; + fp16 const_30_to_fp16 = const()[name = string("const_30_to_fp16"), val = fp16(0x0p+0)]; + tensor input_143_cast_fp16 = pad(constant_val = const_30_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + string h_57_pad_type_0 = const()[name = string("h_57_pad_type_0"), val = string("valid")]; + tensor h_57_dilations_0 = const()[name = string("h_57_dilations_0"), val = tensor([8])]; + int32 h_57_groups_0 = const()[name = string("h_57_groups_0"), val = int32(512)]; + tensor h_57_strides_0 = const()[name = string("h_57_strides_0"), val = tensor([1])]; + tensor h_57_pad_0 = const()[name = string("h_57_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20383744))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20386368))))[name = string("main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20387456)))]; + tensor h_57_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_57_dilations_0, groups = h_57_groups_0, pad = h_57_pad_0, pad_type = h_57_pad_type_0, strides = h_57_strides_0, weight = main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_143_cast_fp16)[name = string("h_57_cast_fp16")]; + tensor x_75_cast_fp16 = mul(x = h_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_901_axes_0 = const()[name = string("op_901_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20388544)))]; + tensor main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20389632)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_75_cast_fp16)[name = string("transpose_82")]; + tensor var_901_cast_fp16 = layer_norm(axes = var_901_axes_0, beta = main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor input_147_perm_0 = const()[name = string("input_147_perm_0"), val = tensor([0, 2, 1])]; + string h_59_pad_type_0 = const()[name = string("h_59_pad_type_0"), val = string("valid")]; + tensor h_59_strides_0 = const()[name = string("h_59_strides_0"), val = tensor([1])]; + tensor h_59_pad_0 = const()[name = string("h_59_pad_0"), val = tensor([0, 0])]; + tensor h_59_dilations_0 = const()[name = string("h_59_dilations_0"), val = tensor([1])]; + int32 h_59_groups_0 = const()[name = string("h_59_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20390720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21439360))))[name = string("main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21443520)))]; + tensor input_147_cast_fp16 = transpose(perm = input_147_perm_0, x = var_901_cast_fp16)[name = string("transpose_81")]; + tensor h_59_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_59_dilations_0, groups = h_59_groups_0, pad = h_59_pad_0, pad_type = h_59_pad_type_0, strides = h_59_strides_0, weight = main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_147_cast_fp16)[name = string("h_59_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = h_59_cast_fp16)[name = string("input_149_cast_fp16")]; + string h_61_pad_type_0 = const()[name = string("h_61_pad_type_0"), val = string("valid")]; + tensor h_61_strides_0 = const()[name = string("h_61_strides_0"), val = tensor([1])]; + tensor h_61_pad_0 = const()[name = string("h_61_pad_0"), val = tensor([0, 0])]; + tensor h_61_dilations_0 = const()[name = string("h_61_dilations_0"), val = tensor([1])]; + int32 h_61_groups_0 = const()[name = string("h_61_groups_0"), val = int32(1)]; + tensor op_918_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21447680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22496320))))[name = string("op_918_weight_0_to_fp16_quantized")]; + tensor var_918_bias_0_to_fp16 = const()[name = string("op_918_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22497408)))]; + tensor var_918_cast_fp16 = conv(bias = var_918_bias_0_to_fp16, dilations = h_61_dilations_0, groups = h_61_groups_0, pad = h_61_pad_0, pad_type = h_61_pad_type_0, strides = h_61_strides_0, weight = op_918_weight_0_to_fp16_quantized, x = input_149_cast_fp16)[name = string("op_918_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = x_73_cast_fp16, y = var_918_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor x_77_cast_fp16 = mul(x = out_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor main_blocks_1_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22498496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22531328))))[name = string("main_blocks_1_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22532416)))]; + tensor linear_11_cast_fp16 = linear(bias = main_blocks_1_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_1_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor t_7_axes_0 = const()[name = string("t_7_axes_0"), val = tensor([-1])]; + tensor t_7_cast_fp16 = expand_dims(axes = t_7_axes_0, x = linear_11_cast_fp16)[name = string("t_7_cast_fp16")]; + tensor var_928_cast_fp16 = add(x = x_77_cast_fp16, y = t_7_cast_fp16)[name = string("op_928_cast_fp16")]; + tensor x_79_cast_fp16 = mul(x = var_928_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor x_81_cast_fp16 = mul(x = x_79_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = x_81_cast_fp16, y = last_1_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor x_last_21_axes_0 = const()[name = string("x_last_21_axes_0"), val = tensor([-1])]; + bool x_last_21_keep_dims_0 = const()[name = string("x_last_21_keep_dims_0"), val = bool(true)]; + tensor x_last_21_cast_fp16 = reduce_sum(axes = x_last_21_axes_0, keep_dims = x_last_21_keep_dims_0, x = var_941_cast_fp16)[name = string("x_last_21_cast_fp16")]; + tensor var_944_cast_fp16 = mul(x = x_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor var_946_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_21_cast_fp16)[name = string("op_946_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = var_944_cast_fp16, y = var_946_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("replicate")]; + fp16 const_32_to_fp16 = const()[name = string("const_32_to_fp16"), val = fp16(0x0p+0)]; + tensor input_157_cast_fp16 = pad(constant_val = const_32_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string h_63_pad_type_0 = const()[name = string("h_63_pad_type_0"), val = string("valid")]; + int32 h_63_groups_0 = const()[name = string("h_63_groups_0"), val = int32(512)]; + tensor h_63_strides_0 = const()[name = string("h_63_strides_0"), val = tensor([1])]; + tensor h_63_pad_0 = const()[name = string("h_63_pad_0"), val = tensor([0, 0])]; + tensor h_63_dilations_0 = const()[name = string("h_63_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22533504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22536128))))[name = string("main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22537216)))]; + tensor h_63_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_63_dilations_0, groups = h_63_groups_0, pad = h_63_pad_0, pad_type = h_63_pad_type_0, strides = h_63_strides_0, weight = main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_157_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor x_83_cast_fp16 = mul(x = h_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor var_964_axes_0 = const()[name = string("op_964_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22538304)))]; + tensor main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22539392)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_83_cast_fp16)[name = string("transpose_80")]; + tensor var_964_cast_fp16 = layer_norm(axes = var_964_axes_0, beta = main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + string h_65_pad_type_0 = const()[name = string("h_65_pad_type_0"), val = string("valid")]; + tensor h_65_strides_0 = const()[name = string("h_65_strides_0"), val = tensor([1])]; + tensor h_65_pad_0 = const()[name = string("h_65_pad_0"), val = tensor([0, 0])]; + tensor h_65_dilations_0 = const()[name = string("h_65_dilations_0"), val = tensor([1])]; + int32 h_65_groups_0 = const()[name = string("h_65_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22540480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23589120))))[name = string("main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23593280)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = var_964_cast_fp16)[name = string("transpose_79")]; + tensor h_65_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_65_dilations_0, groups = h_65_groups_0, pad = h_65_pad_0, pad_type = h_65_pad_type_0, strides = h_65_strides_0, weight = main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_161_cast_fp16)[name = string("h_65_cast_fp16")]; + string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("EXACT")]; + tensor input_163_cast_fp16 = gelu(mode = input_163_mode_0, x = h_65_cast_fp16)[name = string("input_163_cast_fp16")]; + string h_67_pad_type_0 = const()[name = string("h_67_pad_type_0"), val = string("valid")]; + tensor h_67_strides_0 = const()[name = string("h_67_strides_0"), val = tensor([1])]; + tensor h_67_pad_0 = const()[name = string("h_67_pad_0"), val = tensor([0, 0])]; + tensor h_67_dilations_0 = const()[name = string("h_67_dilations_0"), val = tensor([1])]; + int32 h_67_groups_0 = const()[name = string("h_67_groups_0"), val = int32(1)]; + tensor op_981_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23597440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24646080))))[name = string("op_981_weight_0_to_fp16_quantized")]; + tensor var_981_bias_0_to_fp16 = const()[name = string("op_981_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24647168)))]; + tensor var_981_cast_fp16 = conv(bias = var_981_bias_0_to_fp16, dilations = h_67_dilations_0, groups = h_67_groups_0, pad = h_67_pad_0, pad_type = h_67_pad_type_0, strides = h_67_strides_0, weight = op_981_weight_0_to_fp16_quantized, x = input_163_cast_fp16)[name = string("op_981_cast_fp16")]; + tensor out_25_cast_fp16 = add(x = x_81_cast_fp16, y = var_981_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = out_25_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor x_m_5_cast_fp16 = mul(x = x_85_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_5_cast_fp16")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24648256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24910464))))[name = string("main_blocks_1_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24911552)))]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_85_cast_fp16)[name = string("transpose_78")]; + tensor linear_12_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_999 = const()[name = string("op_999"), val = tensor([2, 512, 8, 64])]; + tensor var_1000_cast_fp16 = reshape(shape = var_999, x = linear_12_cast_fp16)[name = string("op_1000_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24912640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25043776))))[name = string("main_blocks_1_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25044864)))]; + tensor linear_13_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_1007 = const()[name = string("op_1007"), val = tensor([2, 128, 8, 64])]; + tensor var_1008_cast_fp16 = reshape(shape = var_1007, x = linear_13_cast_fp16)[name = string("op_1008_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25045952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25177088))))[name = string("main_blocks_1_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25178176)))]; + tensor linear_14_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_1015 = const()[name = string("op_1015"), val = tensor([2, 128, 8, 64])]; + tensor var_1016_cast_fp16 = reshape(shape = var_1015, x = linear_14_cast_fp16)[name = string("op_1016_cast_fp16")]; + tensor v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_5_begin_0 = const()[name = string("x_a_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_5_end_0 = const()[name = string("x_a_5_end_0"), val = tensor([2, 8, 512, 32])]; + tensor x_a_5_end_mask_0 = const()[name = string("x_a_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_1000_cast_fp16)[name = string("transpose_77")]; + tensor x_a_5_cast_fp16 = slice_by_index(begin = x_a_5_begin_0, end = x_a_5_end_0, end_mask = x_a_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_a_5_cast_fp16")]; + tensor x_b_5_begin_0 = const()[name = string("x_b_5_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_5_end_0 = const()[name = string("x_b_5_end_0"), val = tensor([2, 8, 512, 64])]; + tensor x_b_5_end_mask_0 = const()[name = string("x_b_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_5_cast_fp16 = slice_by_index(begin = x_b_5_begin_0, end = x_b_5_end_0, end_mask = x_b_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_b_5_cast_fp16")]; + tensor var_1044_cast_fp16 = mul(x = x_a_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1044_cast_fp16")]; + tensor var_1045_cast_fp16 = mul(x = x_b_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor rot_a_5_cast_fp16 = sub(x = var_1044_cast_fp16, y = var_1045_cast_fp16)[name = string("rot_a_5_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_a_5_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_cast_fp16 = mul(x = x_b_5_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1048_cast_fp16")]; + tensor rot_b_5_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("rot_b_5_cast_fp16")]; + bool q_5_interleave_0 = const()[name = string("q_5_interleave_0"), val = bool(false)]; + tensor q_5_cast_fp16 = concat(axis = var_681, interleave = q_5_interleave_0, values = (rot_a_5_cast_fp16, rot_b_5_cast_fp16))[name = string("q_5_cast_fp16")]; + tensor x_a_7_begin_0 = const()[name = string("x_a_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_7_end_0 = const()[name = string("x_a_7_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_7_end_mask_0 = const()[name = string("x_a_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_1008_cast_fp16)[name = string("transpose_76")]; + tensor x_a_7_cast_fp16 = slice_by_index(begin = x_a_7_begin_0, end = x_a_7_end_0, end_mask = x_a_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_a_7_cast_fp16")]; + tensor x_b_7_begin_0 = const()[name = string("x_b_7_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_7_end_0 = const()[name = string("x_b_7_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_7_end_mask_0 = const()[name = string("x_b_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_7_cast_fp16 = slice_by_index(begin = x_b_7_begin_0, end = x_b_7_end_0, end_mask = x_b_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_b_7_cast_fp16")]; + tensor var_1062_cast_fp16 = mul(x = x_a_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = x_b_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor rot_a_7_cast_fp16 = sub(x = var_1062_cast_fp16, y = var_1063_cast_fp16)[name = string("rot_a_7_cast_fp16")]; + tensor var_1065_cast_fp16 = mul(x = x_a_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1065_cast_fp16")]; + tensor var_1066_cast_fp16 = mul(x = x_b_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1066_cast_fp16")]; + tensor rot_b_7_cast_fp16 = add(x = var_1065_cast_fp16, y = var_1066_cast_fp16)[name = string("rot_b_7_cast_fp16")]; + bool k_5_interleave_0 = const()[name = string("k_5_interleave_0"), val = bool(false)]; + tensor k_5_cast_fp16 = concat(axis = var_681, interleave = k_5_interleave_0, values = (rot_a_7_cast_fp16, rot_b_7_cast_fp16))[name = string("k_5_cast_fp16")]; + bool var_1071_transpose_x_1 = const()[name = string("op_1071_transpose_x_1"), val = bool(false)]; + bool var_1071_transpose_y_1 = const()[name = string("op_1071_transpose_y_1"), val = bool(true)]; + tensor var_1071_cast_fp16 = matmul(transpose_x = var_1071_transpose_x_1, transpose_y = var_1071_transpose_y_1, x = q_5_cast_fp16, y = k_5_cast_fp16)[name = string("op_1071_cast_fp16")]; + fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_3_cast_fp16 = mul(x = var_1071_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")]; + tensor input_171_cast_fp16 = sub(x = _inversed_scores_3_cast_fp16, y = var_530_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_681, x = input_171_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool var_1080_transpose_x_0 = const()[name = string("op_1080_transpose_x_0"), val = bool(false)]; + bool var_1080_transpose_y_0 = const()[name = string("op_1080_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1016_cast_fp16)[name = string("transpose_75")]; + tensor var_1080_cast_fp16 = matmul(transpose_x = var_1080_transpose_x_0, transpose_y = var_1080_transpose_y_0, x = attn_7_cast_fp16, y = v_5_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_perm_0 = const()[name = string("op_1081_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1082 = const()[name = string("op_1082"), val = tensor([2, 512, 512])]; + tensor var_1081_cast_fp16 = transpose(perm = var_1081_perm_0, x = var_1080_cast_fp16)[name = string("transpose_74")]; + tensor input_173_cast_fp16 = reshape(shape = var_1082, x = var_1081_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25179264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25441472))))[name = string("main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25442560)))]; + tensor linear_15_cast_fp16 = linear(bias = main_blocks_1_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_173_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor out_27_perm_0 = const()[name = string("out_27_perm_0"), val = tensor([0, 2, 1])]; + tensor out_27_cast_fp16 = transpose(perm = out_27_perm_0, x = linear_15_cast_fp16)[name = string("transpose_73")]; + tensor var_1089_cast_fp16 = mul(x = out_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor x_91_cast_fp16 = add(x = x_m_5_cast_fp16, y = var_1089_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_175_perm_0 = const()[name = string("input_175_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1096_axes_0 = const()[name = string("op_1096_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25443648)))]; + tensor main_blocks_1_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25444736)))]; + tensor input_175_cast_fp16 = transpose(perm = input_175_perm_0, x = x_91_cast_fp16)[name = string("transpose_72")]; + tensor var_1096_cast_fp16 = layer_norm(axes = var_1096_axes_0, beta = main_blocks_1_text_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_text_norm_norm_weight_to_fp16, x = input_175_cast_fp16)[name = string("op_1096_cast_fp16")]; + tensor var_1097_perm_0 = const()[name = string("op_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1097_cast_fp16 = transpose(perm = var_1097_perm_0, x = var_1096_cast_fp16)[name = string("transpose_71")]; + tensor x_93_cast_fp16 = mul(x = var_1097_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor x_95_cast_fp16 = mul(x = x_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_1110_cast_fp16 = mul(x = x_95_cast_fp16, y = last_1_cast_fp16)[name = string("op_1110_cast_fp16")]; + tensor x_last_23_axes_0 = const()[name = string("x_last_23_axes_0"), val = tensor([-1])]; + bool x_last_23_keep_dims_0 = const()[name = string("x_last_23_keep_dims_0"), val = bool(true)]; + tensor x_last_23_cast_fp16 = reduce_sum(axes = x_last_23_axes_0, keep_dims = x_last_23_keep_dims_0, x = var_1110_cast_fp16)[name = string("x_last_23_cast_fp16")]; + tensor var_1113_cast_fp16 = mul(x = x_95_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1115_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_23_cast_fp16)[name = string("op_1115_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = var_1113_cast_fp16, y = var_1115_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("replicate")]; + fp16 const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = fp16(0x0p+0)]; + tensor input_181_cast_fp16 = pad(constant_val = const_39_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + string h_69_pad_type_0 = const()[name = string("h_69_pad_type_0"), val = string("valid")]; + int32 h_69_groups_0 = const()[name = string("h_69_groups_0"), val = int32(512)]; + tensor h_69_strides_0 = const()[name = string("h_69_strides_0"), val = tensor([1])]; + tensor h_69_pad_0 = const()[name = string("h_69_pad_0"), val = tensor([0, 0])]; + tensor h_69_dilations_0 = const()[name = string("h_69_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25445824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25448448))))[name = string("main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25449536)))]; + tensor h_69_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_69_dilations_0, groups = h_69_groups_0, pad = h_69_pad_0, pad_type = h_69_pad_type_0, strides = h_69_strides_0, weight = main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_181_cast_fp16)[name = string("h_69_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = h_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_183_perm_0 = const()[name = string("input_183_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1133_axes_0 = const()[name = string("op_1133_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25450624)))]; + tensor main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25451712)))]; + tensor input_183_cast_fp16 = transpose(perm = input_183_perm_0, x = x_97_cast_fp16)[name = string("transpose_70")]; + tensor var_1133_cast_fp16 = layer_norm(axes = var_1133_axes_0, beta = main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16, x = input_183_cast_fp16)[name = string("op_1133_cast_fp16")]; + tensor input_185_perm_0 = const()[name = string("input_185_perm_0"), val = tensor([0, 2, 1])]; + string h_71_pad_type_0 = const()[name = string("h_71_pad_type_0"), val = string("valid")]; + tensor h_71_strides_0 = const()[name = string("h_71_strides_0"), val = tensor([1])]; + tensor h_71_pad_0 = const()[name = string("h_71_pad_0"), val = tensor([0, 0])]; + tensor h_71_dilations_0 = const()[name = string("h_71_dilations_0"), val = tensor([1])]; + int32 h_71_groups_0 = const()[name = string("h_71_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25452800))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26501440))))[name = string("main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26505600)))]; + tensor input_185_cast_fp16 = transpose(perm = input_185_perm_0, x = var_1133_cast_fp16)[name = string("transpose_69")]; + tensor h_71_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_71_dilations_0, groups = h_71_groups_0, pad = h_71_pad_0, pad_type = h_71_pad_type_0, strides = h_71_strides_0, weight = main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("h_71_cast_fp16")]; + string input_187_mode_0 = const()[name = string("input_187_mode_0"), val = string("EXACT")]; + tensor input_187_cast_fp16 = gelu(mode = input_187_mode_0, x = h_71_cast_fp16)[name = string("input_187_cast_fp16")]; + string h_73_pad_type_0 = const()[name = string("h_73_pad_type_0"), val = string("valid")]; + tensor h_73_strides_0 = const()[name = string("h_73_strides_0"), val = tensor([1])]; + tensor h_73_pad_0 = const()[name = string("h_73_pad_0"), val = tensor([0, 0])]; + tensor h_73_dilations_0 = const()[name = string("h_73_dilations_0"), val = tensor([1])]; + int32 h_73_groups_0 = const()[name = string("h_73_groups_0"), val = int32(1)]; + tensor op_1150_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26509760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27558400))))[name = string("op_1150_weight_0_to_fp16_quantized")]; + tensor var_1150_bias_0_to_fp16 = const()[name = string("op_1150_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27559488)))]; + tensor var_1150_cast_fp16 = conv(bias = var_1150_bias_0_to_fp16, dilations = h_73_dilations_0, groups = h_73_groups_0, pad = h_73_pad_0, pad_type = h_73_pad_type_0, strides = h_73_strides_0, weight = op_1150_weight_0_to_fp16_quantized, x = input_187_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor out_29_cast_fp16 = add(x = x_95_cast_fp16, y = var_1150_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor x_99_cast_fp16 = mul(x = out_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor x_m_7_cast_fp16 = mul(x = x_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_7_cast_fp16")]; + tensor input_189_perm_0 = const()[name = string("input_189_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27560576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27691712))))[name = string("main_blocks_1_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27692288)))]; + tensor input_189_cast_fp16 = transpose(perm = input_189_perm_0, x = x_99_cast_fp16)[name = string("transpose_68")]; + tensor linear_16_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_1166 = const()[name = string("op_1166"), val = tensor([2, 512, 2, 128])]; + tensor var_1167_cast_fp16 = reshape(shape = var_1166, x = linear_16_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_1_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27692864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27758464))))[name = string("main_blocks_1_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27759040)))]; + tensor linear_18_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_1180 = const()[name = string("op_1180"), val = tensor([2, 50, 2, 128])]; + tensor var_1181_cast_fp16 = reshape(shape = var_1180, x = linear_18_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1185_transpose_x_0 = const()[name = string("op_1185_transpose_x_0"), val = bool(false)]; + bool var_1185_transpose_y_0 = const()[name = string("op_1185_transpose_y_0"), val = bool(false)]; + tensor op_1184_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27759616))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27785280))))[name = string("op_1184_to_fp16_quantized")]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1167_cast_fp16)[name = string("transpose_67")]; + tensor var_1185_cast_fp16 = matmul(transpose_x = var_1185_transpose_x_0, transpose_y = var_1185_transpose_y_0, x = q_7_cast_fp16, y = op_1184_to_fp16_quantized)[name = string("op_1185_cast_fp16")]; + fp16 _inversed_input_191_y_0_to_fp16 = const()[name = string("_inversed_input_191_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_191_cast_fp16 = mul(x = var_1185_cast_fp16, y = _inversed_input_191_y_0_to_fp16)[name = string("_inversed_input_191_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_681, x = _inversed_input_191_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor attn_11_cast_fp16 = mul(x = attn_9_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool var_1192_transpose_x_0 = const()[name = string("op_1192_transpose_x_0"), val = bool(false)]; + bool var_1192_transpose_y_0 = const()[name = string("op_1192_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1181_cast_fp16)[name = string("transpose_66")]; + tensor var_1192_cast_fp16 = matmul(transpose_x = var_1192_transpose_x_0, transpose_y = var_1192_transpose_y_0, x = attn_11_cast_fp16, y = v_7_cast_fp16)[name = string("op_1192_cast_fp16")]; + tensor var_1193_perm_0 = const()[name = string("op_1193_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1194 = const()[name = string("op_1194"), val = tensor([2, 512, 256])]; + tensor var_1193_cast_fp16 = transpose(perm = var_1193_perm_0, x = var_1192_cast_fp16)[name = string("transpose_65")]; + tensor input_193_cast_fp16 = reshape(shape = var_1194, x = var_1193_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27785472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27916608))))[name = string("main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27917696)))]; + tensor linear_19_cast_fp16 = linear(bias = main_blocks_1_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_193_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor out_31_perm_0 = const()[name = string("out_31_perm_0"), val = tensor([0, 2, 1])]; + tensor out_31_cast_fp16 = transpose(perm = out_31_perm_0, x = linear_19_cast_fp16)[name = string("transpose_64")]; + tensor var_1201_cast_fp16 = mul(x = out_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1201_cast_fp16")]; + tensor x_101_cast_fp16 = add(x = x_m_7_cast_fp16, y = var_1201_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_195_perm_0 = const()[name = string("input_195_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1208_axes_0 = const()[name = string("op_1208_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27918784)))]; + tensor main_blocks_1_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27919872)))]; + tensor input_195_cast_fp16 = transpose(perm = input_195_perm_0, x = x_101_cast_fp16)[name = string("transpose_63")]; + tensor var_1208_cast_fp16 = layer_norm(axes = var_1208_axes_0, beta = main_blocks_1_style_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_style_norm_norm_weight_to_fp16, x = input_195_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_perm_0 = const()[name = string("op_1209_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1209_cast_fp16 = transpose(perm = var_1209_perm_0, x = var_1208_cast_fp16)[name = string("transpose_62")]; + tensor x_103_cast_fp16 = mul(x = var_1209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_103_cast_fp16")]; + int32 var_1228 = const()[name = string("op_1228"), val = int32(-1)]; + tensor x_105_cast_fp16 = mul(x = x_103_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_1263_cast_fp16 = mul(x = x_105_cast_fp16, y = last_1_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor x_last_25_axes_0 = const()[name = string("x_last_25_axes_0"), val = tensor([-1])]; + bool x_last_25_keep_dims_0 = const()[name = string("x_last_25_keep_dims_0"), val = bool(true)]; + tensor x_last_25_cast_fp16 = reduce_sum(axes = x_last_25_axes_0, keep_dims = x_last_25_keep_dims_0, x = var_1263_cast_fp16)[name = string("x_last_25_cast_fp16")]; + tensor var_1266_cast_fp16 = mul(x = x_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1266_cast_fp16")]; + tensor var_1268_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_25_cast_fp16)[name = string("op_1268_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1266_cast_fp16, y = var_1268_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_201_mode_0 = const()[name = string("input_201_mode_0"), val = string("replicate")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_201_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")]; + string h_75_pad_type_0 = const()[name = string("h_75_pad_type_0"), val = string("valid")]; + int32 h_75_groups_0 = const()[name = string("h_75_groups_0"), val = int32(512)]; + tensor h_75_strides_0 = const()[name = string("h_75_strides_0"), val = tensor([1])]; + tensor h_75_pad_0 = const()[name = string("h_75_pad_0"), val = tensor([0, 0])]; + tensor h_75_dilations_0 = const()[name = string("h_75_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27920960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27923584))))[name = string("main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27924672)))]; + tensor h_75_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_75_dilations_0, groups = h_75_groups_0, pad = h_75_pad_0, pad_type = h_75_pad_type_0, strides = h_75_strides_0, weight = main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_201_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = h_75_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_203_perm_0 = const()[name = string("input_203_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1286_axes_0 = const()[name = string("op_1286_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27925760)))]; + tensor main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27926848)))]; + fp16 var_1233_to_fp16 = const()[name = string("op_1233_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_203_cast_fp16 = transpose(perm = input_203_perm_0, x = x_107_cast_fp16)[name = string("transpose_61")]; + tensor var_1286_cast_fp16 = layer_norm(axes = var_1286_axes_0, beta = main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16, x = input_203_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string h_77_pad_type_0 = const()[name = string("h_77_pad_type_0"), val = string("valid")]; + tensor h_77_strides_0 = const()[name = string("h_77_strides_0"), val = tensor([1])]; + tensor h_77_pad_0 = const()[name = string("h_77_pad_0"), val = tensor([0, 0])]; + tensor h_77_dilations_0 = const()[name = string("h_77_dilations_0"), val = tensor([1])]; + int32 h_77_groups_0 = const()[name = string("h_77_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27927936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28976576))))[name = string("main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28980736)))]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = var_1286_cast_fp16)[name = string("transpose_60")]; + tensor h_77_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_77_dilations_0, groups = h_77_groups_0, pad = h_77_pad_0, pad_type = h_77_pad_type_0, strides = h_77_strides_0, weight = main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("h_77_cast_fp16")]; + string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")]; + tensor input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = h_77_cast_fp16)[name = string("input_207_cast_fp16")]; + string h_79_pad_type_0 = const()[name = string("h_79_pad_type_0"), val = string("valid")]; + tensor h_79_strides_0 = const()[name = string("h_79_strides_0"), val = tensor([1])]; + tensor h_79_pad_0 = const()[name = string("h_79_pad_0"), val = tensor([0, 0])]; + tensor h_79_dilations_0 = const()[name = string("h_79_dilations_0"), val = tensor([1])]; + int32 h_79_groups_0 = const()[name = string("h_79_groups_0"), val = int32(1)]; + tensor op_1303_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28984896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30033536))))[name = string("op_1303_weight_0_to_fp16_quantized")]; + tensor var_1303_bias_0_to_fp16 = const()[name = string("op_1303_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30034624)))]; + tensor var_1303_cast_fp16 = conv(bias = var_1303_bias_0_to_fp16, dilations = h_79_dilations_0, groups = h_79_groups_0, pad = h_79_pad_0, pad_type = h_79_pad_type_0, strides = h_79_strides_0, weight = op_1303_weight_0_to_fp16_quantized, x = input_207_cast_fp16)[name = string("op_1303_cast_fp16")]; + tensor out_33_cast_fp16 = add(x = x_105_cast_fp16, y = var_1303_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor x_109_cast_fp16 = mul(x = out_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor x_111_cast_fp16 = mul(x = x_109_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_1317_cast_fp16 = mul(x = x_111_cast_fp16, y = last_1_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor x_last_27_axes_0 = const()[name = string("x_last_27_axes_0"), val = tensor([-1])]; + bool x_last_27_keep_dims_0 = const()[name = string("x_last_27_keep_dims_0"), val = bool(true)]; + tensor x_last_27_cast_fp16 = reduce_sum(axes = x_last_27_axes_0, keep_dims = x_last_27_keep_dims_0, x = var_1317_cast_fp16)[name = string("x_last_27_cast_fp16")]; + tensor var_1320_cast_fp16 = mul(x = x_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1322_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_27_cast_fp16)[name = string("op_1322_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1320_cast_fp16, y = var_1322_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("replicate")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor input_213_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = input_213_mode_0, pad = input_213_pad_0, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + string h_81_pad_type_0 = const()[name = string("h_81_pad_type_0"), val = string("valid")]; + tensor h_81_dilations_0 = const()[name = string("h_81_dilations_0"), val = tensor([2])]; + int32 h_81_groups_0 = const()[name = string("h_81_groups_0"), val = int32(512)]; + tensor h_81_strides_0 = const()[name = string("h_81_strides_0"), val = tensor([1])]; + tensor h_81_pad_0 = const()[name = string("h_81_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30035712))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30038336))))[name = string("main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30039424)))]; + tensor h_81_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_81_dilations_0, groups = h_81_groups_0, pad = h_81_pad_0, pad_type = h_81_pad_type_0, strides = h_81_strides_0, weight = main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_213_cast_fp16)[name = string("h_81_cast_fp16")]; + tensor x_113_cast_fp16 = mul(x = h_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor input_215_perm_0 = const()[name = string("input_215_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1340_axes_0 = const()[name = string("op_1340_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30040512)))]; + tensor main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30041600)))]; + tensor input_215_cast_fp16 = transpose(perm = input_215_perm_0, x = x_113_cast_fp16)[name = string("transpose_59")]; + tensor var_1340_cast_fp16 = layer_norm(axes = var_1340_axes_0, beta = main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16, x = input_215_cast_fp16)[name = string("op_1340_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + string h_83_pad_type_0 = const()[name = string("h_83_pad_type_0"), val = string("valid")]; + tensor h_83_strides_0 = const()[name = string("h_83_strides_0"), val = tensor([1])]; + tensor h_83_pad_0 = const()[name = string("h_83_pad_0"), val = tensor([0, 0])]; + tensor h_83_dilations_0 = const()[name = string("h_83_dilations_0"), val = tensor([1])]; + int32 h_83_groups_0 = const()[name = string("h_83_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30042688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31091328))))[name = string("main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31095488)))]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = var_1340_cast_fp16)[name = string("transpose_58")]; + tensor h_83_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_83_dilations_0, groups = h_83_groups_0, pad = h_83_pad_0, pad_type = h_83_pad_type_0, strides = h_83_strides_0, weight = main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_217_cast_fp16)[name = string("h_83_cast_fp16")]; + string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")]; + tensor input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = h_83_cast_fp16)[name = string("input_219_cast_fp16")]; + string h_85_pad_type_0 = const()[name = string("h_85_pad_type_0"), val = string("valid")]; + tensor h_85_strides_0 = const()[name = string("h_85_strides_0"), val = tensor([1])]; + tensor h_85_pad_0 = const()[name = string("h_85_pad_0"), val = tensor([0, 0])]; + tensor h_85_dilations_0 = const()[name = string("h_85_dilations_0"), val = tensor([1])]; + int32 h_85_groups_0 = const()[name = string("h_85_groups_0"), val = int32(1)]; + tensor op_1357_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31099648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32148288))))[name = string("op_1357_weight_0_to_fp16_quantized")]; + tensor var_1357_bias_0_to_fp16 = const()[name = string("op_1357_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32149376)))]; + tensor var_1357_cast_fp16 = conv(bias = var_1357_bias_0_to_fp16, dilations = h_85_dilations_0, groups = h_85_groups_0, pad = h_85_pad_0, pad_type = h_85_pad_type_0, strides = h_85_strides_0, weight = op_1357_weight_0_to_fp16_quantized, x = input_219_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor out_35_cast_fp16 = add(x = x_111_cast_fp16, y = var_1357_cast_fp16)[name = string("out_35_cast_fp16")]; + tensor x_115_cast_fp16 = mul(x = out_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_cast_fp16 = mul(x = x_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1371_cast_fp16 = mul(x = x_117_cast_fp16, y = last_1_cast_fp16)[name = string("op_1371_cast_fp16")]; + tensor x_last_29_axes_0 = const()[name = string("x_last_29_axes_0"), val = tensor([-1])]; + bool x_last_29_keep_dims_0 = const()[name = string("x_last_29_keep_dims_0"), val = bool(true)]; + tensor x_last_29_cast_fp16 = reduce_sum(axes = x_last_29_axes_0, keep_dims = x_last_29_keep_dims_0, x = var_1371_cast_fp16)[name = string("x_last_29_cast_fp16")]; + tensor var_1374_cast_fp16 = mul(x = x_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_29_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1376_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_225_mode_0 = const()[name = string("input_225_mode_0"), val = string("replicate")]; + fp16 const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = fp16(0x0p+0)]; + tensor input_225_cast_fp16 = pad(constant_val = const_48_to_fp16, mode = input_225_mode_0, pad = input_225_pad_0, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")]; + string h_87_pad_type_0 = const()[name = string("h_87_pad_type_0"), val = string("valid")]; + tensor h_87_dilations_0 = const()[name = string("h_87_dilations_0"), val = tensor([4])]; + int32 h_87_groups_0 = const()[name = string("h_87_groups_0"), val = int32(512)]; + tensor h_87_strides_0 = const()[name = string("h_87_strides_0"), val = tensor([1])]; + tensor h_87_pad_0 = const()[name = string("h_87_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32150464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32153088))))[name = string("main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32154176)))]; + tensor h_87_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_87_dilations_0, groups = h_87_groups_0, pad = h_87_pad_0, pad_type = h_87_pad_type_0, strides = h_87_strides_0, weight = main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor x_119_cast_fp16 = mul(x = h_87_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_227_perm_0 = const()[name = string("input_227_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1394_axes_0 = const()[name = string("op_1394_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32155264)))]; + tensor main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32156352)))]; + tensor input_227_cast_fp16 = transpose(perm = input_227_perm_0, x = x_119_cast_fp16)[name = string("transpose_57")]; + tensor var_1394_cast_fp16 = layer_norm(axes = var_1394_axes_0, beta = main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16, x = input_227_cast_fp16)[name = string("op_1394_cast_fp16")]; + tensor input_229_perm_0 = const()[name = string("input_229_perm_0"), val = tensor([0, 2, 1])]; + string h_89_pad_type_0 = const()[name = string("h_89_pad_type_0"), val = string("valid")]; + tensor h_89_strides_0 = const()[name = string("h_89_strides_0"), val = tensor([1])]; + tensor h_89_pad_0 = const()[name = string("h_89_pad_0"), val = tensor([0, 0])]; + tensor h_89_dilations_0 = const()[name = string("h_89_dilations_0"), val = tensor([1])]; + int32 h_89_groups_0 = const()[name = string("h_89_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32157440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33206080))))[name = string("main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33210240)))]; + tensor input_229_cast_fp16 = transpose(perm = input_229_perm_0, x = var_1394_cast_fp16)[name = string("transpose_56")]; + tensor h_89_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_89_dilations_0, groups = h_89_groups_0, pad = h_89_pad_0, pad_type = h_89_pad_type_0, strides = h_89_strides_0, weight = main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_229_cast_fp16)[name = string("h_89_cast_fp16")]; + string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")]; + tensor input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = h_89_cast_fp16)[name = string("input_231_cast_fp16")]; + string h_91_pad_type_0 = const()[name = string("h_91_pad_type_0"), val = string("valid")]; + tensor h_91_strides_0 = const()[name = string("h_91_strides_0"), val = tensor([1])]; + tensor h_91_pad_0 = const()[name = string("h_91_pad_0"), val = tensor([0, 0])]; + tensor h_91_dilations_0 = const()[name = string("h_91_dilations_0"), val = tensor([1])]; + int32 h_91_groups_0 = const()[name = string("h_91_groups_0"), val = int32(1)]; + tensor op_1411_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33214400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34263040))))[name = string("op_1411_weight_0_to_fp16_quantized")]; + tensor var_1411_bias_0_to_fp16 = const()[name = string("op_1411_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34264128)))]; + tensor var_1411_cast_fp16 = conv(bias = var_1411_bias_0_to_fp16, dilations = h_91_dilations_0, groups = h_91_groups_0, pad = h_91_pad_0, pad_type = h_91_pad_type_0, strides = h_91_strides_0, weight = op_1411_weight_0_to_fp16_quantized, x = input_231_cast_fp16)[name = string("op_1411_cast_fp16")]; + tensor out_37_cast_fp16 = add(x = x_117_cast_fp16, y = var_1411_cast_fp16)[name = string("out_37_cast_fp16")]; + tensor x_121_cast_fp16 = mul(x = out_37_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_121_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1425_cast_fp16 = mul(x = x_123_cast_fp16, y = last_1_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor x_last_31_axes_0 = const()[name = string("x_last_31_axes_0"), val = tensor([-1])]; + bool x_last_31_keep_dims_0 = const()[name = string("x_last_31_keep_dims_0"), val = bool(true)]; + tensor x_last_31_cast_fp16 = reduce_sum(axes = x_last_31_axes_0, keep_dims = x_last_31_keep_dims_0, x = var_1425_cast_fp16)[name = string("x_last_31_cast_fp16")]; + tensor var_1428_cast_fp16 = mul(x = x_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_31_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = var_1428_cast_fp16, y = var_1430_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("replicate")]; + fp16 const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = fp16(0x0p+0)]; + tensor input_237_cast_fp16 = pad(constant_val = const_50_to_fp16, mode = input_237_mode_0, pad = input_237_pad_0, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + string h_93_pad_type_0 = const()[name = string("h_93_pad_type_0"), val = string("valid")]; + tensor h_93_dilations_0 = const()[name = string("h_93_dilations_0"), val = tensor([8])]; + int32 h_93_groups_0 = const()[name = string("h_93_groups_0"), val = int32(512)]; + tensor h_93_strides_0 = const()[name = string("h_93_strides_0"), val = tensor([1])]; + tensor h_93_pad_0 = const()[name = string("h_93_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34265216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34267840))))[name = string("main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34268928)))]; + tensor h_93_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_93_dilations_0, groups = h_93_groups_0, pad = h_93_pad_0, pad_type = h_93_pad_type_0, strides = h_93_strides_0, weight = main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_237_cast_fp16)[name = string("h_93_cast_fp16")]; + tensor x_125_cast_fp16 = mul(x = h_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_239_perm_0 = const()[name = string("input_239_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1448_axes_0 = const()[name = string("op_1448_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34270016)))]; + tensor main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34271104)))]; + tensor input_239_cast_fp16 = transpose(perm = input_239_perm_0, x = x_125_cast_fp16)[name = string("transpose_55")]; + tensor var_1448_cast_fp16 = layer_norm(axes = var_1448_axes_0, beta = main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16, x = input_239_cast_fp16)[name = string("op_1448_cast_fp16")]; + tensor input_241_perm_0 = const()[name = string("input_241_perm_0"), val = tensor([0, 2, 1])]; + string h_95_pad_type_0 = const()[name = string("h_95_pad_type_0"), val = string("valid")]; + tensor h_95_strides_0 = const()[name = string("h_95_strides_0"), val = tensor([1])]; + tensor h_95_pad_0 = const()[name = string("h_95_pad_0"), val = tensor([0, 0])]; + tensor h_95_dilations_0 = const()[name = string("h_95_dilations_0"), val = tensor([1])]; + int32 h_95_groups_0 = const()[name = string("h_95_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34272192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35320832))))[name = string("main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35324992)))]; + tensor input_241_cast_fp16 = transpose(perm = input_241_perm_0, x = var_1448_cast_fp16)[name = string("transpose_54")]; + tensor h_95_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_95_dilations_0, groups = h_95_groups_0, pad = h_95_pad_0, pad_type = h_95_pad_type_0, strides = h_95_strides_0, weight = main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_241_cast_fp16)[name = string("h_95_cast_fp16")]; + string input_243_mode_0 = const()[name = string("input_243_mode_0"), val = string("EXACT")]; + tensor input_243_cast_fp16 = gelu(mode = input_243_mode_0, x = h_95_cast_fp16)[name = string("input_243_cast_fp16")]; + string h_97_pad_type_0 = const()[name = string("h_97_pad_type_0"), val = string("valid")]; + tensor h_97_strides_0 = const()[name = string("h_97_strides_0"), val = tensor([1])]; + tensor h_97_pad_0 = const()[name = string("h_97_pad_0"), val = tensor([0, 0])]; + tensor h_97_dilations_0 = const()[name = string("h_97_dilations_0"), val = tensor([1])]; + int32 h_97_groups_0 = const()[name = string("h_97_groups_0"), val = int32(1)]; + tensor op_1465_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35329152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36377792))))[name = string("op_1465_weight_0_to_fp16_quantized")]; + tensor var_1465_bias_0_to_fp16 = const()[name = string("op_1465_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36378880)))]; + tensor var_1465_cast_fp16 = conv(bias = var_1465_bias_0_to_fp16, dilations = h_97_dilations_0, groups = h_97_groups_0, pad = h_97_pad_0, pad_type = h_97_pad_type_0, strides = h_97_strides_0, weight = op_1465_weight_0_to_fp16_quantized, x = input_243_cast_fp16)[name = string("op_1465_cast_fp16")]; + tensor out_39_cast_fp16 = add(x = x_123_cast_fp16, y = var_1465_cast_fp16)[name = string("out_39_cast_fp16")]; + tensor x_127_cast_fp16 = mul(x = out_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor main_blocks_2_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36379968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36412800))))[name = string("main_blocks_2_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36413888)))]; + tensor linear_20_cast_fp16 = linear(bias = main_blocks_2_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_2_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor t_9_axes_0 = const()[name = string("t_9_axes_0"), val = tensor([-1])]; + tensor t_9_cast_fp16 = expand_dims(axes = t_9_axes_0, x = linear_20_cast_fp16)[name = string("t_9_cast_fp16")]; + tensor var_1475_cast_fp16 = add(x = x_127_cast_fp16, y = t_9_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = var_1475_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor x_131_cast_fp16 = mul(x = x_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor var_1488_cast_fp16 = mul(x = x_131_cast_fp16, y = last_1_cast_fp16)[name = string("op_1488_cast_fp16")]; + tensor x_last_33_axes_0 = const()[name = string("x_last_33_axes_0"), val = tensor([-1])]; + bool x_last_33_keep_dims_0 = const()[name = string("x_last_33_keep_dims_0"), val = bool(true)]; + tensor x_last_33_cast_fp16 = reduce_sum(axes = x_last_33_axes_0, keep_dims = x_last_33_keep_dims_0, x = var_1488_cast_fp16)[name = string("x_last_33_cast_fp16")]; + tensor var_1491_cast_fp16 = mul(x = x_131_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1491_cast_fp16")]; + tensor var_1493_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_33_cast_fp16)[name = string("op_1493_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = var_1491_cast_fp16, y = var_1493_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_251_mode_0 = const()[name = string("input_251_mode_0"), val = string("replicate")]; + fp16 const_52_to_fp16 = const()[name = string("const_52_to_fp16"), val = fp16(0x0p+0)]; + tensor input_251_cast_fp16 = pad(constant_val = const_52_to_fp16, mode = input_251_mode_0, pad = input_251_pad_0, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + string h_99_pad_type_0 = const()[name = string("h_99_pad_type_0"), val = string("valid")]; + int32 h_99_groups_0 = const()[name = string("h_99_groups_0"), val = int32(512)]; + tensor h_99_strides_0 = const()[name = string("h_99_strides_0"), val = tensor([1])]; + tensor h_99_pad_0 = const()[name = string("h_99_pad_0"), val = tensor([0, 0])]; + tensor h_99_dilations_0 = const()[name = string("h_99_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36414976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36417600))))[name = string("main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36418688)))]; + tensor h_99_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_99_dilations_0, groups = h_99_groups_0, pad = h_99_pad_0, pad_type = h_99_pad_type_0, strides = h_99_strides_0, weight = main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor x_133_cast_fp16 = mul(x = h_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_253_perm_0 = const()[name = string("input_253_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1511_axes_0 = const()[name = string("op_1511_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36419776)))]; + tensor main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36420864)))]; + tensor input_253_cast_fp16 = transpose(perm = input_253_perm_0, x = x_133_cast_fp16)[name = string("transpose_53")]; + tensor var_1511_cast_fp16 = layer_norm(axes = var_1511_axes_0, beta = main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16, x = input_253_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor input_255_perm_0 = const()[name = string("input_255_perm_0"), val = tensor([0, 2, 1])]; + string h_101_pad_type_0 = const()[name = string("h_101_pad_type_0"), val = string("valid")]; + tensor h_101_strides_0 = const()[name = string("h_101_strides_0"), val = tensor([1])]; + tensor h_101_pad_0 = const()[name = string("h_101_pad_0"), val = tensor([0, 0])]; + tensor h_101_dilations_0 = const()[name = string("h_101_dilations_0"), val = tensor([1])]; + int32 h_101_groups_0 = const()[name = string("h_101_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36421952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37470592))))[name = string("main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37474752)))]; + tensor input_255_cast_fp16 = transpose(perm = input_255_perm_0, x = var_1511_cast_fp16)[name = string("transpose_52")]; + tensor h_101_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_101_dilations_0, groups = h_101_groups_0, pad = h_101_pad_0, pad_type = h_101_pad_type_0, strides = h_101_strides_0, weight = main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("h_101_cast_fp16")]; + string input_257_mode_0 = const()[name = string("input_257_mode_0"), val = string("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = h_101_cast_fp16)[name = string("input_257_cast_fp16")]; + string h_103_pad_type_0 = const()[name = string("h_103_pad_type_0"), val = string("valid")]; + tensor h_103_strides_0 = const()[name = string("h_103_strides_0"), val = tensor([1])]; + tensor h_103_pad_0 = const()[name = string("h_103_pad_0"), val = tensor([0, 0])]; + tensor h_103_dilations_0 = const()[name = string("h_103_dilations_0"), val = tensor([1])]; + int32 h_103_groups_0 = const()[name = string("h_103_groups_0"), val = int32(1)]; + tensor op_1528_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37478912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38527552))))[name = string("op_1528_weight_0_to_fp16_quantized")]; + tensor var_1528_bias_0_to_fp16 = const()[name = string("op_1528_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38528640)))]; + tensor var_1528_cast_fp16 = conv(bias = var_1528_bias_0_to_fp16, dilations = h_103_dilations_0, groups = h_103_groups_0, pad = h_103_pad_0, pad_type = h_103_pad_type_0, strides = h_103_strides_0, weight = op_1528_weight_0_to_fp16_quantized, x = input_257_cast_fp16)[name = string("op_1528_cast_fp16")]; + tensor out_41_cast_fp16 = add(x = x_131_cast_fp16, y = var_1528_cast_fp16)[name = string("out_41_cast_fp16")]; + tensor x_135_cast_fp16 = mul(x = out_41_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor x_m_9_cast_fp16 = mul(x = x_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_9_cast_fp16")]; + tensor input_259_perm_0 = const()[name = string("input_259_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38529728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38791936))))[name = string("main_blocks_2_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38793024)))]; + tensor input_259_cast_fp16 = transpose(perm = input_259_perm_0, x = x_135_cast_fp16)[name = string("transpose_51")]; + tensor linear_21_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_259_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_1546 = const()[name = string("op_1546"), val = tensor([2, 512, 8, 64])]; + tensor var_1547_cast_fp16 = reshape(shape = var_1546, x = linear_21_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor x_137_perm_0 = const()[name = string("x_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38794112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38925248))))[name = string("main_blocks_2_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38926336)))]; + tensor linear_22_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_1554 = const()[name = string("op_1554"), val = tensor([2, 128, 8, 64])]; + tensor var_1555_cast_fp16 = reshape(shape = var_1554, x = linear_22_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_139_perm_0 = const()[name = string("x_139_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38927424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39058560))))[name = string("main_blocks_2_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39059648)))]; + tensor linear_23_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_1562 = const()[name = string("op_1562"), val = tensor([2, 128, 8, 64])]; + tensor var_1563_cast_fp16 = reshape(shape = var_1562, x = linear_23_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_9_begin_0 = const()[name = string("x_a_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_9_end_0 = const()[name = string("x_a_9_end_0"), val = tensor([2, 8, 512, 32])]; + tensor x_a_9_end_mask_0 = const()[name = string("x_a_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_137_cast_fp16 = transpose(perm = x_137_perm_0, x = var_1547_cast_fp16)[name = string("transpose_50")]; + tensor x_a_9_cast_fp16 = slice_by_index(begin = x_a_9_begin_0, end = x_a_9_end_0, end_mask = x_a_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_a_9_cast_fp16")]; + tensor x_b_9_begin_0 = const()[name = string("x_b_9_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_9_end_0 = const()[name = string("x_b_9_end_0"), val = tensor([2, 8, 512, 64])]; + tensor x_b_9_end_mask_0 = const()[name = string("x_b_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_9_cast_fp16 = slice_by_index(begin = x_b_9_begin_0, end = x_b_9_end_0, end_mask = x_b_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_b_9_cast_fp16")]; + tensor var_1591_cast_fp16 = mul(x = x_a_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1591_cast_fp16")]; + tensor var_1592_cast_fp16 = mul(x = x_b_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor rot_a_9_cast_fp16 = sub(x = var_1591_cast_fp16, y = var_1592_cast_fp16)[name = string("rot_a_9_cast_fp16")]; + tensor var_1594_cast_fp16 = mul(x = x_a_9_cast_fp16, y = sin_1_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595_cast_fp16 = mul(x = x_b_9_cast_fp16, y = cos_1_cast_fp16)[name = string("op_1595_cast_fp16")]; + tensor rot_b_9_cast_fp16 = add(x = var_1594_cast_fp16, y = var_1595_cast_fp16)[name = string("rot_b_9_cast_fp16")]; + bool q_9_interleave_0 = const()[name = string("q_9_interleave_0"), val = bool(false)]; + tensor q_9_cast_fp16 = concat(axis = var_1228, interleave = q_9_interleave_0, values = (rot_a_9_cast_fp16, rot_b_9_cast_fp16))[name = string("q_9_cast_fp16")]; + tensor x_a_11_begin_0 = const()[name = string("x_a_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_11_end_0 = const()[name = string("x_a_11_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_11_end_mask_0 = const()[name = string("x_a_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_139_cast_fp16 = transpose(perm = x_139_perm_0, x = var_1555_cast_fp16)[name = string("transpose_49")]; + tensor x_a_11_cast_fp16 = slice_by_index(begin = x_a_11_begin_0, end = x_a_11_end_0, end_mask = x_a_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_a_11_cast_fp16")]; + tensor x_b_11_begin_0 = const()[name = string("x_b_11_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_11_end_0 = const()[name = string("x_b_11_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_11_end_mask_0 = const()[name = string("x_b_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_11_cast_fp16 = slice_by_index(begin = x_b_11_begin_0, end = x_b_11_end_0, end_mask = x_b_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_b_11_cast_fp16")]; + tensor var_1609_cast_fp16 = mul(x = x_a_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor var_1610_cast_fp16 = mul(x = x_b_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor rot_a_11_cast_fp16 = sub(x = var_1609_cast_fp16, y = var_1610_cast_fp16)[name = string("rot_a_11_cast_fp16")]; + tensor var_1612_cast_fp16 = mul(x = x_a_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1612_cast_fp16")]; + tensor var_1613_cast_fp16 = mul(x = x_b_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1613_cast_fp16")]; + tensor rot_b_11_cast_fp16 = add(x = var_1612_cast_fp16, y = var_1613_cast_fp16)[name = string("rot_b_11_cast_fp16")]; + bool k_9_interleave_0 = const()[name = string("k_9_interleave_0"), val = bool(false)]; + tensor k_9_cast_fp16 = concat(axis = var_1228, interleave = k_9_interleave_0, values = (rot_a_11_cast_fp16, rot_b_11_cast_fp16))[name = string("k_9_cast_fp16")]; + bool var_1618_transpose_x_1 = const()[name = string("op_1618_transpose_x_1"), val = bool(false)]; + bool var_1618_transpose_y_1 = const()[name = string("op_1618_transpose_y_1"), val = bool(true)]; + tensor var_1618_cast_fp16 = matmul(transpose_x = var_1618_transpose_x_1, transpose_y = var_1618_transpose_y_1, x = q_9_cast_fp16, y = k_9_cast_fp16)[name = string("op_1618_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_1618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor input_265_cast_fp16 = sub(x = _inversed_scores_5_cast_fp16, y = var_530_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1228, x = input_265_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool var_1627_transpose_x_0 = const()[name = string("op_1627_transpose_x_0"), val = bool(false)]; + bool var_1627_transpose_y_0 = const()[name = string("op_1627_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1563_cast_fp16)[name = string("transpose_48")]; + tensor var_1627_cast_fp16 = matmul(transpose_x = var_1627_transpose_x_0, transpose_y = var_1627_transpose_y_0, x = attn_13_cast_fp16, y = v_9_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor var_1628_perm_0 = const()[name = string("op_1628_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1629 = const()[name = string("op_1629"), val = tensor([2, 512, 512])]; + tensor var_1628_cast_fp16 = transpose(perm = var_1628_perm_0, x = var_1627_cast_fp16)[name = string("transpose_47")]; + tensor input_267_cast_fp16 = reshape(shape = var_1629, x = var_1628_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39060736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39322944))))[name = string("main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39324032)))]; + tensor linear_24_cast_fp16 = linear(bias = main_blocks_2_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_267_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor out_43_perm_0 = const()[name = string("out_43_perm_0"), val = tensor([0, 2, 1])]; + tensor out_43_cast_fp16 = transpose(perm = out_43_perm_0, x = linear_24_cast_fp16)[name = string("transpose_46")]; + tensor var_1636_cast_fp16 = mul(x = out_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1636_cast_fp16")]; + tensor x_141_cast_fp16 = add(x = x_m_9_cast_fp16, y = var_1636_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1643_axes_0 = const()[name = string("op_1643_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39325120)))]; + tensor main_blocks_2_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39326208)))]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_141_cast_fp16)[name = string("transpose_45")]; + tensor var_1643_cast_fp16 = layer_norm(axes = var_1643_axes_0, beta = main_blocks_2_text_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_text_norm_norm_weight_to_fp16, x = input_269_cast_fp16)[name = string("op_1643_cast_fp16")]; + tensor var_1644_perm_0 = const()[name = string("op_1644_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1644_cast_fp16 = transpose(perm = var_1644_perm_0, x = var_1643_cast_fp16)[name = string("transpose_44")]; + tensor x_143_cast_fp16 = mul(x = var_1644_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor x_145_cast_fp16 = mul(x = x_143_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1657_cast_fp16 = mul(x = x_145_cast_fp16, y = last_1_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor x_last_35_axes_0 = const()[name = string("x_last_35_axes_0"), val = tensor([-1])]; + bool x_last_35_keep_dims_0 = const()[name = string("x_last_35_keep_dims_0"), val = bool(true)]; + tensor x_last_35_cast_fp16 = reduce_sum(axes = x_last_35_axes_0, keep_dims = x_last_35_keep_dims_0, x = var_1657_cast_fp16)[name = string("x_last_35_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = x_145_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1662_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_35_cast_fp16)[name = string("op_1662_cast_fp16")]; + tensor input_273_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1662_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("replicate")]; + fp16 const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = fp16(0x0p+0)]; + tensor input_275_cast_fp16 = pad(constant_val = const_59_to_fp16, mode = input_275_mode_0, pad = input_275_pad_0, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")]; + string h_105_pad_type_0 = const()[name = string("h_105_pad_type_0"), val = string("valid")]; + int32 h_105_groups_0 = const()[name = string("h_105_groups_0"), val = int32(512)]; + tensor h_105_strides_0 = const()[name = string("h_105_strides_0"), val = tensor([1])]; + tensor h_105_pad_0 = const()[name = string("h_105_pad_0"), val = tensor([0, 0])]; + tensor h_105_dilations_0 = const()[name = string("h_105_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39327296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39329920))))[name = string("main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39331008)))]; + tensor h_105_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_105_dilations_0, groups = h_105_groups_0, pad = h_105_pad_0, pad_type = h_105_pad_type_0, strides = h_105_strides_0, weight = main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("h_105_cast_fp16")]; + tensor x_147_cast_fp16 = mul(x = h_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_277_perm_0 = const()[name = string("input_277_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1680_axes_0 = const()[name = string("op_1680_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39332096)))]; + tensor main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39333184)))]; + tensor input_277_cast_fp16 = transpose(perm = input_277_perm_0, x = x_147_cast_fp16)[name = string("transpose_43")]; + tensor var_1680_cast_fp16 = layer_norm(axes = var_1680_axes_0, beta = main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16, x = input_277_cast_fp16)[name = string("op_1680_cast_fp16")]; + tensor input_279_perm_0 = const()[name = string("input_279_perm_0"), val = tensor([0, 2, 1])]; + string h_107_pad_type_0 = const()[name = string("h_107_pad_type_0"), val = string("valid")]; + tensor h_107_strides_0 = const()[name = string("h_107_strides_0"), val = tensor([1])]; + tensor h_107_pad_0 = const()[name = string("h_107_pad_0"), val = tensor([0, 0])]; + tensor h_107_dilations_0 = const()[name = string("h_107_dilations_0"), val = tensor([1])]; + int32 h_107_groups_0 = const()[name = string("h_107_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39334272))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40382912))))[name = string("main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40387072)))]; + tensor input_279_cast_fp16 = transpose(perm = input_279_perm_0, x = var_1680_cast_fp16)[name = string("transpose_42")]; + tensor h_107_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_107_dilations_0, groups = h_107_groups_0, pad = h_107_pad_0, pad_type = h_107_pad_type_0, strides = h_107_strides_0, weight = main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_279_cast_fp16)[name = string("h_107_cast_fp16")]; + string input_281_mode_0 = const()[name = string("input_281_mode_0"), val = string("EXACT")]; + tensor input_281_cast_fp16 = gelu(mode = input_281_mode_0, x = h_107_cast_fp16)[name = string("input_281_cast_fp16")]; + string h_109_pad_type_0 = const()[name = string("h_109_pad_type_0"), val = string("valid")]; + tensor h_109_strides_0 = const()[name = string("h_109_strides_0"), val = tensor([1])]; + tensor h_109_pad_0 = const()[name = string("h_109_pad_0"), val = tensor([0, 0])]; + tensor h_109_dilations_0 = const()[name = string("h_109_dilations_0"), val = tensor([1])]; + int32 h_109_groups_0 = const()[name = string("h_109_groups_0"), val = int32(1)]; + tensor op_1697_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40391232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41439872))))[name = string("op_1697_weight_0_to_fp16_quantized")]; + tensor var_1697_bias_0_to_fp16 = const()[name = string("op_1697_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41440960)))]; + tensor var_1697_cast_fp16 = conv(bias = var_1697_bias_0_to_fp16, dilations = h_109_dilations_0, groups = h_109_groups_0, pad = h_109_pad_0, pad_type = h_109_pad_type_0, strides = h_109_strides_0, weight = op_1697_weight_0_to_fp16_quantized, x = input_281_cast_fp16)[name = string("op_1697_cast_fp16")]; + tensor out_45_cast_fp16 = add(x = x_145_cast_fp16, y = var_1697_cast_fp16)[name = string("out_45_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = out_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor x_m_11_cast_fp16 = mul(x = x_149_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_11_cast_fp16")]; + tensor input_283_perm_0 = const()[name = string("input_283_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41442048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41573184))))[name = string("main_blocks_2_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41573760)))]; + tensor input_283_cast_fp16 = transpose(perm = input_283_perm_0, x = x_149_cast_fp16)[name = string("transpose_41")]; + tensor linear_25_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_283_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_1713 = const()[name = string("op_1713"), val = tensor([2, 512, 2, 128])]; + tensor var_1714_cast_fp16 = reshape(shape = var_1713, x = linear_25_cast_fp16)[name = string("op_1714_cast_fp16")]; + tensor q_11_perm_0 = const()[name = string("q_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_2_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41574336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41639936))))[name = string("main_blocks_2_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41640512)))]; + tensor linear_27_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1727 = const()[name = string("op_1727"), val = tensor([2, 50, 2, 128])]; + tensor var_1728_cast_fp16 = reshape(shape = var_1727, x = linear_27_cast_fp16)[name = string("op_1728_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1732_transpose_x_0 = const()[name = string("op_1732_transpose_x_0"), val = bool(false)]; + bool var_1732_transpose_y_0 = const()[name = string("op_1732_transpose_y_0"), val = bool(false)]; + tensor op_1731_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41641088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41666752))))[name = string("op_1731_to_fp16_quantized")]; + tensor q_11_cast_fp16 = transpose(perm = q_11_perm_0, x = var_1714_cast_fp16)[name = string("transpose_40")]; + tensor var_1732_cast_fp16 = matmul(transpose_x = var_1732_transpose_x_0, transpose_y = var_1732_transpose_y_0, x = q_11_cast_fp16, y = op_1731_to_fp16_quantized)[name = string("op_1732_cast_fp16")]; + fp16 _inversed_input_285_y_0_to_fp16 = const()[name = string("_inversed_input_285_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_285_cast_fp16 = mul(x = var_1732_cast_fp16, y = _inversed_input_285_y_0_to_fp16)[name = string("_inversed_input_285_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1228, x = _inversed_input_285_cast_fp16)[name = string("attn_15_cast_fp16")]; + tensor attn_17_cast_fp16 = mul(x = attn_15_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool var_1739_transpose_x_0 = const()[name = string("op_1739_transpose_x_0"), val = bool(false)]; + bool var_1739_transpose_y_0 = const()[name = string("op_1739_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_1728_cast_fp16)[name = string("transpose_39")]; + tensor var_1739_cast_fp16 = matmul(transpose_x = var_1739_transpose_x_0, transpose_y = var_1739_transpose_y_0, x = attn_17_cast_fp16, y = v_11_cast_fp16)[name = string("op_1739_cast_fp16")]; + tensor var_1740_perm_0 = const()[name = string("op_1740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1741 = const()[name = string("op_1741"), val = tensor([2, 512, 256])]; + tensor var_1740_cast_fp16 = transpose(perm = var_1740_perm_0, x = var_1739_cast_fp16)[name = string("transpose_38")]; + tensor input_287_cast_fp16 = reshape(shape = var_1741, x = var_1740_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41666944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41798080))))[name = string("main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41799168)))]; + tensor linear_28_cast_fp16 = linear(bias = main_blocks_2_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_287_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor out_47_perm_0 = const()[name = string("out_47_perm_0"), val = tensor([0, 2, 1])]; + tensor out_47_cast_fp16 = transpose(perm = out_47_perm_0, x = linear_28_cast_fp16)[name = string("transpose_37")]; + tensor var_1748_cast_fp16 = mul(x = out_47_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1748_cast_fp16")]; + tensor x_151_cast_fp16 = add(x = x_m_11_cast_fp16, y = var_1748_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_289_perm_0 = const()[name = string("input_289_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1755_axes_0 = const()[name = string("op_1755_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41800256)))]; + tensor main_blocks_2_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41801344)))]; + tensor input_289_cast_fp16 = transpose(perm = input_289_perm_0, x = x_151_cast_fp16)[name = string("transpose_36")]; + tensor var_1755_cast_fp16 = layer_norm(axes = var_1755_axes_0, beta = main_blocks_2_style_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_style_norm_norm_weight_to_fp16, x = input_289_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor var_1756_perm_0 = const()[name = string("op_1756_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1756_cast_fp16 = transpose(perm = var_1756_perm_0, x = var_1755_cast_fp16)[name = string("transpose_35")]; + tensor x_153_cast_fp16 = mul(x = var_1756_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_153_cast_fp16")]; + int32 var_1775 = const()[name = string("op_1775"), val = int32(-1)]; + tensor x_155_cast_fp16 = mul(x = x_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor var_1810_cast_fp16 = mul(x = x_155_cast_fp16, y = last_1_cast_fp16)[name = string("op_1810_cast_fp16")]; + tensor x_last_37_axes_0 = const()[name = string("x_last_37_axes_0"), val = tensor([-1])]; + bool x_last_37_keep_dims_0 = const()[name = string("x_last_37_keep_dims_0"), val = bool(true)]; + tensor x_last_37_cast_fp16 = reduce_sum(axes = x_last_37_axes_0, keep_dims = x_last_37_keep_dims_0, x = var_1810_cast_fp16)[name = string("x_last_37_cast_fp16")]; + tensor var_1813_cast_fp16 = mul(x = x_155_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1815_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_37_cast_fp16)[name = string("op_1815_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = var_1813_cast_fp16, y = var_1815_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_295_mode_0 = const()[name = string("input_295_mode_0"), val = string("replicate")]; + fp16 const_64_to_fp16 = const()[name = string("const_64_to_fp16"), val = fp16(0x0p+0)]; + tensor input_295_cast_fp16 = pad(constant_val = const_64_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")]; + string h_111_pad_type_0 = const()[name = string("h_111_pad_type_0"), val = string("valid")]; + int32 h_111_groups_0 = const()[name = string("h_111_groups_0"), val = int32(512)]; + tensor h_111_strides_0 = const()[name = string("h_111_strides_0"), val = tensor([1])]; + tensor h_111_pad_0 = const()[name = string("h_111_pad_0"), val = tensor([0, 0])]; + tensor h_111_dilations_0 = const()[name = string("h_111_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41802432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41805056))))[name = string("main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41806144)))]; + tensor h_111_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_111_dilations_0, groups = h_111_groups_0, pad = h_111_pad_0, pad_type = h_111_pad_type_0, strides = h_111_strides_0, weight = main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_295_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor x_157_cast_fp16 = mul(x = h_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor input_297_perm_0 = const()[name = string("input_297_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1833_axes_0 = const()[name = string("op_1833_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41807232)))]; + tensor main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41808320)))]; + fp16 var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_297_cast_fp16 = transpose(perm = input_297_perm_0, x = x_157_cast_fp16)[name = string("transpose_34")]; + tensor var_1833_cast_fp16 = layer_norm(axes = var_1833_axes_0, beta = main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16, x = input_297_cast_fp16)[name = string("op_1833_cast_fp16")]; + tensor input_299_perm_0 = const()[name = string("input_299_perm_0"), val = tensor([0, 2, 1])]; + string h_113_pad_type_0 = const()[name = string("h_113_pad_type_0"), val = string("valid")]; + tensor h_113_strides_0 = const()[name = string("h_113_strides_0"), val = tensor([1])]; + tensor h_113_pad_0 = const()[name = string("h_113_pad_0"), val = tensor([0, 0])]; + tensor h_113_dilations_0 = const()[name = string("h_113_dilations_0"), val = tensor([1])]; + int32 h_113_groups_0 = const()[name = string("h_113_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41809408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42858048))))[name = string("main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42862208)))]; + tensor input_299_cast_fp16 = transpose(perm = input_299_perm_0, x = var_1833_cast_fp16)[name = string("transpose_33")]; + tensor h_113_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_113_dilations_0, groups = h_113_groups_0, pad = h_113_pad_0, pad_type = h_113_pad_type_0, strides = h_113_strides_0, weight = main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_299_cast_fp16)[name = string("h_113_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = h_113_cast_fp16)[name = string("input_301_cast_fp16")]; + string h_115_pad_type_0 = const()[name = string("h_115_pad_type_0"), val = string("valid")]; + tensor h_115_strides_0 = const()[name = string("h_115_strides_0"), val = tensor([1])]; + tensor h_115_pad_0 = const()[name = string("h_115_pad_0"), val = tensor([0, 0])]; + tensor h_115_dilations_0 = const()[name = string("h_115_dilations_0"), val = tensor([1])]; + int32 h_115_groups_0 = const()[name = string("h_115_groups_0"), val = int32(1)]; + tensor op_1850_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42866368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43915008))))[name = string("op_1850_weight_0_to_fp16_quantized")]; + tensor var_1850_bias_0_to_fp16 = const()[name = string("op_1850_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43916096)))]; + tensor var_1850_cast_fp16 = conv(bias = var_1850_bias_0_to_fp16, dilations = h_115_dilations_0, groups = h_115_groups_0, pad = h_115_pad_0, pad_type = h_115_pad_type_0, strides = h_115_strides_0, weight = op_1850_weight_0_to_fp16_quantized, x = input_301_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor out_49_cast_fp16 = add(x = x_155_cast_fp16, y = var_1850_cast_fp16)[name = string("out_49_cast_fp16")]; + tensor x_159_cast_fp16 = mul(x = out_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor x_161_cast_fp16 = mul(x = x_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor var_1864_cast_fp16 = mul(x = x_161_cast_fp16, y = last_1_cast_fp16)[name = string("op_1864_cast_fp16")]; + tensor x_last_39_axes_0 = const()[name = string("x_last_39_axes_0"), val = tensor([-1])]; + bool x_last_39_keep_dims_0 = const()[name = string("x_last_39_keep_dims_0"), val = bool(true)]; + tensor x_last_39_cast_fp16 = reduce_sum(axes = x_last_39_axes_0, keep_dims = x_last_39_keep_dims_0, x = var_1864_cast_fp16)[name = string("x_last_39_cast_fp16")]; + tensor var_1867_cast_fp16 = mul(x = x_161_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_39_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor input_305_cast_fp16 = add(x = var_1867_cast_fp16, y = var_1869_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("replicate")]; + fp16 const_66_to_fp16 = const()[name = string("const_66_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_66_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string h_117_pad_type_0 = const()[name = string("h_117_pad_type_0"), val = string("valid")]; + tensor h_117_dilations_0 = const()[name = string("h_117_dilations_0"), val = tensor([2])]; + int32 h_117_groups_0 = const()[name = string("h_117_groups_0"), val = int32(512)]; + tensor h_117_strides_0 = const()[name = string("h_117_strides_0"), val = tensor([1])]; + tensor h_117_pad_0 = const()[name = string("h_117_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43917184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43919808))))[name = string("main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43920896)))]; + tensor h_117_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_117_dilations_0, groups = h_117_groups_0, pad = h_117_pad_0, pad_type = h_117_pad_type_0, strides = h_117_strides_0, weight = main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_307_cast_fp16)[name = string("h_117_cast_fp16")]; + tensor x_163_cast_fp16 = mul(x = h_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1887_axes_0 = const()[name = string("op_1887_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43921984)))]; + tensor main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43923072)))]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_163_cast_fp16)[name = string("transpose_32")]; + tensor var_1887_cast_fp16 = layer_norm(axes = var_1887_axes_0, beta = main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16, x = input_309_cast_fp16)[name = string("op_1887_cast_fp16")]; + tensor input_311_perm_0 = const()[name = string("input_311_perm_0"), val = tensor([0, 2, 1])]; + string h_119_pad_type_0 = const()[name = string("h_119_pad_type_0"), val = string("valid")]; + tensor h_119_strides_0 = const()[name = string("h_119_strides_0"), val = tensor([1])]; + tensor h_119_pad_0 = const()[name = string("h_119_pad_0"), val = tensor([0, 0])]; + tensor h_119_dilations_0 = const()[name = string("h_119_dilations_0"), val = tensor([1])]; + int32 h_119_groups_0 = const()[name = string("h_119_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43924160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44972800))))[name = string("main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44976960)))]; + tensor input_311_cast_fp16 = transpose(perm = input_311_perm_0, x = var_1887_cast_fp16)[name = string("transpose_31")]; + tensor h_119_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_119_dilations_0, groups = h_119_groups_0, pad = h_119_pad_0, pad_type = h_119_pad_type_0, strides = h_119_strides_0, weight = main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_311_cast_fp16)[name = string("h_119_cast_fp16")]; + string input_313_mode_0 = const()[name = string("input_313_mode_0"), val = string("EXACT")]; + tensor input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = h_119_cast_fp16)[name = string("input_313_cast_fp16")]; + string h_121_pad_type_0 = const()[name = string("h_121_pad_type_0"), val = string("valid")]; + tensor h_121_strides_0 = const()[name = string("h_121_strides_0"), val = tensor([1])]; + tensor h_121_pad_0 = const()[name = string("h_121_pad_0"), val = tensor([0, 0])]; + tensor h_121_dilations_0 = const()[name = string("h_121_dilations_0"), val = tensor([1])]; + int32 h_121_groups_0 = const()[name = string("h_121_groups_0"), val = int32(1)]; + tensor op_1904_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44981120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46029760))))[name = string("op_1904_weight_0_to_fp16_quantized")]; + tensor var_1904_bias_0_to_fp16 = const()[name = string("op_1904_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46030848)))]; + tensor var_1904_cast_fp16 = conv(bias = var_1904_bias_0_to_fp16, dilations = h_121_dilations_0, groups = h_121_groups_0, pad = h_121_pad_0, pad_type = h_121_pad_type_0, strides = h_121_strides_0, weight = op_1904_weight_0_to_fp16_quantized, x = input_313_cast_fp16)[name = string("op_1904_cast_fp16")]; + tensor out_51_cast_fp16 = add(x = x_161_cast_fp16, y = var_1904_cast_fp16)[name = string("out_51_cast_fp16")]; + tensor x_165_cast_fp16 = mul(x = out_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor x_167_cast_fp16 = mul(x = x_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1918_cast_fp16 = mul(x = x_167_cast_fp16, y = last_1_cast_fp16)[name = string("op_1918_cast_fp16")]; + tensor x_last_41_axes_0 = const()[name = string("x_last_41_axes_0"), val = tensor([-1])]; + bool x_last_41_keep_dims_0 = const()[name = string("x_last_41_keep_dims_0"), val = bool(true)]; + tensor x_last_41_cast_fp16 = reduce_sum(axes = x_last_41_axes_0, keep_dims = x_last_41_keep_dims_0, x = var_1918_cast_fp16)[name = string("x_last_41_cast_fp16")]; + tensor var_1921_cast_fp16 = mul(x = x_167_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1921_cast_fp16")]; + tensor var_1923_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_41_cast_fp16)[name = string("op_1923_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = var_1921_cast_fp16, y = var_1923_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("replicate")]; + fp16 const_68_to_fp16 = const()[name = string("const_68_to_fp16"), val = fp16(0x0p+0)]; + tensor input_319_cast_fp16 = pad(constant_val = const_68_to_fp16, mode = input_319_mode_0, pad = input_319_pad_0, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string h_123_pad_type_0 = const()[name = string("h_123_pad_type_0"), val = string("valid")]; + tensor h_123_dilations_0 = const()[name = string("h_123_dilations_0"), val = tensor([4])]; + int32 h_123_groups_0 = const()[name = string("h_123_groups_0"), val = int32(512)]; + tensor h_123_strides_0 = const()[name = string("h_123_strides_0"), val = tensor([1])]; + tensor h_123_pad_0 = const()[name = string("h_123_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46031936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46034560))))[name = string("main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46035648)))]; + tensor h_123_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_123_dilations_0, groups = h_123_groups_0, pad = h_123_pad_0, pad_type = h_123_pad_type_0, strides = h_123_strides_0, weight = main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_319_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor x_169_cast_fp16 = mul(x = h_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1941_axes_0 = const()[name = string("op_1941_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46036736)))]; + tensor main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46037824)))]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_169_cast_fp16)[name = string("transpose_30")]; + tensor var_1941_cast_fp16 = layer_norm(axes = var_1941_axes_0, beta = main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16, x = input_321_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor([0, 2, 1])]; + string h_125_pad_type_0 = const()[name = string("h_125_pad_type_0"), val = string("valid")]; + tensor h_125_strides_0 = const()[name = string("h_125_strides_0"), val = tensor([1])]; + tensor h_125_pad_0 = const()[name = string("h_125_pad_0"), val = tensor([0, 0])]; + tensor h_125_dilations_0 = const()[name = string("h_125_dilations_0"), val = tensor([1])]; + int32 h_125_groups_0 = const()[name = string("h_125_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46038912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47087552))))[name = string("main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47091712)))]; + tensor input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = var_1941_cast_fp16)[name = string("transpose_29")]; + tensor h_125_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_125_dilations_0, groups = h_125_groups_0, pad = h_125_pad_0, pad_type = h_125_pad_type_0, strides = h_125_strides_0, weight = main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_323_cast_fp16)[name = string("h_125_cast_fp16")]; + string input_325_mode_0 = const()[name = string("input_325_mode_0"), val = string("EXACT")]; + tensor input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = h_125_cast_fp16)[name = string("input_325_cast_fp16")]; + string h_127_pad_type_0 = const()[name = string("h_127_pad_type_0"), val = string("valid")]; + tensor h_127_strides_0 = const()[name = string("h_127_strides_0"), val = tensor([1])]; + tensor h_127_pad_0 = const()[name = string("h_127_pad_0"), val = tensor([0, 0])]; + tensor h_127_dilations_0 = const()[name = string("h_127_dilations_0"), val = tensor([1])]; + int32 h_127_groups_0 = const()[name = string("h_127_groups_0"), val = int32(1)]; + tensor op_1958_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47095872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48144512))))[name = string("op_1958_weight_0_to_fp16_quantized")]; + tensor var_1958_bias_0_to_fp16 = const()[name = string("op_1958_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48145600)))]; + tensor var_1958_cast_fp16 = conv(bias = var_1958_bias_0_to_fp16, dilations = h_127_dilations_0, groups = h_127_groups_0, pad = h_127_pad_0, pad_type = h_127_pad_type_0, strides = h_127_strides_0, weight = op_1958_weight_0_to_fp16_quantized, x = input_325_cast_fp16)[name = string("op_1958_cast_fp16")]; + tensor out_53_cast_fp16 = add(x = x_167_cast_fp16, y = var_1958_cast_fp16)[name = string("out_53_cast_fp16")]; + tensor x_171_cast_fp16 = mul(x = out_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_171_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor var_1972_cast_fp16 = mul(x = x_173_cast_fp16, y = last_1_cast_fp16)[name = string("op_1972_cast_fp16")]; + tensor x_last_43_axes_0 = const()[name = string("x_last_43_axes_0"), val = tensor([-1])]; + bool x_last_43_keep_dims_0 = const()[name = string("x_last_43_keep_dims_0"), val = bool(true)]; + tensor x_last_43_cast_fp16 = reduce_sum(axes = x_last_43_axes_0, keep_dims = x_last_43_keep_dims_0, x = var_1972_cast_fp16)[name = string("x_last_43_cast_fp16")]; + tensor var_1975_cast_fp16 = mul(x = x_173_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1975_cast_fp16")]; + tensor var_1977_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_43_cast_fp16)[name = string("op_1977_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1977_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("replicate")]; + fp16 const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = fp16(0x0p+0)]; + tensor input_331_cast_fp16 = pad(constant_val = const_70_to_fp16, mode = input_331_mode_0, pad = input_331_pad_0, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + string h_129_pad_type_0 = const()[name = string("h_129_pad_type_0"), val = string("valid")]; + tensor h_129_dilations_0 = const()[name = string("h_129_dilations_0"), val = tensor([8])]; + int32 h_129_groups_0 = const()[name = string("h_129_groups_0"), val = int32(512)]; + tensor h_129_strides_0 = const()[name = string("h_129_strides_0"), val = tensor([1])]; + tensor h_129_pad_0 = const()[name = string("h_129_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48146688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48149312))))[name = string("main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48150400)))]; + tensor h_129_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_129_dilations_0, groups = h_129_groups_0, pad = h_129_pad_0, pad_type = h_129_pad_type_0, strides = h_129_strides_0, weight = main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_331_cast_fp16)[name = string("h_129_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = h_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_333_perm_0 = const()[name = string("input_333_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1995_axes_0 = const()[name = string("op_1995_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48151488)))]; + tensor main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48152576)))]; + tensor input_333_cast_fp16 = transpose(perm = input_333_perm_0, x = x_175_cast_fp16)[name = string("transpose_28")]; + tensor var_1995_cast_fp16 = layer_norm(axes = var_1995_axes_0, beta = main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16, x = input_333_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor input_335_perm_0 = const()[name = string("input_335_perm_0"), val = tensor([0, 2, 1])]; + string h_131_pad_type_0 = const()[name = string("h_131_pad_type_0"), val = string("valid")]; + tensor h_131_strides_0 = const()[name = string("h_131_strides_0"), val = tensor([1])]; + tensor h_131_pad_0 = const()[name = string("h_131_pad_0"), val = tensor([0, 0])]; + tensor h_131_dilations_0 = const()[name = string("h_131_dilations_0"), val = tensor([1])]; + int32 h_131_groups_0 = const()[name = string("h_131_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48153664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49202304))))[name = string("main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49206464)))]; + tensor input_335_cast_fp16 = transpose(perm = input_335_perm_0, x = var_1995_cast_fp16)[name = string("transpose_27")]; + tensor h_131_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_131_dilations_0, groups = h_131_groups_0, pad = h_131_pad_0, pad_type = h_131_pad_type_0, strides = h_131_strides_0, weight = main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_335_cast_fp16)[name = string("h_131_cast_fp16")]; + string input_337_mode_0 = const()[name = string("input_337_mode_0"), val = string("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = h_131_cast_fp16)[name = string("input_337_cast_fp16")]; + string h_133_pad_type_0 = const()[name = string("h_133_pad_type_0"), val = string("valid")]; + tensor h_133_strides_0 = const()[name = string("h_133_strides_0"), val = tensor([1])]; + tensor h_133_pad_0 = const()[name = string("h_133_pad_0"), val = tensor([0, 0])]; + tensor h_133_dilations_0 = const()[name = string("h_133_dilations_0"), val = tensor([1])]; + int32 h_133_groups_0 = const()[name = string("h_133_groups_0"), val = int32(1)]; + tensor op_2012_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49210624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50259264))))[name = string("op_2012_weight_0_to_fp16_quantized")]; + tensor var_2012_bias_0_to_fp16 = const()[name = string("op_2012_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50260352)))]; + tensor var_2012_cast_fp16 = conv(bias = var_2012_bias_0_to_fp16, dilations = h_133_dilations_0, groups = h_133_groups_0, pad = h_133_pad_0, pad_type = h_133_pad_type_0, strides = h_133_strides_0, weight = op_2012_weight_0_to_fp16_quantized, x = input_337_cast_fp16)[name = string("op_2012_cast_fp16")]; + tensor out_55_cast_fp16 = add(x = x_173_cast_fp16, y = var_2012_cast_fp16)[name = string("out_55_cast_fp16")]; + tensor x_177_cast_fp16 = mul(x = out_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor main_blocks_3_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50261440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50294272))))[name = string("main_blocks_3_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50295360)))]; + tensor linear_29_cast_fp16 = linear(bias = main_blocks_3_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_3_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor t_axes_0 = const()[name = string("t_axes_0"), val = tensor([-1])]; + tensor t_cast_fp16 = expand_dims(axes = t_axes_0, x = linear_29_cast_fp16)[name = string("t_cast_fp16")]; + tensor var_2022_cast_fp16 = add(x = x_177_cast_fp16, y = t_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor x_179_cast_fp16 = mul(x = var_2022_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor x_181_cast_fp16 = mul(x = x_179_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor var_2035_cast_fp16 = mul(x = x_181_cast_fp16, y = last_1_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor x_last_45_axes_0 = const()[name = string("x_last_45_axes_0"), val = tensor([-1])]; + bool x_last_45_keep_dims_0 = const()[name = string("x_last_45_keep_dims_0"), val = bool(true)]; + tensor x_last_45_cast_fp16 = reduce_sum(axes = x_last_45_axes_0, keep_dims = x_last_45_keep_dims_0, x = var_2035_cast_fp16)[name = string("x_last_45_cast_fp16")]; + tensor var_2038_cast_fp16 = mul(x = x_181_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2038_cast_fp16")]; + tensor var_2040_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_45_cast_fp16)[name = string("op_2040_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = var_2038_cast_fp16, y = var_2040_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_345_mode_0 = const()[name = string("input_345_mode_0"), val = string("replicate")]; + fp16 const_72_to_fp16 = const()[name = string("const_72_to_fp16"), val = fp16(0x0p+0)]; + tensor input_345_cast_fp16 = pad(constant_val = const_72_to_fp16, mode = input_345_mode_0, pad = input_345_pad_0, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + string h_135_pad_type_0 = const()[name = string("h_135_pad_type_0"), val = string("valid")]; + int32 h_135_groups_0 = const()[name = string("h_135_groups_0"), val = int32(512)]; + tensor h_135_strides_0 = const()[name = string("h_135_strides_0"), val = tensor([1])]; + tensor h_135_pad_0 = const()[name = string("h_135_pad_0"), val = tensor([0, 0])]; + tensor h_135_dilations_0 = const()[name = string("h_135_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50296448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50299072))))[name = string("main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50300160)))]; + tensor h_135_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_135_dilations_0, groups = h_135_groups_0, pad = h_135_pad_0, pad_type = h_135_pad_type_0, strides = h_135_strides_0, weight = main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_345_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor x_183_cast_fp16 = mul(x = h_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_183_cast_fp16")]; + tensor input_347_perm_0 = const()[name = string("input_347_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2058_axes_0 = const()[name = string("op_2058_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50301248)))]; + tensor main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50302336)))]; + tensor input_347_cast_fp16 = transpose(perm = input_347_perm_0, x = x_183_cast_fp16)[name = string("transpose_26")]; + tensor var_2058_cast_fp16 = layer_norm(axes = var_2058_axes_0, beta = main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16, x = input_347_cast_fp16)[name = string("op_2058_cast_fp16")]; + tensor input_349_perm_0 = const()[name = string("input_349_perm_0"), val = tensor([0, 2, 1])]; + string h_137_pad_type_0 = const()[name = string("h_137_pad_type_0"), val = string("valid")]; + tensor h_137_strides_0 = const()[name = string("h_137_strides_0"), val = tensor([1])]; + tensor h_137_pad_0 = const()[name = string("h_137_pad_0"), val = tensor([0, 0])]; + tensor h_137_dilations_0 = const()[name = string("h_137_dilations_0"), val = tensor([1])]; + int32 h_137_groups_0 = const()[name = string("h_137_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50303424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51352064))))[name = string("main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51356224)))]; + tensor input_349_cast_fp16 = transpose(perm = input_349_perm_0, x = var_2058_cast_fp16)[name = string("transpose_25")]; + tensor h_137_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_137_dilations_0, groups = h_137_groups_0, pad = h_137_pad_0, pad_type = h_137_pad_type_0, strides = h_137_strides_0, weight = main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_349_cast_fp16)[name = string("h_137_cast_fp16")]; + string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")]; + tensor input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = h_137_cast_fp16)[name = string("input_351_cast_fp16")]; + string h_139_pad_type_0 = const()[name = string("h_139_pad_type_0"), val = string("valid")]; + tensor h_139_strides_0 = const()[name = string("h_139_strides_0"), val = tensor([1])]; + tensor h_139_pad_0 = const()[name = string("h_139_pad_0"), val = tensor([0, 0])]; + tensor h_139_dilations_0 = const()[name = string("h_139_dilations_0"), val = tensor([1])]; + int32 h_139_groups_0 = const()[name = string("h_139_groups_0"), val = int32(1)]; + tensor op_2075_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51360384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52409024))))[name = string("op_2075_weight_0_to_fp16_quantized")]; + tensor var_2075_bias_0_to_fp16 = const()[name = string("op_2075_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52410112)))]; + tensor var_2075_cast_fp16 = conv(bias = var_2075_bias_0_to_fp16, dilations = h_139_dilations_0, groups = h_139_groups_0, pad = h_139_pad_0, pad_type = h_139_pad_type_0, strides = h_139_strides_0, weight = op_2075_weight_0_to_fp16_quantized, x = input_351_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor out_57_cast_fp16 = add(x = x_181_cast_fp16, y = var_2075_cast_fp16)[name = string("out_57_cast_fp16")]; + tensor x_185_cast_fp16 = mul(x = out_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor x_m_13_cast_fp16 = mul(x = x_185_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_13_cast_fp16")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52411200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52673408))))[name = string("main_blocks_3_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52674496)))]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_185_cast_fp16)[name = string("transpose_24")]; + tensor linear_30_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_353_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_2093 = const()[name = string("op_2093"), val = tensor([2, 512, 8, 64])]; + tensor var_2094_cast_fp16 = reshape(shape = var_2093, x = linear_30_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor x_187_perm_0 = const()[name = string("x_187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52675584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52806720))))[name = string("main_blocks_3_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52807808)))]; + tensor linear_31_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_2101 = const()[name = string("op_2101"), val = tensor([2, 128, 8, 64])]; + tensor var_2102_cast_fp16 = reshape(shape = var_2101, x = linear_31_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor x_189_perm_0 = const()[name = string("x_189_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52808896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52940032))))[name = string("main_blocks_3_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52941120)))]; + tensor linear_32_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_2109 = const()[name = string("op_2109"), val = tensor([2, 128, 8, 64])]; + tensor var_2110_cast_fp16 = reshape(shape = var_2109, x = linear_32_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor x_a_13_begin_0 = const()[name = string("x_a_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_13_end_0 = const()[name = string("x_a_13_end_0"), val = tensor([2, 8, 512, 32])]; + tensor x_a_13_end_mask_0 = const()[name = string("x_a_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_187_cast_fp16 = transpose(perm = x_187_perm_0, x = var_2094_cast_fp16)[name = string("transpose_23")]; + tensor x_a_13_cast_fp16 = slice_by_index(begin = x_a_13_begin_0, end = x_a_13_end_0, end_mask = x_a_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_a_13_cast_fp16")]; + tensor x_b_13_begin_0 = const()[name = string("x_b_13_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_13_end_0 = const()[name = string("x_b_13_end_0"), val = tensor([2, 8, 512, 64])]; + tensor x_b_13_end_mask_0 = const()[name = string("x_b_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_13_cast_fp16 = slice_by_index(begin = x_b_13_begin_0, end = x_b_13_end_0, end_mask = x_b_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_b_13_cast_fp16")]; + tensor var_2138_cast_fp16 = mul(x = x_a_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2138_cast_fp16")]; + tensor var_2139_cast_fp16 = mul(x = x_b_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2139_cast_fp16")]; + tensor rot_a_13_cast_fp16 = sub(x = var_2138_cast_fp16, y = var_2139_cast_fp16)[name = string("rot_a_13_cast_fp16")]; + tensor var_2141_cast_fp16 = mul(x = x_a_13_cast_fp16, y = sin_1_cast_fp16)[name = string("op_2141_cast_fp16")]; + tensor var_2142_cast_fp16 = mul(x = x_b_13_cast_fp16, y = cos_1_cast_fp16)[name = string("op_2142_cast_fp16")]; + tensor rot_b_13_cast_fp16 = add(x = var_2141_cast_fp16, y = var_2142_cast_fp16)[name = string("rot_b_13_cast_fp16")]; + bool q_13_interleave_0 = const()[name = string("q_13_interleave_0"), val = bool(false)]; + tensor q_13_cast_fp16 = concat(axis = var_1775, interleave = q_13_interleave_0, values = (rot_a_13_cast_fp16, rot_b_13_cast_fp16))[name = string("q_13_cast_fp16")]; + tensor x_a_begin_0 = const()[name = string("x_a_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_end_0 = const()[name = string("x_a_end_0"), val = tensor([2, 8, 128, 32])]; + tensor x_a_end_mask_0 = const()[name = string("x_a_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_189_cast_fp16 = transpose(perm = x_189_perm_0, x = var_2102_cast_fp16)[name = string("transpose_22")]; + tensor x_a_cast_fp16 = slice_by_index(begin = x_a_begin_0, end = x_a_end_0, end_mask = x_a_end_mask_0, x = x_189_cast_fp16)[name = string("x_a_cast_fp16")]; + tensor x_b_begin_0 = const()[name = string("x_b_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_end_0 = const()[name = string("x_b_end_0"), val = tensor([2, 8, 128, 64])]; + tensor x_b_end_mask_0 = const()[name = string("x_b_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_cast_fp16 = slice_by_index(begin = x_b_begin_0, end = x_b_end_0, end_mask = x_b_end_mask_0, x = x_189_cast_fp16)[name = string("x_b_cast_fp16")]; + tensor var_2156_cast_fp16 = mul(x = x_a_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2156_cast_fp16")]; + tensor var_2157_cast_fp16 = mul(x = x_b_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2157_cast_fp16")]; + tensor rot_a_cast_fp16 = sub(x = var_2156_cast_fp16, y = var_2157_cast_fp16)[name = string("rot_a_cast_fp16")]; + tensor var_2159_cast_fp16 = mul(x = x_a_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2159_cast_fp16")]; + tensor var_2160_cast_fp16 = mul(x = x_b_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2160_cast_fp16")]; + tensor rot_b_cast_fp16 = add(x = var_2159_cast_fp16, y = var_2160_cast_fp16)[name = string("rot_b_cast_fp16")]; + bool k_13_interleave_0 = const()[name = string("k_13_interleave_0"), val = bool(false)]; + tensor k_13_cast_fp16 = concat(axis = var_1775, interleave = k_13_interleave_0, values = (rot_a_cast_fp16, rot_b_cast_fp16))[name = string("k_13_cast_fp16")]; + bool var_2165_transpose_x_1 = const()[name = string("op_2165_transpose_x_1"), val = bool(false)]; + bool var_2165_transpose_y_1 = const()[name = string("op_2165_transpose_y_1"), val = bool(true)]; + tensor var_2165_cast_fp16 = matmul(transpose_x = var_2165_transpose_x_1, transpose_y = var_2165_transpose_y_1, x = q_13_cast_fp16, y = k_13_cast_fp16)[name = string("op_2165_cast_fp16")]; + fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_cast_fp16 = mul(x = var_2165_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")]; + tensor input_359_cast_fp16 = sub(x = _inversed_scores_cast_fp16, y = var_530_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_1775, x = input_359_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool var_2174_transpose_x_0 = const()[name = string("op_2174_transpose_x_0"), val = bool(false)]; + bool var_2174_transpose_y_0 = const()[name = string("op_2174_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2110_cast_fp16)[name = string("transpose_21")]; + tensor var_2174_cast_fp16 = matmul(transpose_x = var_2174_transpose_x_0, transpose_y = var_2174_transpose_y_0, x = attn_19_cast_fp16, y = v_13_cast_fp16)[name = string("op_2174_cast_fp16")]; + tensor var_2175_perm_0 = const()[name = string("op_2175_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2176 = const()[name = string("op_2176"), val = tensor([2, 512, 512])]; + tensor var_2175_cast_fp16 = transpose(perm = var_2175_perm_0, x = var_2174_cast_fp16)[name = string("transpose_20")]; + tensor input_361_cast_fp16 = reshape(shape = var_2176, x = var_2175_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52942208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53204416))))[name = string("main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53205504)))]; + tensor linear_33_cast_fp16 = linear(bias = main_blocks_3_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_361_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor out_59_perm_0 = const()[name = string("out_59_perm_0"), val = tensor([0, 2, 1])]; + tensor out_59_cast_fp16 = transpose(perm = out_59_perm_0, x = linear_33_cast_fp16)[name = string("transpose_19")]; + tensor var_2183_cast_fp16 = mul(x = out_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_m_13_cast_fp16, y = var_2183_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor input_363_perm_0 = const()[name = string("input_363_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2190_axes_0 = const()[name = string("op_2190_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53206592)))]; + tensor main_blocks_3_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53207680)))]; + tensor input_363_cast_fp16 = transpose(perm = input_363_perm_0, x = x_191_cast_fp16)[name = string("transpose_18")]; + tensor var_2190_cast_fp16 = layer_norm(axes = var_2190_axes_0, beta = main_blocks_3_text_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_text_norm_norm_weight_to_fp16, x = input_363_cast_fp16)[name = string("op_2190_cast_fp16")]; + tensor var_2191_perm_0 = const()[name = string("op_2191_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2191_perm_0, x = var_2190_cast_fp16)[name = string("transpose_17")]; + tensor x_193_cast_fp16 = mul(x = var_2191_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_193_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2204_cast_fp16 = mul(x = x_195_cast_fp16, y = last_1_cast_fp16)[name = string("op_2204_cast_fp16")]; + tensor x_last_47_axes_0 = const()[name = string("x_last_47_axes_0"), val = tensor([-1])]; + bool x_last_47_keep_dims_0 = const()[name = string("x_last_47_keep_dims_0"), val = bool(true)]; + tensor x_last_47_cast_fp16 = reduce_sum(axes = x_last_47_axes_0, keep_dims = x_last_47_keep_dims_0, x = var_2204_cast_fp16)[name = string("x_last_47_cast_fp16")]; + tensor var_2207_cast_fp16 = mul(x = x_195_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2207_cast_fp16")]; + tensor var_2209_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_47_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor input_367_cast_fp16 = add(x = var_2207_cast_fp16, y = var_2209_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_369_mode_0 = const()[name = string("input_369_mode_0"), val = string("replicate")]; + fp16 const_79_to_fp16 = const()[name = string("const_79_to_fp16"), val = fp16(0x0p+0)]; + tensor input_369_cast_fp16 = pad(constant_val = const_79_to_fp16, mode = input_369_mode_0, pad = input_369_pad_0, x = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + string h_141_pad_type_0 = const()[name = string("h_141_pad_type_0"), val = string("valid")]; + int32 h_141_groups_0 = const()[name = string("h_141_groups_0"), val = int32(512)]; + tensor h_141_strides_0 = const()[name = string("h_141_strides_0"), val = tensor([1])]; + tensor h_141_pad_0 = const()[name = string("h_141_pad_0"), val = tensor([0, 0])]; + tensor h_141_dilations_0 = const()[name = string("h_141_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53208768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53211392))))[name = string("main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53212480)))]; + tensor h_141_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_141_dilations_0, groups = h_141_groups_0, pad = h_141_pad_0, pad_type = h_141_pad_type_0, strides = h_141_strides_0, weight = main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_369_cast_fp16)[name = string("h_141_cast_fp16")]; + tensor x_197_cast_fp16 = mul(x = h_141_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_371_perm_0 = const()[name = string("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2227_axes_0 = const()[name = string("op_2227_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53213568)))]; + tensor main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53214656)))]; + tensor input_371_cast_fp16 = transpose(perm = input_371_perm_0, x = x_197_cast_fp16)[name = string("transpose_16")]; + tensor var_2227_cast_fp16 = layer_norm(axes = var_2227_axes_0, beta = main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16, x = input_371_cast_fp16)[name = string("op_2227_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + string h_143_pad_type_0 = const()[name = string("h_143_pad_type_0"), val = string("valid")]; + tensor h_143_strides_0 = const()[name = string("h_143_strides_0"), val = tensor([1])]; + tensor h_143_pad_0 = const()[name = string("h_143_pad_0"), val = tensor([0, 0])]; + tensor h_143_dilations_0 = const()[name = string("h_143_dilations_0"), val = tensor([1])]; + int32 h_143_groups_0 = const()[name = string("h_143_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53215744))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54264384))))[name = string("main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54268544)))]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = var_2227_cast_fp16)[name = string("transpose_15")]; + tensor h_143_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_143_dilations_0, groups = h_143_groups_0, pad = h_143_pad_0, pad_type = h_143_pad_type_0, strides = h_143_strides_0, weight = main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_373_cast_fp16)[name = string("h_143_cast_fp16")]; + string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")]; + tensor input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = h_143_cast_fp16)[name = string("input_375_cast_fp16")]; + string h_145_pad_type_0 = const()[name = string("h_145_pad_type_0"), val = string("valid")]; + tensor h_145_strides_0 = const()[name = string("h_145_strides_0"), val = tensor([1])]; + tensor h_145_pad_0 = const()[name = string("h_145_pad_0"), val = tensor([0, 0])]; + tensor h_145_dilations_0 = const()[name = string("h_145_dilations_0"), val = tensor([1])]; + int32 h_145_groups_0 = const()[name = string("h_145_groups_0"), val = int32(1)]; + tensor op_2244_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54272704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55321344))))[name = string("op_2244_weight_0_to_fp16_quantized")]; + tensor var_2244_bias_0_to_fp16 = const()[name = string("op_2244_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55322432)))]; + tensor var_2244_cast_fp16 = conv(bias = var_2244_bias_0_to_fp16, dilations = h_145_dilations_0, groups = h_145_groups_0, pad = h_145_pad_0, pad_type = h_145_pad_type_0, strides = h_145_strides_0, weight = op_2244_weight_0_to_fp16_quantized, x = input_375_cast_fp16)[name = string("op_2244_cast_fp16")]; + tensor out_61_cast_fp16 = add(x = x_195_cast_fp16, y = var_2244_cast_fp16)[name = string("out_61_cast_fp16")]; + tensor x_199_cast_fp16 = mul(x = out_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor x_m_cast_fp16 = mul(x = x_199_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_cast_fp16")]; + tensor input_377_perm_0 = const()[name = string("input_377_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55323520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55454656))))[name = string("main_blocks_3_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55455232)))]; + tensor input_377_cast_fp16 = transpose(perm = input_377_perm_0, x = x_199_cast_fp16)[name = string("transpose_14")]; + tensor linear_34_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_377_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_2260 = const()[name = string("op_2260"), val = tensor([2, 512, 2, 128])]; + tensor var_2261_cast_fp16 = reshape(shape = var_2260, x = linear_34_cast_fp16)[name = string("op_2261_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_3_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55455808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55521408))))[name = string("main_blocks_3_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55521984)))]; + tensor linear_36_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_2274 = const()[name = string("op_2274"), val = tensor([2, 50, 2, 128])]; + tensor var_2275_cast_fp16 = reshape(shape = var_2274, x = linear_36_cast_fp16)[name = string("op_2275_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_2279_transpose_x_0 = const()[name = string("op_2279_transpose_x_0"), val = bool(false)]; + bool var_2279_transpose_y_0 = const()[name = string("op_2279_transpose_y_0"), val = bool(false)]; + tensor op_2278_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55522560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55548224))))[name = string("op_2278_to_fp16_quantized")]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_2261_cast_fp16)[name = string("transpose_13")]; + tensor var_2279_cast_fp16 = matmul(transpose_x = var_2279_transpose_x_0, transpose_y = var_2279_transpose_y_0, x = q_cast_fp16, y = op_2278_to_fp16_quantized)[name = string("op_2279_cast_fp16")]; + fp16 _inversed_input_379_y_0_to_fp16 = const()[name = string("_inversed_input_379_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_379_cast_fp16 = mul(x = var_2279_cast_fp16, y = _inversed_input_379_y_0_to_fp16)[name = string("_inversed_input_379_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_1775, x = _inversed_input_379_cast_fp16)[name = string("attn_21_cast_fp16")]; + tensor attn_cast_fp16 = mul(x = attn_21_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_cast_fp16")]; + bool var_2286_transpose_x_0 = const()[name = string("op_2286_transpose_x_0"), val = bool(false)]; + bool var_2286_transpose_y_0 = const()[name = string("op_2286_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_2275_cast_fp16)[name = string("transpose_12")]; + tensor var_2286_cast_fp16 = matmul(transpose_x = var_2286_transpose_x_0, transpose_y = var_2286_transpose_y_0, x = attn_cast_fp16, y = v_15_cast_fp16)[name = string("op_2286_cast_fp16")]; + tensor var_2287_perm_0 = const()[name = string("op_2287_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2288 = const()[name = string("op_2288"), val = tensor([2, 512, 256])]; + tensor var_2287_cast_fp16 = transpose(perm = var_2287_perm_0, x = var_2286_cast_fp16)[name = string("transpose_11")]; + tensor input_381_cast_fp16 = reshape(shape = var_2288, x = var_2287_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55548416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55679552))))[name = string("main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55680640)))]; + tensor linear_37_cast_fp16 = linear(bias = main_blocks_3_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_381_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor out_63_perm_0 = const()[name = string("out_63_perm_0"), val = tensor([0, 2, 1])]; + tensor out_63_cast_fp16 = transpose(perm = out_63_perm_0, x = linear_37_cast_fp16)[name = string("transpose_10")]; + tensor var_2295_cast_fp16 = mul(x = out_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2295_cast_fp16")]; + tensor x_201_cast_fp16 = add(x = x_m_cast_fp16, y = var_2295_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_383_perm_0 = const()[name = string("input_383_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2302_axes_0 = const()[name = string("op_2302_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55681728)))]; + tensor main_blocks_3_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55682816)))]; + tensor input_383_cast_fp16 = transpose(perm = input_383_perm_0, x = x_201_cast_fp16)[name = string("transpose_9")]; + tensor var_2302_cast_fp16 = layer_norm(axes = var_2302_axes_0, beta = main_blocks_3_style_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_style_norm_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_perm_0 = const()[name = string("op_2303_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2303_cast_fp16 = transpose(perm = var_2303_perm_0, x = var_2302_cast_fp16)[name = string("transpose_8")]; + tensor x_203_cast_fp16 = mul(x = var_2303_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor x_205_cast_fp16 = mul(x = x_203_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor var_2330_cast_fp16 = mul(x = x_205_cast_fp16, y = last_1_cast_fp16)[name = string("op_2330_cast_fp16")]; + tensor x_last_49_axes_0 = const()[name = string("x_last_49_axes_0"), val = tensor([-1])]; + bool x_last_49_keep_dims_0 = const()[name = string("x_last_49_keep_dims_0"), val = bool(true)]; + tensor x_last_49_cast_fp16 = reduce_sum(axes = x_last_49_axes_0, keep_dims = x_last_49_keep_dims_0, x = var_2330_cast_fp16)[name = string("x_last_49_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = x_205_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor var_2335_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_49_cast_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2335_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_pad_0 = const()[name = string("input_389_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("replicate")]; + fp16 const_84_to_fp16 = const()[name = string("const_84_to_fp16"), val = fp16(0x0p+0)]; + tensor input_389_cast_fp16 = pad(constant_val = const_84_to_fp16, mode = input_389_mode_0, pad = input_389_pad_0, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + string h_147_pad_type_0 = const()[name = string("h_147_pad_type_0"), val = string("valid")]; + int32 h_147_groups_0 = const()[name = string("h_147_groups_0"), val = int32(512)]; + tensor h_147_strides_0 = const()[name = string("h_147_strides_0"), val = tensor([1])]; + tensor h_147_pad_0 = const()[name = string("h_147_pad_0"), val = tensor([0, 0])]; + tensor h_147_dilations_0 = const()[name = string("h_147_dilations_0"), val = tensor([1])]; + tensor last_convnext_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55683904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55686528))))[name = string("last_convnext_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55687616)))]; + tensor h_147_cast_fp16 = conv(bias = last_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_147_dilations_0, groups = h_147_groups_0, pad = h_147_pad_0, pad_type = h_147_pad_type_0, strides = h_147_strides_0, weight = last_convnext_0_dwconv__conv_weight_to_fp16_quantized, x = input_389_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor x_207_cast_fp16 = mul(x = h_147_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_391_perm_0 = const()[name = string("input_391_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2353_axes_0 = const()[name = string("op_2353_axes_0"), val = tensor([-1])]; + tensor last_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55688704)))]; + tensor last_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55689792)))]; + fp16 var_2307_to_fp16 = const()[name = string("op_2307_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_391_cast_fp16 = transpose(perm = input_391_perm_0, x = x_207_cast_fp16)[name = string("transpose_7")]; + tensor var_2353_cast_fp16 = layer_norm(axes = var_2353_axes_0, beta = last_convnext_0_norm_norm_bias_to_fp16, epsilon = var_2307_to_fp16, gamma = last_convnext_0_norm_norm_weight_to_fp16, x = input_391_cast_fp16)[name = string("op_2353_cast_fp16")]; + tensor input_393_perm_0 = const()[name = string("input_393_perm_0"), val = tensor([0, 2, 1])]; + string h_149_pad_type_0 = const()[name = string("h_149_pad_type_0"), val = string("valid")]; + tensor h_149_strides_0 = const()[name = string("h_149_strides_0"), val = tensor([1])]; + tensor h_149_pad_0 = const()[name = string("h_149_pad_0"), val = tensor([0, 0])]; + tensor h_149_dilations_0 = const()[name = string("h_149_dilations_0"), val = tensor([1])]; + int32 h_149_groups_0 = const()[name = string("h_149_groups_0"), val = int32(1)]; + tensor last_convnext_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55690880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56739520))))[name = string("last_convnext_0_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56743680)))]; + tensor input_393_cast_fp16 = transpose(perm = input_393_perm_0, x = var_2353_cast_fp16)[name = string("transpose_6")]; + tensor h_149_cast_fp16 = conv(bias = last_convnext_0_pwconv1_bias_to_fp16, dilations = h_149_dilations_0, groups = h_149_groups_0, pad = h_149_pad_0, pad_type = h_149_pad_type_0, strides = h_149_strides_0, weight = last_convnext_0_pwconv1_weight_to_fp16_quantized, x = input_393_cast_fp16)[name = string("h_149_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = h_149_cast_fp16)[name = string("input_395_cast_fp16")]; + string h_151_pad_type_0 = const()[name = string("h_151_pad_type_0"), val = string("valid")]; + tensor h_151_strides_0 = const()[name = string("h_151_strides_0"), val = tensor([1])]; + tensor h_151_pad_0 = const()[name = string("h_151_pad_0"), val = tensor([0, 0])]; + tensor h_151_dilations_0 = const()[name = string("h_151_dilations_0"), val = tensor([1])]; + int32 h_151_groups_0 = const()[name = string("h_151_groups_0"), val = int32(1)]; + tensor op_2370_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56747840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57796480))))[name = string("op_2370_weight_0_to_fp16_quantized")]; + tensor var_2370_bias_0_to_fp16 = const()[name = string("op_2370_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57797568)))]; + tensor var_2370_cast_fp16 = conv(bias = var_2370_bias_0_to_fp16, dilations = h_151_dilations_0, groups = h_151_groups_0, pad = h_151_pad_0, pad_type = h_151_pad_type_0, strides = h_151_strides_0, weight = op_2370_weight_0_to_fp16_quantized, x = input_395_cast_fp16)[name = string("op_2370_cast_fp16")]; + tensor out_65_cast_fp16 = add(x = x_205_cast_fp16, y = var_2370_cast_fp16)[name = string("out_65_cast_fp16")]; + tensor x_209_cast_fp16 = mul(x = out_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor x_211_cast_fp16 = mul(x = x_209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_2398_cast_fp16 = mul(x = x_211_cast_fp16, y = last_1_cast_fp16)[name = string("op_2398_cast_fp16")]; + tensor x_last_51_axes_0 = const()[name = string("x_last_51_axes_0"), val = tensor([-1])]; + bool x_last_51_keep_dims_0 = const()[name = string("x_last_51_keep_dims_0"), val = bool(true)]; + tensor x_last_51_cast_fp16 = reduce_sum(axes = x_last_51_axes_0, keep_dims = x_last_51_keep_dims_0, x = var_2398_cast_fp16)[name = string("x_last_51_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = x_211_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_51_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor input_399_cast_fp16 = add(x = var_2401_cast_fp16, y = var_2403_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor input_401_pad_0 = const()[name = string("input_401_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_401_mode_0 = const()[name = string("input_401_mode_0"), val = string("replicate")]; + fp16 const_86_to_fp16 = const()[name = string("const_86_to_fp16"), val = fp16(0x0p+0)]; + tensor input_401_cast_fp16 = pad(constant_val = const_86_to_fp16, mode = input_401_mode_0, pad = input_401_pad_0, x = input_399_cast_fp16)[name = string("input_401_cast_fp16")]; + string h_153_pad_type_0 = const()[name = string("h_153_pad_type_0"), val = string("valid")]; + int32 h_153_groups_0 = const()[name = string("h_153_groups_0"), val = int32(512)]; + tensor h_153_strides_0 = const()[name = string("h_153_strides_0"), val = tensor([1])]; + tensor h_153_pad_0 = const()[name = string("h_153_pad_0"), val = tensor([0, 0])]; + tensor h_153_dilations_0 = const()[name = string("h_153_dilations_0"), val = tensor([1])]; + tensor last_convnext_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57798656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57801280))))[name = string("last_convnext_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57802368)))]; + tensor h_153_cast_fp16 = conv(bias = last_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_153_dilations_0, groups = h_153_groups_0, pad = h_153_pad_0, pad_type = h_153_pad_type_0, strides = h_153_strides_0, weight = last_convnext_1_dwconv__conv_weight_to_fp16_quantized, x = input_401_cast_fp16)[name = string("h_153_cast_fp16")]; + tensor x_213_cast_fp16 = mul(x = h_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_213_cast_fp16")]; + tensor input_403_perm_0 = const()[name = string("input_403_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2421_axes_0 = const()[name = string("op_2421_axes_0"), val = tensor([-1])]; + tensor last_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57803456)))]; + tensor last_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57804544)))]; + fp16 var_2375_to_fp16 = const()[name = string("op_2375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_403_cast_fp16 = transpose(perm = input_403_perm_0, x = x_213_cast_fp16)[name = string("transpose_5")]; + tensor var_2421_cast_fp16 = layer_norm(axes = var_2421_axes_0, beta = last_convnext_1_norm_norm_bias_to_fp16, epsilon = var_2375_to_fp16, gamma = last_convnext_1_norm_norm_weight_to_fp16, x = input_403_cast_fp16)[name = string("op_2421_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string h_155_pad_type_0 = const()[name = string("h_155_pad_type_0"), val = string("valid")]; + tensor h_155_strides_0 = const()[name = string("h_155_strides_0"), val = tensor([1])]; + tensor h_155_pad_0 = const()[name = string("h_155_pad_0"), val = tensor([0, 0])]; + tensor h_155_dilations_0 = const()[name = string("h_155_dilations_0"), val = tensor([1])]; + int32 h_155_groups_0 = const()[name = string("h_155_groups_0"), val = int32(1)]; + tensor last_convnext_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57805632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58854272))))[name = string("last_convnext_1_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58858432)))]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = var_2421_cast_fp16)[name = string("transpose_4")]; + tensor h_155_cast_fp16 = conv(bias = last_convnext_1_pwconv1_bias_to_fp16, dilations = h_155_dilations_0, groups = h_155_groups_0, pad = h_155_pad_0, pad_type = h_155_pad_type_0, strides = h_155_strides_0, weight = last_convnext_1_pwconv1_weight_to_fp16_quantized, x = input_405_cast_fp16)[name = string("h_155_cast_fp16")]; + string input_407_mode_0 = const()[name = string("input_407_mode_0"), val = string("EXACT")]; + tensor input_407_cast_fp16 = gelu(mode = input_407_mode_0, x = h_155_cast_fp16)[name = string("input_407_cast_fp16")]; + string h_157_pad_type_0 = const()[name = string("h_157_pad_type_0"), val = string("valid")]; + tensor h_157_strides_0 = const()[name = string("h_157_strides_0"), val = tensor([1])]; + tensor h_157_pad_0 = const()[name = string("h_157_pad_0"), val = tensor([0, 0])]; + tensor h_157_dilations_0 = const()[name = string("h_157_dilations_0"), val = tensor([1])]; + int32 h_157_groups_0 = const()[name = string("h_157_groups_0"), val = int32(1)]; + tensor op_2438_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58862592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59911232))))[name = string("op_2438_weight_0_to_fp16_quantized")]; + tensor var_2438_bias_0_to_fp16 = const()[name = string("op_2438_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59912320)))]; + tensor var_2438_cast_fp16 = conv(bias = var_2438_bias_0_to_fp16, dilations = h_157_dilations_0, groups = h_157_groups_0, pad = h_157_pad_0, pad_type = h_157_pad_type_0, strides = h_157_strides_0, weight = op_2438_weight_0_to_fp16_quantized, x = input_407_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor out_67_cast_fp16 = add(x = x_211_cast_fp16, y = var_2438_cast_fp16)[name = string("out_67_cast_fp16")]; + tensor x_215_cast_fp16 = mul(x = out_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_215_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2466_cast_fp16 = mul(x = x_217_cast_fp16, y = last_1_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor x_last_53_axes_0 = const()[name = string("x_last_53_axes_0"), val = tensor([-1])]; + bool x_last_53_keep_dims_0 = const()[name = string("x_last_53_keep_dims_0"), val = bool(true)]; + tensor x_last_53_cast_fp16 = reduce_sum(axes = x_last_53_axes_0, keep_dims = x_last_53_keep_dims_0, x = var_2466_cast_fp16)[name = string("x_last_53_cast_fp16")]; + tensor var_2469_cast_fp16 = mul(x = x_217_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor var_2471_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_53_cast_fp16)[name = string("op_2471_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = var_2469_cast_fp16, y = var_2471_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("replicate")]; + fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)]; + tensor input_413_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = input_413_mode_0, pad = input_413_pad_0, x = input_411_cast_fp16)[name = string("input_413_cast_fp16")]; + string h_159_pad_type_0 = const()[name = string("h_159_pad_type_0"), val = string("valid")]; + int32 h_159_groups_0 = const()[name = string("h_159_groups_0"), val = int32(512)]; + tensor h_159_strides_0 = const()[name = string("h_159_strides_0"), val = tensor([1])]; + tensor h_159_pad_0 = const()[name = string("h_159_pad_0"), val = tensor([0, 0])]; + tensor h_159_dilations_0 = const()[name = string("h_159_dilations_0"), val = tensor([1])]; + tensor last_convnext_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59913408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59916032))))[name = string("last_convnext_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59917120)))]; + tensor h_159_cast_fp16 = conv(bias = last_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_159_dilations_0, groups = h_159_groups_0, pad = h_159_pad_0, pad_type = h_159_pad_type_0, strides = h_159_strides_0, weight = last_convnext_2_dwconv__conv_weight_to_fp16_quantized, x = input_413_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor x_219_cast_fp16 = mul(x = h_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_415_perm_0 = const()[name = string("input_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2489_axes_0 = const()[name = string("op_2489_axes_0"), val = tensor([-1])]; + tensor last_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59918208)))]; + tensor last_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59919296)))]; + fp16 var_2443_to_fp16 = const()[name = string("op_2443_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_415_cast_fp16 = transpose(perm = input_415_perm_0, x = x_219_cast_fp16)[name = string("transpose_3")]; + tensor var_2489_cast_fp16 = layer_norm(axes = var_2489_axes_0, beta = last_convnext_2_norm_norm_bias_to_fp16, epsilon = var_2443_to_fp16, gamma = last_convnext_2_norm_norm_weight_to_fp16, x = input_415_cast_fp16)[name = string("op_2489_cast_fp16")]; + tensor input_417_perm_0 = const()[name = string("input_417_perm_0"), val = tensor([0, 2, 1])]; + string h_161_pad_type_0 = const()[name = string("h_161_pad_type_0"), val = string("valid")]; + tensor h_161_strides_0 = const()[name = string("h_161_strides_0"), val = tensor([1])]; + tensor h_161_pad_0 = const()[name = string("h_161_pad_0"), val = tensor([0, 0])]; + tensor h_161_dilations_0 = const()[name = string("h_161_dilations_0"), val = tensor([1])]; + int32 h_161_groups_0 = const()[name = string("h_161_groups_0"), val = int32(1)]; + tensor last_convnext_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59920384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60969024))))[name = string("last_convnext_2_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60973184)))]; + tensor input_417_cast_fp16 = transpose(perm = input_417_perm_0, x = var_2489_cast_fp16)[name = string("transpose_2")]; + tensor h_161_cast_fp16 = conv(bias = last_convnext_2_pwconv1_bias_to_fp16, dilations = h_161_dilations_0, groups = h_161_groups_0, pad = h_161_pad_0, pad_type = h_161_pad_type_0, strides = h_161_strides_0, weight = last_convnext_2_pwconv1_weight_to_fp16_quantized, x = input_417_cast_fp16)[name = string("h_161_cast_fp16")]; + string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("EXACT")]; + tensor input_419_cast_fp16 = gelu(mode = input_419_mode_0, x = h_161_cast_fp16)[name = string("input_419_cast_fp16")]; + string h_163_pad_type_0 = const()[name = string("h_163_pad_type_0"), val = string("valid")]; + tensor h_163_strides_0 = const()[name = string("h_163_strides_0"), val = tensor([1])]; + tensor h_163_pad_0 = const()[name = string("h_163_pad_0"), val = tensor([0, 0])]; + tensor h_163_dilations_0 = const()[name = string("h_163_dilations_0"), val = tensor([1])]; + int32 h_163_groups_0 = const()[name = string("h_163_groups_0"), val = int32(1)]; + tensor op_2506_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60977344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62025984))))[name = string("op_2506_weight_0_to_fp16_quantized")]; + tensor var_2506_bias_0_to_fp16 = const()[name = string("op_2506_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62027072)))]; + tensor var_2506_cast_fp16 = conv(bias = var_2506_bias_0_to_fp16, dilations = h_163_dilations_0, groups = h_163_groups_0, pad = h_163_pad_0, pad_type = h_163_pad_type_0, strides = h_163_strides_0, weight = op_2506_weight_0_to_fp16_quantized, x = input_419_cast_fp16)[name = string("op_2506_cast_fp16")]; + tensor out_69_cast_fp16 = add(x = x_217_cast_fp16, y = var_2506_cast_fp16)[name = string("out_69_cast_fp16")]; + tensor x_221_cast_fp16 = mul(x = out_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor x_223_cast_fp16 = mul(x = x_221_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_2534_cast_fp16 = mul(x = x_223_cast_fp16, y = last_1_cast_fp16)[name = string("op_2534_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_2534_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_2537_cast_fp16 = mul(x = x_223_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2537_cast_fp16")]; + tensor var_2539_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_cast_fp16)[name = string("op_2539_cast_fp16")]; + tensor input_423_cast_fp16 = add(x = var_2537_cast_fp16, y = var_2539_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_425_mode_0 = const()[name = string("input_425_mode_0"), val = string("replicate")]; + fp16 const_90_to_fp16 = const()[name = string("const_90_to_fp16"), val = fp16(0x0p+0)]; + tensor input_425_cast_fp16 = pad(constant_val = const_90_to_fp16, mode = input_425_mode_0, pad = input_425_pad_0, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")]; + string h_165_pad_type_0 = const()[name = string("h_165_pad_type_0"), val = string("valid")]; + int32 h_165_groups_0 = const()[name = string("h_165_groups_0"), val = int32(512)]; + tensor h_165_strides_0 = const()[name = string("h_165_strides_0"), val = tensor([1])]; + tensor h_165_pad_0 = const()[name = string("h_165_pad_0"), val = tensor([0, 0])]; + tensor h_165_dilations_0 = const()[name = string("h_165_dilations_0"), val = tensor([1])]; + tensor last_convnext_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62028160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62030784))))[name = string("last_convnext_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62031872)))]; + tensor h_165_cast_fp16 = conv(bias = last_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_165_dilations_0, groups = h_165_groups_0, pad = h_165_pad_0, pad_type = h_165_pad_type_0, strides = h_165_strides_0, weight = last_convnext_3_dwconv__conv_weight_to_fp16_quantized, x = input_425_cast_fp16)[name = string("h_165_cast_fp16")]; + tensor x_cast_fp16 = mul(x = h_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2557_axes_0 = const()[name = string("op_2557_axes_0"), val = tensor([-1])]; + tensor last_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62032960)))]; + tensor last_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62034048)))]; + fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_cast_fp16)[name = string("transpose_1")]; + tensor var_2557_cast_fp16 = layer_norm(axes = var_2557_axes_0, beta = last_convnext_3_norm_norm_bias_to_fp16, epsilon = var_2511_to_fp16, gamma = last_convnext_3_norm_norm_weight_to_fp16, x = input_427_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor input_429_perm_0 = const()[name = string("input_429_perm_0"), val = tensor([0, 2, 1])]; + string h_167_pad_type_0 = const()[name = string("h_167_pad_type_0"), val = string("valid")]; + tensor h_167_strides_0 = const()[name = string("h_167_strides_0"), val = tensor([1])]; + tensor h_167_pad_0 = const()[name = string("h_167_pad_0"), val = tensor([0, 0])]; + tensor h_167_dilations_0 = const()[name = string("h_167_dilations_0"), val = tensor([1])]; + int32 h_167_groups_0 = const()[name = string("h_167_groups_0"), val = int32(1)]; + tensor last_convnext_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62035136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63083776))))[name = string("last_convnext_3_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63087936)))]; + tensor input_429_cast_fp16 = transpose(perm = input_429_perm_0, x = var_2557_cast_fp16)[name = string("transpose_0")]; + tensor h_167_cast_fp16 = conv(bias = last_convnext_3_pwconv1_bias_to_fp16, dilations = h_167_dilations_0, groups = h_167_groups_0, pad = h_167_pad_0, pad_type = h_167_pad_type_0, strides = h_167_strides_0, weight = last_convnext_3_pwconv1_weight_to_fp16_quantized, x = input_429_cast_fp16)[name = string("h_167_cast_fp16")]; + string input_431_mode_0 = const()[name = string("input_431_mode_0"), val = string("EXACT")]; + tensor input_431_cast_fp16 = gelu(mode = input_431_mode_0, x = h_167_cast_fp16)[name = string("input_431_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor op_2574_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63092096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64140736))))[name = string("op_2574_weight_0_to_fp16_quantized")]; + tensor var_2574_bias_0_to_fp16 = const()[name = string("op_2574_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64141824)))]; + tensor var_2574_cast_fp16 = conv(bias = var_2574_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = op_2574_weight_0_to_fp16_quantized, x = input_431_cast_fp16)[name = string("op_2574_cast_fp16")]; + tensor out_cast_fp16 = add(x = x_223_cast_fp16, y = var_2574_cast_fp16)[name = string("out_cast_fp16")]; + tensor input_cast_fp16 = mul(x = out_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("input_cast_fp16")]; + string var_2587_pad_type_0 = const()[name = string("op_2587_pad_type_0"), val = string("valid")]; + tensor var_2587_strides_0 = const()[name = string("op_2587_strides_0"), val = tensor([1])]; + tensor var_2587_pad_0 = const()[name = string("op_2587_pad_0"), val = tensor([0, 0])]; + tensor var_2587_dilations_0 = const()[name = string("op_2587_dilations_0"), val = tensor([1])]; + int32 var_2587_groups_0 = const()[name = string("op_2587_groups_0"), val = int32(1)]; + tensor proj_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64142912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64216704))))[name = string("proj_out_weight_to_fp16_quantized")]; + tensor var_2587_cast_fp16 = conv(dilations = var_2587_dilations_0, groups = var_2587_groups_0, pad = var_2587_pad_0, pad_type = var_2587_pad_type_0, strides = var_2587_strides_0, weight = proj_out_weight_to_fp16_quantized, x = input_cast_fp16)[name = string("op_2587_cast_fp16")]; + tensor v_cast_fp16 = mul(x = var_2587_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("v_cast_fp16")]; + tensor cond_begin_0 = const()[name = string("cond_begin_0"), val = tensor([0, 0, 0])]; + tensor cond_end_0 = const()[name = string("cond_end_0"), val = tensor([1, 144, 512])]; + tensor cond_end_mask_0 = const()[name = string("cond_end_mask_0"), val = tensor([false, true, true])]; + tensor cond_cast_fp16 = slice_by_index(begin = cond_begin_0, end = cond_end_0, end_mask = cond_end_mask_0, x = v_cast_fp16)[name = string("cond_cast_fp16")]; + tensor uncond_begin_0 = const()[name = string("uncond_begin_0"), val = tensor([1, 0, 0])]; + tensor uncond_end_0 = const()[name = string("uncond_end_0"), val = tensor([2, 144, 512])]; + tensor uncond_end_mask_0 = const()[name = string("uncond_end_mask_0"), val = tensor([true, true, true])]; + tensor uncond_cast_fp16 = slice_by_index(begin = uncond_begin_0, end = uncond_end_0, end_mask = uncond_end_mask_0, x = v_cast_fp16)[name = string("uncond_cast_fp16")]; + fp32 var_2597_epsilon_0 = const()[name = string("op_2597_epsilon_0"), val = fp32(0x1.a36e2ep-14)]; + tensor var_2597_cast_fp16 = inverse(epsilon = var_2597_epsilon_0, x = total_step_to_fp16)[name = string("op_2597_cast_fp16")]; + tensor var_2603 = const()[name = string("op_2603"), val = tensor([-1, 1, 1])]; + tensor step_cast_fp16 = reshape(shape = var_2603, x = var_2597_cast_fp16)[name = string("step_cast_fp16")]; + fp16 var_2605_to_fp16 = const()[name = string("op_2605_to_fp16"), val = fp16(0x1p+2)]; + tensor var_2606_cast_fp16 = mul(x = cond_cast_fp16, y = var_2605_to_fp16)[name = string("op_2606_cast_fp16")]; + fp16 var_2607_to_fp16 = const()[name = string("op_2607_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_2608_cast_fp16 = mul(x = uncond_cast_fp16, y = var_2607_to_fp16)[name = string("op_2608_cast_fp16")]; + tensor var_2610_cast_fp16 = sub(x = var_2606_cast_fp16, y = var_2608_cast_fp16)[name = string("op_2610_cast_fp16")]; + tensor var_2611_cast_fp16 = mul(x = step_cast_fp16, y = var_2610_cast_fp16)[name = string("op_2611_cast_fp16")]; + tensor var_2613_cast_fp16 = add(x = noisy_latent_to_fp16, y = var_2611_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor var_2614_cast_fp16 = mul(x = var_2613_cast_fp16, y = latent_mask_to_fp16)[name = string("op_2614_cast_fp16")]; + string var_2614_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2614_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor denoised_latent = cast(dtype = var_2614_cast_fp16_to_fp32_dtype_0, x = var_2614_cast_fp16)[name = string("cast_0")]; + } -> (denoised_latent); +} \ No newline at end of file diff --git a/VectorEstimatorVariants/VectorEstimator_v2_L512_int8.mlmodelc/weights/weight.bin b/VectorEstimatorVariants/VectorEstimator_v2_L512_int8.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..32687f952a3ea9d8dbfecd02125463708c39edd0 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_L512_int8.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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0000000000000000000000000000000000000000..f6ef5721b007b2696e59aa9731800ba0f33291cf --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_int4.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f204b73d4dfd278f0dcbd245976271dd968527a825f2e6f84118ba98604b6194 +size 634 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_int4.mlmodelc/model.mil b/VectorEstimatorVariants/VectorEstimator_v2_int4.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..2c5c7fda69f5c2dc498dd28384e1121a6814eab3 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_int4.mlmodelc/model.mil @@ -0,0 +1,2269 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor current_step, tensor latent_mask, tensor noisy_latent, tensor style_ttl, tensor text_emb, tensor text_mask, tensor total_step) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"latent_mask", [1, 1, 24]}, {"noisy_latent", [1, 144, 24]}, {"text_emb", [1, 256, 24]}, {"text_mask", [1, 1, 24]}}), ("RangeDims", {{"latent_mask", [[1, 1], [1, 1], [17, 512]]}, {"noisy_latent", [[1, 1], [144, 144], [17, 512]]}, {"text_emb", [[1, 1], [256, 256], [17, 512]]}, {"text_mask", [[1, 1], [1, 1], [17, 512]]}})))] { + string text_emb_to_fp16_dtype_0 = const()[name = string("text_emb_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_emb_to_fp16 = cast(dtype = text_emb_to_fp16_dtype_0, x = text_emb)[name = string("cast_19")]; + tensor var_34_shape_cast_fp16 = shape(x = text_emb_to_fp16)[name = string("op_34_shape_cast_fp16")]; + int32 gather_1_axis_0 = const()[name = string("gather_1_axis_0"), val = int32(0)]; + int32 gather_1_batch_dims_0 = const()[name = string("gather_1_batch_dims_0"), val = int32(0)]; + bool gather_1_validate_indices_0 = const()[name = string("gather_1_validate_indices_0"), val = bool(false)]; + string var_34_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_34_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 gather_1_indices_0_to_uint16 = const()[name = string("gather_1_indices_0_to_uint16"), val = uint16(2)]; + tensor var_34_shape_cast_fp16_to_int16 = cast(dtype = var_34_shape_cast_fp16_to_int16_dtype_0, x = var_34_shape_cast_fp16)[name = string("cast_18")]; + int16 gather_1_cast_uint16 = gather(axis = gather_1_axis_0, batch_dims = gather_1_batch_dims_0, indices = gather_1_indices_0_to_uint16, validate_indices = gather_1_validate_indices_0, x = var_34_shape_cast_fp16_to_int16)[name = string("gather_1_cast_uint16")]; + string gather_1_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_1_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 var_38 = const()[name = string("op_38"), val = int32(-1)]; + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + int32 gather_2 = const()[name = string("gather_2"), val = int32(1)]; + int32 concat_0_axis_0 = const()[name = string("concat_0_axis_0"), val = int32(0)]; + bool concat_0_interleave_0 = const()[name = string("concat_0_interleave_0"), val = bool(false)]; + int32 gather_1_cast_uint16_to_int32 = cast(dtype = gather_1_cast_uint16_to_int32_dtype_0, x = gather_1_cast_uint16)[name = string("cast_17")]; + tensor concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (gather_2, var_38, gather_1_cast_uint16_to_int32))[name = string("concat_0")]; + tensor shape_0 = const()[name = string("shape_0"), val = tensor([1, 256, 1])]; + int32 equal_0_y_0 = const()[name = string("equal_0_y_0"), val = int32(-1)]; + tensor equal_0 = equal(x = concat_0, y = equal_0_y_0)[name = string("equal_0")]; + tensor select_0 = select(a = shape_0, b = concat_0, cond = equal_0)[name = string("select_0")]; + tensor real_div_0 = real_div(x = select_0, y = shape_0)[name = string("real_div_0")]; + tensor uncond_masker_text_special_token_to_fp16 = const()[name = string("uncond_masker_text_special_token_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor text_uncond_cast_fp16 = tile(reps = real_div_0, x = uncond_masker_text_special_token_to_fp16)[name = string("text_uncond_cast_fp16")]; + bool text_emb_interleave_0 = const()[name = string("text_emb_interleave_0"), val = bool(false)]; + tensor text_emb_cast_fp16 = concat(axis = var_39, interleave = text_emb_interleave_0, values = (text_emb_to_fp16, text_uncond_cast_fp16))[name = string("text_emb_cast_fp16")]; + bool input_95_interleave_0 = const()[name = string("input_95_interleave_0"), val = bool(false)]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor uncond_masker_style_value_special_token_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7104))))[name = string("uncond_masker_style_value_special_token_to_fp16_palettized")]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_16")]; + tensor input_95_cast_fp16 = concat(axis = var_39, interleave = input_95_interleave_0, values = (style_ttl_to_fp16, uncond_masker_style_value_special_token_to_fp16_palettized))[name = string("input_95_cast_fp16")]; + int32 var_64 = const()[name = string("op_64"), val = int32(0)]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + string noisy_latent_to_fp16_dtype_0 = const()[name = string("noisy_latent_to_fp16_dtype_0"), val = string("fp16")]; + tensor noisy_latent_to_fp16 = cast(dtype = noisy_latent_to_fp16_dtype_0, x = noisy_latent)[name = string("cast_15")]; + tensor input_5_cast_fp16 = concat(axis = var_64, interleave = input_5_interleave_0, values = (noisy_latent_to_fp16, noisy_latent_to_fp16))[name = string("input_5_cast_fp16")]; + int32 var_67 = const()[name = string("op_67"), val = int32(0)]; + bool latent_mask_b_interleave_0 = const()[name = string("latent_mask_b_interleave_0"), val = bool(false)]; + string latent_mask_to_fp16_dtype_0 = const()[name = string("latent_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor latent_mask_to_fp16 = cast(dtype = latent_mask_to_fp16_dtype_0, x = latent_mask)[name = string("cast_14")]; + tensor latent_mask_b_cast_fp16 = concat(axis = var_67, interleave = latent_mask_b_interleave_0, values = (latent_mask_to_fp16, latent_mask_to_fp16))[name = string("latent_mask_b_cast_fp16")]; + int32 var_70 = const()[name = string("op_70"), val = int32(0)]; + bool text_mask_interleave_0 = const()[name = string("text_mask_interleave_0"), val = bool(false)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_13")]; + tensor text_mask_cast_fp16 = concat(axis = var_70, interleave = text_mask_interleave_0, values = (text_mask_to_fp16, text_mask_to_fp16))[name = string("text_mask_cast_fp16")]; + string current_step_to_fp16_dtype_0 = const()[name = string("current_step_to_fp16_dtype_0"), val = string("fp16")]; + string total_step_to_fp16_dtype_0 = const()[name = string("total_step_to_fp16_dtype_0"), val = string("fp16")]; + tensor current_step_to_fp16 = cast(dtype = current_step_to_fp16_dtype_0, x = current_step)[name = string("cast_11")]; + tensor total_step_to_fp16 = cast(dtype = total_step_to_fp16_dtype_0, x = total_step)[name = string("cast_12")]; + tensor t_1_cast_fp16 = real_div(x = current_step_to_fp16, y = total_step_to_fp16)[name = string("t_1_cast_fp16")]; + int32 var_74 = const()[name = string("op_74"), val = int32(0)]; + bool t_3_interleave_0 = const()[name = string("t_3_interleave_0"), val = bool(false)]; + tensor t_3_cast_fp16 = concat(axis = var_74, interleave = t_3_interleave_0, values = (t_1_cast_fp16, t_1_cast_fp16))[name = string("t_3_cast_fp16")]; + int32 var_78 = const()[name = string("op_78"), val = int32(-1)]; + tensor var_85 = const()[name = string("op_85"), val = tensor([-1, 1])]; + tensor var_86_cast_fp16 = reshape(shape = var_85, x = t_3_cast_fp16)[name = string("op_86_cast_fp16")]; + fp16 time_encoder_time_scale_to_fp16 = const()[name = string("time_encoder_time_scale_to_fp16"), val = fp16(0x1.f4p+9)]; + tensor t_scaled_cast_fp16 = mul(x = var_86_cast_fp16, y = time_encoder_time_scale_to_fp16)[name = string("t_scaled_cast_fp16")]; + tensor time_encoder_omegas_to_fp16 = const()[name = string("time_encoder_omegas_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7232)))]; + tensor phase_cast_fp16 = mul(x = t_scaled_cast_fp16, y = time_encoder_omegas_to_fp16)[name = string("phase_cast_fp16")]; + tensor var_89_cast_fp16 = sin(x = phase_cast_fp16)[name = string("op_89_cast_fp16")]; + tensor var_90_cast_fp16 = cos(x = phase_cast_fp16)[name = string("op_90_cast_fp16")]; + bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)]; + tensor input_1_cast_fp16 = concat(axis = var_78, interleave = input_1_interleave_0, values = (var_89_cast_fp16, var_90_cast_fp16))[name = string("input_1_cast_fp16")]; + tensor time_encoder_mlp_0_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7360))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15616))))[name = string("time_encoder_mlp_0_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_0_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15744)))]; + tensor linear_0_cast_fp16 = linear(bias = time_encoder_mlp_0_linear_bias_to_fp16, weight = time_encoder_mlp_0_linear_weight_to_fp16_palettized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_97_cast_fp16 = softplus(x = linear_0_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = tanh(x = var_97_cast_fp16)[name = string("op_98_cast_fp16")]; + tensor input_3_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_98_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor time_encoder_mlp_2_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24576))))[name = string("time_encoder_mlp_2_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_2_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24704)))]; + tensor linear_1_cast_fp16 = linear(bias = time_encoder_mlp_2_linear_bias_to_fp16, weight = time_encoder_mlp_2_linear_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor time_emb_axes_0 = const()[name = string("time_emb_axes_0"), val = tensor([-1])]; + tensor time_emb_cast_fp16 = expand_dims(axes = time_emb_axes_0, x = linear_1_cast_fp16)[name = string("time_emb_cast_fp16")]; + string var_115_pad_type_0 = const()[name = string("op_115_pad_type_0"), val = string("valid")]; + tensor var_115_strides_0 = const()[name = string("op_115_strides_0"), val = tensor([1])]; + tensor var_115_pad_0 = const()[name = string("op_115_pad_0"), val = tensor([0, 0])]; + tensor var_115_dilations_0 = const()[name = string("op_115_dilations_0"), val = tensor([1])]; + int32 var_115_groups_0 = const()[name = string("op_115_groups_0"), val = int32(1)]; + tensor proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61824))))[name = string("proj_in_weight_to_fp16_palettized")]; + tensor var_115_cast_fp16 = conv(dilations = var_115_dilations_0, groups = var_115_groups_0, pad = var_115_pad_0, pad_type = var_115_pad_type_0, strides = var_115_strides_0, weight = proj_in_weight_to_fp16_palettized, x = input_5_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = var_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_3_cast_fp16")]; + int32 var_134 = const()[name = string("op_134"), val = int32(-1)]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([0, 0, 1])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 0])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([true, true, true])]; + tensor input_7_cast_fp16 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, x = latent_mask_b_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_166_pad_0 = const()[name = string("op_166_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_166_mode_0 = const()[name = string("op_166_mode_0"), val = string("constant")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)]; + tensor var_166_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = var_166_mode_0, pad = var_166_pad_0, x = input_7_cast_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_133_to_fp16 = const()[name = string("op_133_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = sub(x = var_133_to_fp16, y = var_166_cast_fp16)[name = string("op_167_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = latent_mask_b_cast_fp16, y = var_167_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_169_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_172_cast_fp16 = mul(x = x_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor var_173_cast_fp16 = sub(x = var_133_to_fp16, y = latent_mask_b_cast_fp16)[name = string("op_173_cast_fp16")]; + tensor var_174_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_174_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_174_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("replicate")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor input_11_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_11_mode_0, pad = input_11_pad_0, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(512)]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296))))[name = string("main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63424)))]; + tensor h_3_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, 2, 1])]; + tensor var_192_axes_0 = const()[name = string("op_192_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64512)))]; + tensor main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65600)))]; + fp16 var_139_to_fp16 = const()[name = string("op_139_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_7_cast_fp16)[name = string("transpose_117")]; + tensor var_192_cast_fp16 = layer_norm(axes = var_192_axes_0, beta = main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16, x = input_13_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, 2, 1])]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(591040))))[name = string("main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(591168)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = var_192_cast_fp16)[name = string("transpose_116")]; + tensor h_5_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = string("h_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = h_5_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)]; + tensor op_209_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1119680))))[name = string("op_209_weight_0_to_fp16_palettized")]; + tensor var_209_bias_0_to_fp16 = const()[name = string("op_209_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1119808)))]; + tensor var_209_cast_fp16 = conv(bias = var_209_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = op_209_weight_0_to_fp16_palettized, x = input_17_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_209_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_223_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_223_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_226_cast_fp16 = mul(x = x_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_226_cast_fp16")]; + tensor var_228_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_228_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_226_cast_fp16, y = var_228_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor input_23_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([2])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(512)]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1120896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1122240))))[name = string("main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1122368)))]; + tensor h_9_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor var_246_axes_0 = const()[name = string("op_246_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1123456)))]; + tensor main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1124544)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_13_cast_fp16)[name = string("transpose_115")]; + tensor var_246_cast_fp16 = layer_norm(axes = var_246_axes_0, beta = main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_246_cast_fp16")]; + tensor input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor([0, 2, 1])]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1125632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1649984))))[name = string("main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1650112)))]; + tensor input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_246_cast_fp16)[name = string("transpose_114")]; + tensor h_11_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_27_cast_fp16)[name = string("h_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_11_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)]; + tensor op_263_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1654272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2178624))))[name = string("op_263_weight_0_to_fp16_palettized")]; + tensor var_263_bias_0_to_fp16 = const()[name = string("op_263_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2178752)))]; + tensor var_263_cast_fp16 = conv(bias = var_263_bias_0_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = op_263_weight_0_to_fp16_palettized, x = input_29_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_263_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_277_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_277_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_277_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_280_cast_fp16 = mul(x = x_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_280_cast_fp16")]; + tensor var_282_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_280_cast_fp16, y = var_282_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("replicate")]; + fp16 const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = fp16(0x0p+0)]; + tensor input_35_cast_fp16 = pad(constant_val = const_5_to_fp16, mode = input_35_mode_0, pad = input_35_pad_0, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([4])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(512)]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2179840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2181184))))[name = string("main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2181312)))]; + tensor h_15_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor var_300_axes_0 = const()[name = string("op_300_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2182400)))]; + tensor main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2183488)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_19_cast_fp16)[name = string("transpose_113")]; + tensor var_300_cast_fp16 = layer_norm(axes = var_300_axes_0, beta = main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16, x = input_37_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor([0, 2, 1])]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2184576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2708928))))[name = string("main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2709056)))]; + tensor input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_300_cast_fp16)[name = string("transpose_112")]; + tensor h_17_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = string("h_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = h_17_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)]; + tensor op_317_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2713216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3237568))))[name = string("op_317_weight_0_to_fp16_palettized")]; + tensor var_317_bias_0_to_fp16 = const()[name = string("op_317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3237696)))]; + tensor var_317_cast_fp16 = conv(bias = var_317_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = op_317_weight_0_to_fp16_palettized, x = input_41_cast_fp16)[name = string("op_317_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_317_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor var_331_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_331_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_331_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = x_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor var_336_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_336_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_334_cast_fp16, y = var_336_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")]; + fp16 const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_7_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([8])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(512)]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3238784))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3240128))))[name = string("main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3240256)))]; + tensor h_21_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3241344)))]; + tensor main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3242432)))]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_25_cast_fp16)[name = string("transpose_111")]; + tensor var_354_cast_fp16 = layer_norm(axes = var_354_axes_0, beta = main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_354_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3243520))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3767872))))[name = string("main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3768000)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_354_cast_fp16)[name = string("transpose_110")]; + tensor h_23_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("h_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_23_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)]; + tensor op_371_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3772160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4296512))))[name = string("op_371_weight_0_to_fp16_palettized")]; + tensor var_371_bias_0_to_fp16 = const()[name = string("op_371_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4296640)))]; + tensor var_371_cast_fp16 = conv(bias = var_371_bias_0_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = op_371_weight_0_to_fp16_palettized, x = input_53_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_371_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor input_55_cast_fp16 = squeeze(axes = input_55_axes_0, x = time_emb_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4297728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4314176))))[name = string("main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4314304)))]; + tensor linear_2_cast_fp16 = linear(bias = main_blocks_0_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor t_5_axes_0 = const()[name = string("t_5_axes_0"), val = tensor([-1])]; + tensor t_5_cast_fp16 = expand_dims(axes = t_5_axes_0, x = linear_2_cast_fp16)[name = string("t_5_cast_fp16")]; + tensor var_381_cast_fp16 = add(x = x_27_cast_fp16, y = t_5_cast_fp16)[name = string("op_381_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_381_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_394_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_394_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_394_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_397_cast_fp16 = mul(x = x_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = var_397_cast_fp16, y = var_399_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("replicate")]; + fp16 const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = fp16(0x0p+0)]; + tensor input_61_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_61_mode_0, pad = input_61_pad_0, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(512)]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4315392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4316736))))[name = string("main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4316864)))]; + tensor h_27_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417_axes_0 = const()[name = string("op_417_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4317952)))]; + tensor main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4319040)))]; + tensor input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_33_cast_fp16)[name = string("transpose_109")]; + tensor var_417_cast_fp16 = layer_norm(axes = var_417_axes_0, beta = main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16, x = input_63_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4320128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4844480))))[name = string("main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4844608)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = var_417_cast_fp16)[name = string("transpose_108")]; + tensor h_29_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_65_cast_fp16)[name = string("h_29_cast_fp16")]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("EXACT")]; + tensor input_67_cast_fp16 = gelu(mode = input_67_mode_0, x = h_29_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)]; + tensor op_434_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4848768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5373120))))[name = string("op_434_weight_0_to_fp16_palettized")]; + tensor var_434_bias_0_to_fp16 = const()[name = string("op_434_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5373248)))]; + tensor var_434_cast_fp16 = conv(bias = var_434_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = op_434_weight_0_to_fp16_palettized, x = input_67_cast_fp16)[name = string("op_434_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_434_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_m_1_cast_fp16 = mul(x = x_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_1_cast_fp16")]; + tensor var_444_shape_cast_fp16 = shape(x = x_35_cast_fp16)[name = string("op_444_shape_cast_fp16")]; + int32 gather_4_axis_0 = const()[name = string("gather_4_axis_0"), val = int32(0)]; + int32 gather_4_batch_dims_0 = const()[name = string("gather_4_batch_dims_0"), val = int32(0)]; + bool gather_4_validate_indices_0 = const()[name = string("gather_4_validate_indices_0"), val = bool(false)]; + string var_444_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_444_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 gather_4_indices_0_to_uint16 = const()[name = string("gather_4_indices_0_to_uint16"), val = uint16(2)]; + tensor var_444_shape_cast_fp16_to_int16 = cast(dtype = var_444_shape_cast_fp16_to_int16_dtype_0, x = var_444_shape_cast_fp16)[name = string("cast_10")]; + int16 gather_4_cast_uint16 = gather(axis = gather_4_axis_0, batch_dims = gather_4_batch_dims_0, indices = gather_4_indices_0_to_uint16, validate_indices = gather_4_validate_indices_0, x = var_444_shape_cast_fp16_to_int16)[name = string("gather_4_cast_uint16")]; + string gather_4_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_4_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor var_446_shape_cast_fp16 = shape(x = text_emb_cast_fp16)[name = string("op_446_shape_cast_fp16")]; + int32 gather_5_axis_0 = const()[name = string("gather_5_axis_0"), val = int32(0)]; + int32 gather_5_batch_dims_0 = const()[name = string("gather_5_batch_dims_0"), val = int32(0)]; + bool gather_5_validate_indices_0 = const()[name = string("gather_5_validate_indices_0"), val = bool(false)]; + string var_446_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_446_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_5_indices_0_to_uint16 = const()[name = string("gather_5_indices_0_to_uint16"), val = uint16(2)]; + tensor var_446_shape_cast_fp16_to_uint16 = cast(dtype = var_446_shape_cast_fp16_to_uint16_dtype_0, x = var_446_shape_cast_fp16)[name = string("cast_9")]; + uint16 gather_5_cast_uint16 = gather(axis = gather_5_axis_0, batch_dims = gather_5_batch_dims_0, indices = gather_5_indices_0_to_uint16, validate_indices = gather_5_validate_indices_0, x = var_446_shape_cast_fp16_to_uint16)[name = string("gather_5_cast_uint16")]; + string gather_5_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_5_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5374336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5505472))))[name = string("main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5505600)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_35_cast_fp16)[name = string("transpose_107")]; + tensor linear_3_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor concat_4x = const()[name = string("concat_4x"), val = tensor([2, -1, 8, 64])]; + tensor var_453_cast_fp16 = reshape(shape = concat_4x, x = linear_3_cast_fp16)[name = string("op_453_cast_fp16")]; + tensor x_37_perm_0 = const()[name = string("x_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5506688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5572288))))[name = string("main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5572416)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = text_emb_cast_fp16)[name = string("transpose_106")]; + tensor linear_4_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor concat_5x = const()[name = string("concat_5x"), val = tensor([2, -1, 8, 64])]; + tensor var_461_cast_fp16 = reshape(shape = concat_5x, x = linear_4_cast_fp16)[name = string("op_461_cast_fp16")]; + tensor x_39_perm_0 = const()[name = string("x_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5573504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5639104))))[name = string("main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5639232)))]; + tensor linear_5_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor concat_6x = const()[name = string("concat_6x"), val = tensor([2, -1, 8, 64])]; + tensor var_469_cast_fp16 = reshape(shape = concat_6x, x = linear_5_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + int32 concat_7_values0_0 = const()[name = string("concat_7_values0_0"), val = int32(1)]; + int32 concat_7_values2_0 = const()[name = string("concat_7_values2_0"), val = int32(1)]; + int32 concat_7_axis_0 = const()[name = string("concat_7_axis_0"), val = int32(0)]; + bool concat_7_interleave_0 = const()[name = string("concat_7_interleave_0"), val = bool(false)]; + int32 gather_4_cast_uint16_to_int32 = cast(dtype = gather_4_cast_uint16_to_int32_dtype_0, x = gather_4_cast_uint16)[name = string("cast_8")]; + tensor concat_7 = concat(axis = concat_7_axis_0, interleave = concat_7_interleave_0, values = (concat_7_values0_0, gather_4_cast_uint16_to_int32, concat_7_values2_0))[name = string("concat_7")]; + tensor var_472_begin_0 = const()[name = string("op_472_begin_0"), val = tensor([0, 0, 0])]; + tensor var_472_end_mask_0 = const()[name = string("op_472_end_mask_0"), val = tensor([true, false, true])]; + tensor main_blocks_0_text_attn_increments_to_fp16 = const()[name = string("main_blocks_0_text_attn_increments_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5640320)))]; + tensor var_472_cast_fp16 = slice_by_index(begin = var_472_begin_0, end = concat_7, end_mask = var_472_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_472_cast_fp16")]; + tensor concat_8 = const()[name = string("concat_8"), val = tensor([2, -1, -1])]; + tensor shape_1_cast_fp16 = shape(x = var_472_cast_fp16)[name = string("shape_1_cast_fp16")]; + tensor equal_1 = const()[name = string("equal_1"), val = tensor([false, true, true])]; + tensor select_1 = select(a = shape_1_cast_fp16, b = concat_8, cond = equal_1)[name = string("select_1")]; + tensor real_div_1 = real_div(x = select_1, y = shape_1_cast_fp16)[name = string("real_div_1")]; + tensor var_475_cast_fp16 = tile(reps = real_div_1, x = var_472_cast_fp16)[name = string("op_475_cast_fp16")]; + int32 concat_9_values0_0 = const()[name = string("concat_9_values0_0"), val = int32(1)]; + int32 concat_9_values2_0 = const()[name = string("concat_9_values2_0"), val = int32(1)]; + int32 concat_9_axis_0 = const()[name = string("concat_9_axis_0"), val = int32(0)]; + bool concat_9_interleave_0 = const()[name = string("concat_9_interleave_0"), val = bool(false)]; + int32 gather_5_cast_uint16_to_int32 = cast(dtype = gather_5_cast_uint16_to_int32_dtype_0, x = gather_5_cast_uint16)[name = string("cast_7")]; + tensor concat_9 = concat(axis = concat_9_axis_0, interleave = concat_9_interleave_0, values = (concat_9_values0_0, gather_5_cast_uint16_to_int32, concat_9_values2_0))[name = string("concat_9")]; + tensor var_478_begin_0 = const()[name = string("op_478_begin_0"), val = tensor([0, 0, 0])]; + tensor var_478_end_mask_0 = const()[name = string("op_478_end_mask_0"), val = tensor([true, false, true])]; + tensor var_478_cast_fp16 = slice_by_index(begin = var_478_begin_0, end = concat_9, end_mask = var_478_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_478_cast_fp16")]; + tensor concat_10 = const()[name = string("concat_10"), val = tensor([2, -1, -1])]; + tensor shape_2_cast_fp16 = shape(x = var_478_cast_fp16)[name = string("shape_2_cast_fp16")]; + tensor equal_2 = const()[name = string("equal_2"), val = tensor([false, true, true])]; + tensor select_2 = select(a = shape_2_cast_fp16, b = concat_10, cond = equal_2)[name = string("select_2")]; + tensor real_div_2 = real_div(x = select_2, y = shape_2_cast_fp16)[name = string("real_div_2")]; + tensor var_481_cast_fp16 = tile(reps = real_div_2, x = var_478_cast_fp16)[name = string("op_481_cast_fp16")]; + tensor divisor_1_axes_0 = const()[name = string("divisor_1_axes_0"), val = tensor([1, 2])]; + bool divisor_1_keep_dims_0 = const()[name = string("divisor_1_keep_dims_0"), val = bool(false)]; + tensor divisor_1_cast_fp16 = reduce_sum(axes = divisor_1_axes_0, keep_dims = divisor_1_keep_dims_0, x = latent_mask_b_cast_fp16)[name = string("divisor_1_cast_fp16")]; + tensor divisor_3_axes_0 = const()[name = string("divisor_3_axes_0"), val = tensor([1, 2])]; + bool divisor_3_keep_dims_0 = const()[name = string("divisor_3_keep_dims_0"), val = bool(false)]; + tensor divisor_3_cast_fp16 = reduce_sum(axes = divisor_3_axes_0, keep_dims = divisor_3_keep_dims_0, x = text_mask_cast_fp16)[name = string("divisor_3_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([-1, 1, 1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = divisor_1_cast_fp16)[name = string("op_488_cast_fp16")]; + tensor scaled_1_cast_fp16 = real_div(x = var_475_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_1_cast_fp16")]; + tensor main_blocks_0_text_attn_theta_to_fp16 = const()[name = string("main_blocks_0_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5642432)))]; + tensor angles_1_cast_fp16 = mul(x = scaled_1_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_1_cast_fp16")]; + tensor var_491_cast_fp16 = cos(x = angles_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = var_491_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor var_493_cast_fp16 = sin(x = angles_1_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = var_493_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor x_a_1_begin_0 = const()[name = string("x_a_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_1_end_0 = const()[name = string("x_a_1_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_1_end_mask_0 = const()[name = string("x_a_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_37_cast_fp16 = transpose(perm = x_37_perm_0, x = var_453_cast_fp16)[name = string("transpose_105")]; + tensor x_a_1_cast_fp16 = slice_by_index(begin = x_a_1_begin_0, end = x_a_1_end_0, end_mask = x_a_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_a_1_cast_fp16")]; + tensor x_b_1_begin_0 = const()[name = string("x_b_1_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_1_end_0 = const()[name = string("x_b_1_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_1_end_mask_0 = const()[name = string("x_b_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_1_cast_fp16 = slice_by_index(begin = x_b_1_begin_0, end = x_b_1_end_0, end_mask = x_b_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_b_1_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = x_a_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor var_498_cast_fp16 = mul(x = x_b_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_498_cast_fp16")]; + tensor rot_a_1_cast_fp16 = sub(x = var_497_cast_fp16, y = var_498_cast_fp16)[name = string("rot_a_1_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = x_a_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = x_b_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor rot_b_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("rot_b_1_cast_fp16")]; + bool q_1_interleave_0 = const()[name = string("q_1_interleave_0"), val = bool(false)]; + tensor q_1_cast_fp16 = concat(axis = var_134, interleave = q_1_interleave_0, values = (rot_a_1_cast_fp16, rot_b_1_cast_fp16))[name = string("q_1_cast_fp16")]; + tensor var_505 = const()[name = string("op_505"), val = tensor([-1, 1, 1])]; + tensor var_506_cast_fp16 = reshape(shape = var_505, x = divisor_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor scaled_3_cast_fp16 = real_div(x = var_481_cast_fp16, y = var_506_cast_fp16)[name = string("scaled_3_cast_fp16")]; + tensor angles_3_cast_fp16 = mul(x = scaled_3_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_3_cast_fp16")]; + tensor var_509_cast_fp16 = cos(x = angles_3_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor cos_3_axes_0 = const()[name = string("cos_3_axes_0"), val = tensor([1])]; + tensor cos_3_cast_fp16 = expand_dims(axes = cos_3_axes_0, x = var_509_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor var_511_cast_fp16 = sin(x = angles_3_cast_fp16)[name = string("op_511_cast_fp16")]; + tensor sin_3_axes_0 = const()[name = string("sin_3_axes_0"), val = tensor([1])]; + tensor sin_3_cast_fp16 = expand_dims(axes = sin_3_axes_0, x = var_511_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor x_a_3_begin_0 = const()[name = string("x_a_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_3_end_0 = const()[name = string("x_a_3_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_3_end_mask_0 = const()[name = string("x_a_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_39_cast_fp16 = transpose(perm = x_39_perm_0, x = var_461_cast_fp16)[name = string("transpose_104")]; + tensor x_a_3_cast_fp16 = slice_by_index(begin = x_a_3_begin_0, end = x_a_3_end_0, end_mask = x_a_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_a_3_cast_fp16")]; + tensor x_b_3_begin_0 = const()[name = string("x_b_3_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_3_end_0 = const()[name = string("x_b_3_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_3_end_mask_0 = const()[name = string("x_b_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_3_cast_fp16 = slice_by_index(begin = x_b_3_begin_0, end = x_b_3_end_0, end_mask = x_b_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_b_3_cast_fp16")]; + tensor var_515_cast_fp16 = mul(x = x_a_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_515_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = x_b_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor rot_a_3_cast_fp16 = sub(x = var_515_cast_fp16, y = var_516_cast_fp16)[name = string("rot_a_3_cast_fp16")]; + tensor var_518_cast_fp16 = mul(x = x_a_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = mul(x = x_b_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor rot_b_3_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = string("rot_b_3_cast_fp16")]; + bool k_1_interleave_0 = const()[name = string("k_1_interleave_0"), val = bool(false)]; + tensor k_1_cast_fp16 = concat(axis = var_134, interleave = k_1_interleave_0, values = (rot_a_3_cast_fp16, rot_b_3_cast_fp16))[name = string("k_1_cast_fp16")]; + bool var_524_transpose_x_1 = const()[name = string("op_524_transpose_x_1"), val = bool(false)]; + bool var_524_transpose_y_1 = const()[name = string("op_524_transpose_y_1"), val = bool(true)]; + tensor var_524_cast_fp16 = matmul(transpose_x = var_524_transpose_x_1, transpose_y = var_524_transpose_y_1, x = q_1_cast_fp16, y = k_1_cast_fp16)[name = string("op_524_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_524_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([2])]; + tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = text_mask_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528_cast_fp16 = sub(x = var_133_to_fp16, y = var_527_cast_fp16)[name = string("op_528_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_530_cast_fp16 = mul(x = var_528_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_75_cast_fp16 = sub(x = _inversed_scores_1_cast_fp16, y = var_530_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_134, x = input_75_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool var_533_transpose_x_0 = const()[name = string("op_533_transpose_x_0"), val = bool(false)]; + bool var_533_transpose_y_0 = const()[name = string("op_533_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_469_cast_fp16)[name = string("transpose_103")]; + tensor var_533_cast_fp16 = matmul(transpose_x = var_533_transpose_x_0, transpose_y = var_533_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("op_533_cast_fp16")]; + tensor var_534_perm_0 = const()[name = string("op_534_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_11x = const()[name = string("concat_11x"), val = tensor([2, -1, 512])]; + tensor var_534_cast_fp16 = transpose(perm = var_534_perm_0, x = var_533_cast_fp16)[name = string("transpose_102")]; + tensor input_77_cast_fp16 = reshape(shape = concat_11x, x = var_534_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5642560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5773696))))[name = string("main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5773824)))]; + tensor linear_6_cast_fp16 = linear(bias = main_blocks_0_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor out_11_perm_0 = const()[name = string("out_11_perm_0"), val = tensor([0, 2, 1])]; + tensor out_11_cast_fp16 = transpose(perm = out_11_perm_0, x = linear_6_cast_fp16)[name = string("transpose_101")]; + tensor var_542_cast_fp16 = mul(x = out_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_542_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = x_m_1_cast_fp16, y = var_542_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor var_549_axes_0 = const()[name = string("op_549_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5774912)))]; + tensor main_blocks_0_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5776000)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_41_cast_fp16)[name = string("transpose_100")]; + tensor var_549_cast_fp16 = layer_norm(axes = var_549_axes_0, beta = main_blocks_0_text_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_text_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor var_550_perm_0 = const()[name = string("op_550_perm_0"), val = tensor([0, 2, 1])]; + tensor var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = var_549_cast_fp16)[name = string("transpose_99")]; + tensor x_43_cast_fp16 = mul(x = var_550_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = x_45_cast_fp16, y = last_1_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor x_last_11_axes_0 = const()[name = string("x_last_11_axes_0"), val = tensor([-1])]; + bool x_last_11_keep_dims_0 = const()[name = string("x_last_11_keep_dims_0"), val = bool(true)]; + tensor x_last_11_cast_fp16 = reduce_sum(axes = x_last_11_axes_0, keep_dims = x_last_11_keep_dims_0, x = var_563_cast_fp16)[name = string("x_last_11_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = x_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor var_568_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_11_cast_fp16)[name = string("op_568_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = var_566_cast_fp16, y = var_568_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_85_mode_0 = const()[name = string("input_85_mode_0"), val = string("replicate")]; + fp16 const_11_to_fp16 = const()[name = string("const_11_to_fp16"), val = fp16(0x0p+0)]; + tensor input_85_cast_fp16 = pad(constant_val = const_11_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(512)]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5777088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5778432))))[name = string("main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5778560)))]; + tensor h_33_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("h_33_cast_fp16")]; + tensor x_47_cast_fp16 = mul(x = h_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5779648)))]; + tensor main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5780736)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_47_cast_fp16)[name = string("transpose_98")]; + tensor var_586_cast_fp16 = layer_norm(axes = var_586_axes_0, beta = main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_586_cast_fp16")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5781824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6306176))))[name = string("main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6306304)))]; + tensor input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = var_586_cast_fp16)[name = string("transpose_97")]; + tensor h_35_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_89_cast_fp16)[name = string("h_35_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = h_35_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")]; + tensor h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor([1])]; + tensor h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor([0, 0])]; + tensor h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor([1])]; + int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)]; + tensor op_603_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6310464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6834816))))[name = string("op_603_weight_0_to_fp16_palettized")]; + tensor var_603_bias_0_to_fp16 = const()[name = string("op_603_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6834944)))]; + tensor var_603_cast_fp16 = conv(bias = var_603_bias_0_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = op_603_weight_0_to_fp16_palettized, x = input_91_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor out_13_cast_fp16 = add(x = x_45_cast_fp16, y = var_603_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_49_cast_fp16 = mul(x = out_13_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_m_3_cast_fp16 = mul(x = x_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_3_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6836032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6901632))))[name = string("main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6901760)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_49_cast_fp16)[name = string("transpose_96")]; + tensor linear_7_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor concat_12x = const()[name = string("concat_12x"), val = tensor([2, -1, 2, 128])]; + tensor var_620_cast_fp16 = reshape(shape = concat_12x, x = linear_7_cast_fp16)[name = string("op_620_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6902336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6935168))))[name = string("main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6935296)))]; + tensor linear_9_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_633 = const()[name = string("op_633"), val = tensor([2, 50, 2, 128])]; + tensor var_634_cast_fp16 = reshape(shape = var_633, x = linear_9_cast_fp16)[name = string("op_634_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_638_transpose_x_0 = const()[name = string("op_638_transpose_x_0"), val = bool(false)]; + bool var_638_transpose_y_0 = const()[name = string("op_638_transpose_y_0"), val = bool(false)]; + tensor op_637_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6935872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6948736))))[name = string("op_637_to_fp16_palettized")]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_620_cast_fp16)[name = string("transpose_95")]; + tensor var_638_cast_fp16 = matmul(transpose_x = var_638_transpose_x_0, transpose_y = var_638_transpose_y_0, x = q_3_cast_fp16, y = op_637_to_fp16_palettized)[name = string("op_638_cast_fp16")]; + fp16 _inversed_input_97_y_0_to_fp16 = const()[name = string("_inversed_input_97_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_97_cast_fp16 = mul(x = var_638_cast_fp16, y = _inversed_input_97_y_0_to_fp16)[name = string("_inversed_input_97_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_134, x = _inversed_input_97_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_642_perm_0 = const()[name = string("op_642_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_1_axes_0 = const()[name = string("mask_1_axes_0"), val = tensor([1])]; + tensor var_642_cast_fp16 = transpose(perm = var_642_perm_0, x = latent_mask_b_cast_fp16)[name = string("transpose_94")]; + tensor mask_1_cast_fp16 = expand_dims(axes = mask_1_axes_0, x = var_642_cast_fp16)[name = string("mask_1_cast_fp16")]; + tensor attn_5_cast_fp16 = mul(x = attn_3_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool var_645_transpose_x_0 = const()[name = string("op_645_transpose_x_0"), val = bool(false)]; + bool var_645_transpose_y_0 = const()[name = string("op_645_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_634_cast_fp16)[name = string("transpose_93")]; + tensor var_645_cast_fp16 = matmul(transpose_x = var_645_transpose_x_0, transpose_y = var_645_transpose_y_0, x = attn_5_cast_fp16, y = v_3_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_646_perm_0 = const()[name = string("op_646_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_13x = const()[name = string("concat_13x"), val = tensor([2, -1, 256])]; + tensor var_646_cast_fp16 = transpose(perm = var_646_perm_0, x = var_645_cast_fp16)[name = string("transpose_92")]; + tensor input_99_cast_fp16 = reshape(shape = concat_13x, x = var_646_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6948864))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014464))))[name = string("main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014592)))]; + tensor linear_10_cast_fp16 = linear(bias = main_blocks_0_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor out_15_perm_0 = const()[name = string("out_15_perm_0"), val = tensor([0, 2, 1])]; + tensor out_15_cast_fp16 = transpose(perm = out_15_perm_0, x = linear_10_cast_fp16)[name = string("transpose_91")]; + tensor var_654_cast_fp16 = mul(x = out_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor x_51_cast_fp16 = add(x = x_m_3_cast_fp16, y = var_654_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7015680)))]; + tensor main_blocks_0_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7016768)))]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_51_cast_fp16)[name = string("transpose_90")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = main_blocks_0_style_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_style_norm_norm_weight_to_fp16, x = input_101_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_662_perm_0 = const()[name = string("op_662_perm_0"), val = tensor([0, 2, 1])]; + tensor var_662_cast_fp16 = transpose(perm = var_662_perm_0, x = var_661_cast_fp16)[name = string("transpose_89")]; + tensor x_53_cast_fp16 = mul(x = var_662_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_53_cast_fp16")]; + int32 var_681 = const()[name = string("op_681"), val = int32(-1)]; + tensor x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = x_55_cast_fp16, y = last_1_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor x_last_13_axes_0 = const()[name = string("x_last_13_axes_0"), val = tensor([-1])]; + bool x_last_13_keep_dims_0 = const()[name = string("x_last_13_keep_dims_0"), val = bool(true)]; + tensor x_last_13_cast_fp16 = reduce_sum(axes = x_last_13_axes_0, keep_dims = x_last_13_keep_dims_0, x = var_716_cast_fp16)[name = string("x_last_13_cast_fp16")]; + tensor var_719_cast_fp16 = mul(x = x_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_721_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_13_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_719_cast_fp16, y = var_721_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("replicate")]; + fp16 const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(512)]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7017856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7019200))))[name = string("main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7019328)))]; + tensor h_39_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor x_57_cast_fp16 = mul(x = h_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_739_axes_0 = const()[name = string("op_739_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7020416)))]; + tensor main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7021504)))]; + fp16 var_686_to_fp16 = const()[name = string("op_686_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_57_cast_fp16)[name = string("transpose_88")]; + tensor var_739_cast_fp16 = layer_norm(axes = var_739_axes_0, beta = main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_739_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + string h_41_pad_type_0 = const()[name = string("h_41_pad_type_0"), val = string("valid")]; + tensor h_41_strides_0 = const()[name = string("h_41_strides_0"), val = tensor([1])]; + tensor h_41_pad_0 = const()[name = string("h_41_pad_0"), val = tensor([0, 0])]; + tensor h_41_dilations_0 = const()[name = string("h_41_dilations_0"), val = tensor([1])]; + int32 h_41_groups_0 = const()[name = string("h_41_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7022592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7546944))))[name = string("main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7547072)))]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = var_739_cast_fp16)[name = string("transpose_87")]; + tensor h_41_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_41_dilations_0, groups = h_41_groups_0, pad = h_41_pad_0, pad_type = h_41_pad_type_0, strides = h_41_strides_0, weight = main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = h_41_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor op_756_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7551232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8075584))))[name = string("op_756_weight_0_to_fp16_palettized")]; + tensor var_756_bias_0_to_fp16 = const()[name = string("op_756_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8075712)))]; + tensor var_756_cast_fp16 = conv(bias = var_756_bias_0_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = op_756_weight_0_to_fp16_palettized, x = input_113_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor out_17_cast_fp16 = add(x = x_55_cast_fp16, y = var_756_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_59_cast_fp16 = mul(x = out_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor x_61_cast_fp16 = mul(x = x_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_770_cast_fp16 = mul(x = x_61_cast_fp16, y = last_1_cast_fp16)[name = string("op_770_cast_fp16")]; + tensor x_last_15_axes_0 = const()[name = string("x_last_15_axes_0"), val = tensor([-1])]; + bool x_last_15_keep_dims_0 = const()[name = string("x_last_15_keep_dims_0"), val = bool(true)]; + tensor x_last_15_cast_fp16 = reduce_sum(axes = x_last_15_axes_0, keep_dims = x_last_15_keep_dims_0, x = var_770_cast_fp16)[name = string("x_last_15_cast_fp16")]; + tensor var_773_cast_fp16 = mul(x = x_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor var_775_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_15_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = var_773_cast_fp16, y = var_775_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("replicate")]; + fp16 const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = fp16(0x0p+0)]; + tensor input_119_cast_fp16 = pad(constant_val = const_16_to_fp16, mode = input_119_mode_0, pad = input_119_pad_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")]; + string h_45_pad_type_0 = const()[name = string("h_45_pad_type_0"), val = string("valid")]; + tensor h_45_dilations_0 = const()[name = string("h_45_dilations_0"), val = tensor([2])]; + int32 h_45_groups_0 = const()[name = string("h_45_groups_0"), val = int32(512)]; + tensor h_45_strides_0 = const()[name = string("h_45_strides_0"), val = tensor([1])]; + tensor h_45_pad_0 = const()[name = string("h_45_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8076800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8078144))))[name = string("main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8078272)))]; + tensor h_45_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_45_dilations_0, groups = h_45_groups_0, pad = h_45_pad_0, pad_type = h_45_pad_type_0, strides = h_45_strides_0, weight = main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = h_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8079360)))]; + tensor main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8080448)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_63_cast_fp16)[name = string("transpose_86")]; + tensor var_793_cast_fp16 = layer_norm(axes = var_793_axes_0, beta = main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor input_123_perm_0 = const()[name = string("input_123_perm_0"), val = tensor([0, 2, 1])]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8081536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8605888))))[name = string("main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8606016)))]; + tensor input_123_cast_fp16 = transpose(perm = input_123_perm_0, x = var_793_cast_fp16)[name = string("transpose_85")]; + tensor h_47_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = string("h_47_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = h_47_cast_fp16)[name = string("input_125_cast_fp16")]; + string h_49_pad_type_0 = const()[name = string("h_49_pad_type_0"), val = string("valid")]; + tensor h_49_strides_0 = const()[name = string("h_49_strides_0"), val = tensor([1])]; + tensor h_49_pad_0 = const()[name = string("h_49_pad_0"), val = tensor([0, 0])]; + tensor h_49_dilations_0 = const()[name = string("h_49_dilations_0"), val = tensor([1])]; + int32 h_49_groups_0 = const()[name = string("h_49_groups_0"), val = int32(1)]; + tensor op_810_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8610176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9134528))))[name = string("op_810_weight_0_to_fp16_palettized")]; + tensor var_810_bias_0_to_fp16 = const()[name = string("op_810_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9134656)))]; + tensor var_810_cast_fp16 = conv(bias = var_810_bias_0_to_fp16, dilations = h_49_dilations_0, groups = h_49_groups_0, pad = h_49_pad_0, pad_type = h_49_pad_type_0, strides = h_49_strides_0, weight = op_810_weight_0_to_fp16_palettized, x = input_125_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = x_61_cast_fp16, y = var_810_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor x_65_cast_fp16 = mul(x = out_19_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = x_67_cast_fp16, y = last_1_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_last_17_axes_0 = const()[name = string("x_last_17_axes_0"), val = tensor([-1])]; + bool x_last_17_keep_dims_0 = const()[name = string("x_last_17_keep_dims_0"), val = bool(true)]; + tensor x_last_17_cast_fp16 = reduce_sum(axes = x_last_17_axes_0, keep_dims = x_last_17_keep_dims_0, x = var_824_cast_fp16)[name = string("x_last_17_cast_fp16")]; + tensor var_827_cast_fp16 = mul(x = x_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_829_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_17_cast_fp16)[name = string("op_829_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = var_827_cast_fp16, y = var_829_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("replicate")]; + fp16 const_18_to_fp16 = const()[name = string("const_18_to_fp16"), val = fp16(0x0p+0)]; + tensor input_131_cast_fp16 = pad(constant_val = const_18_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + string h_51_pad_type_0 = const()[name = string("h_51_pad_type_0"), val = string("valid")]; + tensor h_51_dilations_0 = const()[name = string("h_51_dilations_0"), val = tensor([4])]; + int32 h_51_groups_0 = const()[name = string("h_51_groups_0"), val = int32(512)]; + tensor h_51_strides_0 = const()[name = string("h_51_strides_0"), val = tensor([1])]; + tensor h_51_pad_0 = const()[name = string("h_51_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9135744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9137088))))[name = string("main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9137216)))]; + tensor h_51_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_51_dilations_0, groups = h_51_groups_0, pad = h_51_pad_0, pad_type = h_51_pad_type_0, strides = h_51_strides_0, weight = main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor x_69_cast_fp16 = mul(x = h_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_847_axes_0 = const()[name = string("op_847_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9138304)))]; + tensor main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9139392)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_69_cast_fp16)[name = string("transpose_84")]; + tensor var_847_cast_fp16 = layer_norm(axes = var_847_axes_0, beta = main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_847_cast_fp16")]; + tensor input_135_perm_0 = const()[name = string("input_135_perm_0"), val = tensor([0, 2, 1])]; + string h_53_pad_type_0 = const()[name = string("h_53_pad_type_0"), val = string("valid")]; + tensor h_53_strides_0 = const()[name = string("h_53_strides_0"), val = tensor([1])]; + tensor h_53_pad_0 = const()[name = string("h_53_pad_0"), val = tensor([0, 0])]; + tensor h_53_dilations_0 = const()[name = string("h_53_dilations_0"), val = tensor([1])]; + int32 h_53_groups_0 = const()[name = string("h_53_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9140480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9664832))))[name = string("main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9664960)))]; + tensor input_135_cast_fp16 = transpose(perm = input_135_perm_0, x = var_847_cast_fp16)[name = string("transpose_83")]; + tensor h_53_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_53_dilations_0, groups = h_53_groups_0, pad = h_53_pad_0, pad_type = h_53_pad_type_0, strides = h_53_strides_0, weight = main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = string("h_53_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = h_53_cast_fp16)[name = string("input_137_cast_fp16")]; + string h_55_pad_type_0 = const()[name = string("h_55_pad_type_0"), val = string("valid")]; + tensor h_55_strides_0 = const()[name = string("h_55_strides_0"), val = tensor([1])]; + tensor h_55_pad_0 = const()[name = string("h_55_pad_0"), val = tensor([0, 0])]; + tensor h_55_dilations_0 = const()[name = string("h_55_dilations_0"), val = tensor([1])]; + int32 h_55_groups_0 = const()[name = string("h_55_groups_0"), val = int32(1)]; + tensor op_864_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9669120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10193472))))[name = string("op_864_weight_0_to_fp16_palettized")]; + tensor var_864_bias_0_to_fp16 = const()[name = string("op_864_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10193600)))]; + tensor var_864_cast_fp16 = conv(bias = var_864_bias_0_to_fp16, dilations = h_55_dilations_0, groups = h_55_groups_0, pad = h_55_pad_0, pad_type = h_55_pad_type_0, strides = h_55_strides_0, weight = op_864_weight_0_to_fp16_palettized, x = input_137_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor out_21_cast_fp16 = add(x = x_67_cast_fp16, y = var_864_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = out_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor x_73_cast_fp16 = mul(x = x_71_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_878_cast_fp16 = mul(x = x_73_cast_fp16, y = last_1_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor x_last_19_axes_0 = const()[name = string("x_last_19_axes_0"), val = tensor([-1])]; + bool x_last_19_keep_dims_0 = const()[name = string("x_last_19_keep_dims_0"), val = bool(true)]; + tensor x_last_19_cast_fp16 = reduce_sum(axes = x_last_19_axes_0, keep_dims = x_last_19_keep_dims_0, x = var_878_cast_fp16)[name = string("x_last_19_cast_fp16")]; + tensor var_881_cast_fp16 = mul(x = x_73_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_881_cast_fp16")]; + tensor var_883_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_19_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = var_881_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("replicate")]; + fp16 const_20_to_fp16 = const()[name = string("const_20_to_fp16"), val = fp16(0x0p+0)]; + tensor input_143_cast_fp16 = pad(constant_val = const_20_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + string h_57_pad_type_0 = const()[name = string("h_57_pad_type_0"), val = string("valid")]; + tensor h_57_dilations_0 = const()[name = string("h_57_dilations_0"), val = tensor([8])]; + int32 h_57_groups_0 = const()[name = string("h_57_groups_0"), val = int32(512)]; + tensor h_57_strides_0 = const()[name = string("h_57_strides_0"), val = tensor([1])]; + tensor h_57_pad_0 = const()[name = string("h_57_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10194688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10196032))))[name = string("main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10196160)))]; + tensor h_57_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_57_dilations_0, groups = h_57_groups_0, pad = h_57_pad_0, pad_type = h_57_pad_type_0, strides = h_57_strides_0, weight = main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = string("h_57_cast_fp16")]; + tensor x_75_cast_fp16 = mul(x = h_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_901_axes_0 = const()[name = string("op_901_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10197248)))]; + tensor main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10198336)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_75_cast_fp16)[name = string("transpose_82")]; + tensor var_901_cast_fp16 = layer_norm(axes = var_901_axes_0, beta = main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor input_147_perm_0 = const()[name = string("input_147_perm_0"), val = tensor([0, 2, 1])]; + string h_59_pad_type_0 = const()[name = string("h_59_pad_type_0"), val = string("valid")]; + tensor h_59_strides_0 = const()[name = string("h_59_strides_0"), val = tensor([1])]; + tensor h_59_pad_0 = const()[name = string("h_59_pad_0"), val = tensor([0, 0])]; + tensor h_59_dilations_0 = const()[name = string("h_59_dilations_0"), val = tensor([1])]; + int32 h_59_groups_0 = const()[name = string("h_59_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10199424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723776))))[name = string("main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723904)))]; + tensor input_147_cast_fp16 = transpose(perm = input_147_perm_0, x = var_901_cast_fp16)[name = string("transpose_81")]; + tensor h_59_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_59_dilations_0, groups = h_59_groups_0, pad = h_59_pad_0, pad_type = h_59_pad_type_0, strides = h_59_strides_0, weight = main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("h_59_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = h_59_cast_fp16)[name = string("input_149_cast_fp16")]; + string h_61_pad_type_0 = const()[name = string("h_61_pad_type_0"), val = string("valid")]; + tensor h_61_strides_0 = const()[name = string("h_61_strides_0"), val = tensor([1])]; + tensor h_61_pad_0 = const()[name = string("h_61_pad_0"), val = tensor([0, 0])]; + tensor h_61_dilations_0 = const()[name = string("h_61_dilations_0"), val = tensor([1])]; + int32 h_61_groups_0 = const()[name = string("h_61_groups_0"), val = int32(1)]; + tensor op_918_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10728064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11252416))))[name = string("op_918_weight_0_to_fp16_palettized")]; + tensor var_918_bias_0_to_fp16 = const()[name = string("op_918_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11252544)))]; + tensor var_918_cast_fp16 = conv(bias = var_918_bias_0_to_fp16, dilations = h_61_dilations_0, groups = h_61_groups_0, pad = h_61_pad_0, pad_type = h_61_pad_type_0, strides = h_61_strides_0, weight = op_918_weight_0_to_fp16_palettized, x = input_149_cast_fp16)[name = string("op_918_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = x_73_cast_fp16, y = var_918_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor x_77_cast_fp16 = mul(x = out_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11253632))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11270080))))[name = string("main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11270208)))]; + tensor linear_11_cast_fp16 = linear(bias = main_blocks_1_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor t_7_axes_0 = const()[name = string("t_7_axes_0"), val = tensor([-1])]; + tensor t_7_cast_fp16 = expand_dims(axes = t_7_axes_0, x = linear_11_cast_fp16)[name = string("t_7_cast_fp16")]; + tensor var_928_cast_fp16 = add(x = x_77_cast_fp16, y = t_7_cast_fp16)[name = string("op_928_cast_fp16")]; + tensor x_79_cast_fp16 = mul(x = var_928_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor x_81_cast_fp16 = mul(x = x_79_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = x_81_cast_fp16, y = last_1_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor x_last_21_axes_0 = const()[name = string("x_last_21_axes_0"), val = tensor([-1])]; + bool x_last_21_keep_dims_0 = const()[name = string("x_last_21_keep_dims_0"), val = bool(true)]; + tensor x_last_21_cast_fp16 = reduce_sum(axes = x_last_21_axes_0, keep_dims = x_last_21_keep_dims_0, x = var_941_cast_fp16)[name = string("x_last_21_cast_fp16")]; + tensor var_944_cast_fp16 = mul(x = x_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor var_946_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_21_cast_fp16)[name = string("op_946_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = var_944_cast_fp16, y = var_946_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("replicate")]; + fp16 const_22_to_fp16 = const()[name = string("const_22_to_fp16"), val = fp16(0x0p+0)]; + tensor input_157_cast_fp16 = pad(constant_val = const_22_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string h_63_pad_type_0 = const()[name = string("h_63_pad_type_0"), val = string("valid")]; + int32 h_63_groups_0 = const()[name = string("h_63_groups_0"), val = int32(512)]; + tensor h_63_strides_0 = const()[name = string("h_63_strides_0"), val = tensor([1])]; + tensor h_63_pad_0 = const()[name = string("h_63_pad_0"), val = tensor([0, 0])]; + tensor h_63_dilations_0 = const()[name = string("h_63_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11271296))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11272640))))[name = string("main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11272768)))]; + tensor h_63_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_63_dilations_0, groups = h_63_groups_0, pad = h_63_pad_0, pad_type = h_63_pad_type_0, strides = h_63_strides_0, weight = main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor x_83_cast_fp16 = mul(x = h_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor var_964_axes_0 = const()[name = string("op_964_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11273856)))]; + tensor main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11274944)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_83_cast_fp16)[name = string("transpose_80")]; + tensor var_964_cast_fp16 = layer_norm(axes = var_964_axes_0, beta = main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + string h_65_pad_type_0 = const()[name = string("h_65_pad_type_0"), val = string("valid")]; + tensor h_65_strides_0 = const()[name = string("h_65_strides_0"), val = tensor([1])]; + tensor h_65_pad_0 = const()[name = string("h_65_pad_0"), val = tensor([0, 0])]; + tensor h_65_dilations_0 = const()[name = string("h_65_dilations_0"), val = tensor([1])]; + int32 h_65_groups_0 = const()[name = string("h_65_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11276032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11800384))))[name = string("main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11800512)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = var_964_cast_fp16)[name = string("transpose_79")]; + tensor h_65_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_65_dilations_0, groups = h_65_groups_0, pad = h_65_pad_0, pad_type = h_65_pad_type_0, strides = h_65_strides_0, weight = main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = string("h_65_cast_fp16")]; + string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("EXACT")]; + tensor input_163_cast_fp16 = gelu(mode = input_163_mode_0, x = h_65_cast_fp16)[name = string("input_163_cast_fp16")]; + string h_67_pad_type_0 = const()[name = string("h_67_pad_type_0"), val = string("valid")]; + tensor h_67_strides_0 = const()[name = string("h_67_strides_0"), val = tensor([1])]; + tensor h_67_pad_0 = const()[name = string("h_67_pad_0"), val = tensor([0, 0])]; + tensor h_67_dilations_0 = const()[name = string("h_67_dilations_0"), val = tensor([1])]; + int32 h_67_groups_0 = const()[name = string("h_67_groups_0"), val = int32(1)]; + tensor op_981_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11804672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12329024))))[name = string("op_981_weight_0_to_fp16_palettized")]; + tensor var_981_bias_0_to_fp16 = const()[name = string("op_981_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12329152)))]; + tensor var_981_cast_fp16 = conv(bias = var_981_bias_0_to_fp16, dilations = h_67_dilations_0, groups = h_67_groups_0, pad = h_67_pad_0, pad_type = h_67_pad_type_0, strides = h_67_strides_0, weight = op_981_weight_0_to_fp16_palettized, x = input_163_cast_fp16)[name = string("op_981_cast_fp16")]; + tensor out_25_cast_fp16 = add(x = x_81_cast_fp16, y = var_981_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = out_25_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor x_m_5_cast_fp16 = mul(x = x_85_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_5_cast_fp16")]; + tensor var_991_shape_cast_fp16 = shape(x = x_85_cast_fp16)[name = string("op_991_shape_cast_fp16")]; + int32 gather_9_axis_0 = const()[name = string("gather_9_axis_0"), val = int32(0)]; + int32 gather_9_batch_dims_0 = const()[name = string("gather_9_batch_dims_0"), val = int32(0)]; + bool gather_9_validate_indices_0 = const()[name = string("gather_9_validate_indices_0"), val = bool(false)]; + string var_991_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_991_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_9_indices_0_to_uint16 = const()[name = string("gather_9_indices_0_to_uint16"), val = uint16(2)]; + tensor var_991_shape_cast_fp16_to_uint16 = cast(dtype = var_991_shape_cast_fp16_to_uint16_dtype_0, x = var_991_shape_cast_fp16)[name = string("cast_6")]; + uint16 gather_9_cast_uint16 = gather(axis = gather_9_axis_0, batch_dims = gather_9_batch_dims_0, indices = gather_9_indices_0_to_uint16, validate_indices = gather_9_validate_indices_0, x = var_991_shape_cast_fp16_to_uint16)[name = string("gather_9_cast_uint16")]; + string gather_9_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_9_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12330240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12461376))))[name = string("main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12461504)))]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_85_cast_fp16)[name = string("transpose_78")]; + tensor linear_12_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor concat_14x = const()[name = string("concat_14x"), val = tensor([2, -1, 8, 64])]; + tensor var_1000_cast_fp16 = reshape(shape = concat_14x, x = linear_12_cast_fp16)[name = string("op_1000_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12462592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12528192))))[name = string("main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12528320)))]; + tensor linear_13_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor concat_15x = const()[name = string("concat_15x"), val = tensor([2, -1, 8, 64])]; + tensor var_1008_cast_fp16 = reshape(shape = concat_15x, x = linear_13_cast_fp16)[name = string("op_1008_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12529408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12595008))))[name = string("main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12595136)))]; + tensor linear_14_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor concat_16x = const()[name = string("concat_16x"), val = tensor([2, -1, 8, 64])]; + tensor var_1016_cast_fp16 = reshape(shape = concat_16x, x = linear_14_cast_fp16)[name = string("op_1016_cast_fp16")]; + tensor v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + int32 concat_17_values0_0 = const()[name = string("concat_17_values0_0"), val = int32(1)]; + int32 concat_17_values2_0 = const()[name = string("concat_17_values2_0"), val = int32(1)]; + int32 concat_17_axis_0 = const()[name = string("concat_17_axis_0"), val = int32(0)]; + bool concat_17_interleave_0 = const()[name = string("concat_17_interleave_0"), val = bool(false)]; + int32 gather_9_cast_uint16_to_int32 = cast(dtype = gather_9_cast_uint16_to_int32_dtype_0, x = gather_9_cast_uint16)[name = string("cast_5")]; + tensor concat_17 = concat(axis = concat_17_axis_0, interleave = concat_17_interleave_0, values = (concat_17_values0_0, gather_9_cast_uint16_to_int32, concat_17_values2_0))[name = string("concat_17")]; + tensor var_1019_begin_0 = const()[name = string("op_1019_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1019_end_mask_0 = const()[name = string("op_1019_end_mask_0"), val = tensor([true, false, true])]; + tensor var_1019_cast_fp16 = slice_by_index(begin = var_1019_begin_0, end = concat_17, end_mask = var_1019_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_1019_cast_fp16")]; + tensor concat_18 = const()[name = string("concat_18"), val = tensor([2, -1, -1])]; + tensor shape_3_cast_fp16 = shape(x = var_1019_cast_fp16)[name = string("shape_3_cast_fp16")]; + tensor equal_3 = const()[name = string("equal_3"), val = tensor([false, true, true])]; + tensor select_3 = select(a = shape_3_cast_fp16, b = concat_18, cond = equal_3)[name = string("select_3")]; + tensor real_div_3 = real_div(x = select_3, y = shape_3_cast_fp16)[name = string("real_div_3")]; + tensor var_1022_cast_fp16 = tile(reps = real_div_3, x = var_1019_cast_fp16)[name = string("op_1022_cast_fp16")]; + tensor scaled_5_cast_fp16 = real_div(x = var_1022_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_5_cast_fp16")]; + tensor main_blocks_1_text_attn_theta_to_fp16 = const()[name = string("main_blocks_1_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12596224)))]; + tensor angles_5_cast_fp16 = mul(x = scaled_5_cast_fp16, y = main_blocks_1_text_attn_theta_to_fp16)[name = string("angles_5_cast_fp16")]; + tensor var_1038_cast_fp16 = cos(x = angles_5_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor cos_5_axes_0 = const()[name = string("cos_5_axes_0"), val = tensor([1])]; + tensor cos_5_cast_fp16 = expand_dims(axes = cos_5_axes_0, x = var_1038_cast_fp16)[name = string("cos_5_cast_fp16")]; + tensor var_1040_cast_fp16 = sin(x = angles_5_cast_fp16)[name = string("op_1040_cast_fp16")]; + tensor sin_5_axes_0 = const()[name = string("sin_5_axes_0"), val = tensor([1])]; + tensor sin_5_cast_fp16 = expand_dims(axes = sin_5_axes_0, x = var_1040_cast_fp16)[name = string("sin_5_cast_fp16")]; + tensor x_a_5_begin_0 = const()[name = string("x_a_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_5_end_0 = const()[name = string("x_a_5_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_5_end_mask_0 = const()[name = string("x_a_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_1000_cast_fp16)[name = string("transpose_77")]; + tensor x_a_5_cast_fp16 = slice_by_index(begin = x_a_5_begin_0, end = x_a_5_end_0, end_mask = x_a_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_a_5_cast_fp16")]; + tensor x_b_5_begin_0 = const()[name = string("x_b_5_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_5_end_0 = const()[name = string("x_b_5_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_5_end_mask_0 = const()[name = string("x_b_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_5_cast_fp16 = slice_by_index(begin = x_b_5_begin_0, end = x_b_5_end_0, end_mask = x_b_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_b_5_cast_fp16")]; + tensor var_1044_cast_fp16 = mul(x = x_a_5_cast_fp16, y = cos_5_cast_fp16)[name = string("op_1044_cast_fp16")]; + tensor var_1045_cast_fp16 = mul(x = x_b_5_cast_fp16, y = sin_5_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor rot_a_5_cast_fp16 = sub(x = var_1044_cast_fp16, y = var_1045_cast_fp16)[name = string("rot_a_5_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_a_5_cast_fp16, y = sin_5_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_cast_fp16 = mul(x = x_b_5_cast_fp16, y = cos_5_cast_fp16)[name = string("op_1048_cast_fp16")]; + tensor rot_b_5_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("rot_b_5_cast_fp16")]; + bool q_5_interleave_0 = const()[name = string("q_5_interleave_0"), val = bool(false)]; + tensor q_5_cast_fp16 = concat(axis = var_681, interleave = q_5_interleave_0, values = (rot_a_5_cast_fp16, rot_b_5_cast_fp16))[name = string("q_5_cast_fp16")]; + tensor x_a_7_begin_0 = const()[name = string("x_a_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_7_end_0 = const()[name = string("x_a_7_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_7_end_mask_0 = const()[name = string("x_a_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_1008_cast_fp16)[name = string("transpose_76")]; + tensor x_a_7_cast_fp16 = slice_by_index(begin = x_a_7_begin_0, end = x_a_7_end_0, end_mask = x_a_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_a_7_cast_fp16")]; + tensor x_b_7_begin_0 = const()[name = string("x_b_7_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_7_end_0 = const()[name = string("x_b_7_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_7_end_mask_0 = const()[name = string("x_b_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_7_cast_fp16 = slice_by_index(begin = x_b_7_begin_0, end = x_b_7_end_0, end_mask = x_b_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_b_7_cast_fp16")]; + tensor var_1062_cast_fp16 = mul(x = x_a_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = x_b_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor rot_a_7_cast_fp16 = sub(x = var_1062_cast_fp16, y = var_1063_cast_fp16)[name = string("rot_a_7_cast_fp16")]; + tensor var_1065_cast_fp16 = mul(x = x_a_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1065_cast_fp16")]; + tensor var_1066_cast_fp16 = mul(x = x_b_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1066_cast_fp16")]; + tensor rot_b_7_cast_fp16 = add(x = var_1065_cast_fp16, y = var_1066_cast_fp16)[name = string("rot_b_7_cast_fp16")]; + bool k_5_interleave_0 = const()[name = string("k_5_interleave_0"), val = bool(false)]; + tensor k_5_cast_fp16 = concat(axis = var_681, interleave = k_5_interleave_0, values = (rot_a_7_cast_fp16, rot_b_7_cast_fp16))[name = string("k_5_cast_fp16")]; + bool var_1071_transpose_x_1 = const()[name = string("op_1071_transpose_x_1"), val = bool(false)]; + bool var_1071_transpose_y_1 = const()[name = string("op_1071_transpose_y_1"), val = bool(true)]; + tensor var_1071_cast_fp16 = matmul(transpose_x = var_1071_transpose_x_1, transpose_y = var_1071_transpose_y_1, x = q_5_cast_fp16, y = k_5_cast_fp16)[name = string("op_1071_cast_fp16")]; + fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_3_cast_fp16 = mul(x = var_1071_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")]; + tensor input_171_cast_fp16 = sub(x = _inversed_scores_3_cast_fp16, y = var_530_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_681, x = input_171_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool var_1080_transpose_x_0 = const()[name = string("op_1080_transpose_x_0"), val = bool(false)]; + bool var_1080_transpose_y_0 = const()[name = string("op_1080_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1016_cast_fp16)[name = string("transpose_75")]; + tensor var_1080_cast_fp16 = matmul(transpose_x = var_1080_transpose_x_0, transpose_y = var_1080_transpose_y_0, x = attn_7_cast_fp16, y = v_5_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_perm_0 = const()[name = string("op_1081_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_21x = const()[name = string("concat_21x"), val = tensor([2, -1, 512])]; + tensor var_1081_cast_fp16 = transpose(perm = var_1081_perm_0, x = var_1080_cast_fp16)[name = string("transpose_74")]; + tensor input_173_cast_fp16 = reshape(shape = concat_21x, x = var_1081_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12596352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12727488))))[name = string("main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12727616)))]; + tensor linear_15_cast_fp16 = linear(bias = main_blocks_1_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor out_27_perm_0 = const()[name = string("out_27_perm_0"), val = tensor([0, 2, 1])]; + tensor out_27_cast_fp16 = transpose(perm = out_27_perm_0, x = linear_15_cast_fp16)[name = string("transpose_73")]; + tensor var_1089_cast_fp16 = mul(x = out_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor x_91_cast_fp16 = add(x = x_m_5_cast_fp16, y = var_1089_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_175_perm_0 = const()[name = string("input_175_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1096_axes_0 = const()[name = string("op_1096_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12728704)))]; + tensor main_blocks_1_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12729792)))]; + tensor input_175_cast_fp16 = transpose(perm = input_175_perm_0, x = x_91_cast_fp16)[name = string("transpose_72")]; + tensor var_1096_cast_fp16 = layer_norm(axes = var_1096_axes_0, beta = main_blocks_1_text_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_text_norm_norm_weight_to_fp16, x = input_175_cast_fp16)[name = string("op_1096_cast_fp16")]; + tensor var_1097_perm_0 = const()[name = string("op_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1097_cast_fp16 = transpose(perm = var_1097_perm_0, x = var_1096_cast_fp16)[name = string("transpose_71")]; + tensor x_93_cast_fp16 = mul(x = var_1097_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor x_95_cast_fp16 = mul(x = x_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_1110_cast_fp16 = mul(x = x_95_cast_fp16, y = last_1_cast_fp16)[name = string("op_1110_cast_fp16")]; + tensor x_last_23_axes_0 = const()[name = string("x_last_23_axes_0"), val = tensor([-1])]; + bool x_last_23_keep_dims_0 = const()[name = string("x_last_23_keep_dims_0"), val = bool(true)]; + tensor x_last_23_cast_fp16 = reduce_sum(axes = x_last_23_axes_0, keep_dims = x_last_23_keep_dims_0, x = var_1110_cast_fp16)[name = string("x_last_23_cast_fp16")]; + tensor var_1113_cast_fp16 = mul(x = x_95_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1115_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_23_cast_fp16)[name = string("op_1115_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = var_1113_cast_fp16, y = var_1115_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("replicate")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor input_181_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + string h_69_pad_type_0 = const()[name = string("h_69_pad_type_0"), val = string("valid")]; + int32 h_69_groups_0 = const()[name = string("h_69_groups_0"), val = int32(512)]; + tensor h_69_strides_0 = const()[name = string("h_69_strides_0"), val = tensor([1])]; + tensor h_69_pad_0 = const()[name = string("h_69_pad_0"), val = tensor([0, 0])]; + tensor h_69_dilations_0 = const()[name = string("h_69_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12730880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12732224))))[name = string("main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12732352)))]; + tensor h_69_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_69_dilations_0, groups = h_69_groups_0, pad = h_69_pad_0, pad_type = h_69_pad_type_0, strides = h_69_strides_0, weight = main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = string("h_69_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = h_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_183_perm_0 = const()[name = string("input_183_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1133_axes_0 = const()[name = string("op_1133_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12733440)))]; + tensor main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12734528)))]; + tensor input_183_cast_fp16 = transpose(perm = input_183_perm_0, x = x_97_cast_fp16)[name = string("transpose_70")]; + tensor var_1133_cast_fp16 = layer_norm(axes = var_1133_axes_0, beta = main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16, x = input_183_cast_fp16)[name = string("op_1133_cast_fp16")]; + tensor input_185_perm_0 = const()[name = string("input_185_perm_0"), val = tensor([0, 2, 1])]; + string h_71_pad_type_0 = const()[name = string("h_71_pad_type_0"), val = string("valid")]; + tensor h_71_strides_0 = const()[name = string("h_71_strides_0"), val = tensor([1])]; + tensor h_71_pad_0 = const()[name = string("h_71_pad_0"), val = tensor([0, 0])]; + tensor h_71_dilations_0 = const()[name = string("h_71_dilations_0"), val = tensor([1])]; + int32 h_71_groups_0 = const()[name = string("h_71_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12735616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13259968))))[name = string("main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13260096)))]; + tensor input_185_cast_fp16 = transpose(perm = input_185_perm_0, x = var_1133_cast_fp16)[name = string("transpose_69")]; + tensor h_71_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_71_dilations_0, groups = h_71_groups_0, pad = h_71_pad_0, pad_type = h_71_pad_type_0, strides = h_71_strides_0, weight = main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = string("h_71_cast_fp16")]; + string input_187_mode_0 = const()[name = string("input_187_mode_0"), val = string("EXACT")]; + tensor input_187_cast_fp16 = gelu(mode = input_187_mode_0, x = h_71_cast_fp16)[name = string("input_187_cast_fp16")]; + string h_73_pad_type_0 = const()[name = string("h_73_pad_type_0"), val = string("valid")]; + tensor h_73_strides_0 = const()[name = string("h_73_strides_0"), val = tensor([1])]; + tensor h_73_pad_0 = const()[name = string("h_73_pad_0"), val = tensor([0, 0])]; + tensor h_73_dilations_0 = const()[name = string("h_73_dilations_0"), val = tensor([1])]; + int32 h_73_groups_0 = const()[name = string("h_73_groups_0"), val = int32(1)]; + tensor op_1150_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13264256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13788608))))[name = string("op_1150_weight_0_to_fp16_palettized")]; + tensor var_1150_bias_0_to_fp16 = const()[name = string("op_1150_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13788736)))]; + tensor var_1150_cast_fp16 = conv(bias = var_1150_bias_0_to_fp16, dilations = h_73_dilations_0, groups = h_73_groups_0, pad = h_73_pad_0, pad_type = h_73_pad_type_0, strides = h_73_strides_0, weight = op_1150_weight_0_to_fp16_palettized, x = input_187_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor out_29_cast_fp16 = add(x = x_95_cast_fp16, y = var_1150_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor x_99_cast_fp16 = mul(x = out_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor x_m_7_cast_fp16 = mul(x = x_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_7_cast_fp16")]; + tensor input_189_perm_0 = const()[name = string("input_189_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13789824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13855424))))[name = string("main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13855552)))]; + tensor input_189_cast_fp16 = transpose(perm = input_189_perm_0, x = x_99_cast_fp16)[name = string("transpose_68")]; + tensor linear_16_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor concat_22x = const()[name = string("concat_22x"), val = tensor([2, -1, 2, 128])]; + tensor var_1167_cast_fp16 = reshape(shape = concat_22x, x = linear_16_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13856128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13888960))))[name = string("main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13889088)))]; + tensor linear_18_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_1180 = const()[name = string("op_1180"), val = tensor([2, 50, 2, 128])]; + tensor var_1181_cast_fp16 = reshape(shape = var_1180, x = linear_18_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1185_transpose_x_0 = const()[name = string("op_1185_transpose_x_0"), val = bool(false)]; + bool var_1185_transpose_y_0 = const()[name = string("op_1185_transpose_y_0"), val = bool(false)]; + tensor op_1184_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13889664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13902528))))[name = string("op_1184_to_fp16_palettized")]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1167_cast_fp16)[name = string("transpose_67")]; + tensor var_1185_cast_fp16 = matmul(transpose_x = var_1185_transpose_x_0, transpose_y = var_1185_transpose_y_0, x = q_7_cast_fp16, y = op_1184_to_fp16_palettized)[name = string("op_1185_cast_fp16")]; + fp16 _inversed_input_191_y_0_to_fp16 = const()[name = string("_inversed_input_191_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_191_cast_fp16 = mul(x = var_1185_cast_fp16, y = _inversed_input_191_y_0_to_fp16)[name = string("_inversed_input_191_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_681, x = _inversed_input_191_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor attn_11_cast_fp16 = mul(x = attn_9_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool var_1192_transpose_x_0 = const()[name = string("op_1192_transpose_x_0"), val = bool(false)]; + bool var_1192_transpose_y_0 = const()[name = string("op_1192_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1181_cast_fp16)[name = string("transpose_66")]; + tensor var_1192_cast_fp16 = matmul(transpose_x = var_1192_transpose_x_0, transpose_y = var_1192_transpose_y_0, x = attn_11_cast_fp16, y = v_7_cast_fp16)[name = string("op_1192_cast_fp16")]; + tensor var_1193_perm_0 = const()[name = string("op_1193_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_23x = const()[name = string("concat_23x"), val = tensor([2, -1, 256])]; + tensor var_1193_cast_fp16 = transpose(perm = var_1193_perm_0, x = var_1192_cast_fp16)[name = string("transpose_65")]; + tensor input_193_cast_fp16 = reshape(shape = concat_23x, x = var_1193_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13902656))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13968256))))[name = string("main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13968384)))]; + tensor linear_19_cast_fp16 = linear(bias = main_blocks_1_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_193_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor out_31_perm_0 = const()[name = string("out_31_perm_0"), val = tensor([0, 2, 1])]; + tensor out_31_cast_fp16 = transpose(perm = out_31_perm_0, x = linear_19_cast_fp16)[name = string("transpose_64")]; + tensor var_1201_cast_fp16 = mul(x = out_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1201_cast_fp16")]; + tensor x_101_cast_fp16 = add(x = x_m_7_cast_fp16, y = var_1201_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_195_perm_0 = const()[name = string("input_195_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1208_axes_0 = const()[name = string("op_1208_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13969472)))]; + tensor main_blocks_1_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13970560)))]; + tensor input_195_cast_fp16 = transpose(perm = input_195_perm_0, x = x_101_cast_fp16)[name = string("transpose_63")]; + tensor var_1208_cast_fp16 = layer_norm(axes = var_1208_axes_0, beta = main_blocks_1_style_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_style_norm_norm_weight_to_fp16, x = input_195_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_perm_0 = const()[name = string("op_1209_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1209_cast_fp16 = transpose(perm = var_1209_perm_0, x = var_1208_cast_fp16)[name = string("transpose_62")]; + tensor x_103_cast_fp16 = mul(x = var_1209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_103_cast_fp16")]; + int32 var_1228 = const()[name = string("op_1228"), val = int32(-1)]; + tensor x_105_cast_fp16 = mul(x = x_103_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_1263_cast_fp16 = mul(x = x_105_cast_fp16, y = last_1_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor x_last_25_axes_0 = const()[name = string("x_last_25_axes_0"), val = tensor([-1])]; + bool x_last_25_keep_dims_0 = const()[name = string("x_last_25_keep_dims_0"), val = bool(true)]; + tensor x_last_25_cast_fp16 = reduce_sum(axes = x_last_25_axes_0, keep_dims = x_last_25_keep_dims_0, x = var_1263_cast_fp16)[name = string("x_last_25_cast_fp16")]; + tensor var_1266_cast_fp16 = mul(x = x_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1266_cast_fp16")]; + tensor var_1268_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_25_cast_fp16)[name = string("op_1268_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1266_cast_fp16, y = var_1268_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_201_mode_0 = const()[name = string("input_201_mode_0"), val = string("replicate")]; + fp16 const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = fp16(0x0p+0)]; + tensor input_201_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")]; + string h_75_pad_type_0 = const()[name = string("h_75_pad_type_0"), val = string("valid")]; + int32 h_75_groups_0 = const()[name = string("h_75_groups_0"), val = int32(512)]; + tensor h_75_strides_0 = const()[name = string("h_75_strides_0"), val = tensor([1])]; + tensor h_75_pad_0 = const()[name = string("h_75_pad_0"), val = tensor([0, 0])]; + tensor h_75_dilations_0 = const()[name = string("h_75_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13971648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13972992))))[name = string("main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13973120)))]; + tensor h_75_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_75_dilations_0, groups = h_75_groups_0, pad = h_75_pad_0, pad_type = h_75_pad_type_0, strides = h_75_strides_0, weight = main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_201_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = h_75_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_203_perm_0 = const()[name = string("input_203_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1286_axes_0 = const()[name = string("op_1286_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13974208)))]; + tensor main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13975296)))]; + fp16 var_1233_to_fp16 = const()[name = string("op_1233_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_203_cast_fp16 = transpose(perm = input_203_perm_0, x = x_107_cast_fp16)[name = string("transpose_61")]; + tensor var_1286_cast_fp16 = layer_norm(axes = var_1286_axes_0, beta = main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16, x = input_203_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string h_77_pad_type_0 = const()[name = string("h_77_pad_type_0"), val = string("valid")]; + tensor h_77_strides_0 = const()[name = string("h_77_strides_0"), val = tensor([1])]; + tensor h_77_pad_0 = const()[name = string("h_77_pad_0"), val = tensor([0, 0])]; + tensor h_77_dilations_0 = const()[name = string("h_77_dilations_0"), val = tensor([1])]; + int32 h_77_groups_0 = const()[name = string("h_77_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13976384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14500736))))[name = string("main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14500864)))]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = var_1286_cast_fp16)[name = string("transpose_60")]; + tensor h_77_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_77_dilations_0, groups = h_77_groups_0, pad = h_77_pad_0, pad_type = h_77_pad_type_0, strides = h_77_strides_0, weight = main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_205_cast_fp16)[name = string("h_77_cast_fp16")]; + string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")]; + tensor input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = h_77_cast_fp16)[name = string("input_207_cast_fp16")]; + string h_79_pad_type_0 = const()[name = string("h_79_pad_type_0"), val = string("valid")]; + tensor h_79_strides_0 = const()[name = string("h_79_strides_0"), val = tensor([1])]; + tensor h_79_pad_0 = const()[name = string("h_79_pad_0"), val = tensor([0, 0])]; + tensor h_79_dilations_0 = const()[name = string("h_79_dilations_0"), val = tensor([1])]; + int32 h_79_groups_0 = const()[name = string("h_79_groups_0"), val = int32(1)]; + tensor op_1303_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14505024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15029376))))[name = string("op_1303_weight_0_to_fp16_palettized")]; + tensor var_1303_bias_0_to_fp16 = const()[name = string("op_1303_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15029504)))]; + tensor var_1303_cast_fp16 = conv(bias = var_1303_bias_0_to_fp16, dilations = h_79_dilations_0, groups = h_79_groups_0, pad = h_79_pad_0, pad_type = h_79_pad_type_0, strides = h_79_strides_0, weight = op_1303_weight_0_to_fp16_palettized, x = input_207_cast_fp16)[name = string("op_1303_cast_fp16")]; + tensor out_33_cast_fp16 = add(x = x_105_cast_fp16, y = var_1303_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor x_109_cast_fp16 = mul(x = out_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor x_111_cast_fp16 = mul(x = x_109_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_1317_cast_fp16 = mul(x = x_111_cast_fp16, y = last_1_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor x_last_27_axes_0 = const()[name = string("x_last_27_axes_0"), val = tensor([-1])]; + bool x_last_27_keep_dims_0 = const()[name = string("x_last_27_keep_dims_0"), val = bool(true)]; + tensor x_last_27_cast_fp16 = reduce_sum(axes = x_last_27_axes_0, keep_dims = x_last_27_keep_dims_0, x = var_1317_cast_fp16)[name = string("x_last_27_cast_fp16")]; + tensor var_1320_cast_fp16 = mul(x = x_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1322_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_27_cast_fp16)[name = string("op_1322_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1320_cast_fp16, y = var_1322_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("replicate")]; + fp16 const_29_to_fp16 = const()[name = string("const_29_to_fp16"), val = fp16(0x0p+0)]; + tensor input_213_cast_fp16 = pad(constant_val = const_29_to_fp16, mode = input_213_mode_0, pad = input_213_pad_0, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + string h_81_pad_type_0 = const()[name = string("h_81_pad_type_0"), val = string("valid")]; + tensor h_81_dilations_0 = const()[name = string("h_81_dilations_0"), val = tensor([2])]; + int32 h_81_groups_0 = const()[name = string("h_81_groups_0"), val = int32(512)]; + tensor h_81_strides_0 = const()[name = string("h_81_strides_0"), val = tensor([1])]; + tensor h_81_pad_0 = const()[name = string("h_81_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15030592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15031936))))[name = string("main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15032064)))]; + tensor h_81_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_81_dilations_0, groups = h_81_groups_0, pad = h_81_pad_0, pad_type = h_81_pad_type_0, strides = h_81_strides_0, weight = main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_213_cast_fp16)[name = string("h_81_cast_fp16")]; + tensor x_113_cast_fp16 = mul(x = h_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor input_215_perm_0 = const()[name = string("input_215_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1340_axes_0 = const()[name = string("op_1340_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15033152)))]; + tensor main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15034240)))]; + tensor input_215_cast_fp16 = transpose(perm = input_215_perm_0, x = x_113_cast_fp16)[name = string("transpose_59")]; + tensor var_1340_cast_fp16 = layer_norm(axes = var_1340_axes_0, beta = main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16, x = input_215_cast_fp16)[name = string("op_1340_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + string h_83_pad_type_0 = const()[name = string("h_83_pad_type_0"), val = string("valid")]; + tensor h_83_strides_0 = const()[name = string("h_83_strides_0"), val = tensor([1])]; + tensor h_83_pad_0 = const()[name = string("h_83_pad_0"), val = tensor([0, 0])]; + tensor h_83_dilations_0 = const()[name = string("h_83_dilations_0"), val = tensor([1])]; + int32 h_83_groups_0 = const()[name = string("h_83_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15035328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15559680))))[name = string("main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15559808)))]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = var_1340_cast_fp16)[name = string("transpose_58")]; + tensor h_83_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_83_dilations_0, groups = h_83_groups_0, pad = h_83_pad_0, pad_type = h_83_pad_type_0, strides = h_83_strides_0, weight = main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = string("h_83_cast_fp16")]; + string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")]; + tensor input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = h_83_cast_fp16)[name = string("input_219_cast_fp16")]; + string h_85_pad_type_0 = const()[name = string("h_85_pad_type_0"), val = string("valid")]; + tensor h_85_strides_0 = const()[name = string("h_85_strides_0"), val = tensor([1])]; + tensor h_85_pad_0 = const()[name = string("h_85_pad_0"), val = tensor([0, 0])]; + tensor h_85_dilations_0 = const()[name = string("h_85_dilations_0"), val = tensor([1])]; + int32 h_85_groups_0 = const()[name = string("h_85_groups_0"), val = int32(1)]; + tensor op_1357_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15563968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16088320))))[name = string("op_1357_weight_0_to_fp16_palettized")]; + tensor var_1357_bias_0_to_fp16 = const()[name = string("op_1357_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16088448)))]; + tensor var_1357_cast_fp16 = conv(bias = var_1357_bias_0_to_fp16, dilations = h_85_dilations_0, groups = h_85_groups_0, pad = h_85_pad_0, pad_type = h_85_pad_type_0, strides = h_85_strides_0, weight = op_1357_weight_0_to_fp16_palettized, x = input_219_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor out_35_cast_fp16 = add(x = x_111_cast_fp16, y = var_1357_cast_fp16)[name = string("out_35_cast_fp16")]; + tensor x_115_cast_fp16 = mul(x = out_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_cast_fp16 = mul(x = x_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1371_cast_fp16 = mul(x = x_117_cast_fp16, y = last_1_cast_fp16)[name = string("op_1371_cast_fp16")]; + tensor x_last_29_axes_0 = const()[name = string("x_last_29_axes_0"), val = tensor([-1])]; + bool x_last_29_keep_dims_0 = const()[name = string("x_last_29_keep_dims_0"), val = bool(true)]; + tensor x_last_29_cast_fp16 = reduce_sum(axes = x_last_29_axes_0, keep_dims = x_last_29_keep_dims_0, x = var_1371_cast_fp16)[name = string("x_last_29_cast_fp16")]; + tensor var_1374_cast_fp16 = mul(x = x_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_29_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1376_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_225_mode_0 = const()[name = string("input_225_mode_0"), val = string("replicate")]; + fp16 const_31_to_fp16 = const()[name = string("const_31_to_fp16"), val = fp16(0x0p+0)]; + tensor input_225_cast_fp16 = pad(constant_val = const_31_to_fp16, mode = input_225_mode_0, pad = input_225_pad_0, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")]; + string h_87_pad_type_0 = const()[name = string("h_87_pad_type_0"), val = string("valid")]; + tensor h_87_dilations_0 = const()[name = string("h_87_dilations_0"), val = tensor([4])]; + int32 h_87_groups_0 = const()[name = string("h_87_groups_0"), val = int32(512)]; + tensor h_87_strides_0 = const()[name = string("h_87_strides_0"), val = tensor([1])]; + tensor h_87_pad_0 = const()[name = string("h_87_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16089536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16090880))))[name = string("main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16091008)))]; + tensor h_87_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_87_dilations_0, groups = h_87_groups_0, pad = h_87_pad_0, pad_type = h_87_pad_type_0, strides = h_87_strides_0, weight = main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor x_119_cast_fp16 = mul(x = h_87_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_227_perm_0 = const()[name = string("input_227_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1394_axes_0 = const()[name = string("op_1394_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16092096)))]; + tensor main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16093184)))]; + tensor input_227_cast_fp16 = transpose(perm = input_227_perm_0, x = x_119_cast_fp16)[name = string("transpose_57")]; + tensor var_1394_cast_fp16 = layer_norm(axes = var_1394_axes_0, beta = main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16, x = input_227_cast_fp16)[name = string("op_1394_cast_fp16")]; + tensor input_229_perm_0 = const()[name = string("input_229_perm_0"), val = tensor([0, 2, 1])]; + string h_89_pad_type_0 = const()[name = string("h_89_pad_type_0"), val = string("valid")]; + tensor h_89_strides_0 = const()[name = string("h_89_strides_0"), val = tensor([1])]; + tensor h_89_pad_0 = const()[name = string("h_89_pad_0"), val = tensor([0, 0])]; + tensor h_89_dilations_0 = const()[name = string("h_89_dilations_0"), val = tensor([1])]; + int32 h_89_groups_0 = const()[name = string("h_89_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16094272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16618624))))[name = string("main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16618752)))]; + tensor input_229_cast_fp16 = transpose(perm = input_229_perm_0, x = var_1394_cast_fp16)[name = string("transpose_56")]; + tensor h_89_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_89_dilations_0, groups = h_89_groups_0, pad = h_89_pad_0, pad_type = h_89_pad_type_0, strides = h_89_strides_0, weight = main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = string("h_89_cast_fp16")]; + string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")]; + tensor input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = h_89_cast_fp16)[name = string("input_231_cast_fp16")]; + string h_91_pad_type_0 = const()[name = string("h_91_pad_type_0"), val = string("valid")]; + tensor h_91_strides_0 = const()[name = string("h_91_strides_0"), val = tensor([1])]; + tensor h_91_pad_0 = const()[name = string("h_91_pad_0"), val = tensor([0, 0])]; + tensor h_91_dilations_0 = const()[name = string("h_91_dilations_0"), val = tensor([1])]; + int32 h_91_groups_0 = const()[name = string("h_91_groups_0"), val = int32(1)]; + tensor op_1411_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16622912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17147264))))[name = string("op_1411_weight_0_to_fp16_palettized")]; + tensor var_1411_bias_0_to_fp16 = const()[name = string("op_1411_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17147392)))]; + tensor var_1411_cast_fp16 = conv(bias = var_1411_bias_0_to_fp16, dilations = h_91_dilations_0, groups = h_91_groups_0, pad = h_91_pad_0, pad_type = h_91_pad_type_0, strides = h_91_strides_0, weight = op_1411_weight_0_to_fp16_palettized, x = input_231_cast_fp16)[name = string("op_1411_cast_fp16")]; + tensor out_37_cast_fp16 = add(x = x_117_cast_fp16, y = var_1411_cast_fp16)[name = string("out_37_cast_fp16")]; + tensor x_121_cast_fp16 = mul(x = out_37_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_121_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1425_cast_fp16 = mul(x = x_123_cast_fp16, y = last_1_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor x_last_31_axes_0 = const()[name = string("x_last_31_axes_0"), val = tensor([-1])]; + bool x_last_31_keep_dims_0 = const()[name = string("x_last_31_keep_dims_0"), val = bool(true)]; + tensor x_last_31_cast_fp16 = reduce_sum(axes = x_last_31_axes_0, keep_dims = x_last_31_keep_dims_0, x = var_1425_cast_fp16)[name = string("x_last_31_cast_fp16")]; + tensor var_1428_cast_fp16 = mul(x = x_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_31_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = var_1428_cast_fp16, y = var_1430_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("replicate")]; + fp16 const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = fp16(0x0p+0)]; + tensor input_237_cast_fp16 = pad(constant_val = const_33_to_fp16, mode = input_237_mode_0, pad = input_237_pad_0, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + string h_93_pad_type_0 = const()[name = string("h_93_pad_type_0"), val = string("valid")]; + tensor h_93_dilations_0 = const()[name = string("h_93_dilations_0"), val = tensor([8])]; + int32 h_93_groups_0 = const()[name = string("h_93_groups_0"), val = int32(512)]; + tensor h_93_strides_0 = const()[name = string("h_93_strides_0"), val = tensor([1])]; + tensor h_93_pad_0 = const()[name = string("h_93_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17148480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17149824))))[name = string("main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17149952)))]; + tensor h_93_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_93_dilations_0, groups = h_93_groups_0, pad = h_93_pad_0, pad_type = h_93_pad_type_0, strides = h_93_strides_0, weight = main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = string("h_93_cast_fp16")]; + tensor x_125_cast_fp16 = mul(x = h_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_239_perm_0 = const()[name = string("input_239_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1448_axes_0 = const()[name = string("op_1448_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17151040)))]; + tensor main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17152128)))]; + tensor input_239_cast_fp16 = transpose(perm = input_239_perm_0, x = x_125_cast_fp16)[name = string("transpose_55")]; + tensor var_1448_cast_fp16 = layer_norm(axes = var_1448_axes_0, beta = main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16, x = input_239_cast_fp16)[name = string("op_1448_cast_fp16")]; + tensor input_241_perm_0 = const()[name = string("input_241_perm_0"), val = tensor([0, 2, 1])]; + string h_95_pad_type_0 = const()[name = string("h_95_pad_type_0"), val = string("valid")]; + tensor h_95_strides_0 = const()[name = string("h_95_strides_0"), val = tensor([1])]; + tensor h_95_pad_0 = const()[name = string("h_95_pad_0"), val = tensor([0, 0])]; + tensor h_95_dilations_0 = const()[name = string("h_95_dilations_0"), val = tensor([1])]; + int32 h_95_groups_0 = const()[name = string("h_95_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17153216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17677568))))[name = string("main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17677696)))]; + tensor input_241_cast_fp16 = transpose(perm = input_241_perm_0, x = var_1448_cast_fp16)[name = string("transpose_54")]; + tensor h_95_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_95_dilations_0, groups = h_95_groups_0, pad = h_95_pad_0, pad_type = h_95_pad_type_0, strides = h_95_strides_0, weight = main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_241_cast_fp16)[name = string("h_95_cast_fp16")]; + string input_243_mode_0 = const()[name = string("input_243_mode_0"), val = string("EXACT")]; + tensor input_243_cast_fp16 = gelu(mode = input_243_mode_0, x = h_95_cast_fp16)[name = string("input_243_cast_fp16")]; + string h_97_pad_type_0 = const()[name = string("h_97_pad_type_0"), val = string("valid")]; + tensor h_97_strides_0 = const()[name = string("h_97_strides_0"), val = tensor([1])]; + tensor h_97_pad_0 = const()[name = string("h_97_pad_0"), val = tensor([0, 0])]; + tensor h_97_dilations_0 = const()[name = string("h_97_dilations_0"), val = tensor([1])]; + int32 h_97_groups_0 = const()[name = string("h_97_groups_0"), val = int32(1)]; + tensor op_1465_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17681856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18206208))))[name = string("op_1465_weight_0_to_fp16_palettized")]; + tensor var_1465_bias_0_to_fp16 = const()[name = string("op_1465_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18206336)))]; + tensor var_1465_cast_fp16 = conv(bias = var_1465_bias_0_to_fp16, dilations = h_97_dilations_0, groups = h_97_groups_0, pad = h_97_pad_0, pad_type = h_97_pad_type_0, strides = h_97_strides_0, weight = op_1465_weight_0_to_fp16_palettized, x = input_243_cast_fp16)[name = string("op_1465_cast_fp16")]; + tensor out_39_cast_fp16 = add(x = x_123_cast_fp16, y = var_1465_cast_fp16)[name = string("out_39_cast_fp16")]; + tensor x_127_cast_fp16 = mul(x = out_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18207424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18223872))))[name = string("main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18224000)))]; + tensor linear_20_cast_fp16 = linear(bias = main_blocks_2_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor t_9_axes_0 = const()[name = string("t_9_axes_0"), val = tensor([-1])]; + tensor t_9_cast_fp16 = expand_dims(axes = t_9_axes_0, x = linear_20_cast_fp16)[name = string("t_9_cast_fp16")]; + tensor var_1475_cast_fp16 = add(x = x_127_cast_fp16, y = t_9_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = var_1475_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor x_131_cast_fp16 = mul(x = x_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor var_1488_cast_fp16 = mul(x = x_131_cast_fp16, y = last_1_cast_fp16)[name = string("op_1488_cast_fp16")]; + tensor x_last_33_axes_0 = const()[name = string("x_last_33_axes_0"), val = tensor([-1])]; + bool x_last_33_keep_dims_0 = const()[name = string("x_last_33_keep_dims_0"), val = bool(true)]; + tensor x_last_33_cast_fp16 = reduce_sum(axes = x_last_33_axes_0, keep_dims = x_last_33_keep_dims_0, x = var_1488_cast_fp16)[name = string("x_last_33_cast_fp16")]; + tensor var_1491_cast_fp16 = mul(x = x_131_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1491_cast_fp16")]; + tensor var_1493_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_33_cast_fp16)[name = string("op_1493_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = var_1491_cast_fp16, y = var_1493_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_251_mode_0 = const()[name = string("input_251_mode_0"), val = string("replicate")]; + fp16 const_35_to_fp16 = const()[name = string("const_35_to_fp16"), val = fp16(0x0p+0)]; + tensor input_251_cast_fp16 = pad(constant_val = const_35_to_fp16, mode = input_251_mode_0, pad = input_251_pad_0, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + string h_99_pad_type_0 = const()[name = string("h_99_pad_type_0"), val = string("valid")]; + int32 h_99_groups_0 = const()[name = string("h_99_groups_0"), val = int32(512)]; + tensor h_99_strides_0 = const()[name = string("h_99_strides_0"), val = tensor([1])]; + tensor h_99_pad_0 = const()[name = string("h_99_pad_0"), val = tensor([0, 0])]; + tensor h_99_dilations_0 = const()[name = string("h_99_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18225088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18226432))))[name = string("main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18226560)))]; + tensor h_99_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_99_dilations_0, groups = h_99_groups_0, pad = h_99_pad_0, pad_type = h_99_pad_type_0, strides = h_99_strides_0, weight = main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor x_133_cast_fp16 = mul(x = h_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_253_perm_0 = const()[name = string("input_253_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1511_axes_0 = const()[name = string("op_1511_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18227648)))]; + tensor main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18228736)))]; + tensor input_253_cast_fp16 = transpose(perm = input_253_perm_0, x = x_133_cast_fp16)[name = string("transpose_53")]; + tensor var_1511_cast_fp16 = layer_norm(axes = var_1511_axes_0, beta = main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16, x = input_253_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor input_255_perm_0 = const()[name = string("input_255_perm_0"), val = tensor([0, 2, 1])]; + string h_101_pad_type_0 = const()[name = string("h_101_pad_type_0"), val = string("valid")]; + tensor h_101_strides_0 = const()[name = string("h_101_strides_0"), val = tensor([1])]; + tensor h_101_pad_0 = const()[name = string("h_101_pad_0"), val = tensor([0, 0])]; + tensor h_101_dilations_0 = const()[name = string("h_101_dilations_0"), val = tensor([1])]; + int32 h_101_groups_0 = const()[name = string("h_101_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18229824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18754176))))[name = string("main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18754304)))]; + tensor input_255_cast_fp16 = transpose(perm = input_255_perm_0, x = var_1511_cast_fp16)[name = string("transpose_52")]; + tensor h_101_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_101_dilations_0, groups = h_101_groups_0, pad = h_101_pad_0, pad_type = h_101_pad_type_0, strides = h_101_strides_0, weight = main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_255_cast_fp16)[name = string("h_101_cast_fp16")]; + string input_257_mode_0 = const()[name = string("input_257_mode_0"), val = string("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = h_101_cast_fp16)[name = string("input_257_cast_fp16")]; + string h_103_pad_type_0 = const()[name = string("h_103_pad_type_0"), val = string("valid")]; + tensor h_103_strides_0 = const()[name = string("h_103_strides_0"), val = tensor([1])]; + tensor h_103_pad_0 = const()[name = string("h_103_pad_0"), val = tensor([0, 0])]; + tensor h_103_dilations_0 = const()[name = string("h_103_dilations_0"), val = tensor([1])]; + int32 h_103_groups_0 = const()[name = string("h_103_groups_0"), val = int32(1)]; + tensor op_1528_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18758464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19282816))))[name = string("op_1528_weight_0_to_fp16_palettized")]; + tensor var_1528_bias_0_to_fp16 = const()[name = string("op_1528_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19282944)))]; + tensor var_1528_cast_fp16 = conv(bias = var_1528_bias_0_to_fp16, dilations = h_103_dilations_0, groups = h_103_groups_0, pad = h_103_pad_0, pad_type = h_103_pad_type_0, strides = h_103_strides_0, weight = op_1528_weight_0_to_fp16_palettized, x = input_257_cast_fp16)[name = string("op_1528_cast_fp16")]; + tensor out_41_cast_fp16 = add(x = x_131_cast_fp16, y = var_1528_cast_fp16)[name = string("out_41_cast_fp16")]; + tensor x_135_cast_fp16 = mul(x = out_41_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor x_m_9_cast_fp16 = mul(x = x_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_9_cast_fp16")]; + tensor var_1538_shape_cast_fp16 = shape(x = x_135_cast_fp16)[name = string("op_1538_shape_cast_fp16")]; + int32 gather_14_axis_0 = const()[name = string("gather_14_axis_0"), val = int32(0)]; + int32 gather_14_batch_dims_0 = const()[name = string("gather_14_batch_dims_0"), val = int32(0)]; + bool gather_14_validate_indices_0 = const()[name = string("gather_14_validate_indices_0"), val = bool(false)]; + string var_1538_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_1538_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_14_indices_0_to_uint16 = const()[name = string("gather_14_indices_0_to_uint16"), val = uint16(2)]; + tensor var_1538_shape_cast_fp16_to_uint16 = cast(dtype = var_1538_shape_cast_fp16_to_uint16_dtype_0, x = var_1538_shape_cast_fp16)[name = string("cast_4")]; + uint16 gather_14_cast_uint16 = gather(axis = gather_14_axis_0, batch_dims = gather_14_batch_dims_0, indices = gather_14_indices_0_to_uint16, validate_indices = gather_14_validate_indices_0, x = var_1538_shape_cast_fp16_to_uint16)[name = string("gather_14_cast_uint16")]; + string gather_14_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_14_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_259_perm_0 = const()[name = string("input_259_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19284032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19415168))))[name = string("main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19415296)))]; + tensor input_259_cast_fp16 = transpose(perm = input_259_perm_0, x = x_135_cast_fp16)[name = string("transpose_51")]; + tensor linear_21_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_259_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor concat_24x = const()[name = string("concat_24x"), val = tensor([2, -1, 8, 64])]; + tensor var_1547_cast_fp16 = reshape(shape = concat_24x, x = linear_21_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor x_137_perm_0 = const()[name = string("x_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19416384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19481984))))[name = string("main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19482112)))]; + tensor linear_22_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor concat_25x = const()[name = string("concat_25x"), val = tensor([2, -1, 8, 64])]; + tensor var_1555_cast_fp16 = reshape(shape = concat_25x, x = linear_22_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_139_perm_0 = const()[name = string("x_139_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19483200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19548800))))[name = string("main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19548928)))]; + tensor linear_23_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor concat_26x = const()[name = string("concat_26x"), val = tensor([2, -1, 8, 64])]; + tensor var_1563_cast_fp16 = reshape(shape = concat_26x, x = linear_23_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + int32 concat_27_values0_0 = const()[name = string("concat_27_values0_0"), val = int32(1)]; + int32 concat_27_values2_0 = const()[name = string("concat_27_values2_0"), val = int32(1)]; + int32 concat_27_axis_0 = const()[name = string("concat_27_axis_0"), val = int32(0)]; + bool concat_27_interleave_0 = const()[name = string("concat_27_interleave_0"), val = bool(false)]; + int32 gather_14_cast_uint16_to_int32 = cast(dtype = gather_14_cast_uint16_to_int32_dtype_0, x = gather_14_cast_uint16)[name = string("cast_3")]; + tensor concat_27 = concat(axis = concat_27_axis_0, interleave = concat_27_interleave_0, values = (concat_27_values0_0, gather_14_cast_uint16_to_int32, concat_27_values2_0))[name = string("concat_27")]; + tensor var_1566_begin_0 = const()[name = string("op_1566_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1566_end_mask_0 = const()[name = string("op_1566_end_mask_0"), val = tensor([true, false, true])]; + tensor var_1566_cast_fp16 = slice_by_index(begin = var_1566_begin_0, end = concat_27, end_mask = var_1566_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_1566_cast_fp16")]; + tensor concat_28 = const()[name = string("concat_28"), val = tensor([2, -1, -1])]; + tensor shape_5_cast_fp16 = shape(x = var_1566_cast_fp16)[name = string("shape_5_cast_fp16")]; + tensor equal_5 = const()[name = string("equal_5"), val = tensor([false, true, true])]; + tensor select_5 = select(a = shape_5_cast_fp16, b = concat_28, cond = equal_5)[name = string("select_5")]; + tensor real_div_5 = real_div(x = select_5, y = shape_5_cast_fp16)[name = string("real_div_5")]; + tensor var_1569_cast_fp16 = tile(reps = real_div_5, x = var_1566_cast_fp16)[name = string("op_1569_cast_fp16")]; + tensor scaled_9_cast_fp16 = real_div(x = var_1569_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_9_cast_fp16")]; + tensor main_blocks_2_text_attn_theta_to_fp16 = const()[name = string("main_blocks_2_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19550016)))]; + tensor angles_9_cast_fp16 = mul(x = scaled_9_cast_fp16, y = main_blocks_2_text_attn_theta_to_fp16)[name = string("angles_9_cast_fp16")]; + tensor var_1585_cast_fp16 = cos(x = angles_9_cast_fp16)[name = string("op_1585_cast_fp16")]; + tensor cos_9_axes_0 = const()[name = string("cos_9_axes_0"), val = tensor([1])]; + tensor cos_9_cast_fp16 = expand_dims(axes = cos_9_axes_0, x = var_1585_cast_fp16)[name = string("cos_9_cast_fp16")]; + tensor var_1587_cast_fp16 = sin(x = angles_9_cast_fp16)[name = string("op_1587_cast_fp16")]; + tensor sin_9_axes_0 = const()[name = string("sin_9_axes_0"), val = tensor([1])]; + tensor sin_9_cast_fp16 = expand_dims(axes = sin_9_axes_0, x = var_1587_cast_fp16)[name = string("sin_9_cast_fp16")]; + tensor x_a_9_begin_0 = const()[name = string("x_a_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_9_end_0 = const()[name = string("x_a_9_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_9_end_mask_0 = const()[name = string("x_a_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_137_cast_fp16 = transpose(perm = x_137_perm_0, x = var_1547_cast_fp16)[name = string("transpose_50")]; + tensor x_a_9_cast_fp16 = slice_by_index(begin = x_a_9_begin_0, end = x_a_9_end_0, end_mask = x_a_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_a_9_cast_fp16")]; + tensor x_b_9_begin_0 = const()[name = string("x_b_9_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_9_end_0 = const()[name = string("x_b_9_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_9_end_mask_0 = const()[name = string("x_b_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_9_cast_fp16 = slice_by_index(begin = x_b_9_begin_0, end = x_b_9_end_0, end_mask = x_b_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_b_9_cast_fp16")]; + tensor var_1591_cast_fp16 = mul(x = x_a_9_cast_fp16, y = cos_9_cast_fp16)[name = string("op_1591_cast_fp16")]; + tensor var_1592_cast_fp16 = mul(x = x_b_9_cast_fp16, y = sin_9_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor rot_a_9_cast_fp16 = sub(x = var_1591_cast_fp16, y = var_1592_cast_fp16)[name = string("rot_a_9_cast_fp16")]; + tensor var_1594_cast_fp16 = mul(x = x_a_9_cast_fp16, y = sin_9_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595_cast_fp16 = mul(x = x_b_9_cast_fp16, y = cos_9_cast_fp16)[name = string("op_1595_cast_fp16")]; + tensor rot_b_9_cast_fp16 = add(x = var_1594_cast_fp16, y = var_1595_cast_fp16)[name = string("rot_b_9_cast_fp16")]; + bool q_9_interleave_0 = const()[name = string("q_9_interleave_0"), val = bool(false)]; + tensor q_9_cast_fp16 = concat(axis = var_1228, interleave = q_9_interleave_0, values = (rot_a_9_cast_fp16, rot_b_9_cast_fp16))[name = string("q_9_cast_fp16")]; + tensor x_a_11_begin_0 = const()[name = string("x_a_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_11_end_0 = const()[name = string("x_a_11_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_11_end_mask_0 = const()[name = string("x_a_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_139_cast_fp16 = transpose(perm = x_139_perm_0, x = var_1555_cast_fp16)[name = string("transpose_49")]; + tensor x_a_11_cast_fp16 = slice_by_index(begin = x_a_11_begin_0, end = x_a_11_end_0, end_mask = x_a_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_a_11_cast_fp16")]; + tensor x_b_11_begin_0 = const()[name = string("x_b_11_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_11_end_0 = const()[name = string("x_b_11_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_11_end_mask_0 = const()[name = string("x_b_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_11_cast_fp16 = slice_by_index(begin = x_b_11_begin_0, end = x_b_11_end_0, end_mask = x_b_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_b_11_cast_fp16")]; + tensor var_1609_cast_fp16 = mul(x = x_a_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor var_1610_cast_fp16 = mul(x = x_b_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor rot_a_11_cast_fp16 = sub(x = var_1609_cast_fp16, y = var_1610_cast_fp16)[name = string("rot_a_11_cast_fp16")]; + tensor var_1612_cast_fp16 = mul(x = x_a_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1612_cast_fp16")]; + tensor var_1613_cast_fp16 = mul(x = x_b_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1613_cast_fp16")]; + tensor rot_b_11_cast_fp16 = add(x = var_1612_cast_fp16, y = var_1613_cast_fp16)[name = string("rot_b_11_cast_fp16")]; + bool k_9_interleave_0 = const()[name = string("k_9_interleave_0"), val = bool(false)]; + tensor k_9_cast_fp16 = concat(axis = var_1228, interleave = k_9_interleave_0, values = (rot_a_11_cast_fp16, rot_b_11_cast_fp16))[name = string("k_9_cast_fp16")]; + bool var_1618_transpose_x_1 = const()[name = string("op_1618_transpose_x_1"), val = bool(false)]; + bool var_1618_transpose_y_1 = const()[name = string("op_1618_transpose_y_1"), val = bool(true)]; + tensor var_1618_cast_fp16 = matmul(transpose_x = var_1618_transpose_x_1, transpose_y = var_1618_transpose_y_1, x = q_9_cast_fp16, y = k_9_cast_fp16)[name = string("op_1618_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_1618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor input_265_cast_fp16 = sub(x = _inversed_scores_5_cast_fp16, y = var_530_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1228, x = input_265_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool var_1627_transpose_x_0 = const()[name = string("op_1627_transpose_x_0"), val = bool(false)]; + bool var_1627_transpose_y_0 = const()[name = string("op_1627_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1563_cast_fp16)[name = string("transpose_48")]; + tensor var_1627_cast_fp16 = matmul(transpose_x = var_1627_transpose_x_0, transpose_y = var_1627_transpose_y_0, x = attn_13_cast_fp16, y = v_9_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor var_1628_perm_0 = const()[name = string("op_1628_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_31x = const()[name = string("concat_31x"), val = tensor([2, -1, 512])]; + tensor var_1628_cast_fp16 = transpose(perm = var_1628_perm_0, x = var_1627_cast_fp16)[name = string("transpose_47")]; + tensor input_267_cast_fp16 = reshape(shape = concat_31x, x = var_1628_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19550144))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19681280))))[name = string("main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19681408)))]; + tensor linear_24_cast_fp16 = linear(bias = main_blocks_2_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor out_43_perm_0 = const()[name = string("out_43_perm_0"), val = tensor([0, 2, 1])]; + tensor out_43_cast_fp16 = transpose(perm = out_43_perm_0, x = linear_24_cast_fp16)[name = string("transpose_46")]; + tensor var_1636_cast_fp16 = mul(x = out_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1636_cast_fp16")]; + tensor x_141_cast_fp16 = add(x = x_m_9_cast_fp16, y = var_1636_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1643_axes_0 = const()[name = string("op_1643_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19682496)))]; + tensor main_blocks_2_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19683584)))]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_141_cast_fp16)[name = string("transpose_45")]; + tensor var_1643_cast_fp16 = layer_norm(axes = var_1643_axes_0, beta = main_blocks_2_text_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_text_norm_norm_weight_to_fp16, x = input_269_cast_fp16)[name = string("op_1643_cast_fp16")]; + tensor var_1644_perm_0 = const()[name = string("op_1644_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1644_cast_fp16 = transpose(perm = var_1644_perm_0, x = var_1643_cast_fp16)[name = string("transpose_44")]; + tensor x_143_cast_fp16 = mul(x = var_1644_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor x_145_cast_fp16 = mul(x = x_143_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1657_cast_fp16 = mul(x = x_145_cast_fp16, y = last_1_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor x_last_35_axes_0 = const()[name = string("x_last_35_axes_0"), val = tensor([-1])]; + bool x_last_35_keep_dims_0 = const()[name = string("x_last_35_keep_dims_0"), val = bool(true)]; + tensor x_last_35_cast_fp16 = reduce_sum(axes = x_last_35_axes_0, keep_dims = x_last_35_keep_dims_0, x = var_1657_cast_fp16)[name = string("x_last_35_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = x_145_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1662_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_35_cast_fp16)[name = string("op_1662_cast_fp16")]; + tensor input_273_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1662_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("replicate")]; + fp16 const_37_to_fp16 = const()[name = string("const_37_to_fp16"), val = fp16(0x0p+0)]; + tensor input_275_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_275_mode_0, pad = input_275_pad_0, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")]; + string h_105_pad_type_0 = const()[name = string("h_105_pad_type_0"), val = string("valid")]; + int32 h_105_groups_0 = const()[name = string("h_105_groups_0"), val = int32(512)]; + tensor h_105_strides_0 = const()[name = string("h_105_strides_0"), val = tensor([1])]; + tensor h_105_pad_0 = const()[name = string("h_105_pad_0"), val = tensor([0, 0])]; + tensor h_105_dilations_0 = const()[name = string("h_105_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19684672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19686016))))[name = string("main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19686144)))]; + tensor h_105_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_105_dilations_0, groups = h_105_groups_0, pad = h_105_pad_0, pad_type = h_105_pad_type_0, strides = h_105_strides_0, weight = main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_275_cast_fp16)[name = string("h_105_cast_fp16")]; + tensor x_147_cast_fp16 = mul(x = h_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_277_perm_0 = const()[name = string("input_277_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1680_axes_0 = const()[name = string("op_1680_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19687232)))]; + tensor main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19688320)))]; + tensor input_277_cast_fp16 = transpose(perm = input_277_perm_0, x = x_147_cast_fp16)[name = string("transpose_43")]; + tensor var_1680_cast_fp16 = layer_norm(axes = var_1680_axes_0, beta = main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16, x = input_277_cast_fp16)[name = string("op_1680_cast_fp16")]; + tensor input_279_perm_0 = const()[name = string("input_279_perm_0"), val = tensor([0, 2, 1])]; + string h_107_pad_type_0 = const()[name = string("h_107_pad_type_0"), val = string("valid")]; + tensor h_107_strides_0 = const()[name = string("h_107_strides_0"), val = tensor([1])]; + tensor h_107_pad_0 = const()[name = string("h_107_pad_0"), val = tensor([0, 0])]; + tensor h_107_dilations_0 = const()[name = string("h_107_dilations_0"), val = tensor([1])]; + int32 h_107_groups_0 = const()[name = string("h_107_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19689408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20213760))))[name = string("main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20213888)))]; + tensor input_279_cast_fp16 = transpose(perm = input_279_perm_0, x = var_1680_cast_fp16)[name = string("transpose_42")]; + tensor h_107_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_107_dilations_0, groups = h_107_groups_0, pad = h_107_pad_0, pad_type = h_107_pad_type_0, strides = h_107_strides_0, weight = main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_279_cast_fp16)[name = string("h_107_cast_fp16")]; + string input_281_mode_0 = const()[name = string("input_281_mode_0"), val = string("EXACT")]; + tensor input_281_cast_fp16 = gelu(mode = input_281_mode_0, x = h_107_cast_fp16)[name = string("input_281_cast_fp16")]; + string h_109_pad_type_0 = const()[name = string("h_109_pad_type_0"), val = string("valid")]; + tensor h_109_strides_0 = const()[name = string("h_109_strides_0"), val = tensor([1])]; + tensor h_109_pad_0 = const()[name = string("h_109_pad_0"), val = tensor([0, 0])]; + tensor h_109_dilations_0 = const()[name = string("h_109_dilations_0"), val = tensor([1])]; + int32 h_109_groups_0 = const()[name = string("h_109_groups_0"), val = int32(1)]; + tensor op_1697_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20218048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20742400))))[name = string("op_1697_weight_0_to_fp16_palettized")]; + tensor var_1697_bias_0_to_fp16 = const()[name = string("op_1697_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20742528)))]; + tensor var_1697_cast_fp16 = conv(bias = var_1697_bias_0_to_fp16, dilations = h_109_dilations_0, groups = h_109_groups_0, pad = h_109_pad_0, pad_type = h_109_pad_type_0, strides = h_109_strides_0, weight = op_1697_weight_0_to_fp16_palettized, x = input_281_cast_fp16)[name = string("op_1697_cast_fp16")]; + tensor out_45_cast_fp16 = add(x = x_145_cast_fp16, y = var_1697_cast_fp16)[name = string("out_45_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = out_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor x_m_11_cast_fp16 = mul(x = x_149_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_11_cast_fp16")]; + tensor input_283_perm_0 = const()[name = string("input_283_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20743616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20809216))))[name = string("main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20809344)))]; + tensor input_283_cast_fp16 = transpose(perm = input_283_perm_0, x = x_149_cast_fp16)[name = string("transpose_41")]; + tensor linear_25_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_283_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor concat_32x = const()[name = string("concat_32x"), val = tensor([2, -1, 2, 128])]; + tensor var_1714_cast_fp16 = reshape(shape = concat_32x, x = linear_25_cast_fp16)[name = string("op_1714_cast_fp16")]; + tensor q_11_perm_0 = const()[name = string("q_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20809920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20842752))))[name = string("main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20842880)))]; + tensor linear_27_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1727 = const()[name = string("op_1727"), val = tensor([2, 50, 2, 128])]; + tensor var_1728_cast_fp16 = reshape(shape = var_1727, x = linear_27_cast_fp16)[name = string("op_1728_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1732_transpose_x_0 = const()[name = string("op_1732_transpose_x_0"), val = bool(false)]; + bool var_1732_transpose_y_0 = const()[name = string("op_1732_transpose_y_0"), val = bool(false)]; + tensor op_1731_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20843456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20856320))))[name = string("op_1731_to_fp16_palettized")]; + tensor q_11_cast_fp16 = transpose(perm = q_11_perm_0, x = var_1714_cast_fp16)[name = string("transpose_40")]; + tensor var_1732_cast_fp16 = matmul(transpose_x = var_1732_transpose_x_0, transpose_y = var_1732_transpose_y_0, x = q_11_cast_fp16, y = op_1731_to_fp16_palettized)[name = string("op_1732_cast_fp16")]; + fp16 _inversed_input_285_y_0_to_fp16 = const()[name = string("_inversed_input_285_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_285_cast_fp16 = mul(x = var_1732_cast_fp16, y = _inversed_input_285_y_0_to_fp16)[name = string("_inversed_input_285_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1228, x = _inversed_input_285_cast_fp16)[name = string("attn_15_cast_fp16")]; + tensor attn_17_cast_fp16 = mul(x = attn_15_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool var_1739_transpose_x_0 = const()[name = string("op_1739_transpose_x_0"), val = bool(false)]; + bool var_1739_transpose_y_0 = const()[name = string("op_1739_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_1728_cast_fp16)[name = string("transpose_39")]; + tensor var_1739_cast_fp16 = matmul(transpose_x = var_1739_transpose_x_0, transpose_y = var_1739_transpose_y_0, x = attn_17_cast_fp16, y = v_11_cast_fp16)[name = string("op_1739_cast_fp16")]; + tensor var_1740_perm_0 = const()[name = string("op_1740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_33x = const()[name = string("concat_33x"), val = tensor([2, -1, 256])]; + tensor var_1740_cast_fp16 = transpose(perm = var_1740_perm_0, x = var_1739_cast_fp16)[name = string("transpose_38")]; + tensor input_287_cast_fp16 = reshape(shape = concat_33x, x = var_1740_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20856448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20922048))))[name = string("main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20922176)))]; + tensor linear_28_cast_fp16 = linear(bias = main_blocks_2_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor out_47_perm_0 = const()[name = string("out_47_perm_0"), val = tensor([0, 2, 1])]; + tensor out_47_cast_fp16 = transpose(perm = out_47_perm_0, x = linear_28_cast_fp16)[name = string("transpose_37")]; + tensor var_1748_cast_fp16 = mul(x = out_47_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1748_cast_fp16")]; + tensor x_151_cast_fp16 = add(x = x_m_11_cast_fp16, y = var_1748_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_289_perm_0 = const()[name = string("input_289_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1755_axes_0 = const()[name = string("op_1755_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20923264)))]; + tensor main_blocks_2_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20924352)))]; + tensor input_289_cast_fp16 = transpose(perm = input_289_perm_0, x = x_151_cast_fp16)[name = string("transpose_36")]; + tensor var_1755_cast_fp16 = layer_norm(axes = var_1755_axes_0, beta = main_blocks_2_style_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_style_norm_norm_weight_to_fp16, x = input_289_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor var_1756_perm_0 = const()[name = string("op_1756_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1756_cast_fp16 = transpose(perm = var_1756_perm_0, x = var_1755_cast_fp16)[name = string("transpose_35")]; + tensor x_153_cast_fp16 = mul(x = var_1756_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_153_cast_fp16")]; + int32 var_1775 = const()[name = string("op_1775"), val = int32(-1)]; + tensor x_155_cast_fp16 = mul(x = x_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor var_1810_cast_fp16 = mul(x = x_155_cast_fp16, y = last_1_cast_fp16)[name = string("op_1810_cast_fp16")]; + tensor x_last_37_axes_0 = const()[name = string("x_last_37_axes_0"), val = tensor([-1])]; + bool x_last_37_keep_dims_0 = const()[name = string("x_last_37_keep_dims_0"), val = bool(true)]; + tensor x_last_37_cast_fp16 = reduce_sum(axes = x_last_37_axes_0, keep_dims = x_last_37_keep_dims_0, x = var_1810_cast_fp16)[name = string("x_last_37_cast_fp16")]; + tensor var_1813_cast_fp16 = mul(x = x_155_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1815_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_37_cast_fp16)[name = string("op_1815_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = var_1813_cast_fp16, y = var_1815_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_295_mode_0 = const()[name = string("input_295_mode_0"), val = string("replicate")]; + fp16 const_40_to_fp16 = const()[name = string("const_40_to_fp16"), val = fp16(0x0p+0)]; + tensor input_295_cast_fp16 = pad(constant_val = const_40_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")]; + string h_111_pad_type_0 = const()[name = string("h_111_pad_type_0"), val = string("valid")]; + int32 h_111_groups_0 = const()[name = string("h_111_groups_0"), val = int32(512)]; + tensor h_111_strides_0 = const()[name = string("h_111_strides_0"), val = tensor([1])]; + tensor h_111_pad_0 = const()[name = string("h_111_pad_0"), val = tensor([0, 0])]; + tensor h_111_dilations_0 = const()[name = string("h_111_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20925440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20926784))))[name = string("main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20926912)))]; + tensor h_111_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_111_dilations_0, groups = h_111_groups_0, pad = h_111_pad_0, pad_type = h_111_pad_type_0, strides = h_111_strides_0, weight = main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_295_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor x_157_cast_fp16 = mul(x = h_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor input_297_perm_0 = const()[name = string("input_297_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1833_axes_0 = const()[name = string("op_1833_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20928000)))]; + tensor main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20929088)))]; + fp16 var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_297_cast_fp16 = transpose(perm = input_297_perm_0, x = x_157_cast_fp16)[name = string("transpose_34")]; + tensor var_1833_cast_fp16 = layer_norm(axes = var_1833_axes_0, beta = main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16, x = input_297_cast_fp16)[name = string("op_1833_cast_fp16")]; + tensor input_299_perm_0 = const()[name = string("input_299_perm_0"), val = tensor([0, 2, 1])]; + string h_113_pad_type_0 = const()[name = string("h_113_pad_type_0"), val = string("valid")]; + tensor h_113_strides_0 = const()[name = string("h_113_strides_0"), val = tensor([1])]; + tensor h_113_pad_0 = const()[name = string("h_113_pad_0"), val = tensor([0, 0])]; + tensor h_113_dilations_0 = const()[name = string("h_113_dilations_0"), val = tensor([1])]; + int32 h_113_groups_0 = const()[name = string("h_113_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20930176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21454528))))[name = string("main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21454656)))]; + tensor input_299_cast_fp16 = transpose(perm = input_299_perm_0, x = var_1833_cast_fp16)[name = string("transpose_33")]; + tensor h_113_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_113_dilations_0, groups = h_113_groups_0, pad = h_113_pad_0, pad_type = h_113_pad_type_0, strides = h_113_strides_0, weight = main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("h_113_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = h_113_cast_fp16)[name = string("input_301_cast_fp16")]; + string h_115_pad_type_0 = const()[name = string("h_115_pad_type_0"), val = string("valid")]; + tensor h_115_strides_0 = const()[name = string("h_115_strides_0"), val = tensor([1])]; + tensor h_115_pad_0 = const()[name = string("h_115_pad_0"), val = tensor([0, 0])]; + tensor h_115_dilations_0 = const()[name = string("h_115_dilations_0"), val = tensor([1])]; + int32 h_115_groups_0 = const()[name = string("h_115_groups_0"), val = int32(1)]; + tensor op_1850_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21458816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21983168))))[name = string("op_1850_weight_0_to_fp16_palettized")]; + tensor var_1850_bias_0_to_fp16 = const()[name = string("op_1850_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21983296)))]; + tensor var_1850_cast_fp16 = conv(bias = var_1850_bias_0_to_fp16, dilations = h_115_dilations_0, groups = h_115_groups_0, pad = h_115_pad_0, pad_type = h_115_pad_type_0, strides = h_115_strides_0, weight = op_1850_weight_0_to_fp16_palettized, x = input_301_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor out_49_cast_fp16 = add(x = x_155_cast_fp16, y = var_1850_cast_fp16)[name = string("out_49_cast_fp16")]; + tensor x_159_cast_fp16 = mul(x = out_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor x_161_cast_fp16 = mul(x = x_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor var_1864_cast_fp16 = mul(x = x_161_cast_fp16, y = last_1_cast_fp16)[name = string("op_1864_cast_fp16")]; + tensor x_last_39_axes_0 = const()[name = string("x_last_39_axes_0"), val = tensor([-1])]; + bool x_last_39_keep_dims_0 = const()[name = string("x_last_39_keep_dims_0"), val = bool(true)]; + tensor x_last_39_cast_fp16 = reduce_sum(axes = x_last_39_axes_0, keep_dims = x_last_39_keep_dims_0, x = var_1864_cast_fp16)[name = string("x_last_39_cast_fp16")]; + tensor var_1867_cast_fp16 = mul(x = x_161_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_39_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor input_305_cast_fp16 = add(x = var_1867_cast_fp16, y = var_1869_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("replicate")]; + fp16 const_42_to_fp16 = const()[name = string("const_42_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_42_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string h_117_pad_type_0 = const()[name = string("h_117_pad_type_0"), val = string("valid")]; + tensor h_117_dilations_0 = const()[name = string("h_117_dilations_0"), val = tensor([2])]; + int32 h_117_groups_0 = const()[name = string("h_117_groups_0"), val = int32(512)]; + tensor h_117_strides_0 = const()[name = string("h_117_strides_0"), val = tensor([1])]; + tensor h_117_pad_0 = const()[name = string("h_117_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21984384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21985728))))[name = string("main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21985856)))]; + tensor h_117_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_117_dilations_0, groups = h_117_groups_0, pad = h_117_pad_0, pad_type = h_117_pad_type_0, strides = h_117_strides_0, weight = main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_307_cast_fp16)[name = string("h_117_cast_fp16")]; + tensor x_163_cast_fp16 = mul(x = h_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1887_axes_0 = const()[name = string("op_1887_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21986944)))]; + tensor main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21988032)))]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_163_cast_fp16)[name = string("transpose_32")]; + tensor var_1887_cast_fp16 = layer_norm(axes = var_1887_axes_0, beta = main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16, x = input_309_cast_fp16)[name = string("op_1887_cast_fp16")]; + tensor input_311_perm_0 = const()[name = string("input_311_perm_0"), val = tensor([0, 2, 1])]; + string h_119_pad_type_0 = const()[name = string("h_119_pad_type_0"), val = string("valid")]; + tensor h_119_strides_0 = const()[name = string("h_119_strides_0"), val = tensor([1])]; + tensor h_119_pad_0 = const()[name = string("h_119_pad_0"), val = tensor([0, 0])]; + tensor h_119_dilations_0 = const()[name = string("h_119_dilations_0"), val = tensor([1])]; + int32 h_119_groups_0 = const()[name = string("h_119_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21989120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22513472))))[name = string("main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22513600)))]; + tensor input_311_cast_fp16 = transpose(perm = input_311_perm_0, x = var_1887_cast_fp16)[name = string("transpose_31")]; + tensor h_119_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_119_dilations_0, groups = h_119_groups_0, pad = h_119_pad_0, pad_type = h_119_pad_type_0, strides = h_119_strides_0, weight = main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_311_cast_fp16)[name = string("h_119_cast_fp16")]; + string input_313_mode_0 = const()[name = string("input_313_mode_0"), val = string("EXACT")]; + tensor input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = h_119_cast_fp16)[name = string("input_313_cast_fp16")]; + string h_121_pad_type_0 = const()[name = string("h_121_pad_type_0"), val = string("valid")]; + tensor h_121_strides_0 = const()[name = string("h_121_strides_0"), val = tensor([1])]; + tensor h_121_pad_0 = const()[name = string("h_121_pad_0"), val = tensor([0, 0])]; + tensor h_121_dilations_0 = const()[name = string("h_121_dilations_0"), val = tensor([1])]; + int32 h_121_groups_0 = const()[name = string("h_121_groups_0"), val = int32(1)]; + tensor op_1904_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22517760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23042112))))[name = string("op_1904_weight_0_to_fp16_palettized")]; + tensor var_1904_bias_0_to_fp16 = const()[name = string("op_1904_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23042240)))]; + tensor var_1904_cast_fp16 = conv(bias = var_1904_bias_0_to_fp16, dilations = h_121_dilations_0, groups = h_121_groups_0, pad = h_121_pad_0, pad_type = h_121_pad_type_0, strides = h_121_strides_0, weight = op_1904_weight_0_to_fp16_palettized, x = input_313_cast_fp16)[name = string("op_1904_cast_fp16")]; + tensor out_51_cast_fp16 = add(x = x_161_cast_fp16, y = var_1904_cast_fp16)[name = string("out_51_cast_fp16")]; + tensor x_165_cast_fp16 = mul(x = out_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor x_167_cast_fp16 = mul(x = x_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1918_cast_fp16 = mul(x = x_167_cast_fp16, y = last_1_cast_fp16)[name = string("op_1918_cast_fp16")]; + tensor x_last_41_axes_0 = const()[name = string("x_last_41_axes_0"), val = tensor([-1])]; + bool x_last_41_keep_dims_0 = const()[name = string("x_last_41_keep_dims_0"), val = bool(true)]; + tensor x_last_41_cast_fp16 = reduce_sum(axes = x_last_41_axes_0, keep_dims = x_last_41_keep_dims_0, x = var_1918_cast_fp16)[name = string("x_last_41_cast_fp16")]; + tensor var_1921_cast_fp16 = mul(x = x_167_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1921_cast_fp16")]; + tensor var_1923_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_41_cast_fp16)[name = string("op_1923_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = var_1921_cast_fp16, y = var_1923_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("replicate")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_319_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = input_319_mode_0, pad = input_319_pad_0, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string h_123_pad_type_0 = const()[name = string("h_123_pad_type_0"), val = string("valid")]; + tensor h_123_dilations_0 = const()[name = string("h_123_dilations_0"), val = tensor([4])]; + int32 h_123_groups_0 = const()[name = string("h_123_groups_0"), val = int32(512)]; + tensor h_123_strides_0 = const()[name = string("h_123_strides_0"), val = tensor([1])]; + tensor h_123_pad_0 = const()[name = string("h_123_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23043328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23044672))))[name = string("main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23044800)))]; + tensor h_123_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_123_dilations_0, groups = h_123_groups_0, pad = h_123_pad_0, pad_type = h_123_pad_type_0, strides = h_123_strides_0, weight = main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_319_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor x_169_cast_fp16 = mul(x = h_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1941_axes_0 = const()[name = string("op_1941_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23045888)))]; + tensor main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23046976)))]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_169_cast_fp16)[name = string("transpose_30")]; + tensor var_1941_cast_fp16 = layer_norm(axes = var_1941_axes_0, beta = main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16, x = input_321_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor([0, 2, 1])]; + string h_125_pad_type_0 = const()[name = string("h_125_pad_type_0"), val = string("valid")]; + tensor h_125_strides_0 = const()[name = string("h_125_strides_0"), val = tensor([1])]; + tensor h_125_pad_0 = const()[name = string("h_125_pad_0"), val = tensor([0, 0])]; + tensor h_125_dilations_0 = const()[name = string("h_125_dilations_0"), val = tensor([1])]; + int32 h_125_groups_0 = const()[name = string("h_125_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23048064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23572416))))[name = string("main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23572544)))]; + tensor input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = var_1941_cast_fp16)[name = string("transpose_29")]; + tensor h_125_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_125_dilations_0, groups = h_125_groups_0, pad = h_125_pad_0, pad_type = h_125_pad_type_0, strides = h_125_strides_0, weight = main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = string("h_125_cast_fp16")]; + string input_325_mode_0 = const()[name = string("input_325_mode_0"), val = string("EXACT")]; + tensor input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = h_125_cast_fp16)[name = string("input_325_cast_fp16")]; + string h_127_pad_type_0 = const()[name = string("h_127_pad_type_0"), val = string("valid")]; + tensor h_127_strides_0 = const()[name = string("h_127_strides_0"), val = tensor([1])]; + tensor h_127_pad_0 = const()[name = string("h_127_pad_0"), val = tensor([0, 0])]; + tensor h_127_dilations_0 = const()[name = string("h_127_dilations_0"), val = tensor([1])]; + int32 h_127_groups_0 = const()[name = string("h_127_groups_0"), val = int32(1)]; + tensor op_1958_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23576704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24101056))))[name = string("op_1958_weight_0_to_fp16_palettized")]; + tensor var_1958_bias_0_to_fp16 = const()[name = string("op_1958_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24101184)))]; + tensor var_1958_cast_fp16 = conv(bias = var_1958_bias_0_to_fp16, dilations = h_127_dilations_0, groups = h_127_groups_0, pad = h_127_pad_0, pad_type = h_127_pad_type_0, strides = h_127_strides_0, weight = op_1958_weight_0_to_fp16_palettized, x = input_325_cast_fp16)[name = string("op_1958_cast_fp16")]; + tensor out_53_cast_fp16 = add(x = x_167_cast_fp16, y = var_1958_cast_fp16)[name = string("out_53_cast_fp16")]; + tensor x_171_cast_fp16 = mul(x = out_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_171_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor var_1972_cast_fp16 = mul(x = x_173_cast_fp16, y = last_1_cast_fp16)[name = string("op_1972_cast_fp16")]; + tensor x_last_43_axes_0 = const()[name = string("x_last_43_axes_0"), val = tensor([-1])]; + bool x_last_43_keep_dims_0 = const()[name = string("x_last_43_keep_dims_0"), val = bool(true)]; + tensor x_last_43_cast_fp16 = reduce_sum(axes = x_last_43_axes_0, keep_dims = x_last_43_keep_dims_0, x = var_1972_cast_fp16)[name = string("x_last_43_cast_fp16")]; + tensor var_1975_cast_fp16 = mul(x = x_173_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1975_cast_fp16")]; + tensor var_1977_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_43_cast_fp16)[name = string("op_1977_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1977_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("replicate")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor input_331_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = input_331_mode_0, pad = input_331_pad_0, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + string h_129_pad_type_0 = const()[name = string("h_129_pad_type_0"), val = string("valid")]; + tensor h_129_dilations_0 = const()[name = string("h_129_dilations_0"), val = tensor([8])]; + int32 h_129_groups_0 = const()[name = string("h_129_groups_0"), val = int32(512)]; + tensor h_129_strides_0 = const()[name = string("h_129_strides_0"), val = tensor([1])]; + tensor h_129_pad_0 = const()[name = string("h_129_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24102272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24103616))))[name = string("main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24103744)))]; + tensor h_129_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_129_dilations_0, groups = h_129_groups_0, pad = h_129_pad_0, pad_type = h_129_pad_type_0, strides = h_129_strides_0, weight = main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = string("h_129_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = h_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_333_perm_0 = const()[name = string("input_333_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1995_axes_0 = const()[name = string("op_1995_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24104832)))]; + tensor main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24105920)))]; + tensor input_333_cast_fp16 = transpose(perm = input_333_perm_0, x = x_175_cast_fp16)[name = string("transpose_28")]; + tensor var_1995_cast_fp16 = layer_norm(axes = var_1995_axes_0, beta = main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16, x = input_333_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor input_335_perm_0 = const()[name = string("input_335_perm_0"), val = tensor([0, 2, 1])]; + string h_131_pad_type_0 = const()[name = string("h_131_pad_type_0"), val = string("valid")]; + tensor h_131_strides_0 = const()[name = string("h_131_strides_0"), val = tensor([1])]; + tensor h_131_pad_0 = const()[name = string("h_131_pad_0"), val = tensor([0, 0])]; + tensor h_131_dilations_0 = const()[name = string("h_131_dilations_0"), val = tensor([1])]; + int32 h_131_groups_0 = const()[name = string("h_131_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24107008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24631360))))[name = string("main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24631488)))]; + tensor input_335_cast_fp16 = transpose(perm = input_335_perm_0, x = var_1995_cast_fp16)[name = string("transpose_27")]; + tensor h_131_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_131_dilations_0, groups = h_131_groups_0, pad = h_131_pad_0, pad_type = h_131_pad_type_0, strides = h_131_strides_0, weight = main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_335_cast_fp16)[name = string("h_131_cast_fp16")]; + string input_337_mode_0 = const()[name = string("input_337_mode_0"), val = string("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = h_131_cast_fp16)[name = string("input_337_cast_fp16")]; + string h_133_pad_type_0 = const()[name = string("h_133_pad_type_0"), val = string("valid")]; + tensor h_133_strides_0 = const()[name = string("h_133_strides_0"), val = tensor([1])]; + tensor h_133_pad_0 = const()[name = string("h_133_pad_0"), val = tensor([0, 0])]; + tensor h_133_dilations_0 = const()[name = string("h_133_dilations_0"), val = tensor([1])]; + int32 h_133_groups_0 = const()[name = string("h_133_groups_0"), val = int32(1)]; + tensor op_2012_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24635648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25160000))))[name = string("op_2012_weight_0_to_fp16_palettized")]; + tensor var_2012_bias_0_to_fp16 = const()[name = string("op_2012_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25160128)))]; + tensor var_2012_cast_fp16 = conv(bias = var_2012_bias_0_to_fp16, dilations = h_133_dilations_0, groups = h_133_groups_0, pad = h_133_pad_0, pad_type = h_133_pad_type_0, strides = h_133_strides_0, weight = op_2012_weight_0_to_fp16_palettized, x = input_337_cast_fp16)[name = string("op_2012_cast_fp16")]; + tensor out_55_cast_fp16 = add(x = x_173_cast_fp16, y = var_2012_cast_fp16)[name = string("out_55_cast_fp16")]; + tensor x_177_cast_fp16 = mul(x = out_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25161216))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25177664))))[name = string("main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25177792)))]; + tensor linear_29_cast_fp16 = linear(bias = main_blocks_3_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor t_axes_0 = const()[name = string("t_axes_0"), val = tensor([-1])]; + tensor t_cast_fp16 = expand_dims(axes = t_axes_0, x = linear_29_cast_fp16)[name = string("t_cast_fp16")]; + tensor var_2022_cast_fp16 = add(x = x_177_cast_fp16, y = t_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor x_179_cast_fp16 = mul(x = var_2022_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor x_181_cast_fp16 = mul(x = x_179_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor var_2035_cast_fp16 = mul(x = x_181_cast_fp16, y = last_1_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor x_last_45_axes_0 = const()[name = string("x_last_45_axes_0"), val = tensor([-1])]; + bool x_last_45_keep_dims_0 = const()[name = string("x_last_45_keep_dims_0"), val = bool(true)]; + tensor x_last_45_cast_fp16 = reduce_sum(axes = x_last_45_axes_0, keep_dims = x_last_45_keep_dims_0, x = var_2035_cast_fp16)[name = string("x_last_45_cast_fp16")]; + tensor var_2038_cast_fp16 = mul(x = x_181_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2038_cast_fp16")]; + tensor var_2040_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_45_cast_fp16)[name = string("op_2040_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = var_2038_cast_fp16, y = var_2040_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_345_mode_0 = const()[name = string("input_345_mode_0"), val = string("replicate")]; + fp16 const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = fp16(0x0p+0)]; + tensor input_345_cast_fp16 = pad(constant_val = const_48_to_fp16, mode = input_345_mode_0, pad = input_345_pad_0, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + string h_135_pad_type_0 = const()[name = string("h_135_pad_type_0"), val = string("valid")]; + int32 h_135_groups_0 = const()[name = string("h_135_groups_0"), val = int32(512)]; + tensor h_135_strides_0 = const()[name = string("h_135_strides_0"), val = tensor([1])]; + tensor h_135_pad_0 = const()[name = string("h_135_pad_0"), val = tensor([0, 0])]; + tensor h_135_dilations_0 = const()[name = string("h_135_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25178880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25180224))))[name = string("main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25180352)))]; + tensor h_135_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_135_dilations_0, groups = h_135_groups_0, pad = h_135_pad_0, pad_type = h_135_pad_type_0, strides = h_135_strides_0, weight = main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_345_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor x_183_cast_fp16 = mul(x = h_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_183_cast_fp16")]; + tensor input_347_perm_0 = const()[name = string("input_347_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2058_axes_0 = const()[name = string("op_2058_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25181440)))]; + tensor main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25182528)))]; + tensor input_347_cast_fp16 = transpose(perm = input_347_perm_0, x = x_183_cast_fp16)[name = string("transpose_26")]; + tensor var_2058_cast_fp16 = layer_norm(axes = var_2058_axes_0, beta = main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16, x = input_347_cast_fp16)[name = string("op_2058_cast_fp16")]; + tensor input_349_perm_0 = const()[name = string("input_349_perm_0"), val = tensor([0, 2, 1])]; + string h_137_pad_type_0 = const()[name = string("h_137_pad_type_0"), val = string("valid")]; + tensor h_137_strides_0 = const()[name = string("h_137_strides_0"), val = tensor([1])]; + tensor h_137_pad_0 = const()[name = string("h_137_pad_0"), val = tensor([0, 0])]; + tensor h_137_dilations_0 = const()[name = string("h_137_dilations_0"), val = tensor([1])]; + int32 h_137_groups_0 = const()[name = string("h_137_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25183616))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25707968))))[name = string("main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25708096)))]; + tensor input_349_cast_fp16 = transpose(perm = input_349_perm_0, x = var_2058_cast_fp16)[name = string("transpose_25")]; + tensor h_137_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_137_dilations_0, groups = h_137_groups_0, pad = h_137_pad_0, pad_type = h_137_pad_type_0, strides = h_137_strides_0, weight = main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_349_cast_fp16)[name = string("h_137_cast_fp16")]; + string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")]; + tensor input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = h_137_cast_fp16)[name = string("input_351_cast_fp16")]; + string h_139_pad_type_0 = const()[name = string("h_139_pad_type_0"), val = string("valid")]; + tensor h_139_strides_0 = const()[name = string("h_139_strides_0"), val = tensor([1])]; + tensor h_139_pad_0 = const()[name = string("h_139_pad_0"), val = tensor([0, 0])]; + tensor h_139_dilations_0 = const()[name = string("h_139_dilations_0"), val = tensor([1])]; + int32 h_139_groups_0 = const()[name = string("h_139_groups_0"), val = int32(1)]; + tensor op_2075_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25712256))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26236608))))[name = string("op_2075_weight_0_to_fp16_palettized")]; + tensor var_2075_bias_0_to_fp16 = const()[name = string("op_2075_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26236736)))]; + tensor var_2075_cast_fp16 = conv(bias = var_2075_bias_0_to_fp16, dilations = h_139_dilations_0, groups = h_139_groups_0, pad = h_139_pad_0, pad_type = h_139_pad_type_0, strides = h_139_strides_0, weight = op_2075_weight_0_to_fp16_palettized, x = input_351_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor out_57_cast_fp16 = add(x = x_181_cast_fp16, y = var_2075_cast_fp16)[name = string("out_57_cast_fp16")]; + tensor x_185_cast_fp16 = mul(x = out_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor x_m_13_cast_fp16 = mul(x = x_185_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_13_cast_fp16")]; + tensor var_2085_shape_cast_fp16 = shape(x = x_185_cast_fp16)[name = string("op_2085_shape_cast_fp16")]; + int32 gather_19_axis_0 = const()[name = string("gather_19_axis_0"), val = int32(0)]; + int32 gather_19_batch_dims_0 = const()[name = string("gather_19_batch_dims_0"), val = int32(0)]; + bool gather_19_validate_indices_0 = const()[name = string("gather_19_validate_indices_0"), val = bool(false)]; + string var_2085_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_2085_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_19_indices_0_to_uint16 = const()[name = string("gather_19_indices_0_to_uint16"), val = uint16(2)]; + tensor var_2085_shape_cast_fp16_to_uint16 = cast(dtype = var_2085_shape_cast_fp16_to_uint16_dtype_0, x = var_2085_shape_cast_fp16)[name = string("cast_2")]; + uint16 gather_19_cast_uint16 = gather(axis = gather_19_axis_0, batch_dims = gather_19_batch_dims_0, indices = gather_19_indices_0_to_uint16, validate_indices = gather_19_validate_indices_0, x = var_2085_shape_cast_fp16_to_uint16)[name = string("gather_19_cast_uint16")]; + string gather_19_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_19_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26237824))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26368960))))[name = string("main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26369088)))]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_185_cast_fp16)[name = string("transpose_24")]; + tensor linear_30_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor concat_34x = const()[name = string("concat_34x"), val = tensor([2, -1, 8, 64])]; + tensor var_2094_cast_fp16 = reshape(shape = concat_34x, x = linear_30_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor x_187_perm_0 = const()[name = string("x_187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26370176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26435776))))[name = string("main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26435904)))]; + tensor linear_31_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor concat_35x = const()[name = string("concat_35x"), val = tensor([2, -1, 8, 64])]; + tensor var_2102_cast_fp16 = reshape(shape = concat_35x, x = linear_31_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor x_189_perm_0 = const()[name = string("x_189_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26436992))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26502592))))[name = string("main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26502720)))]; + tensor linear_32_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor concat_36x = const()[name = string("concat_36x"), val = tensor([2, -1, 8, 64])]; + tensor var_2110_cast_fp16 = reshape(shape = concat_36x, x = linear_32_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + int32 concat_37_values0_0 = const()[name = string("concat_37_values0_0"), val = int32(1)]; + int32 concat_37_values2_0 = const()[name = string("concat_37_values2_0"), val = int32(1)]; + int32 concat_37_axis_0 = const()[name = string("concat_37_axis_0"), val = int32(0)]; + bool concat_37_interleave_0 = const()[name = string("concat_37_interleave_0"), val = bool(false)]; + int32 gather_19_cast_uint16_to_int32 = cast(dtype = gather_19_cast_uint16_to_int32_dtype_0, x = gather_19_cast_uint16)[name = string("cast_1")]; + tensor concat_37 = concat(axis = concat_37_axis_0, interleave = concat_37_interleave_0, values = (concat_37_values0_0, gather_19_cast_uint16_to_int32, concat_37_values2_0))[name = string("concat_37")]; + tensor var_2113_begin_0 = const()[name = string("op_2113_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2113_end_mask_0 = const()[name = string("op_2113_end_mask_0"), val = tensor([true, false, true])]; + tensor var_2113_cast_fp16 = slice_by_index(begin = var_2113_begin_0, end = concat_37, end_mask = var_2113_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_2113_cast_fp16")]; + tensor concat_38 = const()[name = string("concat_38"), val = tensor([2, -1, -1])]; + tensor shape_7_cast_fp16 = shape(x = var_2113_cast_fp16)[name = string("shape_7_cast_fp16")]; + tensor equal_7 = const()[name = string("equal_7"), val = tensor([false, true, true])]; + tensor select_7 = select(a = shape_7_cast_fp16, b = concat_38, cond = equal_7)[name = string("select_7")]; + tensor real_div_7 = real_div(x = select_7, y = shape_7_cast_fp16)[name = string("real_div_7")]; + tensor var_2116_cast_fp16 = tile(reps = real_div_7, x = var_2113_cast_fp16)[name = string("op_2116_cast_fp16")]; + tensor scaled_13_cast_fp16 = real_div(x = var_2116_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_13_cast_fp16")]; + tensor main_blocks_3_text_attn_theta_to_fp16 = const()[name = string("main_blocks_3_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26503808)))]; + tensor angles_13_cast_fp16 = mul(x = scaled_13_cast_fp16, y = main_blocks_3_text_attn_theta_to_fp16)[name = string("angles_13_cast_fp16")]; + tensor var_2132_cast_fp16 = cos(x = angles_13_cast_fp16)[name = string("op_2132_cast_fp16")]; + tensor cos_13_axes_0 = const()[name = string("cos_13_axes_0"), val = tensor([1])]; + tensor cos_13_cast_fp16 = expand_dims(axes = cos_13_axes_0, x = var_2132_cast_fp16)[name = string("cos_13_cast_fp16")]; + tensor var_2134_cast_fp16 = sin(x = angles_13_cast_fp16)[name = string("op_2134_cast_fp16")]; + tensor sin_13_axes_0 = const()[name = string("sin_13_axes_0"), val = tensor([1])]; + tensor sin_13_cast_fp16 = expand_dims(axes = sin_13_axes_0, x = var_2134_cast_fp16)[name = string("sin_13_cast_fp16")]; + tensor x_a_13_begin_0 = const()[name = string("x_a_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_13_end_0 = const()[name = string("x_a_13_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_13_end_mask_0 = const()[name = string("x_a_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_187_cast_fp16 = transpose(perm = x_187_perm_0, x = var_2094_cast_fp16)[name = string("transpose_23")]; + tensor x_a_13_cast_fp16 = slice_by_index(begin = x_a_13_begin_0, end = x_a_13_end_0, end_mask = x_a_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_a_13_cast_fp16")]; + tensor x_b_13_begin_0 = const()[name = string("x_b_13_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_13_end_0 = const()[name = string("x_b_13_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_13_end_mask_0 = const()[name = string("x_b_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_13_cast_fp16 = slice_by_index(begin = x_b_13_begin_0, end = x_b_13_end_0, end_mask = x_b_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_b_13_cast_fp16")]; + tensor var_2138_cast_fp16 = mul(x = x_a_13_cast_fp16, y = cos_13_cast_fp16)[name = string("op_2138_cast_fp16")]; + tensor var_2139_cast_fp16 = mul(x = x_b_13_cast_fp16, y = sin_13_cast_fp16)[name = string("op_2139_cast_fp16")]; + tensor rot_a_13_cast_fp16 = sub(x = var_2138_cast_fp16, y = var_2139_cast_fp16)[name = string("rot_a_13_cast_fp16")]; + tensor var_2141_cast_fp16 = mul(x = x_a_13_cast_fp16, y = sin_13_cast_fp16)[name = string("op_2141_cast_fp16")]; + tensor var_2142_cast_fp16 = mul(x = x_b_13_cast_fp16, y = cos_13_cast_fp16)[name = string("op_2142_cast_fp16")]; + tensor rot_b_13_cast_fp16 = add(x = var_2141_cast_fp16, y = var_2142_cast_fp16)[name = string("rot_b_13_cast_fp16")]; + bool q_13_interleave_0 = const()[name = string("q_13_interleave_0"), val = bool(false)]; + tensor q_13_cast_fp16 = concat(axis = var_1775, interleave = q_13_interleave_0, values = (rot_a_13_cast_fp16, rot_b_13_cast_fp16))[name = string("q_13_cast_fp16")]; + tensor x_a_begin_0 = const()[name = string("x_a_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_end_0 = const()[name = string("x_a_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_end_mask_0 = const()[name = string("x_a_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_189_cast_fp16 = transpose(perm = x_189_perm_0, x = var_2102_cast_fp16)[name = string("transpose_22")]; + tensor x_a_cast_fp16 = slice_by_index(begin = x_a_begin_0, end = x_a_end_0, end_mask = x_a_end_mask_0, x = x_189_cast_fp16)[name = string("x_a_cast_fp16")]; + tensor x_b_begin_0 = const()[name = string("x_b_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_end_0 = const()[name = string("x_b_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_end_mask_0 = const()[name = string("x_b_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_cast_fp16 = slice_by_index(begin = x_b_begin_0, end = x_b_end_0, end_mask = x_b_end_mask_0, x = x_189_cast_fp16)[name = string("x_b_cast_fp16")]; + tensor var_2156_cast_fp16 = mul(x = x_a_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2156_cast_fp16")]; + tensor var_2157_cast_fp16 = mul(x = x_b_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2157_cast_fp16")]; + tensor rot_a_cast_fp16 = sub(x = var_2156_cast_fp16, y = var_2157_cast_fp16)[name = string("rot_a_cast_fp16")]; + tensor var_2159_cast_fp16 = mul(x = x_a_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2159_cast_fp16")]; + tensor var_2160_cast_fp16 = mul(x = x_b_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2160_cast_fp16")]; + tensor rot_b_cast_fp16 = add(x = var_2159_cast_fp16, y = var_2160_cast_fp16)[name = string("rot_b_cast_fp16")]; + bool k_13_interleave_0 = const()[name = string("k_13_interleave_0"), val = bool(false)]; + tensor k_13_cast_fp16 = concat(axis = var_1775, interleave = k_13_interleave_0, values = (rot_a_cast_fp16, rot_b_cast_fp16))[name = string("k_13_cast_fp16")]; + bool var_2165_transpose_x_1 = const()[name = string("op_2165_transpose_x_1"), val = bool(false)]; + bool var_2165_transpose_y_1 = const()[name = string("op_2165_transpose_y_1"), val = bool(true)]; + tensor var_2165_cast_fp16 = matmul(transpose_x = var_2165_transpose_x_1, transpose_y = var_2165_transpose_y_1, x = q_13_cast_fp16, y = k_13_cast_fp16)[name = string("op_2165_cast_fp16")]; + fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_cast_fp16 = mul(x = var_2165_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")]; + tensor input_359_cast_fp16 = sub(x = _inversed_scores_cast_fp16, y = var_530_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_1775, x = input_359_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool var_2174_transpose_x_0 = const()[name = string("op_2174_transpose_x_0"), val = bool(false)]; + bool var_2174_transpose_y_0 = const()[name = string("op_2174_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2110_cast_fp16)[name = string("transpose_21")]; + tensor var_2174_cast_fp16 = matmul(transpose_x = var_2174_transpose_x_0, transpose_y = var_2174_transpose_y_0, x = attn_19_cast_fp16, y = v_13_cast_fp16)[name = string("op_2174_cast_fp16")]; + tensor var_2175_perm_0 = const()[name = string("op_2175_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_41x = const()[name = string("concat_41x"), val = tensor([2, -1, 512])]; + tensor var_2175_cast_fp16 = transpose(perm = var_2175_perm_0, x = var_2174_cast_fp16)[name = string("transpose_20")]; + tensor input_361_cast_fp16 = reshape(shape = concat_41x, x = var_2175_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26503936))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26635072))))[name = string("main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26635200)))]; + tensor linear_33_cast_fp16 = linear(bias = main_blocks_3_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor out_59_perm_0 = const()[name = string("out_59_perm_0"), val = tensor([0, 2, 1])]; + tensor out_59_cast_fp16 = transpose(perm = out_59_perm_0, x = linear_33_cast_fp16)[name = string("transpose_19")]; + tensor var_2183_cast_fp16 = mul(x = out_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_m_13_cast_fp16, y = var_2183_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor input_363_perm_0 = const()[name = string("input_363_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2190_axes_0 = const()[name = string("op_2190_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26636288)))]; + tensor main_blocks_3_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26637376)))]; + tensor input_363_cast_fp16 = transpose(perm = input_363_perm_0, x = x_191_cast_fp16)[name = string("transpose_18")]; + tensor var_2190_cast_fp16 = layer_norm(axes = var_2190_axes_0, beta = main_blocks_3_text_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_text_norm_norm_weight_to_fp16, x = input_363_cast_fp16)[name = string("op_2190_cast_fp16")]; + tensor var_2191_perm_0 = const()[name = string("op_2191_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2191_perm_0, x = var_2190_cast_fp16)[name = string("transpose_17")]; + tensor x_193_cast_fp16 = mul(x = var_2191_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_193_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2204_cast_fp16 = mul(x = x_195_cast_fp16, y = last_1_cast_fp16)[name = string("op_2204_cast_fp16")]; + tensor x_last_47_axes_0 = const()[name = string("x_last_47_axes_0"), val = tensor([-1])]; + bool x_last_47_keep_dims_0 = const()[name = string("x_last_47_keep_dims_0"), val = bool(true)]; + tensor x_last_47_cast_fp16 = reduce_sum(axes = x_last_47_axes_0, keep_dims = x_last_47_keep_dims_0, x = var_2204_cast_fp16)[name = string("x_last_47_cast_fp16")]; + tensor var_2207_cast_fp16 = mul(x = x_195_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2207_cast_fp16")]; + tensor var_2209_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_47_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor input_367_cast_fp16 = add(x = var_2207_cast_fp16, y = var_2209_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_369_mode_0 = const()[name = string("input_369_mode_0"), val = string("replicate")]; + fp16 const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = fp16(0x0p+0)]; + tensor input_369_cast_fp16 = pad(constant_val = const_50_to_fp16, mode = input_369_mode_0, pad = input_369_pad_0, x = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + string h_141_pad_type_0 = const()[name = string("h_141_pad_type_0"), val = string("valid")]; + int32 h_141_groups_0 = const()[name = string("h_141_groups_0"), val = int32(512)]; + tensor h_141_strides_0 = const()[name = string("h_141_strides_0"), val = tensor([1])]; + tensor h_141_pad_0 = const()[name = string("h_141_pad_0"), val = tensor([0, 0])]; + tensor h_141_dilations_0 = const()[name = string("h_141_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26638464))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26639808))))[name = string("main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26639936)))]; + tensor h_141_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_141_dilations_0, groups = h_141_groups_0, pad = h_141_pad_0, pad_type = h_141_pad_type_0, strides = h_141_strides_0, weight = main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_369_cast_fp16)[name = string("h_141_cast_fp16")]; + tensor x_197_cast_fp16 = mul(x = h_141_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_371_perm_0 = const()[name = string("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2227_axes_0 = const()[name = string("op_2227_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26641024)))]; + tensor main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26642112)))]; + tensor input_371_cast_fp16 = transpose(perm = input_371_perm_0, x = x_197_cast_fp16)[name = string("transpose_16")]; + tensor var_2227_cast_fp16 = layer_norm(axes = var_2227_axes_0, beta = main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16, x = input_371_cast_fp16)[name = string("op_2227_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + string h_143_pad_type_0 = const()[name = string("h_143_pad_type_0"), val = string("valid")]; + tensor h_143_strides_0 = const()[name = string("h_143_strides_0"), val = tensor([1])]; + tensor h_143_pad_0 = const()[name = string("h_143_pad_0"), val = tensor([0, 0])]; + tensor h_143_dilations_0 = const()[name = string("h_143_dilations_0"), val = tensor([1])]; + int32 h_143_groups_0 = const()[name = string("h_143_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26643200))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27167552))))[name = string("main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27167680)))]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = var_2227_cast_fp16)[name = string("transpose_15")]; + tensor h_143_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_143_dilations_0, groups = h_143_groups_0, pad = h_143_pad_0, pad_type = h_143_pad_type_0, strides = h_143_strides_0, weight = main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = string("h_143_cast_fp16")]; + string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")]; + tensor input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = h_143_cast_fp16)[name = string("input_375_cast_fp16")]; + string h_145_pad_type_0 = const()[name = string("h_145_pad_type_0"), val = string("valid")]; + tensor h_145_strides_0 = const()[name = string("h_145_strides_0"), val = tensor([1])]; + tensor h_145_pad_0 = const()[name = string("h_145_pad_0"), val = tensor([0, 0])]; + tensor h_145_dilations_0 = const()[name = string("h_145_dilations_0"), val = tensor([1])]; + int32 h_145_groups_0 = const()[name = string("h_145_groups_0"), val = int32(1)]; + tensor op_2244_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27171840))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27696192))))[name = string("op_2244_weight_0_to_fp16_palettized")]; + tensor var_2244_bias_0_to_fp16 = const()[name = string("op_2244_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27696320)))]; + tensor var_2244_cast_fp16 = conv(bias = var_2244_bias_0_to_fp16, dilations = h_145_dilations_0, groups = h_145_groups_0, pad = h_145_pad_0, pad_type = h_145_pad_type_0, strides = h_145_strides_0, weight = op_2244_weight_0_to_fp16_palettized, x = input_375_cast_fp16)[name = string("op_2244_cast_fp16")]; + tensor out_61_cast_fp16 = add(x = x_195_cast_fp16, y = var_2244_cast_fp16)[name = string("out_61_cast_fp16")]; + tensor x_199_cast_fp16 = mul(x = out_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor x_m_cast_fp16 = mul(x = x_199_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_cast_fp16")]; + tensor input_377_perm_0 = const()[name = string("input_377_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27697408))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27763008))))[name = string("main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27763136)))]; + tensor input_377_cast_fp16 = transpose(perm = input_377_perm_0, x = x_199_cast_fp16)[name = string("transpose_14")]; + tensor linear_34_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor concat_42x = const()[name = string("concat_42x"), val = tensor([2, -1, 2, 128])]; + tensor var_2261_cast_fp16 = reshape(shape = concat_42x, x = linear_34_cast_fp16)[name = string("op_2261_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27763712))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27796544))))[name = string("main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27796672)))]; + tensor linear_36_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_2274 = const()[name = string("op_2274"), val = tensor([2, 50, 2, 128])]; + tensor var_2275_cast_fp16 = reshape(shape = var_2274, x = linear_36_cast_fp16)[name = string("op_2275_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_2279_transpose_x_0 = const()[name = string("op_2279_transpose_x_0"), val = bool(false)]; + bool var_2279_transpose_y_0 = const()[name = string("op_2279_transpose_y_0"), val = bool(false)]; + tensor op_2278_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27797248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27810112))))[name = string("op_2278_to_fp16_palettized")]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_2261_cast_fp16)[name = string("transpose_13")]; + tensor var_2279_cast_fp16 = matmul(transpose_x = var_2279_transpose_x_0, transpose_y = var_2279_transpose_y_0, x = q_cast_fp16, y = op_2278_to_fp16_palettized)[name = string("op_2279_cast_fp16")]; + fp16 _inversed_input_379_y_0_to_fp16 = const()[name = string("_inversed_input_379_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_379_cast_fp16 = mul(x = var_2279_cast_fp16, y = _inversed_input_379_y_0_to_fp16)[name = string("_inversed_input_379_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_1775, x = _inversed_input_379_cast_fp16)[name = string("attn_21_cast_fp16")]; + tensor attn_cast_fp16 = mul(x = attn_21_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_cast_fp16")]; + bool var_2286_transpose_x_0 = const()[name = string("op_2286_transpose_x_0"), val = bool(false)]; + bool var_2286_transpose_y_0 = const()[name = string("op_2286_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_2275_cast_fp16)[name = string("transpose_12")]; + tensor var_2286_cast_fp16 = matmul(transpose_x = var_2286_transpose_x_0, transpose_y = var_2286_transpose_y_0, x = attn_cast_fp16, y = v_15_cast_fp16)[name = string("op_2286_cast_fp16")]; + tensor var_2287_perm_0 = const()[name = string("op_2287_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_43x = const()[name = string("concat_43x"), val = tensor([2, -1, 256])]; + tensor var_2287_cast_fp16 = transpose(perm = var_2287_perm_0, x = var_2286_cast_fp16)[name = string("transpose_11")]; + tensor input_381_cast_fp16 = reshape(shape = concat_43x, x = var_2287_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27810240))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27875840))))[name = string("main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27875968)))]; + tensor linear_37_cast_fp16 = linear(bias = main_blocks_3_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor out_63_perm_0 = const()[name = string("out_63_perm_0"), val = tensor([0, 2, 1])]; + tensor out_63_cast_fp16 = transpose(perm = out_63_perm_0, x = linear_37_cast_fp16)[name = string("transpose_10")]; + tensor var_2295_cast_fp16 = mul(x = out_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2295_cast_fp16")]; + tensor x_201_cast_fp16 = add(x = x_m_cast_fp16, y = var_2295_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_383_perm_0 = const()[name = string("input_383_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2302_axes_0 = const()[name = string("op_2302_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27877056)))]; + tensor main_blocks_3_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27878144)))]; + tensor input_383_cast_fp16 = transpose(perm = input_383_perm_0, x = x_201_cast_fp16)[name = string("transpose_9")]; + tensor var_2302_cast_fp16 = layer_norm(axes = var_2302_axes_0, beta = main_blocks_3_style_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_style_norm_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_perm_0 = const()[name = string("op_2303_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2303_cast_fp16 = transpose(perm = var_2303_perm_0, x = var_2302_cast_fp16)[name = string("transpose_8")]; + tensor x_203_cast_fp16 = mul(x = var_2303_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor x_205_cast_fp16 = mul(x = x_203_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor var_2330_cast_fp16 = mul(x = x_205_cast_fp16, y = last_1_cast_fp16)[name = string("op_2330_cast_fp16")]; + tensor x_last_49_axes_0 = const()[name = string("x_last_49_axes_0"), val = tensor([-1])]; + bool x_last_49_keep_dims_0 = const()[name = string("x_last_49_keep_dims_0"), val = bool(true)]; + tensor x_last_49_cast_fp16 = reduce_sum(axes = x_last_49_axes_0, keep_dims = x_last_49_keep_dims_0, x = var_2330_cast_fp16)[name = string("x_last_49_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = x_205_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor var_2335_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_49_cast_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2335_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_pad_0 = const()[name = string("input_389_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("replicate")]; + fp16 const_53_to_fp16 = const()[name = string("const_53_to_fp16"), val = fp16(0x0p+0)]; + tensor input_389_cast_fp16 = pad(constant_val = const_53_to_fp16, mode = input_389_mode_0, pad = input_389_pad_0, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + string h_147_pad_type_0 = const()[name = string("h_147_pad_type_0"), val = string("valid")]; + int32 h_147_groups_0 = const()[name = string("h_147_groups_0"), val = int32(512)]; + tensor h_147_strides_0 = const()[name = string("h_147_strides_0"), val = tensor([1])]; + tensor h_147_pad_0 = const()[name = string("h_147_pad_0"), val = tensor([0, 0])]; + tensor h_147_dilations_0 = const()[name = string("h_147_dilations_0"), val = tensor([1])]; + tensor last_convnext_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27879232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27880576))))[name = string("last_convnext_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27880704)))]; + tensor h_147_cast_fp16 = conv(bias = last_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_147_dilations_0, groups = h_147_groups_0, pad = h_147_pad_0, pad_type = h_147_pad_type_0, strides = h_147_strides_0, weight = last_convnext_0_dwconv__conv_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor x_207_cast_fp16 = mul(x = h_147_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_391_perm_0 = const()[name = string("input_391_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2353_axes_0 = const()[name = string("op_2353_axes_0"), val = tensor([-1])]; + tensor last_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27881792)))]; + tensor last_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27882880)))]; + fp16 var_2307_to_fp16 = const()[name = string("op_2307_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_391_cast_fp16 = transpose(perm = input_391_perm_0, x = x_207_cast_fp16)[name = string("transpose_7")]; + tensor var_2353_cast_fp16 = layer_norm(axes = var_2353_axes_0, beta = last_convnext_0_norm_norm_bias_to_fp16, epsilon = var_2307_to_fp16, gamma = last_convnext_0_norm_norm_weight_to_fp16, x = input_391_cast_fp16)[name = string("op_2353_cast_fp16")]; + tensor input_393_perm_0 = const()[name = string("input_393_perm_0"), val = tensor([0, 2, 1])]; + string h_149_pad_type_0 = const()[name = string("h_149_pad_type_0"), val = string("valid")]; + tensor h_149_strides_0 = const()[name = string("h_149_strides_0"), val = tensor([1])]; + tensor h_149_pad_0 = const()[name = string("h_149_pad_0"), val = tensor([0, 0])]; + tensor h_149_dilations_0 = const()[name = string("h_149_dilations_0"), val = tensor([1])]; + int32 h_149_groups_0 = const()[name = string("h_149_groups_0"), val = int32(1)]; + tensor last_convnext_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27883968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28408320))))[name = string("last_convnext_0_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28408448)))]; + tensor input_393_cast_fp16 = transpose(perm = input_393_perm_0, x = var_2353_cast_fp16)[name = string("transpose_6")]; + tensor h_149_cast_fp16 = conv(bias = last_convnext_0_pwconv1_bias_to_fp16, dilations = h_149_dilations_0, groups = h_149_groups_0, pad = h_149_pad_0, pad_type = h_149_pad_type_0, strides = h_149_strides_0, weight = last_convnext_0_pwconv1_weight_to_fp16_palettized, x = input_393_cast_fp16)[name = string("h_149_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = h_149_cast_fp16)[name = string("input_395_cast_fp16")]; + string h_151_pad_type_0 = const()[name = string("h_151_pad_type_0"), val = string("valid")]; + tensor h_151_strides_0 = const()[name = string("h_151_strides_0"), val = tensor([1])]; + tensor h_151_pad_0 = const()[name = string("h_151_pad_0"), val = tensor([0, 0])]; + tensor h_151_dilations_0 = const()[name = string("h_151_dilations_0"), val = tensor([1])]; + int32 h_151_groups_0 = const()[name = string("h_151_groups_0"), val = int32(1)]; + tensor op_2370_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28412608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28936960))))[name = string("op_2370_weight_0_to_fp16_palettized")]; + tensor var_2370_bias_0_to_fp16 = const()[name = string("op_2370_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28937088)))]; + tensor var_2370_cast_fp16 = conv(bias = var_2370_bias_0_to_fp16, dilations = h_151_dilations_0, groups = h_151_groups_0, pad = h_151_pad_0, pad_type = h_151_pad_type_0, strides = h_151_strides_0, weight = op_2370_weight_0_to_fp16_palettized, x = input_395_cast_fp16)[name = string("op_2370_cast_fp16")]; + tensor out_65_cast_fp16 = add(x = x_205_cast_fp16, y = var_2370_cast_fp16)[name = string("out_65_cast_fp16")]; + tensor x_209_cast_fp16 = mul(x = out_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor x_211_cast_fp16 = mul(x = x_209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_2398_cast_fp16 = mul(x = x_211_cast_fp16, y = last_1_cast_fp16)[name = string("op_2398_cast_fp16")]; + tensor x_last_51_axes_0 = const()[name = string("x_last_51_axes_0"), val = tensor([-1])]; + bool x_last_51_keep_dims_0 = const()[name = string("x_last_51_keep_dims_0"), val = bool(true)]; + tensor x_last_51_cast_fp16 = reduce_sum(axes = x_last_51_axes_0, keep_dims = x_last_51_keep_dims_0, x = var_2398_cast_fp16)[name = string("x_last_51_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = x_211_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_51_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor input_399_cast_fp16 = add(x = var_2401_cast_fp16, y = var_2403_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor input_401_pad_0 = const()[name = string("input_401_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_401_mode_0 = const()[name = string("input_401_mode_0"), val = string("replicate")]; + fp16 const_55_to_fp16 = const()[name = string("const_55_to_fp16"), val = fp16(0x0p+0)]; + tensor input_401_cast_fp16 = pad(constant_val = const_55_to_fp16, mode = input_401_mode_0, pad = input_401_pad_0, x = input_399_cast_fp16)[name = string("input_401_cast_fp16")]; + string h_153_pad_type_0 = const()[name = string("h_153_pad_type_0"), val = string("valid")]; + int32 h_153_groups_0 = const()[name = string("h_153_groups_0"), val = int32(512)]; + tensor h_153_strides_0 = const()[name = string("h_153_strides_0"), val = tensor([1])]; + tensor h_153_pad_0 = const()[name = string("h_153_pad_0"), val = tensor([0, 0])]; + tensor h_153_dilations_0 = const()[name = string("h_153_dilations_0"), val = tensor([1])]; + tensor last_convnext_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28938176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28939520))))[name = string("last_convnext_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28939648)))]; + tensor h_153_cast_fp16 = conv(bias = last_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_153_dilations_0, groups = h_153_groups_0, pad = h_153_pad_0, pad_type = h_153_pad_type_0, strides = h_153_strides_0, weight = last_convnext_1_dwconv__conv_weight_to_fp16_palettized, x = input_401_cast_fp16)[name = string("h_153_cast_fp16")]; + tensor x_213_cast_fp16 = mul(x = h_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_213_cast_fp16")]; + tensor input_403_perm_0 = const()[name = string("input_403_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2421_axes_0 = const()[name = string("op_2421_axes_0"), val = tensor([-1])]; + tensor last_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28940736)))]; + tensor last_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28941824)))]; + fp16 var_2375_to_fp16 = const()[name = string("op_2375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_403_cast_fp16 = transpose(perm = input_403_perm_0, x = x_213_cast_fp16)[name = string("transpose_5")]; + tensor var_2421_cast_fp16 = layer_norm(axes = var_2421_axes_0, beta = last_convnext_1_norm_norm_bias_to_fp16, epsilon = var_2375_to_fp16, gamma = last_convnext_1_norm_norm_weight_to_fp16, x = input_403_cast_fp16)[name = string("op_2421_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string h_155_pad_type_0 = const()[name = string("h_155_pad_type_0"), val = string("valid")]; + tensor h_155_strides_0 = const()[name = string("h_155_strides_0"), val = tensor([1])]; + tensor h_155_pad_0 = const()[name = string("h_155_pad_0"), val = tensor([0, 0])]; + tensor h_155_dilations_0 = const()[name = string("h_155_dilations_0"), val = tensor([1])]; + int32 h_155_groups_0 = const()[name = string("h_155_groups_0"), val = int32(1)]; + tensor last_convnext_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28942912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29467264))))[name = string("last_convnext_1_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29467392)))]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = var_2421_cast_fp16)[name = string("transpose_4")]; + tensor h_155_cast_fp16 = conv(bias = last_convnext_1_pwconv1_bias_to_fp16, dilations = h_155_dilations_0, groups = h_155_groups_0, pad = h_155_pad_0, pad_type = h_155_pad_type_0, strides = h_155_strides_0, weight = last_convnext_1_pwconv1_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = string("h_155_cast_fp16")]; + string input_407_mode_0 = const()[name = string("input_407_mode_0"), val = string("EXACT")]; + tensor input_407_cast_fp16 = gelu(mode = input_407_mode_0, x = h_155_cast_fp16)[name = string("input_407_cast_fp16")]; + string h_157_pad_type_0 = const()[name = string("h_157_pad_type_0"), val = string("valid")]; + tensor h_157_strides_0 = const()[name = string("h_157_strides_0"), val = tensor([1])]; + tensor h_157_pad_0 = const()[name = string("h_157_pad_0"), val = tensor([0, 0])]; + tensor h_157_dilations_0 = const()[name = string("h_157_dilations_0"), val = tensor([1])]; + int32 h_157_groups_0 = const()[name = string("h_157_groups_0"), val = int32(1)]; + tensor op_2438_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29471552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29995904))))[name = string("op_2438_weight_0_to_fp16_palettized")]; + tensor var_2438_bias_0_to_fp16 = const()[name = string("op_2438_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29996032)))]; + tensor var_2438_cast_fp16 = conv(bias = var_2438_bias_0_to_fp16, dilations = h_157_dilations_0, groups = h_157_groups_0, pad = h_157_pad_0, pad_type = h_157_pad_type_0, strides = h_157_strides_0, weight = op_2438_weight_0_to_fp16_palettized, x = input_407_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor out_67_cast_fp16 = add(x = x_211_cast_fp16, y = var_2438_cast_fp16)[name = string("out_67_cast_fp16")]; + tensor x_215_cast_fp16 = mul(x = out_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_215_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2466_cast_fp16 = mul(x = x_217_cast_fp16, y = last_1_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor x_last_53_axes_0 = const()[name = string("x_last_53_axes_0"), val = tensor([-1])]; + bool x_last_53_keep_dims_0 = const()[name = string("x_last_53_keep_dims_0"), val = bool(true)]; + tensor x_last_53_cast_fp16 = reduce_sum(axes = x_last_53_axes_0, keep_dims = x_last_53_keep_dims_0, x = var_2466_cast_fp16)[name = string("x_last_53_cast_fp16")]; + tensor var_2469_cast_fp16 = mul(x = x_217_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor var_2471_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_53_cast_fp16)[name = string("op_2471_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = var_2469_cast_fp16, y = var_2471_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("replicate")]; + fp16 const_57_to_fp16 = const()[name = string("const_57_to_fp16"), val = fp16(0x0p+0)]; + tensor input_413_cast_fp16 = pad(constant_val = const_57_to_fp16, mode = input_413_mode_0, pad = input_413_pad_0, x = input_411_cast_fp16)[name = string("input_413_cast_fp16")]; + string h_159_pad_type_0 = const()[name = string("h_159_pad_type_0"), val = string("valid")]; + int32 h_159_groups_0 = const()[name = string("h_159_groups_0"), val = int32(512)]; + tensor h_159_strides_0 = const()[name = string("h_159_strides_0"), val = tensor([1])]; + tensor h_159_pad_0 = const()[name = string("h_159_pad_0"), val = tensor([0, 0])]; + tensor h_159_dilations_0 = const()[name = string("h_159_dilations_0"), val = tensor([1])]; + tensor last_convnext_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29997120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29998464))))[name = string("last_convnext_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29998592)))]; + tensor h_159_cast_fp16 = conv(bias = last_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_159_dilations_0, groups = h_159_groups_0, pad = h_159_pad_0, pad_type = h_159_pad_type_0, strides = h_159_strides_0, weight = last_convnext_2_dwconv__conv_weight_to_fp16_palettized, x = input_413_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor x_219_cast_fp16 = mul(x = h_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_415_perm_0 = const()[name = string("input_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2489_axes_0 = const()[name = string("op_2489_axes_0"), val = tensor([-1])]; + tensor last_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29999680)))]; + tensor last_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30000768)))]; + fp16 var_2443_to_fp16 = const()[name = string("op_2443_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_415_cast_fp16 = transpose(perm = input_415_perm_0, x = x_219_cast_fp16)[name = string("transpose_3")]; + tensor var_2489_cast_fp16 = layer_norm(axes = var_2489_axes_0, beta = last_convnext_2_norm_norm_bias_to_fp16, epsilon = var_2443_to_fp16, gamma = last_convnext_2_norm_norm_weight_to_fp16, x = input_415_cast_fp16)[name = string("op_2489_cast_fp16")]; + tensor input_417_perm_0 = const()[name = string("input_417_perm_0"), val = tensor([0, 2, 1])]; + string h_161_pad_type_0 = const()[name = string("h_161_pad_type_0"), val = string("valid")]; + tensor h_161_strides_0 = const()[name = string("h_161_strides_0"), val = tensor([1])]; + tensor h_161_pad_0 = const()[name = string("h_161_pad_0"), val = tensor([0, 0])]; + tensor h_161_dilations_0 = const()[name = string("h_161_dilations_0"), val = tensor([1])]; + int32 h_161_groups_0 = const()[name = string("h_161_groups_0"), val = int32(1)]; + tensor last_convnext_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30001856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30526208))))[name = string("last_convnext_2_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30526336)))]; + tensor input_417_cast_fp16 = transpose(perm = input_417_perm_0, x = var_2489_cast_fp16)[name = string("transpose_2")]; + tensor h_161_cast_fp16 = conv(bias = last_convnext_2_pwconv1_bias_to_fp16, dilations = h_161_dilations_0, groups = h_161_groups_0, pad = h_161_pad_0, pad_type = h_161_pad_type_0, strides = h_161_strides_0, weight = last_convnext_2_pwconv1_weight_to_fp16_palettized, x = input_417_cast_fp16)[name = string("h_161_cast_fp16")]; + string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("EXACT")]; + tensor input_419_cast_fp16 = gelu(mode = input_419_mode_0, x = h_161_cast_fp16)[name = string("input_419_cast_fp16")]; + string h_163_pad_type_0 = const()[name = string("h_163_pad_type_0"), val = string("valid")]; + tensor h_163_strides_0 = const()[name = string("h_163_strides_0"), val = tensor([1])]; + tensor h_163_pad_0 = const()[name = string("h_163_pad_0"), val = tensor([0, 0])]; + tensor h_163_dilations_0 = const()[name = string("h_163_dilations_0"), val = tensor([1])]; + int32 h_163_groups_0 = const()[name = string("h_163_groups_0"), val = int32(1)]; + tensor op_2506_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30530496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31054848))))[name = string("op_2506_weight_0_to_fp16_palettized")]; + tensor var_2506_bias_0_to_fp16 = const()[name = string("op_2506_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31054976)))]; + tensor var_2506_cast_fp16 = conv(bias = var_2506_bias_0_to_fp16, dilations = h_163_dilations_0, groups = h_163_groups_0, pad = h_163_pad_0, pad_type = h_163_pad_type_0, strides = h_163_strides_0, weight = op_2506_weight_0_to_fp16_palettized, x = input_419_cast_fp16)[name = string("op_2506_cast_fp16")]; + tensor out_69_cast_fp16 = add(x = x_217_cast_fp16, y = var_2506_cast_fp16)[name = string("out_69_cast_fp16")]; + tensor x_221_cast_fp16 = mul(x = out_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor x_223_cast_fp16 = mul(x = x_221_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_2534_cast_fp16 = mul(x = x_223_cast_fp16, y = last_1_cast_fp16)[name = string("op_2534_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_2534_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_2537_cast_fp16 = mul(x = x_223_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2537_cast_fp16")]; + tensor var_2539_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_cast_fp16)[name = string("op_2539_cast_fp16")]; + tensor input_423_cast_fp16 = add(x = var_2537_cast_fp16, y = var_2539_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_425_mode_0 = const()[name = string("input_425_mode_0"), val = string("replicate")]; + fp16 const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = fp16(0x0p+0)]; + tensor input_425_cast_fp16 = pad(constant_val = const_59_to_fp16, mode = input_425_mode_0, pad = input_425_pad_0, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")]; + string h_165_pad_type_0 = const()[name = string("h_165_pad_type_0"), val = string("valid")]; + int32 h_165_groups_0 = const()[name = string("h_165_groups_0"), val = int32(512)]; + tensor h_165_strides_0 = const()[name = string("h_165_strides_0"), val = tensor([1])]; + tensor h_165_pad_0 = const()[name = string("h_165_pad_0"), val = tensor([0, 0])]; + tensor h_165_dilations_0 = const()[name = string("h_165_dilations_0"), val = tensor([1])]; + tensor last_convnext_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31056064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31057408))))[name = string("last_convnext_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31057536)))]; + tensor h_165_cast_fp16 = conv(bias = last_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_165_dilations_0, groups = h_165_groups_0, pad = h_165_pad_0, pad_type = h_165_pad_type_0, strides = h_165_strides_0, weight = last_convnext_3_dwconv__conv_weight_to_fp16_palettized, x = input_425_cast_fp16)[name = string("h_165_cast_fp16")]; + tensor x_cast_fp16 = mul(x = h_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2557_axes_0 = const()[name = string("op_2557_axes_0"), val = tensor([-1])]; + tensor last_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31058624)))]; + tensor last_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31059712)))]; + fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_cast_fp16)[name = string("transpose_1")]; + tensor var_2557_cast_fp16 = layer_norm(axes = var_2557_axes_0, beta = last_convnext_3_norm_norm_bias_to_fp16, epsilon = var_2511_to_fp16, gamma = last_convnext_3_norm_norm_weight_to_fp16, x = input_427_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor input_429_perm_0 = const()[name = string("input_429_perm_0"), val = tensor([0, 2, 1])]; + string h_167_pad_type_0 = const()[name = string("h_167_pad_type_0"), val = string("valid")]; + tensor h_167_strides_0 = const()[name = string("h_167_strides_0"), val = tensor([1])]; + tensor h_167_pad_0 = const()[name = string("h_167_pad_0"), val = tensor([0, 0])]; + tensor h_167_dilations_0 = const()[name = string("h_167_dilations_0"), val = tensor([1])]; + int32 h_167_groups_0 = const()[name = string("h_167_groups_0"), val = int32(1)]; + tensor last_convnext_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31060800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31585152))))[name = string("last_convnext_3_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31585280)))]; + tensor input_429_cast_fp16 = transpose(perm = input_429_perm_0, x = var_2557_cast_fp16)[name = string("transpose_0")]; + tensor h_167_cast_fp16 = conv(bias = last_convnext_3_pwconv1_bias_to_fp16, dilations = h_167_dilations_0, groups = h_167_groups_0, pad = h_167_pad_0, pad_type = h_167_pad_type_0, strides = h_167_strides_0, weight = last_convnext_3_pwconv1_weight_to_fp16_palettized, x = input_429_cast_fp16)[name = string("h_167_cast_fp16")]; + string input_431_mode_0 = const()[name = string("input_431_mode_0"), val = string("EXACT")]; + tensor input_431_cast_fp16 = gelu(mode = input_431_mode_0, x = h_167_cast_fp16)[name = string("input_431_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor op_2574_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31589440))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32113792))))[name = string("op_2574_weight_0_to_fp16_palettized")]; + tensor var_2574_bias_0_to_fp16 = const()[name = string("op_2574_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32113920)))]; + tensor var_2574_cast_fp16 = conv(bias = var_2574_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = op_2574_weight_0_to_fp16_palettized, x = input_431_cast_fp16)[name = string("op_2574_cast_fp16")]; + tensor out_cast_fp16 = add(x = x_223_cast_fp16, y = var_2574_cast_fp16)[name = string("out_cast_fp16")]; + tensor input_cast_fp16 = mul(x = out_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("input_cast_fp16")]; + string var_2587_pad_type_0 = const()[name = string("op_2587_pad_type_0"), val = string("valid")]; + tensor var_2587_strides_0 = const()[name = string("op_2587_strides_0"), val = tensor([1])]; + tensor var_2587_pad_0 = const()[name = string("op_2587_pad_0"), val = tensor([0, 0])]; + tensor var_2587_dilations_0 = const()[name = string("op_2587_dilations_0"), val = tensor([1])]; + int32 var_2587_groups_0 = const()[name = string("op_2587_groups_0"), val = int32(1)]; + tensor proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32115008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32151936))))[name = string("proj_out_weight_to_fp16_palettized")]; + tensor var_2587_cast_fp16 = conv(dilations = var_2587_dilations_0, groups = var_2587_groups_0, pad = var_2587_pad_0, pad_type = var_2587_pad_type_0, strides = var_2587_strides_0, weight = proj_out_weight_to_fp16_palettized, x = input_cast_fp16)[name = string("op_2587_cast_fp16")]; + tensor v_cast_fp16 = mul(x = var_2587_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("v_cast_fp16")]; + tensor cond_begin_0 = const()[name = string("cond_begin_0"), val = tensor([0, 0, 0])]; + tensor cond_end_0 = const()[name = string("cond_end_0"), val = tensor([1, 144, 0])]; + tensor cond_end_mask_0 = const()[name = string("cond_end_mask_0"), val = tensor([false, true, true])]; + tensor cond_cast_fp16 = slice_by_index(begin = cond_begin_0, end = cond_end_0, end_mask = cond_end_mask_0, x = v_cast_fp16)[name = string("cond_cast_fp16")]; + tensor uncond_begin_0 = const()[name = string("uncond_begin_0"), val = tensor([1, 0, 0])]; + tensor uncond_end_0 = const()[name = string("uncond_end_0"), val = tensor([2, 144, 0])]; + tensor uncond_end_mask_0 = const()[name = string("uncond_end_mask_0"), val = tensor([true, true, true])]; + tensor uncond_cast_fp16 = slice_by_index(begin = uncond_begin_0, end = uncond_end_0, end_mask = uncond_end_mask_0, x = v_cast_fp16)[name = string("uncond_cast_fp16")]; + fp32 var_2597_epsilon_0 = const()[name = string("op_2597_epsilon_0"), val = fp32(0x1.a36e2ep-14)]; + tensor var_2597_cast_fp16 = inverse(epsilon = var_2597_epsilon_0, x = total_step_to_fp16)[name = string("op_2597_cast_fp16")]; + tensor var_2603 = const()[name = string("op_2603"), val = tensor([-1, 1, 1])]; + tensor step_cast_fp16 = reshape(shape = var_2603, x = var_2597_cast_fp16)[name = string("step_cast_fp16")]; + fp16 var_2605_to_fp16 = const()[name = string("op_2605_to_fp16"), val = fp16(0x1p+2)]; + tensor var_2606_cast_fp16 = mul(x = cond_cast_fp16, y = var_2605_to_fp16)[name = string("op_2606_cast_fp16")]; + fp16 var_2607_to_fp16 = const()[name = string("op_2607_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_2608_cast_fp16 = mul(x = uncond_cast_fp16, y = var_2607_to_fp16)[name = string("op_2608_cast_fp16")]; + tensor var_2610_cast_fp16 = sub(x = var_2606_cast_fp16, y = var_2608_cast_fp16)[name = string("op_2610_cast_fp16")]; + tensor var_2611_cast_fp16 = mul(x = step_cast_fp16, y = var_2610_cast_fp16)[name = string("op_2611_cast_fp16")]; + tensor var_2613_cast_fp16 = add(x = noisy_latent_to_fp16, y = var_2611_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor var_2614_cast_fp16 = mul(x = var_2613_cast_fp16, y = latent_mask_to_fp16)[name = string("op_2614_cast_fp16")]; + string var_2614_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2614_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor denoised_latent = cast(dtype = var_2614_cast_fp16_to_fp32_dtype_0, x = var_2614_cast_fp16)[name = string("cast_0")]; + } -> (denoised_latent); +} \ No newline at end of file diff --git a/VectorEstimatorVariants/VectorEstimator_v2_int4.mlmodelc/weights/weight.bin b/VectorEstimatorVariants/VectorEstimator_v2_int4.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..a3e765eea67138a48712c7a83f38816e2e323c73 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_int4.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:16ec8917b2424f1004b9cd395912c74ab47ff4bc3e1712c7bf06c07a0d9729d2 +size 32152032 diff --git 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Weights", + "name": "weights", + "path": "com.apple.CoreML/weights" + } + }, + "rootModelIdentifier": "41077186-4683-48B3-A43A-34221FD3DE0F" +} diff --git a/VectorEstimatorVariants/VectorEstimator_v2_int6.mlmodelc/analytics/coremldata.bin b/VectorEstimatorVariants/VectorEstimator_v2_int6.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..128f67f80b9850c452b2205cf4eecdb88aac53f2 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_int6.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6dcd39890d74c8125775901edc3e8978acb83c7e2ac1c6df6b6212a481c65437 +size 243 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_int6.mlmodelc/coremldata.bin b/VectorEstimatorVariants/VectorEstimator_v2_int6.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..e237ecc876948bf42454b5a72e77538c19381c6c --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_int6.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a61ba6647e635b19ba1bbc39d4e3fa35ad5c18bef4f9513c3137a2a0d4a6705 +size 634 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_int6.mlmodelc/model.mil b/VectorEstimatorVariants/VectorEstimator_v2_int6.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..a6cf43c8f419100d986887aed0e88c5ce3033c64 --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_int6.mlmodelc/model.mil @@ -0,0 +1,2269 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor current_step, tensor latent_mask, tensor noisy_latent, tensor style_ttl, tensor text_emb, tensor text_mask, tensor total_step) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"latent_mask", [1, 1, 24]}, {"noisy_latent", [1, 144, 24]}, {"text_emb", [1, 256, 24]}, {"text_mask", [1, 1, 24]}}), ("RangeDims", {{"latent_mask", [[1, 1], [1, 1], [17, 512]]}, {"noisy_latent", [[1, 1], [144, 144], [17, 512]]}, {"text_emb", [[1, 1], [256, 256], [17, 512]]}, {"text_mask", [[1, 1], [1, 1], [17, 512]]}})))] { + string text_emb_to_fp16_dtype_0 = const()[name = string("text_emb_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_emb_to_fp16 = cast(dtype = text_emb_to_fp16_dtype_0, x = text_emb)[name = string("cast_19")]; + tensor var_34_shape_cast_fp16 = shape(x = text_emb_to_fp16)[name = string("op_34_shape_cast_fp16")]; + int32 gather_1_axis_0 = const()[name = string("gather_1_axis_0"), val = int32(0)]; + int32 gather_1_batch_dims_0 = const()[name = string("gather_1_batch_dims_0"), val = int32(0)]; + bool gather_1_validate_indices_0 = const()[name = string("gather_1_validate_indices_0"), val = bool(false)]; + string var_34_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_34_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 gather_1_indices_0_to_uint16 = const()[name = string("gather_1_indices_0_to_uint16"), val = uint16(2)]; + tensor var_34_shape_cast_fp16_to_int16 = cast(dtype = var_34_shape_cast_fp16_to_int16_dtype_0, x = var_34_shape_cast_fp16)[name = string("cast_18")]; + int16 gather_1_cast_uint16 = gather(axis = gather_1_axis_0, batch_dims = gather_1_batch_dims_0, indices = gather_1_indices_0_to_uint16, validate_indices = gather_1_validate_indices_0, x = var_34_shape_cast_fp16_to_int16)[name = string("gather_1_cast_uint16")]; + string gather_1_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_1_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 var_38 = const()[name = string("op_38"), val = int32(-1)]; + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + int32 gather_2 = const()[name = string("gather_2"), val = int32(1)]; + int32 concat_0_axis_0 = const()[name = string("concat_0_axis_0"), val = int32(0)]; + bool concat_0_interleave_0 = const()[name = string("concat_0_interleave_0"), val = bool(false)]; + int32 gather_1_cast_uint16_to_int32 = cast(dtype = gather_1_cast_uint16_to_int32_dtype_0, x = gather_1_cast_uint16)[name = string("cast_17")]; + tensor concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (gather_2, var_38, gather_1_cast_uint16_to_int32))[name = string("concat_0")]; + tensor shape_0 = const()[name = string("shape_0"), val = tensor([1, 256, 1])]; + int32 equal_0_y_0 = const()[name = string("equal_0_y_0"), val = int32(-1)]; + tensor equal_0 = equal(x = concat_0, y = equal_0_y_0)[name = string("equal_0")]; + tensor select_0 = select(a = shape_0, b = concat_0, cond = equal_0)[name = string("select_0")]; + tensor real_div_0 = real_div(x = select_0, y = shape_0)[name = string("real_div_0")]; + tensor uncond_masker_text_special_token_to_fp16 = const()[name = string("uncond_masker_text_special_token_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor text_uncond_cast_fp16 = tile(reps = real_div_0, x = uncond_masker_text_special_token_to_fp16)[name = string("text_uncond_cast_fp16")]; + bool text_emb_interleave_0 = const()[name = string("text_emb_interleave_0"), val = bool(false)]; + tensor text_emb_cast_fp16 = concat(axis = var_39, interleave = text_emb_interleave_0, values = (text_emb_to_fp16, text_uncond_cast_fp16))[name = string("text_emb_cast_fp16")]; + bool input_95_interleave_0 = const()[name = string("input_95_interleave_0"), val = bool(false)]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor uncond_masker_style_value_special_token_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10304))))[name = string("uncond_masker_style_value_special_token_to_fp16_palettized")]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_16")]; + tensor input_95_cast_fp16 = concat(axis = var_39, interleave = input_95_interleave_0, values = (style_ttl_to_fp16, uncond_masker_style_value_special_token_to_fp16_palettized))[name = string("input_95_cast_fp16")]; + int32 var_64 = const()[name = string("op_64"), val = int32(0)]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + string noisy_latent_to_fp16_dtype_0 = const()[name = string("noisy_latent_to_fp16_dtype_0"), val = string("fp16")]; + tensor noisy_latent_to_fp16 = cast(dtype = noisy_latent_to_fp16_dtype_0, x = noisy_latent)[name = string("cast_15")]; + tensor input_5_cast_fp16 = concat(axis = var_64, interleave = input_5_interleave_0, values = (noisy_latent_to_fp16, noisy_latent_to_fp16))[name = string("input_5_cast_fp16")]; + int32 var_67 = const()[name = string("op_67"), val = int32(0)]; + bool latent_mask_b_interleave_0 = const()[name = string("latent_mask_b_interleave_0"), val = bool(false)]; + string latent_mask_to_fp16_dtype_0 = const()[name = string("latent_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor latent_mask_to_fp16 = cast(dtype = latent_mask_to_fp16_dtype_0, x = latent_mask)[name = string("cast_14")]; + tensor latent_mask_b_cast_fp16 = concat(axis = var_67, interleave = latent_mask_b_interleave_0, values = (latent_mask_to_fp16, latent_mask_to_fp16))[name = string("latent_mask_b_cast_fp16")]; + int32 var_70 = const()[name = string("op_70"), val = int32(0)]; + bool text_mask_interleave_0 = const()[name = string("text_mask_interleave_0"), val = bool(false)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_13")]; + tensor text_mask_cast_fp16 = concat(axis = var_70, interleave = text_mask_interleave_0, values = (text_mask_to_fp16, text_mask_to_fp16))[name = string("text_mask_cast_fp16")]; + string current_step_to_fp16_dtype_0 = const()[name = string("current_step_to_fp16_dtype_0"), val = string("fp16")]; + string total_step_to_fp16_dtype_0 = const()[name = string("total_step_to_fp16_dtype_0"), val = string("fp16")]; + tensor current_step_to_fp16 = cast(dtype = current_step_to_fp16_dtype_0, x = current_step)[name = string("cast_11")]; + tensor total_step_to_fp16 = cast(dtype = total_step_to_fp16_dtype_0, x = total_step)[name = string("cast_12")]; + tensor t_1_cast_fp16 = real_div(x = current_step_to_fp16, y = total_step_to_fp16)[name = string("t_1_cast_fp16")]; + int32 var_74 = const()[name = string("op_74"), val = int32(0)]; + bool t_3_interleave_0 = const()[name = string("t_3_interleave_0"), val = bool(false)]; + tensor t_3_cast_fp16 = concat(axis = var_74, interleave = t_3_interleave_0, values = (t_1_cast_fp16, t_1_cast_fp16))[name = string("t_3_cast_fp16")]; + int32 var_78 = const()[name = string("op_78"), val = int32(-1)]; + tensor var_85 = const()[name = string("op_85"), val = tensor([-1, 1])]; + tensor var_86_cast_fp16 = reshape(shape = var_85, x = t_3_cast_fp16)[name = string("op_86_cast_fp16")]; + fp16 time_encoder_time_scale_to_fp16 = const()[name = string("time_encoder_time_scale_to_fp16"), val = fp16(0x1.f4p+9)]; + tensor t_scaled_cast_fp16 = mul(x = var_86_cast_fp16, y = time_encoder_time_scale_to_fp16)[name = string("t_scaled_cast_fp16")]; + tensor time_encoder_omegas_to_fp16 = const()[name = string("time_encoder_omegas_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10496)))]; + tensor phase_cast_fp16 = mul(x = t_scaled_cast_fp16, y = time_encoder_omegas_to_fp16)[name = string("phase_cast_fp16")]; + tensor var_89_cast_fp16 = sin(x = phase_cast_fp16)[name = string("op_89_cast_fp16")]; + tensor var_90_cast_fp16 = cos(x = phase_cast_fp16)[name = string("op_90_cast_fp16")]; + bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)]; + tensor input_1_cast_fp16 = concat(axis = var_78, interleave = input_1_interleave_0, values = (var_89_cast_fp16, var_90_cast_fp16))[name = string("input_1_cast_fp16")]; + tensor time_encoder_mlp_0_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22976))))[name = string("time_encoder_mlp_0_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_0_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23168)))]; + tensor linear_0_cast_fp16 = linear(bias = time_encoder_mlp_0_linear_bias_to_fp16, weight = time_encoder_mlp_0_linear_weight_to_fp16_palettized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_97_cast_fp16 = softplus(x = linear_0_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = tanh(x = var_97_cast_fp16)[name = string("op_98_cast_fp16")]; + tensor input_3_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_98_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor time_encoder_mlp_2_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36096))))[name = string("time_encoder_mlp_2_linear_weight_to_fp16_palettized")]; + tensor time_encoder_mlp_2_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36288)))]; + tensor linear_1_cast_fp16 = linear(bias = time_encoder_mlp_2_linear_bias_to_fp16, weight = time_encoder_mlp_2_linear_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor time_emb_axes_0 = const()[name = string("time_emb_axes_0"), val = tensor([-1])]; + tensor time_emb_cast_fp16 = expand_dims(axes = time_emb_axes_0, x = linear_1_cast_fp16)[name = string("time_emb_cast_fp16")]; + string var_115_pad_type_0 = const()[name = string("op_115_pad_type_0"), val = string("valid")]; + tensor var_115_strides_0 = const()[name = string("op_115_strides_0"), val = tensor([1])]; + tensor var_115_pad_0 = const()[name = string("op_115_pad_0"), val = tensor([0, 0])]; + tensor var_115_dilations_0 = const()[name = string("op_115_dilations_0"), val = tensor([1])]; + int32 var_115_groups_0 = const()[name = string("op_115_groups_0"), val = int32(1)]; + tensor proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91840))))[name = string("proj_in_weight_to_fp16_palettized")]; + tensor var_115_cast_fp16 = conv(dilations = var_115_dilations_0, groups = var_115_groups_0, pad = var_115_pad_0, pad_type = var_115_pad_type_0, strides = var_115_strides_0, weight = proj_in_weight_to_fp16_palettized, x = input_5_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = var_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_3_cast_fp16")]; + int32 var_134 = const()[name = string("op_134"), val = int32(-1)]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([0, 0, 1])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 0])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([true, true, true])]; + tensor input_7_cast_fp16 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, x = latent_mask_b_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_166_pad_0 = const()[name = string("op_166_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_166_mode_0 = const()[name = string("op_166_mode_0"), val = string("constant")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)]; + tensor var_166_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = var_166_mode_0, pad = var_166_pad_0, x = input_7_cast_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_133_to_fp16 = const()[name = string("op_133_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = sub(x = var_133_to_fp16, y = var_166_cast_fp16)[name = string("op_167_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = latent_mask_b_cast_fp16, y = var_167_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_169_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_172_cast_fp16 = mul(x = x_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor var_173_cast_fp16 = sub(x = var_133_to_fp16, y = latent_mask_b_cast_fp16)[name = string("op_173_cast_fp16")]; + tensor var_174_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_174_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_174_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("replicate")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor input_11_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_11_mode_0, pad = input_11_pad_0, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(512)]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94016))))[name = string("main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94208)))]; + tensor h_3_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, 2, 1])]; + tensor var_192_axes_0 = const()[name = string("op_192_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95296)))]; + tensor main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96384)))]; + fp16 var_139_to_fp16 = const()[name = string("op_139_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_7_cast_fp16)[name = string("transpose_117")]; + tensor var_192_cast_fp16 = layer_norm(axes = var_192_axes_0, beta = main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16, x = input_13_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, 2, 1])]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97472))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(883968))))[name = string("main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(884160)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = var_192_cast_fp16)[name = string("transpose_116")]; + tensor h_5_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = string("h_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = h_5_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)]; + tensor op_209_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(888320))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1674816))))[name = string("op_209_weight_0_to_fp16_palettized")]; + tensor var_209_bias_0_to_fp16 = const()[name = string("op_209_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675008)))]; + tensor var_209_cast_fp16 = conv(bias = var_209_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = op_209_weight_0_to_fp16_palettized, x = input_17_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_209_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_223_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_223_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_226_cast_fp16 = mul(x = x_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_226_cast_fp16")]; + tensor var_228_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_228_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_226_cast_fp16, y = var_228_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor input_23_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([2])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(512)]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1676096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1678080))))[name = string("main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1678272)))]; + tensor h_9_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor var_246_axes_0 = const()[name = string("op_246_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1679360)))]; + tensor main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1680448)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_13_cast_fp16)[name = string("transpose_115")]; + tensor var_246_cast_fp16 = layer_norm(axes = var_246_axes_0, beta = main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_246_cast_fp16")]; + tensor input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor([0, 2, 1])]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1681536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2468032))))[name = string("main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2468224)))]; + tensor input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_246_cast_fp16)[name = string("transpose_114")]; + tensor h_11_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_27_cast_fp16)[name = string("h_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_11_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)]; + tensor op_263_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2472384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3258880))))[name = string("op_263_weight_0_to_fp16_palettized")]; + tensor var_263_bias_0_to_fp16 = const()[name = string("op_263_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3259072)))]; + tensor var_263_cast_fp16 = conv(bias = var_263_bias_0_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = op_263_weight_0_to_fp16_palettized, x = input_29_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_263_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_277_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_277_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_277_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_280_cast_fp16 = mul(x = x_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_280_cast_fp16")]; + tensor var_282_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_280_cast_fp16, y = var_282_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("replicate")]; + fp16 const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = fp16(0x0p+0)]; + tensor input_35_cast_fp16 = pad(constant_val = const_5_to_fp16, mode = input_35_mode_0, pad = input_35_pad_0, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([4])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(512)]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3260160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3262144))))[name = string("main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3262336)))]; + tensor h_15_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor var_300_axes_0 = const()[name = string("op_300_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3263424)))]; + tensor main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3264512)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_19_cast_fp16)[name = string("transpose_113")]; + tensor var_300_cast_fp16 = layer_norm(axes = var_300_axes_0, beta = main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16, x = input_37_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor([0, 2, 1])]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3265600))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4052096))))[name = string("main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4052288)))]; + tensor input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_300_cast_fp16)[name = string("transpose_112")]; + tensor h_17_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = string("h_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = h_17_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)]; + tensor op_317_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4056448))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4842944))))[name = string("op_317_weight_0_to_fp16_palettized")]; + tensor var_317_bias_0_to_fp16 = const()[name = string("op_317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4843136)))]; + tensor var_317_cast_fp16 = conv(bias = var_317_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = op_317_weight_0_to_fp16_palettized, x = input_41_cast_fp16)[name = string("op_317_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_317_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor var_331_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_331_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_331_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = x_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor var_336_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_336_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_334_cast_fp16, y = var_336_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")]; + fp16 const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_7_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([8])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(512)]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4844224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4846208))))[name = string("main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4846400)))]; + tensor h_21_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4847488)))]; + tensor main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4848576)))]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_25_cast_fp16)[name = string("transpose_111")]; + tensor var_354_cast_fp16 = layer_norm(axes = var_354_axes_0, beta = main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_354_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4849664))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5636160))))[name = string("main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5636352)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_354_cast_fp16)[name = string("transpose_110")]; + tensor h_23_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("h_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_23_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)]; + tensor op_371_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5640512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6427008))))[name = string("op_371_weight_0_to_fp16_palettized")]; + tensor var_371_bias_0_to_fp16 = const()[name = string("op_371_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6427200)))]; + tensor var_371_cast_fp16 = conv(bias = var_371_bias_0_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = op_371_weight_0_to_fp16_palettized, x = input_53_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_371_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor input_55_cast_fp16 = squeeze(axes = input_55_axes_0, x = time_emb_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6428288))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6452928))))[name = string("main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6453120)))]; + tensor linear_2_cast_fp16 = linear(bias = main_blocks_0_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_0_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor t_5_axes_0 = const()[name = string("t_5_axes_0"), val = tensor([-1])]; + tensor t_5_cast_fp16 = expand_dims(axes = t_5_axes_0, x = linear_2_cast_fp16)[name = string("t_5_cast_fp16")]; + tensor var_381_cast_fp16 = add(x = x_27_cast_fp16, y = t_5_cast_fp16)[name = string("op_381_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_381_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_394_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_394_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_394_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_397_cast_fp16 = mul(x = x_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = var_397_cast_fp16, y = var_399_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("replicate")]; + fp16 const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = fp16(0x0p+0)]; + tensor input_61_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_61_mode_0, pad = input_61_pad_0, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(512)]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6454208))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6456192))))[name = string("main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6456384)))]; + tensor h_27_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417_axes_0 = const()[name = string("op_417_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6457472)))]; + tensor main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6458560)))]; + tensor input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_33_cast_fp16)[name = string("transpose_109")]; + tensor var_417_cast_fp16 = layer_norm(axes = var_417_axes_0, beta = main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16, x = input_63_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6459648))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7246144))))[name = string("main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7246336)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = var_417_cast_fp16)[name = string("transpose_108")]; + tensor h_29_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_65_cast_fp16)[name = string("h_29_cast_fp16")]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("EXACT")]; + tensor input_67_cast_fp16 = gelu(mode = input_67_mode_0, x = h_29_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)]; + tensor op_434_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7250496))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8036992))))[name = string("op_434_weight_0_to_fp16_palettized")]; + tensor var_434_bias_0_to_fp16 = const()[name = string("op_434_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8037184)))]; + tensor var_434_cast_fp16 = conv(bias = var_434_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = op_434_weight_0_to_fp16_palettized, x = input_67_cast_fp16)[name = string("op_434_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_434_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_m_1_cast_fp16 = mul(x = x_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_1_cast_fp16")]; + tensor var_444_shape_cast_fp16 = shape(x = x_35_cast_fp16)[name = string("op_444_shape_cast_fp16")]; + int32 gather_4_axis_0 = const()[name = string("gather_4_axis_0"), val = int32(0)]; + int32 gather_4_batch_dims_0 = const()[name = string("gather_4_batch_dims_0"), val = int32(0)]; + bool gather_4_validate_indices_0 = const()[name = string("gather_4_validate_indices_0"), val = bool(false)]; + string var_444_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_444_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 gather_4_indices_0_to_uint16 = const()[name = string("gather_4_indices_0_to_uint16"), val = uint16(2)]; + tensor var_444_shape_cast_fp16_to_int16 = cast(dtype = var_444_shape_cast_fp16_to_int16_dtype_0, x = var_444_shape_cast_fp16)[name = string("cast_10")]; + int16 gather_4_cast_uint16 = gather(axis = gather_4_axis_0, batch_dims = gather_4_batch_dims_0, indices = gather_4_indices_0_to_uint16, validate_indices = gather_4_validate_indices_0, x = var_444_shape_cast_fp16_to_int16)[name = string("gather_4_cast_uint16")]; + string gather_4_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_4_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor var_446_shape_cast_fp16 = shape(x = text_emb_cast_fp16)[name = string("op_446_shape_cast_fp16")]; + int32 gather_5_axis_0 = const()[name = string("gather_5_axis_0"), val = int32(0)]; + int32 gather_5_batch_dims_0 = const()[name = string("gather_5_batch_dims_0"), val = int32(0)]; + bool gather_5_validate_indices_0 = const()[name = string("gather_5_validate_indices_0"), val = bool(false)]; + string var_446_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_446_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_5_indices_0_to_uint16 = const()[name = string("gather_5_indices_0_to_uint16"), val = uint16(2)]; + tensor var_446_shape_cast_fp16_to_uint16 = cast(dtype = var_446_shape_cast_fp16_to_uint16_dtype_0, x = var_446_shape_cast_fp16)[name = string("cast_9")]; + uint16 gather_5_cast_uint16 = gather(axis = gather_5_axis_0, batch_dims = gather_5_batch_dims_0, indices = gather_5_indices_0_to_uint16, validate_indices = gather_5_validate_indices_0, x = var_446_shape_cast_fp16_to_uint16)[name = string("gather_5_cast_uint16")]; + string gather_5_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_5_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8038272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8234944))))[name = string("main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8235136)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_35_cast_fp16)[name = string("transpose_107")]; + tensor linear_3_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_69_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor concat_4x = const()[name = string("concat_4x"), val = tensor([2, -1, 8, 64])]; + tensor var_453_cast_fp16 = reshape(shape = concat_4x, x = linear_3_cast_fp16)[name = string("op_453_cast_fp16")]; + tensor x_37_perm_0 = const()[name = string("x_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8236224))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8334592))))[name = string("main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8334784)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = text_emb_cast_fp16)[name = string("transpose_106")]; + tensor linear_4_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor concat_5x = const()[name = string("concat_5x"), val = tensor([2, -1, 8, 64])]; + tensor var_461_cast_fp16 = reshape(shape = concat_5x, x = linear_4_cast_fp16)[name = string("op_461_cast_fp16")]; + tensor x_39_perm_0 = const()[name = string("x_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8335872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8434240))))[name = string("main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8434432)))]; + tensor linear_5_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor concat_6x = const()[name = string("concat_6x"), val = tensor([2, -1, 8, 64])]; + tensor var_469_cast_fp16 = reshape(shape = concat_6x, x = linear_5_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + int32 concat_7_values0_0 = const()[name = string("concat_7_values0_0"), val = int32(1)]; + int32 concat_7_values2_0 = const()[name = string("concat_7_values2_0"), val = int32(1)]; + int32 concat_7_axis_0 = const()[name = string("concat_7_axis_0"), val = int32(0)]; + bool concat_7_interleave_0 = const()[name = string("concat_7_interleave_0"), val = bool(false)]; + int32 gather_4_cast_uint16_to_int32 = cast(dtype = gather_4_cast_uint16_to_int32_dtype_0, x = gather_4_cast_uint16)[name = string("cast_8")]; + tensor concat_7 = concat(axis = concat_7_axis_0, interleave = concat_7_interleave_0, values = (concat_7_values0_0, gather_4_cast_uint16_to_int32, concat_7_values2_0))[name = string("concat_7")]; + tensor var_472_begin_0 = const()[name = string("op_472_begin_0"), val = tensor([0, 0, 0])]; + tensor var_472_end_mask_0 = const()[name = string("op_472_end_mask_0"), val = tensor([true, false, true])]; + tensor main_blocks_0_text_attn_increments_to_fp16 = const()[name = string("main_blocks_0_text_attn_increments_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8435520)))]; + tensor var_472_cast_fp16 = slice_by_index(begin = var_472_begin_0, end = concat_7, end_mask = var_472_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_472_cast_fp16")]; + tensor concat_8 = const()[name = string("concat_8"), val = tensor([2, -1, -1])]; + tensor shape_1_cast_fp16 = shape(x = var_472_cast_fp16)[name = string("shape_1_cast_fp16")]; + tensor equal_1 = const()[name = string("equal_1"), val = tensor([false, true, true])]; + tensor select_1 = select(a = shape_1_cast_fp16, b = concat_8, cond = equal_1)[name = string("select_1")]; + tensor real_div_1 = real_div(x = select_1, y = shape_1_cast_fp16)[name = string("real_div_1")]; + tensor var_475_cast_fp16 = tile(reps = real_div_1, x = var_472_cast_fp16)[name = string("op_475_cast_fp16")]; + int32 concat_9_values0_0 = const()[name = string("concat_9_values0_0"), val = int32(1)]; + int32 concat_9_values2_0 = const()[name = string("concat_9_values2_0"), val = int32(1)]; + int32 concat_9_axis_0 = const()[name = string("concat_9_axis_0"), val = int32(0)]; + bool concat_9_interleave_0 = const()[name = string("concat_9_interleave_0"), val = bool(false)]; + int32 gather_5_cast_uint16_to_int32 = cast(dtype = gather_5_cast_uint16_to_int32_dtype_0, x = gather_5_cast_uint16)[name = string("cast_7")]; + tensor concat_9 = concat(axis = concat_9_axis_0, interleave = concat_9_interleave_0, values = (concat_9_values0_0, gather_5_cast_uint16_to_int32, concat_9_values2_0))[name = string("concat_9")]; + tensor var_478_begin_0 = const()[name = string("op_478_begin_0"), val = tensor([0, 0, 0])]; + tensor var_478_end_mask_0 = const()[name = string("op_478_end_mask_0"), val = tensor([true, false, true])]; + tensor var_478_cast_fp16 = slice_by_index(begin = var_478_begin_0, end = concat_9, end_mask = var_478_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_478_cast_fp16")]; + tensor concat_10 = const()[name = string("concat_10"), val = tensor([2, -1, -1])]; + tensor shape_2_cast_fp16 = shape(x = var_478_cast_fp16)[name = string("shape_2_cast_fp16")]; + tensor equal_2 = const()[name = string("equal_2"), val = tensor([false, true, true])]; + tensor select_2 = select(a = shape_2_cast_fp16, b = concat_10, cond = equal_2)[name = string("select_2")]; + tensor real_div_2 = real_div(x = select_2, y = shape_2_cast_fp16)[name = string("real_div_2")]; + tensor var_481_cast_fp16 = tile(reps = real_div_2, x = var_478_cast_fp16)[name = string("op_481_cast_fp16")]; + tensor divisor_1_axes_0 = const()[name = string("divisor_1_axes_0"), val = tensor([1, 2])]; + bool divisor_1_keep_dims_0 = const()[name = string("divisor_1_keep_dims_0"), val = bool(false)]; + tensor divisor_1_cast_fp16 = reduce_sum(axes = divisor_1_axes_0, keep_dims = divisor_1_keep_dims_0, x = latent_mask_b_cast_fp16)[name = string("divisor_1_cast_fp16")]; + tensor divisor_3_axes_0 = const()[name = string("divisor_3_axes_0"), val = tensor([1, 2])]; + bool divisor_3_keep_dims_0 = const()[name = string("divisor_3_keep_dims_0"), val = bool(false)]; + tensor divisor_3_cast_fp16 = reduce_sum(axes = divisor_3_axes_0, keep_dims = divisor_3_keep_dims_0, x = text_mask_cast_fp16)[name = string("divisor_3_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([-1, 1, 1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = divisor_1_cast_fp16)[name = string("op_488_cast_fp16")]; + tensor scaled_1_cast_fp16 = real_div(x = var_475_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_1_cast_fp16")]; + tensor main_blocks_0_text_attn_theta_to_fp16 = const()[name = string("main_blocks_0_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8437632)))]; + tensor angles_1_cast_fp16 = mul(x = scaled_1_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_1_cast_fp16")]; + tensor var_491_cast_fp16 = cos(x = angles_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = var_491_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor var_493_cast_fp16 = sin(x = angles_1_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = var_493_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor x_a_1_begin_0 = const()[name = string("x_a_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_1_end_0 = const()[name = string("x_a_1_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_1_end_mask_0 = const()[name = string("x_a_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_37_cast_fp16 = transpose(perm = x_37_perm_0, x = var_453_cast_fp16)[name = string("transpose_105")]; + tensor x_a_1_cast_fp16 = slice_by_index(begin = x_a_1_begin_0, end = x_a_1_end_0, end_mask = x_a_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_a_1_cast_fp16")]; + tensor x_b_1_begin_0 = const()[name = string("x_b_1_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_1_end_0 = const()[name = string("x_b_1_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_1_end_mask_0 = const()[name = string("x_b_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_1_cast_fp16 = slice_by_index(begin = x_b_1_begin_0, end = x_b_1_end_0, end_mask = x_b_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_b_1_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = x_a_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor var_498_cast_fp16 = mul(x = x_b_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_498_cast_fp16")]; + tensor rot_a_1_cast_fp16 = sub(x = var_497_cast_fp16, y = var_498_cast_fp16)[name = string("rot_a_1_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = x_a_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = x_b_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor rot_b_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("rot_b_1_cast_fp16")]; + bool q_1_interleave_0 = const()[name = string("q_1_interleave_0"), val = bool(false)]; + tensor q_1_cast_fp16 = concat(axis = var_134, interleave = q_1_interleave_0, values = (rot_a_1_cast_fp16, rot_b_1_cast_fp16))[name = string("q_1_cast_fp16")]; + tensor var_505 = const()[name = string("op_505"), val = tensor([-1, 1, 1])]; + tensor var_506_cast_fp16 = reshape(shape = var_505, x = divisor_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor scaled_3_cast_fp16 = real_div(x = var_481_cast_fp16, y = var_506_cast_fp16)[name = string("scaled_3_cast_fp16")]; + tensor angles_3_cast_fp16 = mul(x = scaled_3_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_3_cast_fp16")]; + tensor var_509_cast_fp16 = cos(x = angles_3_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor cos_3_axes_0 = const()[name = string("cos_3_axes_0"), val = tensor([1])]; + tensor cos_3_cast_fp16 = expand_dims(axes = cos_3_axes_0, x = var_509_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor var_511_cast_fp16 = sin(x = angles_3_cast_fp16)[name = string("op_511_cast_fp16")]; + tensor sin_3_axes_0 = const()[name = string("sin_3_axes_0"), val = tensor([1])]; + tensor sin_3_cast_fp16 = expand_dims(axes = sin_3_axes_0, x = var_511_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor x_a_3_begin_0 = const()[name = string("x_a_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_3_end_0 = const()[name = string("x_a_3_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_3_end_mask_0 = const()[name = string("x_a_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_39_cast_fp16 = transpose(perm = x_39_perm_0, x = var_461_cast_fp16)[name = string("transpose_104")]; + tensor x_a_3_cast_fp16 = slice_by_index(begin = x_a_3_begin_0, end = x_a_3_end_0, end_mask = x_a_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_a_3_cast_fp16")]; + tensor x_b_3_begin_0 = const()[name = string("x_b_3_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_3_end_0 = const()[name = string("x_b_3_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_3_end_mask_0 = const()[name = string("x_b_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_3_cast_fp16 = slice_by_index(begin = x_b_3_begin_0, end = x_b_3_end_0, end_mask = x_b_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_b_3_cast_fp16")]; + tensor var_515_cast_fp16 = mul(x = x_a_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_515_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = x_b_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor rot_a_3_cast_fp16 = sub(x = var_515_cast_fp16, y = var_516_cast_fp16)[name = string("rot_a_3_cast_fp16")]; + tensor var_518_cast_fp16 = mul(x = x_a_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = mul(x = x_b_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor rot_b_3_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = string("rot_b_3_cast_fp16")]; + bool k_1_interleave_0 = const()[name = string("k_1_interleave_0"), val = bool(false)]; + tensor k_1_cast_fp16 = concat(axis = var_134, interleave = k_1_interleave_0, values = (rot_a_3_cast_fp16, rot_b_3_cast_fp16))[name = string("k_1_cast_fp16")]; + bool var_524_transpose_x_1 = const()[name = string("op_524_transpose_x_1"), val = bool(false)]; + bool var_524_transpose_y_1 = const()[name = string("op_524_transpose_y_1"), val = bool(true)]; + tensor var_524_cast_fp16 = matmul(transpose_x = var_524_transpose_x_1, transpose_y = var_524_transpose_y_1, x = q_1_cast_fp16, y = k_1_cast_fp16)[name = string("op_524_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_524_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([2])]; + tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = text_mask_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528_cast_fp16 = sub(x = var_133_to_fp16, y = var_527_cast_fp16)[name = string("op_528_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_530_cast_fp16 = mul(x = var_528_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_75_cast_fp16 = sub(x = _inversed_scores_1_cast_fp16, y = var_530_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_134, x = input_75_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool var_533_transpose_x_0 = const()[name = string("op_533_transpose_x_0"), val = bool(false)]; + bool var_533_transpose_y_0 = const()[name = string("op_533_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_469_cast_fp16)[name = string("transpose_103")]; + tensor var_533_cast_fp16 = matmul(transpose_x = var_533_transpose_x_0, transpose_y = var_533_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("op_533_cast_fp16")]; + tensor var_534_perm_0 = const()[name = string("op_534_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_11x = const()[name = string("concat_11x"), val = tensor([2, -1, 512])]; + tensor var_534_cast_fp16 = transpose(perm = var_534_perm_0, x = var_533_cast_fp16)[name = string("transpose_102")]; + tensor input_77_cast_fp16 = reshape(shape = concat_11x, x = var_534_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8437760))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8634432))))[name = string("main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8634624)))]; + tensor linear_6_cast_fp16 = linear(bias = main_blocks_0_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor out_11_perm_0 = const()[name = string("out_11_perm_0"), val = tensor([0, 2, 1])]; + tensor out_11_cast_fp16 = transpose(perm = out_11_perm_0, x = linear_6_cast_fp16)[name = string("transpose_101")]; + tensor var_542_cast_fp16 = mul(x = out_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_542_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = x_m_1_cast_fp16, y = var_542_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor var_549_axes_0 = const()[name = string("op_549_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8635712)))]; + tensor main_blocks_0_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8636800)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_41_cast_fp16)[name = string("transpose_100")]; + tensor var_549_cast_fp16 = layer_norm(axes = var_549_axes_0, beta = main_blocks_0_text_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_text_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor var_550_perm_0 = const()[name = string("op_550_perm_0"), val = tensor([0, 2, 1])]; + tensor var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = var_549_cast_fp16)[name = string("transpose_99")]; + tensor x_43_cast_fp16 = mul(x = var_550_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = x_45_cast_fp16, y = last_1_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor x_last_11_axes_0 = const()[name = string("x_last_11_axes_0"), val = tensor([-1])]; + bool x_last_11_keep_dims_0 = const()[name = string("x_last_11_keep_dims_0"), val = bool(true)]; + tensor x_last_11_cast_fp16 = reduce_sum(axes = x_last_11_axes_0, keep_dims = x_last_11_keep_dims_0, x = var_563_cast_fp16)[name = string("x_last_11_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = x_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor var_568_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_11_cast_fp16)[name = string("op_568_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = var_566_cast_fp16, y = var_568_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_85_mode_0 = const()[name = string("input_85_mode_0"), val = string("replicate")]; + fp16 const_11_to_fp16 = const()[name = string("const_11_to_fp16"), val = fp16(0x0p+0)]; + tensor input_85_cast_fp16 = pad(constant_val = const_11_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(512)]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8637888))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8639872))))[name = string("main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8640064)))]; + tensor h_33_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("h_33_cast_fp16")]; + tensor x_47_cast_fp16 = mul(x = h_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8641152)))]; + tensor main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8642240)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_47_cast_fp16)[name = string("transpose_98")]; + tensor var_586_cast_fp16 = layer_norm(axes = var_586_axes_0, beta = main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_586_cast_fp16")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8643328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9429824))))[name = string("main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9430016)))]; + tensor input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = var_586_cast_fp16)[name = string("transpose_97")]; + tensor h_35_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_89_cast_fp16)[name = string("h_35_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = h_35_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")]; + tensor h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor([1])]; + tensor h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor([0, 0])]; + tensor h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor([1])]; + int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)]; + tensor op_603_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9434176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10220672))))[name = string("op_603_weight_0_to_fp16_palettized")]; + tensor var_603_bias_0_to_fp16 = const()[name = string("op_603_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10220864)))]; + tensor var_603_cast_fp16 = conv(bias = var_603_bias_0_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = op_603_weight_0_to_fp16_palettized, x = input_91_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor out_13_cast_fp16 = add(x = x_45_cast_fp16, y = var_603_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_49_cast_fp16 = mul(x = out_13_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_m_3_cast_fp16 = mul(x = x_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_3_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10221952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10320320))))[name = string("main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10320512)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_49_cast_fp16)[name = string("transpose_96")]; + tensor linear_7_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_93_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor concat_12x = const()[name = string("concat_12x"), val = tensor([2, -1, 2, 128])]; + tensor var_620_cast_fp16 = reshape(shape = concat_12x, x = linear_7_cast_fp16)[name = string("op_620_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10321088))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10370304))))[name = string("main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10370496)))]; + tensor linear_9_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_633 = const()[name = string("op_633"), val = tensor([2, 50, 2, 128])]; + tensor var_634_cast_fp16 = reshape(shape = var_633, x = linear_9_cast_fp16)[name = string("op_634_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_638_transpose_x_0 = const()[name = string("op_638_transpose_x_0"), val = bool(false)]; + bool var_638_transpose_y_0 = const()[name = string("op_638_transpose_y_0"), val = bool(false)]; + tensor op_637_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10371072))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10390336))))[name = string("op_637_to_fp16_palettized")]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_620_cast_fp16)[name = string("transpose_95")]; + tensor var_638_cast_fp16 = matmul(transpose_x = var_638_transpose_x_0, transpose_y = var_638_transpose_y_0, x = q_3_cast_fp16, y = op_637_to_fp16_palettized)[name = string("op_638_cast_fp16")]; + fp16 _inversed_input_97_y_0_to_fp16 = const()[name = string("_inversed_input_97_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_97_cast_fp16 = mul(x = var_638_cast_fp16, y = _inversed_input_97_y_0_to_fp16)[name = string("_inversed_input_97_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_134, x = _inversed_input_97_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_642_perm_0 = const()[name = string("op_642_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_1_axes_0 = const()[name = string("mask_1_axes_0"), val = tensor([1])]; + tensor var_642_cast_fp16 = transpose(perm = var_642_perm_0, x = latent_mask_b_cast_fp16)[name = string("transpose_94")]; + tensor mask_1_cast_fp16 = expand_dims(axes = mask_1_axes_0, x = var_642_cast_fp16)[name = string("mask_1_cast_fp16")]; + tensor attn_5_cast_fp16 = mul(x = attn_3_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool var_645_transpose_x_0 = const()[name = string("op_645_transpose_x_0"), val = bool(false)]; + bool var_645_transpose_y_0 = const()[name = string("op_645_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_634_cast_fp16)[name = string("transpose_93")]; + tensor var_645_cast_fp16 = matmul(transpose_x = var_645_transpose_x_0, transpose_y = var_645_transpose_y_0, x = attn_5_cast_fp16, y = v_3_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_646_perm_0 = const()[name = string("op_646_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_13x = const()[name = string("concat_13x"), val = tensor([2, -1, 256])]; + tensor var_646_cast_fp16 = transpose(perm = var_646_perm_0, x = var_645_cast_fp16)[name = string("transpose_92")]; + tensor input_99_cast_fp16 = reshape(shape = concat_13x, x = var_646_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10390528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10488896))))[name = string("main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_0_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10489088)))]; + tensor linear_10_cast_fp16 = linear(bias = main_blocks_0_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor out_15_perm_0 = const()[name = string("out_15_perm_0"), val = tensor([0, 2, 1])]; + tensor out_15_cast_fp16 = transpose(perm = out_15_perm_0, x = linear_10_cast_fp16)[name = string("transpose_91")]; + tensor var_654_cast_fp16 = mul(x = out_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor x_51_cast_fp16 = add(x = x_m_3_cast_fp16, y = var_654_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10490176)))]; + tensor main_blocks_0_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10491264)))]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_51_cast_fp16)[name = string("transpose_90")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = main_blocks_0_style_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_style_norm_norm_weight_to_fp16, x = input_101_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_662_perm_0 = const()[name = string("op_662_perm_0"), val = tensor([0, 2, 1])]; + tensor var_662_cast_fp16 = transpose(perm = var_662_perm_0, x = var_661_cast_fp16)[name = string("transpose_89")]; + tensor x_53_cast_fp16 = mul(x = var_662_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_53_cast_fp16")]; + int32 var_681 = const()[name = string("op_681"), val = int32(-1)]; + tensor x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = x_55_cast_fp16, y = last_1_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor x_last_13_axes_0 = const()[name = string("x_last_13_axes_0"), val = tensor([-1])]; + bool x_last_13_keep_dims_0 = const()[name = string("x_last_13_keep_dims_0"), val = bool(true)]; + tensor x_last_13_cast_fp16 = reduce_sum(axes = x_last_13_axes_0, keep_dims = x_last_13_keep_dims_0, x = var_716_cast_fp16)[name = string("x_last_13_cast_fp16")]; + tensor var_719_cast_fp16 = mul(x = x_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_721_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_13_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_719_cast_fp16, y = var_721_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("replicate")]; + fp16 const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(512)]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10492352))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10494336))))[name = string("main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10494528)))]; + tensor h_39_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor x_57_cast_fp16 = mul(x = h_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_739_axes_0 = const()[name = string("op_739_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10495616)))]; + tensor main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10496704)))]; + fp16 var_686_to_fp16 = const()[name = string("op_686_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_57_cast_fp16)[name = string("transpose_88")]; + tensor var_739_cast_fp16 = layer_norm(axes = var_739_axes_0, beta = main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_739_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + string h_41_pad_type_0 = const()[name = string("h_41_pad_type_0"), val = string("valid")]; + tensor h_41_strides_0 = const()[name = string("h_41_strides_0"), val = tensor([1])]; + tensor h_41_pad_0 = const()[name = string("h_41_pad_0"), val = tensor([0, 0])]; + tensor h_41_dilations_0 = const()[name = string("h_41_dilations_0"), val = tensor([1])]; + int32 h_41_groups_0 = const()[name = string("h_41_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10497792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11284288))))[name = string("main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11284480)))]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = var_739_cast_fp16)[name = string("transpose_87")]; + tensor h_41_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_41_dilations_0, groups = h_41_groups_0, pad = h_41_pad_0, pad_type = h_41_pad_type_0, strides = h_41_strides_0, weight = main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = h_41_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor op_756_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11288640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12075136))))[name = string("op_756_weight_0_to_fp16_palettized")]; + tensor var_756_bias_0_to_fp16 = const()[name = string("op_756_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12075328)))]; + tensor var_756_cast_fp16 = conv(bias = var_756_bias_0_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = op_756_weight_0_to_fp16_palettized, x = input_113_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor out_17_cast_fp16 = add(x = x_55_cast_fp16, y = var_756_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_59_cast_fp16 = mul(x = out_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor x_61_cast_fp16 = mul(x = x_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_770_cast_fp16 = mul(x = x_61_cast_fp16, y = last_1_cast_fp16)[name = string("op_770_cast_fp16")]; + tensor x_last_15_axes_0 = const()[name = string("x_last_15_axes_0"), val = tensor([-1])]; + bool x_last_15_keep_dims_0 = const()[name = string("x_last_15_keep_dims_0"), val = bool(true)]; + tensor x_last_15_cast_fp16 = reduce_sum(axes = x_last_15_axes_0, keep_dims = x_last_15_keep_dims_0, x = var_770_cast_fp16)[name = string("x_last_15_cast_fp16")]; + tensor var_773_cast_fp16 = mul(x = x_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor var_775_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_15_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = var_773_cast_fp16, y = var_775_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("replicate")]; + fp16 const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = fp16(0x0p+0)]; + tensor input_119_cast_fp16 = pad(constant_val = const_16_to_fp16, mode = input_119_mode_0, pad = input_119_pad_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")]; + string h_45_pad_type_0 = const()[name = string("h_45_pad_type_0"), val = string("valid")]; + tensor h_45_dilations_0 = const()[name = string("h_45_dilations_0"), val = tensor([2])]; + int32 h_45_groups_0 = const()[name = string("h_45_groups_0"), val = int32(512)]; + tensor h_45_strides_0 = const()[name = string("h_45_strides_0"), val = tensor([1])]; + tensor h_45_pad_0 = const()[name = string("h_45_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12076416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12078400))))[name = string("main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12078592)))]; + tensor h_45_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_45_dilations_0, groups = h_45_groups_0, pad = h_45_pad_0, pad_type = h_45_pad_type_0, strides = h_45_strides_0, weight = main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = h_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12079680)))]; + tensor main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12080768)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_63_cast_fp16)[name = string("transpose_86")]; + tensor var_793_cast_fp16 = layer_norm(axes = var_793_axes_0, beta = main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor input_123_perm_0 = const()[name = string("input_123_perm_0"), val = tensor([0, 2, 1])]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12081856))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12868352))))[name = string("main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12868544)))]; + tensor input_123_cast_fp16 = transpose(perm = input_123_perm_0, x = var_793_cast_fp16)[name = string("transpose_85")]; + tensor h_47_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = string("h_47_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = h_47_cast_fp16)[name = string("input_125_cast_fp16")]; + string h_49_pad_type_0 = const()[name = string("h_49_pad_type_0"), val = string("valid")]; + tensor h_49_strides_0 = const()[name = string("h_49_strides_0"), val = tensor([1])]; + tensor h_49_pad_0 = const()[name = string("h_49_pad_0"), val = tensor([0, 0])]; + tensor h_49_dilations_0 = const()[name = string("h_49_dilations_0"), val = tensor([1])]; + int32 h_49_groups_0 = const()[name = string("h_49_groups_0"), val = int32(1)]; + tensor op_810_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12872704))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13659200))))[name = string("op_810_weight_0_to_fp16_palettized")]; + tensor var_810_bias_0_to_fp16 = const()[name = string("op_810_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13659392)))]; + tensor var_810_cast_fp16 = conv(bias = var_810_bias_0_to_fp16, dilations = h_49_dilations_0, groups = h_49_groups_0, pad = h_49_pad_0, pad_type = h_49_pad_type_0, strides = h_49_strides_0, weight = op_810_weight_0_to_fp16_palettized, x = input_125_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = x_61_cast_fp16, y = var_810_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor x_65_cast_fp16 = mul(x = out_19_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = x_67_cast_fp16, y = last_1_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_last_17_axes_0 = const()[name = string("x_last_17_axes_0"), val = tensor([-1])]; + bool x_last_17_keep_dims_0 = const()[name = string("x_last_17_keep_dims_0"), val = bool(true)]; + tensor x_last_17_cast_fp16 = reduce_sum(axes = x_last_17_axes_0, keep_dims = x_last_17_keep_dims_0, x = var_824_cast_fp16)[name = string("x_last_17_cast_fp16")]; + tensor var_827_cast_fp16 = mul(x = x_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_829_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_17_cast_fp16)[name = string("op_829_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = var_827_cast_fp16, y = var_829_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("replicate")]; + fp16 const_18_to_fp16 = const()[name = string("const_18_to_fp16"), val = fp16(0x0p+0)]; + tensor input_131_cast_fp16 = pad(constant_val = const_18_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + string h_51_pad_type_0 = const()[name = string("h_51_pad_type_0"), val = string("valid")]; + tensor h_51_dilations_0 = const()[name = string("h_51_dilations_0"), val = tensor([4])]; + int32 h_51_groups_0 = const()[name = string("h_51_groups_0"), val = int32(512)]; + tensor h_51_strides_0 = const()[name = string("h_51_strides_0"), val = tensor([1])]; + tensor h_51_pad_0 = const()[name = string("h_51_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13660480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13662464))))[name = string("main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13662656)))]; + tensor h_51_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_51_dilations_0, groups = h_51_groups_0, pad = h_51_pad_0, pad_type = h_51_pad_type_0, strides = h_51_strides_0, weight = main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor x_69_cast_fp16 = mul(x = h_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_847_axes_0 = const()[name = string("op_847_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13663744)))]; + tensor main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13664832)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_69_cast_fp16)[name = string("transpose_84")]; + tensor var_847_cast_fp16 = layer_norm(axes = var_847_axes_0, beta = main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_847_cast_fp16")]; + tensor input_135_perm_0 = const()[name = string("input_135_perm_0"), val = tensor([0, 2, 1])]; + string h_53_pad_type_0 = const()[name = string("h_53_pad_type_0"), val = string("valid")]; + tensor h_53_strides_0 = const()[name = string("h_53_strides_0"), val = tensor([1])]; + tensor h_53_pad_0 = const()[name = string("h_53_pad_0"), val = tensor([0, 0])]; + tensor h_53_dilations_0 = const()[name = string("h_53_dilations_0"), val = tensor([1])]; + int32 h_53_groups_0 = const()[name = string("h_53_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13665920))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14452416))))[name = string("main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14452608)))]; + tensor input_135_cast_fp16 = transpose(perm = input_135_perm_0, x = var_847_cast_fp16)[name = string("transpose_83")]; + tensor h_53_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_53_dilations_0, groups = h_53_groups_0, pad = h_53_pad_0, pad_type = h_53_pad_type_0, strides = h_53_strides_0, weight = main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = string("h_53_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = h_53_cast_fp16)[name = string("input_137_cast_fp16")]; + string h_55_pad_type_0 = const()[name = string("h_55_pad_type_0"), val = string("valid")]; + tensor h_55_strides_0 = const()[name = string("h_55_strides_0"), val = tensor([1])]; + tensor h_55_pad_0 = const()[name = string("h_55_pad_0"), val = tensor([0, 0])]; + tensor h_55_dilations_0 = const()[name = string("h_55_dilations_0"), val = tensor([1])]; + int32 h_55_groups_0 = const()[name = string("h_55_groups_0"), val = int32(1)]; + tensor op_864_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14456768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15243264))))[name = string("op_864_weight_0_to_fp16_palettized")]; + tensor var_864_bias_0_to_fp16 = const()[name = string("op_864_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15243456)))]; + tensor var_864_cast_fp16 = conv(bias = var_864_bias_0_to_fp16, dilations = h_55_dilations_0, groups = h_55_groups_0, pad = h_55_pad_0, pad_type = h_55_pad_type_0, strides = h_55_strides_0, weight = op_864_weight_0_to_fp16_palettized, x = input_137_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor out_21_cast_fp16 = add(x = x_67_cast_fp16, y = var_864_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = out_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor x_73_cast_fp16 = mul(x = x_71_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_878_cast_fp16 = mul(x = x_73_cast_fp16, y = last_1_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor x_last_19_axes_0 = const()[name = string("x_last_19_axes_0"), val = tensor([-1])]; + bool x_last_19_keep_dims_0 = const()[name = string("x_last_19_keep_dims_0"), val = bool(true)]; + tensor x_last_19_cast_fp16 = reduce_sum(axes = x_last_19_axes_0, keep_dims = x_last_19_keep_dims_0, x = var_878_cast_fp16)[name = string("x_last_19_cast_fp16")]; + tensor var_881_cast_fp16 = mul(x = x_73_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_881_cast_fp16")]; + tensor var_883_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_19_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = var_881_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("replicate")]; + fp16 const_20_to_fp16 = const()[name = string("const_20_to_fp16"), val = fp16(0x0p+0)]; + tensor input_143_cast_fp16 = pad(constant_val = const_20_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + string h_57_pad_type_0 = const()[name = string("h_57_pad_type_0"), val = string("valid")]; + tensor h_57_dilations_0 = const()[name = string("h_57_dilations_0"), val = tensor([8])]; + int32 h_57_groups_0 = const()[name = string("h_57_groups_0"), val = int32(512)]; + tensor h_57_strides_0 = const()[name = string("h_57_strides_0"), val = tensor([1])]; + tensor h_57_pad_0 = const()[name = string("h_57_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15244544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15246528))))[name = string("main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15246720)))]; + tensor h_57_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_57_dilations_0, groups = h_57_groups_0, pad = h_57_pad_0, pad_type = h_57_pad_type_0, strides = h_57_strides_0, weight = main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = string("h_57_cast_fp16")]; + tensor x_75_cast_fp16 = mul(x = h_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_901_axes_0 = const()[name = string("op_901_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15247808)))]; + tensor main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15248896)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_75_cast_fp16)[name = string("transpose_82")]; + tensor var_901_cast_fp16 = layer_norm(axes = var_901_axes_0, beta = main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor input_147_perm_0 = const()[name = string("input_147_perm_0"), val = tensor([0, 2, 1])]; + string h_59_pad_type_0 = const()[name = string("h_59_pad_type_0"), val = string("valid")]; + tensor h_59_strides_0 = const()[name = string("h_59_strides_0"), val = tensor([1])]; + tensor h_59_pad_0 = const()[name = string("h_59_pad_0"), val = tensor([0, 0])]; + tensor h_59_dilations_0 = const()[name = string("h_59_dilations_0"), val = tensor([1])]; + int32 h_59_groups_0 = const()[name = string("h_59_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15249984))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16036480))))[name = string("main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16036672)))]; + tensor input_147_cast_fp16 = transpose(perm = input_147_perm_0, x = var_901_cast_fp16)[name = string("transpose_81")]; + tensor h_59_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_59_dilations_0, groups = h_59_groups_0, pad = h_59_pad_0, pad_type = h_59_pad_type_0, strides = h_59_strides_0, weight = main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("h_59_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = h_59_cast_fp16)[name = string("input_149_cast_fp16")]; + string h_61_pad_type_0 = const()[name = string("h_61_pad_type_0"), val = string("valid")]; + tensor h_61_strides_0 = const()[name = string("h_61_strides_0"), val = tensor([1])]; + tensor h_61_pad_0 = const()[name = string("h_61_pad_0"), val = tensor([0, 0])]; + tensor h_61_dilations_0 = const()[name = string("h_61_dilations_0"), val = tensor([1])]; + int32 h_61_groups_0 = const()[name = string("h_61_groups_0"), val = int32(1)]; + tensor op_918_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16040832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16827328))))[name = string("op_918_weight_0_to_fp16_palettized")]; + tensor var_918_bias_0_to_fp16 = const()[name = string("op_918_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16827520)))]; + tensor var_918_cast_fp16 = conv(bias = var_918_bias_0_to_fp16, dilations = h_61_dilations_0, groups = h_61_groups_0, pad = h_61_pad_0, pad_type = h_61_pad_type_0, strides = h_61_strides_0, weight = op_918_weight_0_to_fp16_palettized, x = input_149_cast_fp16)[name = string("op_918_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = x_73_cast_fp16, y = var_918_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor x_77_cast_fp16 = mul(x = out_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16828608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16853248))))[name = string("main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16853440)))]; + tensor linear_11_cast_fp16 = linear(bias = main_blocks_1_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_1_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor t_7_axes_0 = const()[name = string("t_7_axes_0"), val = tensor([-1])]; + tensor t_7_cast_fp16 = expand_dims(axes = t_7_axes_0, x = linear_11_cast_fp16)[name = string("t_7_cast_fp16")]; + tensor var_928_cast_fp16 = add(x = x_77_cast_fp16, y = t_7_cast_fp16)[name = string("op_928_cast_fp16")]; + tensor x_79_cast_fp16 = mul(x = var_928_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor x_81_cast_fp16 = mul(x = x_79_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = x_81_cast_fp16, y = last_1_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor x_last_21_axes_0 = const()[name = string("x_last_21_axes_0"), val = tensor([-1])]; + bool x_last_21_keep_dims_0 = const()[name = string("x_last_21_keep_dims_0"), val = bool(true)]; + tensor x_last_21_cast_fp16 = reduce_sum(axes = x_last_21_axes_0, keep_dims = x_last_21_keep_dims_0, x = var_941_cast_fp16)[name = string("x_last_21_cast_fp16")]; + tensor var_944_cast_fp16 = mul(x = x_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor var_946_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_21_cast_fp16)[name = string("op_946_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = var_944_cast_fp16, y = var_946_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("replicate")]; + fp16 const_22_to_fp16 = const()[name = string("const_22_to_fp16"), val = fp16(0x0p+0)]; + tensor input_157_cast_fp16 = pad(constant_val = const_22_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string h_63_pad_type_0 = const()[name = string("h_63_pad_type_0"), val = string("valid")]; + int32 h_63_groups_0 = const()[name = string("h_63_groups_0"), val = int32(512)]; + tensor h_63_strides_0 = const()[name = string("h_63_strides_0"), val = tensor([1])]; + tensor h_63_pad_0 = const()[name = string("h_63_pad_0"), val = tensor([0, 0])]; + tensor h_63_dilations_0 = const()[name = string("h_63_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16854528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16856512))))[name = string("main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16856704)))]; + tensor h_63_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_63_dilations_0, groups = h_63_groups_0, pad = h_63_pad_0, pad_type = h_63_pad_type_0, strides = h_63_strides_0, weight = main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor x_83_cast_fp16 = mul(x = h_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor var_964_axes_0 = const()[name = string("op_964_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16857792)))]; + tensor main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16858880)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_83_cast_fp16)[name = string("transpose_80")]; + tensor var_964_cast_fp16 = layer_norm(axes = var_964_axes_0, beta = main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + string h_65_pad_type_0 = const()[name = string("h_65_pad_type_0"), val = string("valid")]; + tensor h_65_strides_0 = const()[name = string("h_65_strides_0"), val = tensor([1])]; + tensor h_65_pad_0 = const()[name = string("h_65_pad_0"), val = tensor([0, 0])]; + tensor h_65_dilations_0 = const()[name = string("h_65_dilations_0"), val = tensor([1])]; + int32 h_65_groups_0 = const()[name = string("h_65_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16859968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17646464))))[name = string("main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17646656)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = var_964_cast_fp16)[name = string("transpose_79")]; + tensor h_65_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_65_dilations_0, groups = h_65_groups_0, pad = h_65_pad_0, pad_type = h_65_pad_type_0, strides = h_65_strides_0, weight = main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = string("h_65_cast_fp16")]; + string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("EXACT")]; + tensor input_163_cast_fp16 = gelu(mode = input_163_mode_0, x = h_65_cast_fp16)[name = string("input_163_cast_fp16")]; + string h_67_pad_type_0 = const()[name = string("h_67_pad_type_0"), val = string("valid")]; + tensor h_67_strides_0 = const()[name = string("h_67_strides_0"), val = tensor([1])]; + tensor h_67_pad_0 = const()[name = string("h_67_pad_0"), val = tensor([0, 0])]; + tensor h_67_dilations_0 = const()[name = string("h_67_dilations_0"), val = tensor([1])]; + int32 h_67_groups_0 = const()[name = string("h_67_groups_0"), val = int32(1)]; + tensor op_981_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17650816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18437312))))[name = string("op_981_weight_0_to_fp16_palettized")]; + tensor var_981_bias_0_to_fp16 = const()[name = string("op_981_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18437504)))]; + tensor var_981_cast_fp16 = conv(bias = var_981_bias_0_to_fp16, dilations = h_67_dilations_0, groups = h_67_groups_0, pad = h_67_pad_0, pad_type = h_67_pad_type_0, strides = h_67_strides_0, weight = op_981_weight_0_to_fp16_palettized, x = input_163_cast_fp16)[name = string("op_981_cast_fp16")]; + tensor out_25_cast_fp16 = add(x = x_81_cast_fp16, y = var_981_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = out_25_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor x_m_5_cast_fp16 = mul(x = x_85_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_5_cast_fp16")]; + tensor var_991_shape_cast_fp16 = shape(x = x_85_cast_fp16)[name = string("op_991_shape_cast_fp16")]; + int32 gather_9_axis_0 = const()[name = string("gather_9_axis_0"), val = int32(0)]; + int32 gather_9_batch_dims_0 = const()[name = string("gather_9_batch_dims_0"), val = int32(0)]; + bool gather_9_validate_indices_0 = const()[name = string("gather_9_validate_indices_0"), val = bool(false)]; + string var_991_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_991_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_9_indices_0_to_uint16 = const()[name = string("gather_9_indices_0_to_uint16"), val = uint16(2)]; + tensor var_991_shape_cast_fp16_to_uint16 = cast(dtype = var_991_shape_cast_fp16_to_uint16_dtype_0, x = var_991_shape_cast_fp16)[name = string("cast_6")]; + uint16 gather_9_cast_uint16 = gather(axis = gather_9_axis_0, batch_dims = gather_9_batch_dims_0, indices = gather_9_indices_0_to_uint16, validate_indices = gather_9_validate_indices_0, x = var_991_shape_cast_fp16_to_uint16)[name = string("gather_9_cast_uint16")]; + string gather_9_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_9_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18438592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18635264))))[name = string("main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18635456)))]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_85_cast_fp16)[name = string("transpose_78")]; + tensor linear_12_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor concat_14x = const()[name = string("concat_14x"), val = tensor([2, -1, 8, 64])]; + tensor var_1000_cast_fp16 = reshape(shape = concat_14x, x = linear_12_cast_fp16)[name = string("op_1000_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18636544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18734912))))[name = string("main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18735104)))]; + tensor linear_13_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor concat_15x = const()[name = string("concat_15x"), val = tensor([2, -1, 8, 64])]; + tensor var_1008_cast_fp16 = reshape(shape = concat_15x, x = linear_13_cast_fp16)[name = string("op_1008_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18736192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18834560))))[name = string("main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18834752)))]; + tensor linear_14_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor concat_16x = const()[name = string("concat_16x"), val = tensor([2, -1, 8, 64])]; + tensor var_1016_cast_fp16 = reshape(shape = concat_16x, x = linear_14_cast_fp16)[name = string("op_1016_cast_fp16")]; + tensor v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + int32 concat_17_values0_0 = const()[name = string("concat_17_values0_0"), val = int32(1)]; + int32 concat_17_values2_0 = const()[name = string("concat_17_values2_0"), val = int32(1)]; + int32 concat_17_axis_0 = const()[name = string("concat_17_axis_0"), val = int32(0)]; + bool concat_17_interleave_0 = const()[name = string("concat_17_interleave_0"), val = bool(false)]; + int32 gather_9_cast_uint16_to_int32 = cast(dtype = gather_9_cast_uint16_to_int32_dtype_0, x = gather_9_cast_uint16)[name = string("cast_5")]; + tensor concat_17 = concat(axis = concat_17_axis_0, interleave = concat_17_interleave_0, values = (concat_17_values0_0, gather_9_cast_uint16_to_int32, concat_17_values2_0))[name = string("concat_17")]; + tensor var_1019_begin_0 = const()[name = string("op_1019_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1019_end_mask_0 = const()[name = string("op_1019_end_mask_0"), val = tensor([true, false, true])]; + tensor var_1019_cast_fp16 = slice_by_index(begin = var_1019_begin_0, end = concat_17, end_mask = var_1019_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_1019_cast_fp16")]; + tensor concat_18 = const()[name = string("concat_18"), val = tensor([2, -1, -1])]; + tensor shape_3_cast_fp16 = shape(x = var_1019_cast_fp16)[name = string("shape_3_cast_fp16")]; + tensor equal_3 = const()[name = string("equal_3"), val = tensor([false, true, true])]; + tensor select_3 = select(a = shape_3_cast_fp16, b = concat_18, cond = equal_3)[name = string("select_3")]; + tensor real_div_3 = real_div(x = select_3, y = shape_3_cast_fp16)[name = string("real_div_3")]; + tensor var_1022_cast_fp16 = tile(reps = real_div_3, x = var_1019_cast_fp16)[name = string("op_1022_cast_fp16")]; + tensor scaled_5_cast_fp16 = real_div(x = var_1022_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_5_cast_fp16")]; + tensor main_blocks_1_text_attn_theta_to_fp16 = const()[name = string("main_blocks_1_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18835840)))]; + tensor angles_5_cast_fp16 = mul(x = scaled_5_cast_fp16, y = main_blocks_1_text_attn_theta_to_fp16)[name = string("angles_5_cast_fp16")]; + tensor var_1038_cast_fp16 = cos(x = angles_5_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor cos_5_axes_0 = const()[name = string("cos_5_axes_0"), val = tensor([1])]; + tensor cos_5_cast_fp16 = expand_dims(axes = cos_5_axes_0, x = var_1038_cast_fp16)[name = string("cos_5_cast_fp16")]; + tensor var_1040_cast_fp16 = sin(x = angles_5_cast_fp16)[name = string("op_1040_cast_fp16")]; + tensor sin_5_axes_0 = const()[name = string("sin_5_axes_0"), val = tensor([1])]; + tensor sin_5_cast_fp16 = expand_dims(axes = sin_5_axes_0, x = var_1040_cast_fp16)[name = string("sin_5_cast_fp16")]; + tensor x_a_5_begin_0 = const()[name = string("x_a_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_5_end_0 = const()[name = string("x_a_5_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_5_end_mask_0 = const()[name = string("x_a_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_1000_cast_fp16)[name = string("transpose_77")]; + tensor x_a_5_cast_fp16 = slice_by_index(begin = x_a_5_begin_0, end = x_a_5_end_0, end_mask = x_a_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_a_5_cast_fp16")]; + tensor x_b_5_begin_0 = const()[name = string("x_b_5_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_5_end_0 = const()[name = string("x_b_5_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_5_end_mask_0 = const()[name = string("x_b_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_5_cast_fp16 = slice_by_index(begin = x_b_5_begin_0, end = x_b_5_end_0, end_mask = x_b_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_b_5_cast_fp16")]; + tensor var_1044_cast_fp16 = mul(x = x_a_5_cast_fp16, y = cos_5_cast_fp16)[name = string("op_1044_cast_fp16")]; + tensor var_1045_cast_fp16 = mul(x = x_b_5_cast_fp16, y = sin_5_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor rot_a_5_cast_fp16 = sub(x = var_1044_cast_fp16, y = var_1045_cast_fp16)[name = string("rot_a_5_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_a_5_cast_fp16, y = sin_5_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_cast_fp16 = mul(x = x_b_5_cast_fp16, y = cos_5_cast_fp16)[name = string("op_1048_cast_fp16")]; + tensor rot_b_5_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("rot_b_5_cast_fp16")]; + bool q_5_interleave_0 = const()[name = string("q_5_interleave_0"), val = bool(false)]; + tensor q_5_cast_fp16 = concat(axis = var_681, interleave = q_5_interleave_0, values = (rot_a_5_cast_fp16, rot_b_5_cast_fp16))[name = string("q_5_cast_fp16")]; + tensor x_a_7_begin_0 = const()[name = string("x_a_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_7_end_0 = const()[name = string("x_a_7_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_7_end_mask_0 = const()[name = string("x_a_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_1008_cast_fp16)[name = string("transpose_76")]; + tensor x_a_7_cast_fp16 = slice_by_index(begin = x_a_7_begin_0, end = x_a_7_end_0, end_mask = x_a_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_a_7_cast_fp16")]; + tensor x_b_7_begin_0 = const()[name = string("x_b_7_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_7_end_0 = const()[name = string("x_b_7_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_7_end_mask_0 = const()[name = string("x_b_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_7_cast_fp16 = slice_by_index(begin = x_b_7_begin_0, end = x_b_7_end_0, end_mask = x_b_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_b_7_cast_fp16")]; + tensor var_1062_cast_fp16 = mul(x = x_a_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = x_b_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor rot_a_7_cast_fp16 = sub(x = var_1062_cast_fp16, y = var_1063_cast_fp16)[name = string("rot_a_7_cast_fp16")]; + tensor var_1065_cast_fp16 = mul(x = x_a_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1065_cast_fp16")]; + tensor var_1066_cast_fp16 = mul(x = x_b_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1066_cast_fp16")]; + tensor rot_b_7_cast_fp16 = add(x = var_1065_cast_fp16, y = var_1066_cast_fp16)[name = string("rot_b_7_cast_fp16")]; + bool k_5_interleave_0 = const()[name = string("k_5_interleave_0"), val = bool(false)]; + tensor k_5_cast_fp16 = concat(axis = var_681, interleave = k_5_interleave_0, values = (rot_a_7_cast_fp16, rot_b_7_cast_fp16))[name = string("k_5_cast_fp16")]; + bool var_1071_transpose_x_1 = const()[name = string("op_1071_transpose_x_1"), val = bool(false)]; + bool var_1071_transpose_y_1 = const()[name = string("op_1071_transpose_y_1"), val = bool(true)]; + tensor var_1071_cast_fp16 = matmul(transpose_x = var_1071_transpose_x_1, transpose_y = var_1071_transpose_y_1, x = q_5_cast_fp16, y = k_5_cast_fp16)[name = string("op_1071_cast_fp16")]; + fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_3_cast_fp16 = mul(x = var_1071_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")]; + tensor input_171_cast_fp16 = sub(x = _inversed_scores_3_cast_fp16, y = var_530_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_681, x = input_171_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool var_1080_transpose_x_0 = const()[name = string("op_1080_transpose_x_0"), val = bool(false)]; + bool var_1080_transpose_y_0 = const()[name = string("op_1080_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1016_cast_fp16)[name = string("transpose_75")]; + tensor var_1080_cast_fp16 = matmul(transpose_x = var_1080_transpose_x_0, transpose_y = var_1080_transpose_y_0, x = attn_7_cast_fp16, y = v_5_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_perm_0 = const()[name = string("op_1081_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_21x = const()[name = string("concat_21x"), val = tensor([2, -1, 512])]; + tensor var_1081_cast_fp16 = transpose(perm = var_1081_perm_0, x = var_1080_cast_fp16)[name = string("transpose_74")]; + tensor input_173_cast_fp16 = reshape(shape = concat_21x, x = var_1081_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18835968))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19032640))))[name = string("main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19032832)))]; + tensor linear_15_cast_fp16 = linear(bias = main_blocks_1_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_173_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor out_27_perm_0 = const()[name = string("out_27_perm_0"), val = tensor([0, 2, 1])]; + tensor out_27_cast_fp16 = transpose(perm = out_27_perm_0, x = linear_15_cast_fp16)[name = string("transpose_73")]; + tensor var_1089_cast_fp16 = mul(x = out_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor x_91_cast_fp16 = add(x = x_m_5_cast_fp16, y = var_1089_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_175_perm_0 = const()[name = string("input_175_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1096_axes_0 = const()[name = string("op_1096_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19033920)))]; + tensor main_blocks_1_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19035008)))]; + tensor input_175_cast_fp16 = transpose(perm = input_175_perm_0, x = x_91_cast_fp16)[name = string("transpose_72")]; + tensor var_1096_cast_fp16 = layer_norm(axes = var_1096_axes_0, beta = main_blocks_1_text_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_text_norm_norm_weight_to_fp16, x = input_175_cast_fp16)[name = string("op_1096_cast_fp16")]; + tensor var_1097_perm_0 = const()[name = string("op_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1097_cast_fp16 = transpose(perm = var_1097_perm_0, x = var_1096_cast_fp16)[name = string("transpose_71")]; + tensor x_93_cast_fp16 = mul(x = var_1097_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor x_95_cast_fp16 = mul(x = x_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_1110_cast_fp16 = mul(x = x_95_cast_fp16, y = last_1_cast_fp16)[name = string("op_1110_cast_fp16")]; + tensor x_last_23_axes_0 = const()[name = string("x_last_23_axes_0"), val = tensor([-1])]; + bool x_last_23_keep_dims_0 = const()[name = string("x_last_23_keep_dims_0"), val = bool(true)]; + tensor x_last_23_cast_fp16 = reduce_sum(axes = x_last_23_axes_0, keep_dims = x_last_23_keep_dims_0, x = var_1110_cast_fp16)[name = string("x_last_23_cast_fp16")]; + tensor var_1113_cast_fp16 = mul(x = x_95_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1115_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_23_cast_fp16)[name = string("op_1115_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = var_1113_cast_fp16, y = var_1115_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("replicate")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor input_181_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + string h_69_pad_type_0 = const()[name = string("h_69_pad_type_0"), val = string("valid")]; + int32 h_69_groups_0 = const()[name = string("h_69_groups_0"), val = int32(512)]; + tensor h_69_strides_0 = const()[name = string("h_69_strides_0"), val = tensor([1])]; + tensor h_69_pad_0 = const()[name = string("h_69_pad_0"), val = tensor([0, 0])]; + tensor h_69_dilations_0 = const()[name = string("h_69_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19036096))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19038080))))[name = string("main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19038272)))]; + tensor h_69_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_69_dilations_0, groups = h_69_groups_0, pad = h_69_pad_0, pad_type = h_69_pad_type_0, strides = h_69_strides_0, weight = main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = string("h_69_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = h_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_183_perm_0 = const()[name = string("input_183_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1133_axes_0 = const()[name = string("op_1133_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19039360)))]; + tensor main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19040448)))]; + tensor input_183_cast_fp16 = transpose(perm = input_183_perm_0, x = x_97_cast_fp16)[name = string("transpose_70")]; + tensor var_1133_cast_fp16 = layer_norm(axes = var_1133_axes_0, beta = main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16, x = input_183_cast_fp16)[name = string("op_1133_cast_fp16")]; + tensor input_185_perm_0 = const()[name = string("input_185_perm_0"), val = tensor([0, 2, 1])]; + string h_71_pad_type_0 = const()[name = string("h_71_pad_type_0"), val = string("valid")]; + tensor h_71_strides_0 = const()[name = string("h_71_strides_0"), val = tensor([1])]; + tensor h_71_pad_0 = const()[name = string("h_71_pad_0"), val = tensor([0, 0])]; + tensor h_71_dilations_0 = const()[name = string("h_71_dilations_0"), val = tensor([1])]; + int32 h_71_groups_0 = const()[name = string("h_71_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19041536))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19828032))))[name = string("main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19828224)))]; + tensor input_185_cast_fp16 = transpose(perm = input_185_perm_0, x = var_1133_cast_fp16)[name = string("transpose_69")]; + tensor h_71_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_71_dilations_0, groups = h_71_groups_0, pad = h_71_pad_0, pad_type = h_71_pad_type_0, strides = h_71_strides_0, weight = main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = string("h_71_cast_fp16")]; + string input_187_mode_0 = const()[name = string("input_187_mode_0"), val = string("EXACT")]; + tensor input_187_cast_fp16 = gelu(mode = input_187_mode_0, x = h_71_cast_fp16)[name = string("input_187_cast_fp16")]; + string h_73_pad_type_0 = const()[name = string("h_73_pad_type_0"), val = string("valid")]; + tensor h_73_strides_0 = const()[name = string("h_73_strides_0"), val = tensor([1])]; + tensor h_73_pad_0 = const()[name = string("h_73_pad_0"), val = tensor([0, 0])]; + tensor h_73_dilations_0 = const()[name = string("h_73_dilations_0"), val = tensor([1])]; + int32 h_73_groups_0 = const()[name = string("h_73_groups_0"), val = int32(1)]; + tensor op_1150_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19832384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20618880))))[name = string("op_1150_weight_0_to_fp16_palettized")]; + tensor var_1150_bias_0_to_fp16 = const()[name = string("op_1150_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20619072)))]; + tensor var_1150_cast_fp16 = conv(bias = var_1150_bias_0_to_fp16, dilations = h_73_dilations_0, groups = h_73_groups_0, pad = h_73_pad_0, pad_type = h_73_pad_type_0, strides = h_73_strides_0, weight = op_1150_weight_0_to_fp16_palettized, x = input_187_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor out_29_cast_fp16 = add(x = x_95_cast_fp16, y = var_1150_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor x_99_cast_fp16 = mul(x = out_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor x_m_7_cast_fp16 = mul(x = x_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_7_cast_fp16")]; + tensor input_189_perm_0 = const()[name = string("input_189_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20620160))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20718528))))[name = string("main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20718720)))]; + tensor input_189_cast_fp16 = transpose(perm = input_189_perm_0, x = x_99_cast_fp16)[name = string("transpose_68")]; + tensor linear_16_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor concat_22x = const()[name = string("concat_22x"), val = tensor([2, -1, 2, 128])]; + tensor var_1167_cast_fp16 = reshape(shape = concat_22x, x = linear_16_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20719296))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20768512))))[name = string("main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20768704)))]; + tensor linear_18_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_1180 = const()[name = string("op_1180"), val = tensor([2, 50, 2, 128])]; + tensor var_1181_cast_fp16 = reshape(shape = var_1180, x = linear_18_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1185_transpose_x_0 = const()[name = string("op_1185_transpose_x_0"), val = bool(false)]; + bool var_1185_transpose_y_0 = const()[name = string("op_1185_transpose_y_0"), val = bool(false)]; + tensor op_1184_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20769280))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20788544))))[name = string("op_1184_to_fp16_palettized")]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1167_cast_fp16)[name = string("transpose_67")]; + tensor var_1185_cast_fp16 = matmul(transpose_x = var_1185_transpose_x_0, transpose_y = var_1185_transpose_y_0, x = q_7_cast_fp16, y = op_1184_to_fp16_palettized)[name = string("op_1185_cast_fp16")]; + fp16 _inversed_input_191_y_0_to_fp16 = const()[name = string("_inversed_input_191_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_191_cast_fp16 = mul(x = var_1185_cast_fp16, y = _inversed_input_191_y_0_to_fp16)[name = string("_inversed_input_191_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_681, x = _inversed_input_191_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor attn_11_cast_fp16 = mul(x = attn_9_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool var_1192_transpose_x_0 = const()[name = string("op_1192_transpose_x_0"), val = bool(false)]; + bool var_1192_transpose_y_0 = const()[name = string("op_1192_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1181_cast_fp16)[name = string("transpose_66")]; + tensor var_1192_cast_fp16 = matmul(transpose_x = var_1192_transpose_x_0, transpose_y = var_1192_transpose_y_0, x = attn_11_cast_fp16, y = v_7_cast_fp16)[name = string("op_1192_cast_fp16")]; + tensor var_1193_perm_0 = const()[name = string("op_1193_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_23x = const()[name = string("concat_23x"), val = tensor([2, -1, 256])]; + tensor var_1193_cast_fp16 = transpose(perm = var_1193_perm_0, x = var_1192_cast_fp16)[name = string("transpose_65")]; + tensor input_193_cast_fp16 = reshape(shape = concat_23x, x = var_1193_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20788736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20887104))))[name = string("main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_1_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20887296)))]; + tensor linear_19_cast_fp16 = linear(bias = main_blocks_1_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_193_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor out_31_perm_0 = const()[name = string("out_31_perm_0"), val = tensor([0, 2, 1])]; + tensor out_31_cast_fp16 = transpose(perm = out_31_perm_0, x = linear_19_cast_fp16)[name = string("transpose_64")]; + tensor var_1201_cast_fp16 = mul(x = out_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1201_cast_fp16")]; + tensor x_101_cast_fp16 = add(x = x_m_7_cast_fp16, y = var_1201_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_195_perm_0 = const()[name = string("input_195_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1208_axes_0 = const()[name = string("op_1208_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20888384)))]; + tensor main_blocks_1_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20889472)))]; + tensor input_195_cast_fp16 = transpose(perm = input_195_perm_0, x = x_101_cast_fp16)[name = string("transpose_63")]; + tensor var_1208_cast_fp16 = layer_norm(axes = var_1208_axes_0, beta = main_blocks_1_style_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_style_norm_norm_weight_to_fp16, x = input_195_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_perm_0 = const()[name = string("op_1209_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1209_cast_fp16 = transpose(perm = var_1209_perm_0, x = var_1208_cast_fp16)[name = string("transpose_62")]; + tensor x_103_cast_fp16 = mul(x = var_1209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_103_cast_fp16")]; + int32 var_1228 = const()[name = string("op_1228"), val = int32(-1)]; + tensor x_105_cast_fp16 = mul(x = x_103_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_1263_cast_fp16 = mul(x = x_105_cast_fp16, y = last_1_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor x_last_25_axes_0 = const()[name = string("x_last_25_axes_0"), val = tensor([-1])]; + bool x_last_25_keep_dims_0 = const()[name = string("x_last_25_keep_dims_0"), val = bool(true)]; + tensor x_last_25_cast_fp16 = reduce_sum(axes = x_last_25_axes_0, keep_dims = x_last_25_keep_dims_0, x = var_1263_cast_fp16)[name = string("x_last_25_cast_fp16")]; + tensor var_1266_cast_fp16 = mul(x = x_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1266_cast_fp16")]; + tensor var_1268_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_25_cast_fp16)[name = string("op_1268_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1266_cast_fp16, y = var_1268_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_201_mode_0 = const()[name = string("input_201_mode_0"), val = string("replicate")]; + fp16 const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = fp16(0x0p+0)]; + tensor input_201_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")]; + string h_75_pad_type_0 = const()[name = string("h_75_pad_type_0"), val = string("valid")]; + int32 h_75_groups_0 = const()[name = string("h_75_groups_0"), val = int32(512)]; + tensor h_75_strides_0 = const()[name = string("h_75_strides_0"), val = tensor([1])]; + tensor h_75_pad_0 = const()[name = string("h_75_pad_0"), val = tensor([0, 0])]; + tensor h_75_dilations_0 = const()[name = string("h_75_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20890560))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20892544))))[name = string("main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20892736)))]; + tensor h_75_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_75_dilations_0, groups = h_75_groups_0, pad = h_75_pad_0, pad_type = h_75_pad_type_0, strides = h_75_strides_0, weight = main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_201_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = h_75_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_203_perm_0 = const()[name = string("input_203_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1286_axes_0 = const()[name = string("op_1286_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20893824)))]; + tensor main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20894912)))]; + fp16 var_1233_to_fp16 = const()[name = string("op_1233_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_203_cast_fp16 = transpose(perm = input_203_perm_0, x = x_107_cast_fp16)[name = string("transpose_61")]; + tensor var_1286_cast_fp16 = layer_norm(axes = var_1286_axes_0, beta = main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16, x = input_203_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string h_77_pad_type_0 = const()[name = string("h_77_pad_type_0"), val = string("valid")]; + tensor h_77_strides_0 = const()[name = string("h_77_strides_0"), val = tensor([1])]; + tensor h_77_pad_0 = const()[name = string("h_77_pad_0"), val = tensor([0, 0])]; + tensor h_77_dilations_0 = const()[name = string("h_77_dilations_0"), val = tensor([1])]; + int32 h_77_groups_0 = const()[name = string("h_77_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20896000))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21682496))))[name = string("main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21682688)))]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = var_1286_cast_fp16)[name = string("transpose_60")]; + tensor h_77_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_77_dilations_0, groups = h_77_groups_0, pad = h_77_pad_0, pad_type = h_77_pad_type_0, strides = h_77_strides_0, weight = main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_205_cast_fp16)[name = string("h_77_cast_fp16")]; + string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")]; + tensor input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = h_77_cast_fp16)[name = string("input_207_cast_fp16")]; + string h_79_pad_type_0 = const()[name = string("h_79_pad_type_0"), val = string("valid")]; + tensor h_79_strides_0 = const()[name = string("h_79_strides_0"), val = tensor([1])]; + tensor h_79_pad_0 = const()[name = string("h_79_pad_0"), val = tensor([0, 0])]; + tensor h_79_dilations_0 = const()[name = string("h_79_dilations_0"), val = tensor([1])]; + int32 h_79_groups_0 = const()[name = string("h_79_groups_0"), val = int32(1)]; + tensor op_1303_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21686848))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22473344))))[name = string("op_1303_weight_0_to_fp16_palettized")]; + tensor var_1303_bias_0_to_fp16 = const()[name = string("op_1303_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22473536)))]; + tensor var_1303_cast_fp16 = conv(bias = var_1303_bias_0_to_fp16, dilations = h_79_dilations_0, groups = h_79_groups_0, pad = h_79_pad_0, pad_type = h_79_pad_type_0, strides = h_79_strides_0, weight = op_1303_weight_0_to_fp16_palettized, x = input_207_cast_fp16)[name = string("op_1303_cast_fp16")]; + tensor out_33_cast_fp16 = add(x = x_105_cast_fp16, y = var_1303_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor x_109_cast_fp16 = mul(x = out_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor x_111_cast_fp16 = mul(x = x_109_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_1317_cast_fp16 = mul(x = x_111_cast_fp16, y = last_1_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor x_last_27_axes_0 = const()[name = string("x_last_27_axes_0"), val = tensor([-1])]; + bool x_last_27_keep_dims_0 = const()[name = string("x_last_27_keep_dims_0"), val = bool(true)]; + tensor x_last_27_cast_fp16 = reduce_sum(axes = x_last_27_axes_0, keep_dims = x_last_27_keep_dims_0, x = var_1317_cast_fp16)[name = string("x_last_27_cast_fp16")]; + tensor var_1320_cast_fp16 = mul(x = x_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1322_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_27_cast_fp16)[name = string("op_1322_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1320_cast_fp16, y = var_1322_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("replicate")]; + fp16 const_29_to_fp16 = const()[name = string("const_29_to_fp16"), val = fp16(0x0p+0)]; + tensor input_213_cast_fp16 = pad(constant_val = const_29_to_fp16, mode = input_213_mode_0, pad = input_213_pad_0, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + string h_81_pad_type_0 = const()[name = string("h_81_pad_type_0"), val = string("valid")]; + tensor h_81_dilations_0 = const()[name = string("h_81_dilations_0"), val = tensor([2])]; + int32 h_81_groups_0 = const()[name = string("h_81_groups_0"), val = int32(512)]; + tensor h_81_strides_0 = const()[name = string("h_81_strides_0"), val = tensor([1])]; + tensor h_81_pad_0 = const()[name = string("h_81_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22474624))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22476608))))[name = string("main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22476800)))]; + tensor h_81_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_81_dilations_0, groups = h_81_groups_0, pad = h_81_pad_0, pad_type = h_81_pad_type_0, strides = h_81_strides_0, weight = main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_213_cast_fp16)[name = string("h_81_cast_fp16")]; + tensor x_113_cast_fp16 = mul(x = h_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor input_215_perm_0 = const()[name = string("input_215_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1340_axes_0 = const()[name = string("op_1340_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22477888)))]; + tensor main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22478976)))]; + tensor input_215_cast_fp16 = transpose(perm = input_215_perm_0, x = x_113_cast_fp16)[name = string("transpose_59")]; + tensor var_1340_cast_fp16 = layer_norm(axes = var_1340_axes_0, beta = main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16, x = input_215_cast_fp16)[name = string("op_1340_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + string h_83_pad_type_0 = const()[name = string("h_83_pad_type_0"), val = string("valid")]; + tensor h_83_strides_0 = const()[name = string("h_83_strides_0"), val = tensor([1])]; + tensor h_83_pad_0 = const()[name = string("h_83_pad_0"), val = tensor([0, 0])]; + tensor h_83_dilations_0 = const()[name = string("h_83_dilations_0"), val = tensor([1])]; + int32 h_83_groups_0 = const()[name = string("h_83_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22480064))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23266560))))[name = string("main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23266752)))]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = var_1340_cast_fp16)[name = string("transpose_58")]; + tensor h_83_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_83_dilations_0, groups = h_83_groups_0, pad = h_83_pad_0, pad_type = h_83_pad_type_0, strides = h_83_strides_0, weight = main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = string("h_83_cast_fp16")]; + string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")]; + tensor input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = h_83_cast_fp16)[name = string("input_219_cast_fp16")]; + string h_85_pad_type_0 = const()[name = string("h_85_pad_type_0"), val = string("valid")]; + tensor h_85_strides_0 = const()[name = string("h_85_strides_0"), val = tensor([1])]; + tensor h_85_pad_0 = const()[name = string("h_85_pad_0"), val = tensor([0, 0])]; + tensor h_85_dilations_0 = const()[name = string("h_85_dilations_0"), val = tensor([1])]; + int32 h_85_groups_0 = const()[name = string("h_85_groups_0"), val = int32(1)]; + tensor op_1357_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23270912))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24057408))))[name = string("op_1357_weight_0_to_fp16_palettized")]; + tensor var_1357_bias_0_to_fp16 = const()[name = string("op_1357_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24057600)))]; + tensor var_1357_cast_fp16 = conv(bias = var_1357_bias_0_to_fp16, dilations = h_85_dilations_0, groups = h_85_groups_0, pad = h_85_pad_0, pad_type = h_85_pad_type_0, strides = h_85_strides_0, weight = op_1357_weight_0_to_fp16_palettized, x = input_219_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor out_35_cast_fp16 = add(x = x_111_cast_fp16, y = var_1357_cast_fp16)[name = string("out_35_cast_fp16")]; + tensor x_115_cast_fp16 = mul(x = out_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_cast_fp16 = mul(x = x_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1371_cast_fp16 = mul(x = x_117_cast_fp16, y = last_1_cast_fp16)[name = string("op_1371_cast_fp16")]; + tensor x_last_29_axes_0 = const()[name = string("x_last_29_axes_0"), val = tensor([-1])]; + bool x_last_29_keep_dims_0 = const()[name = string("x_last_29_keep_dims_0"), val = bool(true)]; + tensor x_last_29_cast_fp16 = reduce_sum(axes = x_last_29_axes_0, keep_dims = x_last_29_keep_dims_0, x = var_1371_cast_fp16)[name = string("x_last_29_cast_fp16")]; + tensor var_1374_cast_fp16 = mul(x = x_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_29_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1376_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_225_mode_0 = const()[name = string("input_225_mode_0"), val = string("replicate")]; + fp16 const_31_to_fp16 = const()[name = string("const_31_to_fp16"), val = fp16(0x0p+0)]; + tensor input_225_cast_fp16 = pad(constant_val = const_31_to_fp16, mode = input_225_mode_0, pad = input_225_pad_0, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")]; + string h_87_pad_type_0 = const()[name = string("h_87_pad_type_0"), val = string("valid")]; + tensor h_87_dilations_0 = const()[name = string("h_87_dilations_0"), val = tensor([4])]; + int32 h_87_groups_0 = const()[name = string("h_87_groups_0"), val = int32(512)]; + tensor h_87_strides_0 = const()[name = string("h_87_strides_0"), val = tensor([1])]; + tensor h_87_pad_0 = const()[name = string("h_87_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24058688))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24060672))))[name = string("main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24060864)))]; + tensor h_87_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_87_dilations_0, groups = h_87_groups_0, pad = h_87_pad_0, pad_type = h_87_pad_type_0, strides = h_87_strides_0, weight = main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_225_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor x_119_cast_fp16 = mul(x = h_87_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_227_perm_0 = const()[name = string("input_227_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1394_axes_0 = const()[name = string("op_1394_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24061952)))]; + tensor main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24063040)))]; + tensor input_227_cast_fp16 = transpose(perm = input_227_perm_0, x = x_119_cast_fp16)[name = string("transpose_57")]; + tensor var_1394_cast_fp16 = layer_norm(axes = var_1394_axes_0, beta = main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16, x = input_227_cast_fp16)[name = string("op_1394_cast_fp16")]; + tensor input_229_perm_0 = const()[name = string("input_229_perm_0"), val = tensor([0, 2, 1])]; + string h_89_pad_type_0 = const()[name = string("h_89_pad_type_0"), val = string("valid")]; + tensor h_89_strides_0 = const()[name = string("h_89_strides_0"), val = tensor([1])]; + tensor h_89_pad_0 = const()[name = string("h_89_pad_0"), val = tensor([0, 0])]; + tensor h_89_dilations_0 = const()[name = string("h_89_dilations_0"), val = tensor([1])]; + int32 h_89_groups_0 = const()[name = string("h_89_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24064128))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24850624))))[name = string("main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24850816)))]; + tensor input_229_cast_fp16 = transpose(perm = input_229_perm_0, x = var_1394_cast_fp16)[name = string("transpose_56")]; + tensor h_89_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_89_dilations_0, groups = h_89_groups_0, pad = h_89_pad_0, pad_type = h_89_pad_type_0, strides = h_89_strides_0, weight = main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_229_cast_fp16)[name = string("h_89_cast_fp16")]; + string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")]; + tensor input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = h_89_cast_fp16)[name = string("input_231_cast_fp16")]; + string h_91_pad_type_0 = const()[name = string("h_91_pad_type_0"), val = string("valid")]; + tensor h_91_strides_0 = const()[name = string("h_91_strides_0"), val = tensor([1])]; + tensor h_91_pad_0 = const()[name = string("h_91_pad_0"), val = tensor([0, 0])]; + tensor h_91_dilations_0 = const()[name = string("h_91_dilations_0"), val = tensor([1])]; + int32 h_91_groups_0 = const()[name = string("h_91_groups_0"), val = int32(1)]; + tensor op_1411_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24854976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25641472))))[name = string("op_1411_weight_0_to_fp16_palettized")]; + tensor var_1411_bias_0_to_fp16 = const()[name = string("op_1411_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25641664)))]; + tensor var_1411_cast_fp16 = conv(bias = var_1411_bias_0_to_fp16, dilations = h_91_dilations_0, groups = h_91_groups_0, pad = h_91_pad_0, pad_type = h_91_pad_type_0, strides = h_91_strides_0, weight = op_1411_weight_0_to_fp16_palettized, x = input_231_cast_fp16)[name = string("op_1411_cast_fp16")]; + tensor out_37_cast_fp16 = add(x = x_117_cast_fp16, y = var_1411_cast_fp16)[name = string("out_37_cast_fp16")]; + tensor x_121_cast_fp16 = mul(x = out_37_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_121_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1425_cast_fp16 = mul(x = x_123_cast_fp16, y = last_1_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor x_last_31_axes_0 = const()[name = string("x_last_31_axes_0"), val = tensor([-1])]; + bool x_last_31_keep_dims_0 = const()[name = string("x_last_31_keep_dims_0"), val = bool(true)]; + tensor x_last_31_cast_fp16 = reduce_sum(axes = x_last_31_axes_0, keep_dims = x_last_31_keep_dims_0, x = var_1425_cast_fp16)[name = string("x_last_31_cast_fp16")]; + tensor var_1428_cast_fp16 = mul(x = x_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_31_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = var_1428_cast_fp16, y = var_1430_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("replicate")]; + fp16 const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = fp16(0x0p+0)]; + tensor input_237_cast_fp16 = pad(constant_val = const_33_to_fp16, mode = input_237_mode_0, pad = input_237_pad_0, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + string h_93_pad_type_0 = const()[name = string("h_93_pad_type_0"), val = string("valid")]; + tensor h_93_dilations_0 = const()[name = string("h_93_dilations_0"), val = tensor([8])]; + int32 h_93_groups_0 = const()[name = string("h_93_groups_0"), val = int32(512)]; + tensor h_93_strides_0 = const()[name = string("h_93_strides_0"), val = tensor([1])]; + tensor h_93_pad_0 = const()[name = string("h_93_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25642752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25644736))))[name = string("main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25644928)))]; + tensor h_93_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_93_dilations_0, groups = h_93_groups_0, pad = h_93_pad_0, pad_type = h_93_pad_type_0, strides = h_93_strides_0, weight = main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = string("h_93_cast_fp16")]; + tensor x_125_cast_fp16 = mul(x = h_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_239_perm_0 = const()[name = string("input_239_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1448_axes_0 = const()[name = string("op_1448_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25646016)))]; + tensor main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25647104)))]; + tensor input_239_cast_fp16 = transpose(perm = input_239_perm_0, x = x_125_cast_fp16)[name = string("transpose_55")]; + tensor var_1448_cast_fp16 = layer_norm(axes = var_1448_axes_0, beta = main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16, x = input_239_cast_fp16)[name = string("op_1448_cast_fp16")]; + tensor input_241_perm_0 = const()[name = string("input_241_perm_0"), val = tensor([0, 2, 1])]; + string h_95_pad_type_0 = const()[name = string("h_95_pad_type_0"), val = string("valid")]; + tensor h_95_strides_0 = const()[name = string("h_95_strides_0"), val = tensor([1])]; + tensor h_95_pad_0 = const()[name = string("h_95_pad_0"), val = tensor([0, 0])]; + tensor h_95_dilations_0 = const()[name = string("h_95_dilations_0"), val = tensor([1])]; + int32 h_95_groups_0 = const()[name = string("h_95_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25648192))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26434688))))[name = string("main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26434880)))]; + tensor input_241_cast_fp16 = transpose(perm = input_241_perm_0, x = var_1448_cast_fp16)[name = string("transpose_54")]; + tensor h_95_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_95_dilations_0, groups = h_95_groups_0, pad = h_95_pad_0, pad_type = h_95_pad_type_0, strides = h_95_strides_0, weight = main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_241_cast_fp16)[name = string("h_95_cast_fp16")]; + string input_243_mode_0 = const()[name = string("input_243_mode_0"), val = string("EXACT")]; + tensor input_243_cast_fp16 = gelu(mode = input_243_mode_0, x = h_95_cast_fp16)[name = string("input_243_cast_fp16")]; + string h_97_pad_type_0 = const()[name = string("h_97_pad_type_0"), val = string("valid")]; + tensor h_97_strides_0 = const()[name = string("h_97_strides_0"), val = tensor([1])]; + tensor h_97_pad_0 = const()[name = string("h_97_pad_0"), val = tensor([0, 0])]; + tensor h_97_dilations_0 = const()[name = string("h_97_dilations_0"), val = tensor([1])]; + int32 h_97_groups_0 = const()[name = string("h_97_groups_0"), val = int32(1)]; + tensor op_1465_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26439040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27225536))))[name = string("op_1465_weight_0_to_fp16_palettized")]; + tensor var_1465_bias_0_to_fp16 = const()[name = string("op_1465_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27225728)))]; + tensor var_1465_cast_fp16 = conv(bias = var_1465_bias_0_to_fp16, dilations = h_97_dilations_0, groups = h_97_groups_0, pad = h_97_pad_0, pad_type = h_97_pad_type_0, strides = h_97_strides_0, weight = op_1465_weight_0_to_fp16_palettized, x = input_243_cast_fp16)[name = string("op_1465_cast_fp16")]; + tensor out_39_cast_fp16 = add(x = x_123_cast_fp16, y = var_1465_cast_fp16)[name = string("out_39_cast_fp16")]; + tensor x_127_cast_fp16 = mul(x = out_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27226816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27251456))))[name = string("main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27251648)))]; + tensor linear_20_cast_fp16 = linear(bias = main_blocks_2_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_2_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor t_9_axes_0 = const()[name = string("t_9_axes_0"), val = tensor([-1])]; + tensor t_9_cast_fp16 = expand_dims(axes = t_9_axes_0, x = linear_20_cast_fp16)[name = string("t_9_cast_fp16")]; + tensor var_1475_cast_fp16 = add(x = x_127_cast_fp16, y = t_9_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = var_1475_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor x_131_cast_fp16 = mul(x = x_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor var_1488_cast_fp16 = mul(x = x_131_cast_fp16, y = last_1_cast_fp16)[name = string("op_1488_cast_fp16")]; + tensor x_last_33_axes_0 = const()[name = string("x_last_33_axes_0"), val = tensor([-1])]; + bool x_last_33_keep_dims_0 = const()[name = string("x_last_33_keep_dims_0"), val = bool(true)]; + tensor x_last_33_cast_fp16 = reduce_sum(axes = x_last_33_axes_0, keep_dims = x_last_33_keep_dims_0, x = var_1488_cast_fp16)[name = string("x_last_33_cast_fp16")]; + tensor var_1491_cast_fp16 = mul(x = x_131_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1491_cast_fp16")]; + tensor var_1493_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_33_cast_fp16)[name = string("op_1493_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = var_1491_cast_fp16, y = var_1493_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_251_mode_0 = const()[name = string("input_251_mode_0"), val = string("replicate")]; + fp16 const_35_to_fp16 = const()[name = string("const_35_to_fp16"), val = fp16(0x0p+0)]; + tensor input_251_cast_fp16 = pad(constant_val = const_35_to_fp16, mode = input_251_mode_0, pad = input_251_pad_0, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + string h_99_pad_type_0 = const()[name = string("h_99_pad_type_0"), val = string("valid")]; + int32 h_99_groups_0 = const()[name = string("h_99_groups_0"), val = int32(512)]; + tensor h_99_strides_0 = const()[name = string("h_99_strides_0"), val = tensor([1])]; + tensor h_99_pad_0 = const()[name = string("h_99_pad_0"), val = tensor([0, 0])]; + tensor h_99_dilations_0 = const()[name = string("h_99_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27252736))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27254720))))[name = string("main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27254912)))]; + tensor h_99_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_99_dilations_0, groups = h_99_groups_0, pad = h_99_pad_0, pad_type = h_99_pad_type_0, strides = h_99_strides_0, weight = main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor x_133_cast_fp16 = mul(x = h_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_253_perm_0 = const()[name = string("input_253_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1511_axes_0 = const()[name = string("op_1511_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27256000)))]; + tensor main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27257088)))]; + tensor input_253_cast_fp16 = transpose(perm = input_253_perm_0, x = x_133_cast_fp16)[name = string("transpose_53")]; + tensor var_1511_cast_fp16 = layer_norm(axes = var_1511_axes_0, beta = main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16, x = input_253_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor input_255_perm_0 = const()[name = string("input_255_perm_0"), val = tensor([0, 2, 1])]; + string h_101_pad_type_0 = const()[name = string("h_101_pad_type_0"), val = string("valid")]; + tensor h_101_strides_0 = const()[name = string("h_101_strides_0"), val = tensor([1])]; + tensor h_101_pad_0 = const()[name = string("h_101_pad_0"), val = tensor([0, 0])]; + tensor h_101_dilations_0 = const()[name = string("h_101_dilations_0"), val = tensor([1])]; + int32 h_101_groups_0 = const()[name = string("h_101_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27258176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28044672))))[name = string("main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28044864)))]; + tensor input_255_cast_fp16 = transpose(perm = input_255_perm_0, x = var_1511_cast_fp16)[name = string("transpose_52")]; + tensor h_101_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_101_dilations_0, groups = h_101_groups_0, pad = h_101_pad_0, pad_type = h_101_pad_type_0, strides = h_101_strides_0, weight = main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_255_cast_fp16)[name = string("h_101_cast_fp16")]; + string input_257_mode_0 = const()[name = string("input_257_mode_0"), val = string("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = h_101_cast_fp16)[name = string("input_257_cast_fp16")]; + string h_103_pad_type_0 = const()[name = string("h_103_pad_type_0"), val = string("valid")]; + tensor h_103_strides_0 = const()[name = string("h_103_strides_0"), val = tensor([1])]; + tensor h_103_pad_0 = const()[name = string("h_103_pad_0"), val = tensor([0, 0])]; + tensor h_103_dilations_0 = const()[name = string("h_103_dilations_0"), val = tensor([1])]; + int32 h_103_groups_0 = const()[name = string("h_103_groups_0"), val = int32(1)]; + tensor op_1528_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28049024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28835520))))[name = string("op_1528_weight_0_to_fp16_palettized")]; + tensor var_1528_bias_0_to_fp16 = const()[name = string("op_1528_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28835712)))]; + tensor var_1528_cast_fp16 = conv(bias = var_1528_bias_0_to_fp16, dilations = h_103_dilations_0, groups = h_103_groups_0, pad = h_103_pad_0, pad_type = h_103_pad_type_0, strides = h_103_strides_0, weight = op_1528_weight_0_to_fp16_palettized, x = input_257_cast_fp16)[name = string("op_1528_cast_fp16")]; + tensor out_41_cast_fp16 = add(x = x_131_cast_fp16, y = var_1528_cast_fp16)[name = string("out_41_cast_fp16")]; + tensor x_135_cast_fp16 = mul(x = out_41_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor x_m_9_cast_fp16 = mul(x = x_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_9_cast_fp16")]; + tensor var_1538_shape_cast_fp16 = shape(x = x_135_cast_fp16)[name = string("op_1538_shape_cast_fp16")]; + int32 gather_14_axis_0 = const()[name = string("gather_14_axis_0"), val = int32(0)]; + int32 gather_14_batch_dims_0 = const()[name = string("gather_14_batch_dims_0"), val = int32(0)]; + bool gather_14_validate_indices_0 = const()[name = string("gather_14_validate_indices_0"), val = bool(false)]; + string var_1538_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_1538_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_14_indices_0_to_uint16 = const()[name = string("gather_14_indices_0_to_uint16"), val = uint16(2)]; + tensor var_1538_shape_cast_fp16_to_uint16 = cast(dtype = var_1538_shape_cast_fp16_to_uint16_dtype_0, x = var_1538_shape_cast_fp16)[name = string("cast_4")]; + uint16 gather_14_cast_uint16 = gather(axis = gather_14_axis_0, batch_dims = gather_14_batch_dims_0, indices = gather_14_indices_0_to_uint16, validate_indices = gather_14_validate_indices_0, x = var_1538_shape_cast_fp16_to_uint16)[name = string("gather_14_cast_uint16")]; + string gather_14_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_14_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_259_perm_0 = const()[name = string("input_259_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28836800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29033472))))[name = string("main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29033664)))]; + tensor input_259_cast_fp16 = transpose(perm = input_259_perm_0, x = x_135_cast_fp16)[name = string("transpose_51")]; + tensor linear_21_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_259_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor concat_24x = const()[name = string("concat_24x"), val = tensor([2, -1, 8, 64])]; + tensor var_1547_cast_fp16 = reshape(shape = concat_24x, x = linear_21_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor x_137_perm_0 = const()[name = string("x_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29034752))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29133120))))[name = string("main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29133312)))]; + tensor linear_22_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor concat_25x = const()[name = string("concat_25x"), val = tensor([2, -1, 8, 64])]; + tensor var_1555_cast_fp16 = reshape(shape = concat_25x, x = linear_22_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_139_perm_0 = const()[name = string("x_139_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29134400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29232768))))[name = string("main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29232960)))]; + tensor linear_23_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor concat_26x = const()[name = string("concat_26x"), val = tensor([2, -1, 8, 64])]; + tensor var_1563_cast_fp16 = reshape(shape = concat_26x, x = linear_23_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + int32 concat_27_values0_0 = const()[name = string("concat_27_values0_0"), val = int32(1)]; + int32 concat_27_values2_0 = const()[name = string("concat_27_values2_0"), val = int32(1)]; + int32 concat_27_axis_0 = const()[name = string("concat_27_axis_0"), val = int32(0)]; + bool concat_27_interleave_0 = const()[name = string("concat_27_interleave_0"), val = bool(false)]; + int32 gather_14_cast_uint16_to_int32 = cast(dtype = gather_14_cast_uint16_to_int32_dtype_0, x = gather_14_cast_uint16)[name = string("cast_3")]; + tensor concat_27 = concat(axis = concat_27_axis_0, interleave = concat_27_interleave_0, values = (concat_27_values0_0, gather_14_cast_uint16_to_int32, concat_27_values2_0))[name = string("concat_27")]; + tensor var_1566_begin_0 = const()[name = string("op_1566_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1566_end_mask_0 = const()[name = string("op_1566_end_mask_0"), val = tensor([true, false, true])]; + tensor var_1566_cast_fp16 = slice_by_index(begin = var_1566_begin_0, end = concat_27, end_mask = var_1566_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_1566_cast_fp16")]; + tensor concat_28 = const()[name = string("concat_28"), val = tensor([2, -1, -1])]; + tensor shape_5_cast_fp16 = shape(x = var_1566_cast_fp16)[name = string("shape_5_cast_fp16")]; + tensor equal_5 = const()[name = string("equal_5"), val = tensor([false, true, true])]; + tensor select_5 = select(a = shape_5_cast_fp16, b = concat_28, cond = equal_5)[name = string("select_5")]; + tensor real_div_5 = real_div(x = select_5, y = shape_5_cast_fp16)[name = string("real_div_5")]; + tensor var_1569_cast_fp16 = tile(reps = real_div_5, x = var_1566_cast_fp16)[name = string("op_1569_cast_fp16")]; + tensor scaled_9_cast_fp16 = real_div(x = var_1569_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_9_cast_fp16")]; + tensor main_blocks_2_text_attn_theta_to_fp16 = const()[name = string("main_blocks_2_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29234048)))]; + tensor angles_9_cast_fp16 = mul(x = scaled_9_cast_fp16, y = main_blocks_2_text_attn_theta_to_fp16)[name = string("angles_9_cast_fp16")]; + tensor var_1585_cast_fp16 = cos(x = angles_9_cast_fp16)[name = string("op_1585_cast_fp16")]; + tensor cos_9_axes_0 = const()[name = string("cos_9_axes_0"), val = tensor([1])]; + tensor cos_9_cast_fp16 = expand_dims(axes = cos_9_axes_0, x = var_1585_cast_fp16)[name = string("cos_9_cast_fp16")]; + tensor var_1587_cast_fp16 = sin(x = angles_9_cast_fp16)[name = string("op_1587_cast_fp16")]; + tensor sin_9_axes_0 = const()[name = string("sin_9_axes_0"), val = tensor([1])]; + tensor sin_9_cast_fp16 = expand_dims(axes = sin_9_axes_0, x = var_1587_cast_fp16)[name = string("sin_9_cast_fp16")]; + tensor x_a_9_begin_0 = const()[name = string("x_a_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_9_end_0 = const()[name = string("x_a_9_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_9_end_mask_0 = const()[name = string("x_a_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_137_cast_fp16 = transpose(perm = x_137_perm_0, x = var_1547_cast_fp16)[name = string("transpose_50")]; + tensor x_a_9_cast_fp16 = slice_by_index(begin = x_a_9_begin_0, end = x_a_9_end_0, end_mask = x_a_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_a_9_cast_fp16")]; + tensor x_b_9_begin_0 = const()[name = string("x_b_9_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_9_end_0 = const()[name = string("x_b_9_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_9_end_mask_0 = const()[name = string("x_b_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_9_cast_fp16 = slice_by_index(begin = x_b_9_begin_0, end = x_b_9_end_0, end_mask = x_b_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_b_9_cast_fp16")]; + tensor var_1591_cast_fp16 = mul(x = x_a_9_cast_fp16, y = cos_9_cast_fp16)[name = string("op_1591_cast_fp16")]; + tensor var_1592_cast_fp16 = mul(x = x_b_9_cast_fp16, y = sin_9_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor rot_a_9_cast_fp16 = sub(x = var_1591_cast_fp16, y = var_1592_cast_fp16)[name = string("rot_a_9_cast_fp16")]; + tensor var_1594_cast_fp16 = mul(x = x_a_9_cast_fp16, y = sin_9_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595_cast_fp16 = mul(x = x_b_9_cast_fp16, y = cos_9_cast_fp16)[name = string("op_1595_cast_fp16")]; + tensor rot_b_9_cast_fp16 = add(x = var_1594_cast_fp16, y = var_1595_cast_fp16)[name = string("rot_b_9_cast_fp16")]; + bool q_9_interleave_0 = const()[name = string("q_9_interleave_0"), val = bool(false)]; + tensor q_9_cast_fp16 = concat(axis = var_1228, interleave = q_9_interleave_0, values = (rot_a_9_cast_fp16, rot_b_9_cast_fp16))[name = string("q_9_cast_fp16")]; + tensor x_a_11_begin_0 = const()[name = string("x_a_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_11_end_0 = const()[name = string("x_a_11_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_11_end_mask_0 = const()[name = string("x_a_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_139_cast_fp16 = transpose(perm = x_139_perm_0, x = var_1555_cast_fp16)[name = string("transpose_49")]; + tensor x_a_11_cast_fp16 = slice_by_index(begin = x_a_11_begin_0, end = x_a_11_end_0, end_mask = x_a_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_a_11_cast_fp16")]; + tensor x_b_11_begin_0 = const()[name = string("x_b_11_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_11_end_0 = const()[name = string("x_b_11_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_11_end_mask_0 = const()[name = string("x_b_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_11_cast_fp16 = slice_by_index(begin = x_b_11_begin_0, end = x_b_11_end_0, end_mask = x_b_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_b_11_cast_fp16")]; + tensor var_1609_cast_fp16 = mul(x = x_a_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor var_1610_cast_fp16 = mul(x = x_b_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor rot_a_11_cast_fp16 = sub(x = var_1609_cast_fp16, y = var_1610_cast_fp16)[name = string("rot_a_11_cast_fp16")]; + tensor var_1612_cast_fp16 = mul(x = x_a_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1612_cast_fp16")]; + tensor var_1613_cast_fp16 = mul(x = x_b_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1613_cast_fp16")]; + tensor rot_b_11_cast_fp16 = add(x = var_1612_cast_fp16, y = var_1613_cast_fp16)[name = string("rot_b_11_cast_fp16")]; + bool k_9_interleave_0 = const()[name = string("k_9_interleave_0"), val = bool(false)]; + tensor k_9_cast_fp16 = concat(axis = var_1228, interleave = k_9_interleave_0, values = (rot_a_11_cast_fp16, rot_b_11_cast_fp16))[name = string("k_9_cast_fp16")]; + bool var_1618_transpose_x_1 = const()[name = string("op_1618_transpose_x_1"), val = bool(false)]; + bool var_1618_transpose_y_1 = const()[name = string("op_1618_transpose_y_1"), val = bool(true)]; + tensor var_1618_cast_fp16 = matmul(transpose_x = var_1618_transpose_x_1, transpose_y = var_1618_transpose_y_1, x = q_9_cast_fp16, y = k_9_cast_fp16)[name = string("op_1618_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_1618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor input_265_cast_fp16 = sub(x = _inversed_scores_5_cast_fp16, y = var_530_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1228, x = input_265_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool var_1627_transpose_x_0 = const()[name = string("op_1627_transpose_x_0"), val = bool(false)]; + bool var_1627_transpose_y_0 = const()[name = string("op_1627_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1563_cast_fp16)[name = string("transpose_48")]; + tensor var_1627_cast_fp16 = matmul(transpose_x = var_1627_transpose_x_0, transpose_y = var_1627_transpose_y_0, x = attn_13_cast_fp16, y = v_9_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor var_1628_perm_0 = const()[name = string("op_1628_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_31x = const()[name = string("concat_31x"), val = tensor([2, -1, 512])]; + tensor var_1628_cast_fp16 = transpose(perm = var_1628_perm_0, x = var_1627_cast_fp16)[name = string("transpose_47")]; + tensor input_267_cast_fp16 = reshape(shape = concat_31x, x = var_1628_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29234176))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29430848))))[name = string("main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29431040)))]; + tensor linear_24_cast_fp16 = linear(bias = main_blocks_2_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor out_43_perm_0 = const()[name = string("out_43_perm_0"), val = tensor([0, 2, 1])]; + tensor out_43_cast_fp16 = transpose(perm = out_43_perm_0, x = linear_24_cast_fp16)[name = string("transpose_46")]; + tensor var_1636_cast_fp16 = mul(x = out_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1636_cast_fp16")]; + tensor x_141_cast_fp16 = add(x = x_m_9_cast_fp16, y = var_1636_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1643_axes_0 = const()[name = string("op_1643_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29432128)))]; + tensor main_blocks_2_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29433216)))]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_141_cast_fp16)[name = string("transpose_45")]; + tensor var_1643_cast_fp16 = layer_norm(axes = var_1643_axes_0, beta = main_blocks_2_text_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_text_norm_norm_weight_to_fp16, x = input_269_cast_fp16)[name = string("op_1643_cast_fp16")]; + tensor var_1644_perm_0 = const()[name = string("op_1644_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1644_cast_fp16 = transpose(perm = var_1644_perm_0, x = var_1643_cast_fp16)[name = string("transpose_44")]; + tensor x_143_cast_fp16 = mul(x = var_1644_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor x_145_cast_fp16 = mul(x = x_143_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1657_cast_fp16 = mul(x = x_145_cast_fp16, y = last_1_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor x_last_35_axes_0 = const()[name = string("x_last_35_axes_0"), val = tensor([-1])]; + bool x_last_35_keep_dims_0 = const()[name = string("x_last_35_keep_dims_0"), val = bool(true)]; + tensor x_last_35_cast_fp16 = reduce_sum(axes = x_last_35_axes_0, keep_dims = x_last_35_keep_dims_0, x = var_1657_cast_fp16)[name = string("x_last_35_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = x_145_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1662_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_35_cast_fp16)[name = string("op_1662_cast_fp16")]; + tensor input_273_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1662_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("replicate")]; + fp16 const_37_to_fp16 = const()[name = string("const_37_to_fp16"), val = fp16(0x0p+0)]; + tensor input_275_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_275_mode_0, pad = input_275_pad_0, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")]; + string h_105_pad_type_0 = const()[name = string("h_105_pad_type_0"), val = string("valid")]; + int32 h_105_groups_0 = const()[name = string("h_105_groups_0"), val = int32(512)]; + tensor h_105_strides_0 = const()[name = string("h_105_strides_0"), val = tensor([1])]; + tensor h_105_pad_0 = const()[name = string("h_105_pad_0"), val = tensor([0, 0])]; + tensor h_105_dilations_0 = const()[name = string("h_105_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29434304))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29436288))))[name = string("main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29436480)))]; + tensor h_105_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_105_dilations_0, groups = h_105_groups_0, pad = h_105_pad_0, pad_type = h_105_pad_type_0, strides = h_105_strides_0, weight = main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_275_cast_fp16)[name = string("h_105_cast_fp16")]; + tensor x_147_cast_fp16 = mul(x = h_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_277_perm_0 = const()[name = string("input_277_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1680_axes_0 = const()[name = string("op_1680_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29437568)))]; + tensor main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29438656)))]; + tensor input_277_cast_fp16 = transpose(perm = input_277_perm_0, x = x_147_cast_fp16)[name = string("transpose_43")]; + tensor var_1680_cast_fp16 = layer_norm(axes = var_1680_axes_0, beta = main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16, x = input_277_cast_fp16)[name = string("op_1680_cast_fp16")]; + tensor input_279_perm_0 = const()[name = string("input_279_perm_0"), val = tensor([0, 2, 1])]; + string h_107_pad_type_0 = const()[name = string("h_107_pad_type_0"), val = string("valid")]; + tensor h_107_strides_0 = const()[name = string("h_107_strides_0"), val = tensor([1])]; + tensor h_107_pad_0 = const()[name = string("h_107_pad_0"), val = tensor([0, 0])]; + tensor h_107_dilations_0 = const()[name = string("h_107_dilations_0"), val = tensor([1])]; + int32 h_107_groups_0 = const()[name = string("h_107_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29439744))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30226240))))[name = string("main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30226432)))]; + tensor input_279_cast_fp16 = transpose(perm = input_279_perm_0, x = var_1680_cast_fp16)[name = string("transpose_42")]; + tensor h_107_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_107_dilations_0, groups = h_107_groups_0, pad = h_107_pad_0, pad_type = h_107_pad_type_0, strides = h_107_strides_0, weight = main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_279_cast_fp16)[name = string("h_107_cast_fp16")]; + string input_281_mode_0 = const()[name = string("input_281_mode_0"), val = string("EXACT")]; + tensor input_281_cast_fp16 = gelu(mode = input_281_mode_0, x = h_107_cast_fp16)[name = string("input_281_cast_fp16")]; + string h_109_pad_type_0 = const()[name = string("h_109_pad_type_0"), val = string("valid")]; + tensor h_109_strides_0 = const()[name = string("h_109_strides_0"), val = tensor([1])]; + tensor h_109_pad_0 = const()[name = string("h_109_pad_0"), val = tensor([0, 0])]; + tensor h_109_dilations_0 = const()[name = string("h_109_dilations_0"), val = tensor([1])]; + int32 h_109_groups_0 = const()[name = string("h_109_groups_0"), val = int32(1)]; + tensor op_1697_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30230592))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31017088))))[name = string("op_1697_weight_0_to_fp16_palettized")]; + tensor var_1697_bias_0_to_fp16 = const()[name = string("op_1697_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31017280)))]; + tensor var_1697_cast_fp16 = conv(bias = var_1697_bias_0_to_fp16, dilations = h_109_dilations_0, groups = h_109_groups_0, pad = h_109_pad_0, pad_type = h_109_pad_type_0, strides = h_109_strides_0, weight = op_1697_weight_0_to_fp16_palettized, x = input_281_cast_fp16)[name = string("op_1697_cast_fp16")]; + tensor out_45_cast_fp16 = add(x = x_145_cast_fp16, y = var_1697_cast_fp16)[name = string("out_45_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = out_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor x_m_11_cast_fp16 = mul(x = x_149_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_11_cast_fp16")]; + tensor input_283_perm_0 = const()[name = string("input_283_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31018368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31116736))))[name = string("main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31116928)))]; + tensor input_283_cast_fp16 = transpose(perm = input_283_perm_0, x = x_149_cast_fp16)[name = string("transpose_41")]; + tensor linear_25_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_283_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor concat_32x = const()[name = string("concat_32x"), val = tensor([2, -1, 2, 128])]; + tensor var_1714_cast_fp16 = reshape(shape = concat_32x, x = linear_25_cast_fp16)[name = string("op_1714_cast_fp16")]; + tensor q_11_perm_0 = const()[name = string("q_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31117504))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31166720))))[name = string("main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31166912)))]; + tensor linear_27_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1727 = const()[name = string("op_1727"), val = tensor([2, 50, 2, 128])]; + tensor var_1728_cast_fp16 = reshape(shape = var_1727, x = linear_27_cast_fp16)[name = string("op_1728_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1732_transpose_x_0 = const()[name = string("op_1732_transpose_x_0"), val = bool(false)]; + bool var_1732_transpose_y_0 = const()[name = string("op_1732_transpose_y_0"), val = bool(false)]; + tensor op_1731_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31167488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31186752))))[name = string("op_1731_to_fp16_palettized")]; + tensor q_11_cast_fp16 = transpose(perm = q_11_perm_0, x = var_1714_cast_fp16)[name = string("transpose_40")]; + tensor var_1732_cast_fp16 = matmul(transpose_x = var_1732_transpose_x_0, transpose_y = var_1732_transpose_y_0, x = q_11_cast_fp16, y = op_1731_to_fp16_palettized)[name = string("op_1732_cast_fp16")]; + fp16 _inversed_input_285_y_0_to_fp16 = const()[name = string("_inversed_input_285_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_285_cast_fp16 = mul(x = var_1732_cast_fp16, y = _inversed_input_285_y_0_to_fp16)[name = string("_inversed_input_285_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1228, x = _inversed_input_285_cast_fp16)[name = string("attn_15_cast_fp16")]; + tensor attn_17_cast_fp16 = mul(x = attn_15_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool var_1739_transpose_x_0 = const()[name = string("op_1739_transpose_x_0"), val = bool(false)]; + bool var_1739_transpose_y_0 = const()[name = string("op_1739_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_1728_cast_fp16)[name = string("transpose_39")]; + tensor var_1739_cast_fp16 = matmul(transpose_x = var_1739_transpose_x_0, transpose_y = var_1739_transpose_y_0, x = attn_17_cast_fp16, y = v_11_cast_fp16)[name = string("op_1739_cast_fp16")]; + tensor var_1740_perm_0 = const()[name = string("op_1740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_33x = const()[name = string("concat_33x"), val = tensor([2, -1, 256])]; + tensor var_1740_cast_fp16 = transpose(perm = var_1740_perm_0, x = var_1739_cast_fp16)[name = string("transpose_38")]; + tensor input_287_cast_fp16 = reshape(shape = concat_33x, x = var_1740_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31186944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31285312))))[name = string("main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_2_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31285504)))]; + tensor linear_28_cast_fp16 = linear(bias = main_blocks_2_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor out_47_perm_0 = const()[name = string("out_47_perm_0"), val = tensor([0, 2, 1])]; + tensor out_47_cast_fp16 = transpose(perm = out_47_perm_0, x = linear_28_cast_fp16)[name = string("transpose_37")]; + tensor var_1748_cast_fp16 = mul(x = out_47_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1748_cast_fp16")]; + tensor x_151_cast_fp16 = add(x = x_m_11_cast_fp16, y = var_1748_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_289_perm_0 = const()[name = string("input_289_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1755_axes_0 = const()[name = string("op_1755_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31286592)))]; + tensor main_blocks_2_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31287680)))]; + tensor input_289_cast_fp16 = transpose(perm = input_289_perm_0, x = x_151_cast_fp16)[name = string("transpose_36")]; + tensor var_1755_cast_fp16 = layer_norm(axes = var_1755_axes_0, beta = main_blocks_2_style_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_style_norm_norm_weight_to_fp16, x = input_289_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor var_1756_perm_0 = const()[name = string("op_1756_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1756_cast_fp16 = transpose(perm = var_1756_perm_0, x = var_1755_cast_fp16)[name = string("transpose_35")]; + tensor x_153_cast_fp16 = mul(x = var_1756_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_153_cast_fp16")]; + int32 var_1775 = const()[name = string("op_1775"), val = int32(-1)]; + tensor x_155_cast_fp16 = mul(x = x_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor var_1810_cast_fp16 = mul(x = x_155_cast_fp16, y = last_1_cast_fp16)[name = string("op_1810_cast_fp16")]; + tensor x_last_37_axes_0 = const()[name = string("x_last_37_axes_0"), val = tensor([-1])]; + bool x_last_37_keep_dims_0 = const()[name = string("x_last_37_keep_dims_0"), val = bool(true)]; + tensor x_last_37_cast_fp16 = reduce_sum(axes = x_last_37_axes_0, keep_dims = x_last_37_keep_dims_0, x = var_1810_cast_fp16)[name = string("x_last_37_cast_fp16")]; + tensor var_1813_cast_fp16 = mul(x = x_155_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1815_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_37_cast_fp16)[name = string("op_1815_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = var_1813_cast_fp16, y = var_1815_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_295_mode_0 = const()[name = string("input_295_mode_0"), val = string("replicate")]; + fp16 const_40_to_fp16 = const()[name = string("const_40_to_fp16"), val = fp16(0x0p+0)]; + tensor input_295_cast_fp16 = pad(constant_val = const_40_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")]; + string h_111_pad_type_0 = const()[name = string("h_111_pad_type_0"), val = string("valid")]; + int32 h_111_groups_0 = const()[name = string("h_111_groups_0"), val = int32(512)]; + tensor h_111_strides_0 = const()[name = string("h_111_strides_0"), val = tensor([1])]; + tensor h_111_pad_0 = const()[name = string("h_111_pad_0"), val = tensor([0, 0])]; + tensor h_111_dilations_0 = const()[name = string("h_111_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31288768))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31290752))))[name = string("main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31290944)))]; + tensor h_111_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_111_dilations_0, groups = h_111_groups_0, pad = h_111_pad_0, pad_type = h_111_pad_type_0, strides = h_111_strides_0, weight = main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_palettized, x = input_295_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor x_157_cast_fp16 = mul(x = h_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor input_297_perm_0 = const()[name = string("input_297_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1833_axes_0 = const()[name = string("op_1833_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31292032)))]; + tensor main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31293120)))]; + fp16 var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_297_cast_fp16 = transpose(perm = input_297_perm_0, x = x_157_cast_fp16)[name = string("transpose_34")]; + tensor var_1833_cast_fp16 = layer_norm(axes = var_1833_axes_0, beta = main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16, x = input_297_cast_fp16)[name = string("op_1833_cast_fp16")]; + tensor input_299_perm_0 = const()[name = string("input_299_perm_0"), val = tensor([0, 2, 1])]; + string h_113_pad_type_0 = const()[name = string("h_113_pad_type_0"), val = string("valid")]; + tensor h_113_strides_0 = const()[name = string("h_113_strides_0"), val = tensor([1])]; + tensor h_113_pad_0 = const()[name = string("h_113_pad_0"), val = tensor([0, 0])]; + tensor h_113_dilations_0 = const()[name = string("h_113_dilations_0"), val = tensor([1])]; + int32 h_113_groups_0 = const()[name = string("h_113_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31294208))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32080704))))[name = string("main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32080896)))]; + tensor input_299_cast_fp16 = transpose(perm = input_299_perm_0, x = var_1833_cast_fp16)[name = string("transpose_33")]; + tensor h_113_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_113_dilations_0, groups = h_113_groups_0, pad = h_113_pad_0, pad_type = h_113_pad_type_0, strides = h_113_strides_0, weight = main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_palettized, x = input_299_cast_fp16)[name = string("h_113_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = h_113_cast_fp16)[name = string("input_301_cast_fp16")]; + string h_115_pad_type_0 = const()[name = string("h_115_pad_type_0"), val = string("valid")]; + tensor h_115_strides_0 = const()[name = string("h_115_strides_0"), val = tensor([1])]; + tensor h_115_pad_0 = const()[name = string("h_115_pad_0"), val = tensor([0, 0])]; + tensor h_115_dilations_0 = const()[name = string("h_115_dilations_0"), val = tensor([1])]; + int32 h_115_groups_0 = const()[name = string("h_115_groups_0"), val = int32(1)]; + tensor op_1850_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32085056))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32871552))))[name = string("op_1850_weight_0_to_fp16_palettized")]; + tensor var_1850_bias_0_to_fp16 = const()[name = string("op_1850_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32871744)))]; + tensor var_1850_cast_fp16 = conv(bias = var_1850_bias_0_to_fp16, dilations = h_115_dilations_0, groups = h_115_groups_0, pad = h_115_pad_0, pad_type = h_115_pad_type_0, strides = h_115_strides_0, weight = op_1850_weight_0_to_fp16_palettized, x = input_301_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor out_49_cast_fp16 = add(x = x_155_cast_fp16, y = var_1850_cast_fp16)[name = string("out_49_cast_fp16")]; + tensor x_159_cast_fp16 = mul(x = out_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor x_161_cast_fp16 = mul(x = x_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor var_1864_cast_fp16 = mul(x = x_161_cast_fp16, y = last_1_cast_fp16)[name = string("op_1864_cast_fp16")]; + tensor x_last_39_axes_0 = const()[name = string("x_last_39_axes_0"), val = tensor([-1])]; + bool x_last_39_keep_dims_0 = const()[name = string("x_last_39_keep_dims_0"), val = bool(true)]; + tensor x_last_39_cast_fp16 = reduce_sum(axes = x_last_39_axes_0, keep_dims = x_last_39_keep_dims_0, x = var_1864_cast_fp16)[name = string("x_last_39_cast_fp16")]; + tensor var_1867_cast_fp16 = mul(x = x_161_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_39_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor input_305_cast_fp16 = add(x = var_1867_cast_fp16, y = var_1869_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("replicate")]; + fp16 const_42_to_fp16 = const()[name = string("const_42_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_42_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string h_117_pad_type_0 = const()[name = string("h_117_pad_type_0"), val = string("valid")]; + tensor h_117_dilations_0 = const()[name = string("h_117_dilations_0"), val = tensor([2])]; + int32 h_117_groups_0 = const()[name = string("h_117_groups_0"), val = int32(512)]; + tensor h_117_strides_0 = const()[name = string("h_117_strides_0"), val = tensor([1])]; + tensor h_117_pad_0 = const()[name = string("h_117_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32872832))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32874816))))[name = string("main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32875008)))]; + tensor h_117_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_117_dilations_0, groups = h_117_groups_0, pad = h_117_pad_0, pad_type = h_117_pad_type_0, strides = h_117_strides_0, weight = main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_palettized, x = input_307_cast_fp16)[name = string("h_117_cast_fp16")]; + tensor x_163_cast_fp16 = mul(x = h_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1887_axes_0 = const()[name = string("op_1887_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32876096)))]; + tensor main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32877184)))]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_163_cast_fp16)[name = string("transpose_32")]; + tensor var_1887_cast_fp16 = layer_norm(axes = var_1887_axes_0, beta = main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16, x = input_309_cast_fp16)[name = string("op_1887_cast_fp16")]; + tensor input_311_perm_0 = const()[name = string("input_311_perm_0"), val = tensor([0, 2, 1])]; + string h_119_pad_type_0 = const()[name = string("h_119_pad_type_0"), val = string("valid")]; + tensor h_119_strides_0 = const()[name = string("h_119_strides_0"), val = tensor([1])]; + tensor h_119_pad_0 = const()[name = string("h_119_pad_0"), val = tensor([0, 0])]; + tensor h_119_dilations_0 = const()[name = string("h_119_dilations_0"), val = tensor([1])]; + int32 h_119_groups_0 = const()[name = string("h_119_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32878272))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33664768))))[name = string("main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33664960)))]; + tensor input_311_cast_fp16 = transpose(perm = input_311_perm_0, x = var_1887_cast_fp16)[name = string("transpose_31")]; + tensor h_119_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_119_dilations_0, groups = h_119_groups_0, pad = h_119_pad_0, pad_type = h_119_pad_type_0, strides = h_119_strides_0, weight = main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_palettized, x = input_311_cast_fp16)[name = string("h_119_cast_fp16")]; + string input_313_mode_0 = const()[name = string("input_313_mode_0"), val = string("EXACT")]; + tensor input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = h_119_cast_fp16)[name = string("input_313_cast_fp16")]; + string h_121_pad_type_0 = const()[name = string("h_121_pad_type_0"), val = string("valid")]; + tensor h_121_strides_0 = const()[name = string("h_121_strides_0"), val = tensor([1])]; + tensor h_121_pad_0 = const()[name = string("h_121_pad_0"), val = tensor([0, 0])]; + tensor h_121_dilations_0 = const()[name = string("h_121_dilations_0"), val = tensor([1])]; + int32 h_121_groups_0 = const()[name = string("h_121_groups_0"), val = int32(1)]; + tensor op_1904_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33669120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34455616))))[name = string("op_1904_weight_0_to_fp16_palettized")]; + tensor var_1904_bias_0_to_fp16 = const()[name = string("op_1904_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34455808)))]; + tensor var_1904_cast_fp16 = conv(bias = var_1904_bias_0_to_fp16, dilations = h_121_dilations_0, groups = h_121_groups_0, pad = h_121_pad_0, pad_type = h_121_pad_type_0, strides = h_121_strides_0, weight = op_1904_weight_0_to_fp16_palettized, x = input_313_cast_fp16)[name = string("op_1904_cast_fp16")]; + tensor out_51_cast_fp16 = add(x = x_161_cast_fp16, y = var_1904_cast_fp16)[name = string("out_51_cast_fp16")]; + tensor x_165_cast_fp16 = mul(x = out_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor x_167_cast_fp16 = mul(x = x_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1918_cast_fp16 = mul(x = x_167_cast_fp16, y = last_1_cast_fp16)[name = string("op_1918_cast_fp16")]; + tensor x_last_41_axes_0 = const()[name = string("x_last_41_axes_0"), val = tensor([-1])]; + bool x_last_41_keep_dims_0 = const()[name = string("x_last_41_keep_dims_0"), val = bool(true)]; + tensor x_last_41_cast_fp16 = reduce_sum(axes = x_last_41_axes_0, keep_dims = x_last_41_keep_dims_0, x = var_1918_cast_fp16)[name = string("x_last_41_cast_fp16")]; + tensor var_1921_cast_fp16 = mul(x = x_167_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1921_cast_fp16")]; + tensor var_1923_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_41_cast_fp16)[name = string("op_1923_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = var_1921_cast_fp16, y = var_1923_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("replicate")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_319_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = input_319_mode_0, pad = input_319_pad_0, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string h_123_pad_type_0 = const()[name = string("h_123_pad_type_0"), val = string("valid")]; + tensor h_123_dilations_0 = const()[name = string("h_123_dilations_0"), val = tensor([4])]; + int32 h_123_groups_0 = const()[name = string("h_123_groups_0"), val = int32(512)]; + tensor h_123_strides_0 = const()[name = string("h_123_strides_0"), val = tensor([1])]; + tensor h_123_pad_0 = const()[name = string("h_123_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34456896))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34458880))))[name = string("main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34459072)))]; + tensor h_123_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_123_dilations_0, groups = h_123_groups_0, pad = h_123_pad_0, pad_type = h_123_pad_type_0, strides = h_123_strides_0, weight = main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_palettized, x = input_319_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor x_169_cast_fp16 = mul(x = h_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1941_axes_0 = const()[name = string("op_1941_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34460160)))]; + tensor main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34461248)))]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_169_cast_fp16)[name = string("transpose_30")]; + tensor var_1941_cast_fp16 = layer_norm(axes = var_1941_axes_0, beta = main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16, x = input_321_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor([0, 2, 1])]; + string h_125_pad_type_0 = const()[name = string("h_125_pad_type_0"), val = string("valid")]; + tensor h_125_strides_0 = const()[name = string("h_125_strides_0"), val = tensor([1])]; + tensor h_125_pad_0 = const()[name = string("h_125_pad_0"), val = tensor([0, 0])]; + tensor h_125_dilations_0 = const()[name = string("h_125_dilations_0"), val = tensor([1])]; + int32 h_125_groups_0 = const()[name = string("h_125_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34462336))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35248832))))[name = string("main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35249024)))]; + tensor input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = var_1941_cast_fp16)[name = string("transpose_29")]; + tensor h_125_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_125_dilations_0, groups = h_125_groups_0, pad = h_125_pad_0, pad_type = h_125_pad_type_0, strides = h_125_strides_0, weight = main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_palettized, x = input_323_cast_fp16)[name = string("h_125_cast_fp16")]; + string input_325_mode_0 = const()[name = string("input_325_mode_0"), val = string("EXACT")]; + tensor input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = h_125_cast_fp16)[name = string("input_325_cast_fp16")]; + string h_127_pad_type_0 = const()[name = string("h_127_pad_type_0"), val = string("valid")]; + tensor h_127_strides_0 = const()[name = string("h_127_strides_0"), val = tensor([1])]; + tensor h_127_pad_0 = const()[name = string("h_127_pad_0"), val = tensor([0, 0])]; + tensor h_127_dilations_0 = const()[name = string("h_127_dilations_0"), val = tensor([1])]; + int32 h_127_groups_0 = const()[name = string("h_127_groups_0"), val = int32(1)]; + tensor op_1958_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35253184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36039680))))[name = string("op_1958_weight_0_to_fp16_palettized")]; + tensor var_1958_bias_0_to_fp16 = const()[name = string("op_1958_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36039872)))]; + tensor var_1958_cast_fp16 = conv(bias = var_1958_bias_0_to_fp16, dilations = h_127_dilations_0, groups = h_127_groups_0, pad = h_127_pad_0, pad_type = h_127_pad_type_0, strides = h_127_strides_0, weight = op_1958_weight_0_to_fp16_palettized, x = input_325_cast_fp16)[name = string("op_1958_cast_fp16")]; + tensor out_53_cast_fp16 = add(x = x_167_cast_fp16, y = var_1958_cast_fp16)[name = string("out_53_cast_fp16")]; + tensor x_171_cast_fp16 = mul(x = out_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_171_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor var_1972_cast_fp16 = mul(x = x_173_cast_fp16, y = last_1_cast_fp16)[name = string("op_1972_cast_fp16")]; + tensor x_last_43_axes_0 = const()[name = string("x_last_43_axes_0"), val = tensor([-1])]; + bool x_last_43_keep_dims_0 = const()[name = string("x_last_43_keep_dims_0"), val = bool(true)]; + tensor x_last_43_cast_fp16 = reduce_sum(axes = x_last_43_axes_0, keep_dims = x_last_43_keep_dims_0, x = var_1972_cast_fp16)[name = string("x_last_43_cast_fp16")]; + tensor var_1975_cast_fp16 = mul(x = x_173_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1975_cast_fp16")]; + tensor var_1977_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_43_cast_fp16)[name = string("op_1977_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1977_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("replicate")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor input_331_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = input_331_mode_0, pad = input_331_pad_0, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + string h_129_pad_type_0 = const()[name = string("h_129_pad_type_0"), val = string("valid")]; + tensor h_129_dilations_0 = const()[name = string("h_129_dilations_0"), val = tensor([8])]; + int32 h_129_groups_0 = const()[name = string("h_129_groups_0"), val = int32(512)]; + tensor h_129_strides_0 = const()[name = string("h_129_strides_0"), val = tensor([1])]; + tensor h_129_pad_0 = const()[name = string("h_129_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36040960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36042944))))[name = string("main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36043136)))]; + tensor h_129_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_129_dilations_0, groups = h_129_groups_0, pad = h_129_pad_0, pad_type = h_129_pad_type_0, strides = h_129_strides_0, weight = main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = string("h_129_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = h_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_333_perm_0 = const()[name = string("input_333_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1995_axes_0 = const()[name = string("op_1995_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36044224)))]; + tensor main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36045312)))]; + tensor input_333_cast_fp16 = transpose(perm = input_333_perm_0, x = x_175_cast_fp16)[name = string("transpose_28")]; + tensor var_1995_cast_fp16 = layer_norm(axes = var_1995_axes_0, beta = main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16, x = input_333_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor input_335_perm_0 = const()[name = string("input_335_perm_0"), val = tensor([0, 2, 1])]; + string h_131_pad_type_0 = const()[name = string("h_131_pad_type_0"), val = string("valid")]; + tensor h_131_strides_0 = const()[name = string("h_131_strides_0"), val = tensor([1])]; + tensor h_131_pad_0 = const()[name = string("h_131_pad_0"), val = tensor([0, 0])]; + tensor h_131_dilations_0 = const()[name = string("h_131_dilations_0"), val = tensor([1])]; + int32 h_131_groups_0 = const()[name = string("h_131_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36046400))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36832896))))[name = string("main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36833088)))]; + tensor input_335_cast_fp16 = transpose(perm = input_335_perm_0, x = var_1995_cast_fp16)[name = string("transpose_27")]; + tensor h_131_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_131_dilations_0, groups = h_131_groups_0, pad = h_131_pad_0, pad_type = h_131_pad_type_0, strides = h_131_strides_0, weight = main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_palettized, x = input_335_cast_fp16)[name = string("h_131_cast_fp16")]; + string input_337_mode_0 = const()[name = string("input_337_mode_0"), val = string("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = h_131_cast_fp16)[name = string("input_337_cast_fp16")]; + string h_133_pad_type_0 = const()[name = string("h_133_pad_type_0"), val = string("valid")]; + tensor h_133_strides_0 = const()[name = string("h_133_strides_0"), val = tensor([1])]; + tensor h_133_pad_0 = const()[name = string("h_133_pad_0"), val = tensor([0, 0])]; + tensor h_133_dilations_0 = const()[name = string("h_133_dilations_0"), val = tensor([1])]; + int32 h_133_groups_0 = const()[name = string("h_133_groups_0"), val = int32(1)]; + tensor op_2012_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36837248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37623744))))[name = string("op_2012_weight_0_to_fp16_palettized")]; + tensor var_2012_bias_0_to_fp16 = const()[name = string("op_2012_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37623936)))]; + tensor var_2012_cast_fp16 = conv(bias = var_2012_bias_0_to_fp16, dilations = h_133_dilations_0, groups = h_133_groups_0, pad = h_133_pad_0, pad_type = h_133_pad_type_0, strides = h_133_strides_0, weight = op_2012_weight_0_to_fp16_palettized, x = input_337_cast_fp16)[name = string("op_2012_cast_fp16")]; + tensor out_55_cast_fp16 = add(x = x_173_cast_fp16, y = var_2012_cast_fp16)[name = string("out_55_cast_fp16")]; + tensor x_177_cast_fp16 = mul(x = out_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37625024))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37649664))))[name = string("main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37649856)))]; + tensor linear_29_cast_fp16 = linear(bias = main_blocks_3_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_3_time_cond_linear_linear_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor t_axes_0 = const()[name = string("t_axes_0"), val = tensor([-1])]; + tensor t_cast_fp16 = expand_dims(axes = t_axes_0, x = linear_29_cast_fp16)[name = string("t_cast_fp16")]; + tensor var_2022_cast_fp16 = add(x = x_177_cast_fp16, y = t_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor x_179_cast_fp16 = mul(x = var_2022_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor x_181_cast_fp16 = mul(x = x_179_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor var_2035_cast_fp16 = mul(x = x_181_cast_fp16, y = last_1_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor x_last_45_axes_0 = const()[name = string("x_last_45_axes_0"), val = tensor([-1])]; + bool x_last_45_keep_dims_0 = const()[name = string("x_last_45_keep_dims_0"), val = bool(true)]; + tensor x_last_45_cast_fp16 = reduce_sum(axes = x_last_45_axes_0, keep_dims = x_last_45_keep_dims_0, x = var_2035_cast_fp16)[name = string("x_last_45_cast_fp16")]; + tensor var_2038_cast_fp16 = mul(x = x_181_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2038_cast_fp16")]; + tensor var_2040_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_45_cast_fp16)[name = string("op_2040_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = var_2038_cast_fp16, y = var_2040_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_345_mode_0 = const()[name = string("input_345_mode_0"), val = string("replicate")]; + fp16 const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = fp16(0x0p+0)]; + tensor input_345_cast_fp16 = pad(constant_val = const_48_to_fp16, mode = input_345_mode_0, pad = input_345_pad_0, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + string h_135_pad_type_0 = const()[name = string("h_135_pad_type_0"), val = string("valid")]; + int32 h_135_groups_0 = const()[name = string("h_135_groups_0"), val = int32(512)]; + tensor h_135_strides_0 = const()[name = string("h_135_strides_0"), val = tensor([1])]; + tensor h_135_pad_0 = const()[name = string("h_135_pad_0"), val = tensor([0, 0])]; + tensor h_135_dilations_0 = const()[name = string("h_135_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37650944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37652928))))[name = string("main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37653120)))]; + tensor h_135_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_135_dilations_0, groups = h_135_groups_0, pad = h_135_pad_0, pad_type = h_135_pad_type_0, strides = h_135_strides_0, weight = main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_palettized, x = input_345_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor x_183_cast_fp16 = mul(x = h_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_183_cast_fp16")]; + tensor input_347_perm_0 = const()[name = string("input_347_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2058_axes_0 = const()[name = string("op_2058_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37654208)))]; + tensor main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37655296)))]; + tensor input_347_cast_fp16 = transpose(perm = input_347_perm_0, x = x_183_cast_fp16)[name = string("transpose_26")]; + tensor var_2058_cast_fp16 = layer_norm(axes = var_2058_axes_0, beta = main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16, x = input_347_cast_fp16)[name = string("op_2058_cast_fp16")]; + tensor input_349_perm_0 = const()[name = string("input_349_perm_0"), val = tensor([0, 2, 1])]; + string h_137_pad_type_0 = const()[name = string("h_137_pad_type_0"), val = string("valid")]; + tensor h_137_strides_0 = const()[name = string("h_137_strides_0"), val = tensor([1])]; + tensor h_137_pad_0 = const()[name = string("h_137_pad_0"), val = tensor([0, 0])]; + tensor h_137_dilations_0 = const()[name = string("h_137_dilations_0"), val = tensor([1])]; + int32 h_137_groups_0 = const()[name = string("h_137_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37656384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38442880))))[name = string("main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38443072)))]; + tensor input_349_cast_fp16 = transpose(perm = input_349_perm_0, x = var_2058_cast_fp16)[name = string("transpose_25")]; + tensor h_137_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_137_dilations_0, groups = h_137_groups_0, pad = h_137_pad_0, pad_type = h_137_pad_type_0, strides = h_137_strides_0, weight = main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_palettized, x = input_349_cast_fp16)[name = string("h_137_cast_fp16")]; + string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")]; + tensor input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = h_137_cast_fp16)[name = string("input_351_cast_fp16")]; + string h_139_pad_type_0 = const()[name = string("h_139_pad_type_0"), val = string("valid")]; + tensor h_139_strides_0 = const()[name = string("h_139_strides_0"), val = tensor([1])]; + tensor h_139_pad_0 = const()[name = string("h_139_pad_0"), val = tensor([0, 0])]; + tensor h_139_dilations_0 = const()[name = string("h_139_dilations_0"), val = tensor([1])]; + int32 h_139_groups_0 = const()[name = string("h_139_groups_0"), val = int32(1)]; + tensor op_2075_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38447232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39233728))))[name = string("op_2075_weight_0_to_fp16_palettized")]; + tensor var_2075_bias_0_to_fp16 = const()[name = string("op_2075_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39233920)))]; + tensor var_2075_cast_fp16 = conv(bias = var_2075_bias_0_to_fp16, dilations = h_139_dilations_0, groups = h_139_groups_0, pad = h_139_pad_0, pad_type = h_139_pad_type_0, strides = h_139_strides_0, weight = op_2075_weight_0_to_fp16_palettized, x = input_351_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor out_57_cast_fp16 = add(x = x_181_cast_fp16, y = var_2075_cast_fp16)[name = string("out_57_cast_fp16")]; + tensor x_185_cast_fp16 = mul(x = out_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor x_m_13_cast_fp16 = mul(x = x_185_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_13_cast_fp16")]; + tensor var_2085_shape_cast_fp16 = shape(x = x_185_cast_fp16)[name = string("op_2085_shape_cast_fp16")]; + int32 gather_19_axis_0 = const()[name = string("gather_19_axis_0"), val = int32(0)]; + int32 gather_19_batch_dims_0 = const()[name = string("gather_19_batch_dims_0"), val = int32(0)]; + bool gather_19_validate_indices_0 = const()[name = string("gather_19_validate_indices_0"), val = bool(false)]; + string var_2085_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_2085_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_19_indices_0_to_uint16 = const()[name = string("gather_19_indices_0_to_uint16"), val = uint16(2)]; + tensor var_2085_shape_cast_fp16_to_uint16 = cast(dtype = var_2085_shape_cast_fp16_to_uint16_dtype_0, x = var_2085_shape_cast_fp16)[name = string("cast_2")]; + uint16 gather_19_cast_uint16 = gather(axis = gather_19_axis_0, batch_dims = gather_19_batch_dims_0, indices = gather_19_indices_0_to_uint16, validate_indices = gather_19_validate_indices_0, x = var_2085_shape_cast_fp16_to_uint16)[name = string("gather_19_cast_uint16")]; + string gather_19_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_19_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39235008))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39431680))))[name = string("main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39431872)))]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_185_cast_fp16)[name = string("transpose_24")]; + tensor linear_30_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_query_linear_weight_to_fp16_palettized, x = input_353_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor concat_34x = const()[name = string("concat_34x"), val = tensor([2, -1, 8, 64])]; + tensor var_2094_cast_fp16 = reshape(shape = concat_34x, x = linear_30_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor x_187_perm_0 = const()[name = string("x_187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39432960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39531328))))[name = string("main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39531520)))]; + tensor linear_31_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_key_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor concat_35x = const()[name = string("concat_35x"), val = tensor([2, -1, 8, 64])]; + tensor var_2102_cast_fp16 = reshape(shape = concat_35x, x = linear_31_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor x_189_perm_0 = const()[name = string("x_189_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39532608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39630976))))[name = string("main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39631168)))]; + tensor linear_32_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_value_linear_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor concat_36x = const()[name = string("concat_36x"), val = tensor([2, -1, 8, 64])]; + tensor var_2110_cast_fp16 = reshape(shape = concat_36x, x = linear_32_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + int32 concat_37_values0_0 = const()[name = string("concat_37_values0_0"), val = int32(1)]; + int32 concat_37_values2_0 = const()[name = string("concat_37_values2_0"), val = int32(1)]; + int32 concat_37_axis_0 = const()[name = string("concat_37_axis_0"), val = int32(0)]; + bool concat_37_interleave_0 = const()[name = string("concat_37_interleave_0"), val = bool(false)]; + int32 gather_19_cast_uint16_to_int32 = cast(dtype = gather_19_cast_uint16_to_int32_dtype_0, x = gather_19_cast_uint16)[name = string("cast_1")]; + tensor concat_37 = concat(axis = concat_37_axis_0, interleave = concat_37_interleave_0, values = (concat_37_values0_0, gather_19_cast_uint16_to_int32, concat_37_values2_0))[name = string("concat_37")]; + tensor var_2113_begin_0 = const()[name = string("op_2113_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2113_end_mask_0 = const()[name = string("op_2113_end_mask_0"), val = tensor([true, false, true])]; + tensor var_2113_cast_fp16 = slice_by_index(begin = var_2113_begin_0, end = concat_37, end_mask = var_2113_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_2113_cast_fp16")]; + tensor concat_38 = const()[name = string("concat_38"), val = tensor([2, -1, -1])]; + tensor shape_7_cast_fp16 = shape(x = var_2113_cast_fp16)[name = string("shape_7_cast_fp16")]; + tensor equal_7 = const()[name = string("equal_7"), val = tensor([false, true, true])]; + tensor select_7 = select(a = shape_7_cast_fp16, b = concat_38, cond = equal_7)[name = string("select_7")]; + tensor real_div_7 = real_div(x = select_7, y = shape_7_cast_fp16)[name = string("real_div_7")]; + tensor var_2116_cast_fp16 = tile(reps = real_div_7, x = var_2113_cast_fp16)[name = string("op_2116_cast_fp16")]; + tensor scaled_13_cast_fp16 = real_div(x = var_2116_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_13_cast_fp16")]; + tensor main_blocks_3_text_attn_theta_to_fp16 = const()[name = string("main_blocks_3_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39632256)))]; + tensor angles_13_cast_fp16 = mul(x = scaled_13_cast_fp16, y = main_blocks_3_text_attn_theta_to_fp16)[name = string("angles_13_cast_fp16")]; + tensor var_2132_cast_fp16 = cos(x = angles_13_cast_fp16)[name = string("op_2132_cast_fp16")]; + tensor cos_13_axes_0 = const()[name = string("cos_13_axes_0"), val = tensor([1])]; + tensor cos_13_cast_fp16 = expand_dims(axes = cos_13_axes_0, x = var_2132_cast_fp16)[name = string("cos_13_cast_fp16")]; + tensor var_2134_cast_fp16 = sin(x = angles_13_cast_fp16)[name = string("op_2134_cast_fp16")]; + tensor sin_13_axes_0 = const()[name = string("sin_13_axes_0"), val = tensor([1])]; + tensor sin_13_cast_fp16 = expand_dims(axes = sin_13_axes_0, x = var_2134_cast_fp16)[name = string("sin_13_cast_fp16")]; + tensor x_a_13_begin_0 = const()[name = string("x_a_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_13_end_0 = const()[name = string("x_a_13_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_13_end_mask_0 = const()[name = string("x_a_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_187_cast_fp16 = transpose(perm = x_187_perm_0, x = var_2094_cast_fp16)[name = string("transpose_23")]; + tensor x_a_13_cast_fp16 = slice_by_index(begin = x_a_13_begin_0, end = x_a_13_end_0, end_mask = x_a_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_a_13_cast_fp16")]; + tensor x_b_13_begin_0 = const()[name = string("x_b_13_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_13_end_0 = const()[name = string("x_b_13_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_13_end_mask_0 = const()[name = string("x_b_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_13_cast_fp16 = slice_by_index(begin = x_b_13_begin_0, end = x_b_13_end_0, end_mask = x_b_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_b_13_cast_fp16")]; + tensor var_2138_cast_fp16 = mul(x = x_a_13_cast_fp16, y = cos_13_cast_fp16)[name = string("op_2138_cast_fp16")]; + tensor var_2139_cast_fp16 = mul(x = x_b_13_cast_fp16, y = sin_13_cast_fp16)[name = string("op_2139_cast_fp16")]; + tensor rot_a_13_cast_fp16 = sub(x = var_2138_cast_fp16, y = var_2139_cast_fp16)[name = string("rot_a_13_cast_fp16")]; + tensor var_2141_cast_fp16 = mul(x = x_a_13_cast_fp16, y = sin_13_cast_fp16)[name = string("op_2141_cast_fp16")]; + tensor var_2142_cast_fp16 = mul(x = x_b_13_cast_fp16, y = cos_13_cast_fp16)[name = string("op_2142_cast_fp16")]; + tensor rot_b_13_cast_fp16 = add(x = var_2141_cast_fp16, y = var_2142_cast_fp16)[name = string("rot_b_13_cast_fp16")]; + bool q_13_interleave_0 = const()[name = string("q_13_interleave_0"), val = bool(false)]; + tensor q_13_cast_fp16 = concat(axis = var_1775, interleave = q_13_interleave_0, values = (rot_a_13_cast_fp16, rot_b_13_cast_fp16))[name = string("q_13_cast_fp16")]; + tensor x_a_begin_0 = const()[name = string("x_a_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_end_0 = const()[name = string("x_a_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_end_mask_0 = const()[name = string("x_a_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_189_cast_fp16 = transpose(perm = x_189_perm_0, x = var_2102_cast_fp16)[name = string("transpose_22")]; + tensor x_a_cast_fp16 = slice_by_index(begin = x_a_begin_0, end = x_a_end_0, end_mask = x_a_end_mask_0, x = x_189_cast_fp16)[name = string("x_a_cast_fp16")]; + tensor x_b_begin_0 = const()[name = string("x_b_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_end_0 = const()[name = string("x_b_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_end_mask_0 = const()[name = string("x_b_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_cast_fp16 = slice_by_index(begin = x_b_begin_0, end = x_b_end_0, end_mask = x_b_end_mask_0, x = x_189_cast_fp16)[name = string("x_b_cast_fp16")]; + tensor var_2156_cast_fp16 = mul(x = x_a_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2156_cast_fp16")]; + tensor var_2157_cast_fp16 = mul(x = x_b_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2157_cast_fp16")]; + tensor rot_a_cast_fp16 = sub(x = var_2156_cast_fp16, y = var_2157_cast_fp16)[name = string("rot_a_cast_fp16")]; + tensor var_2159_cast_fp16 = mul(x = x_a_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2159_cast_fp16")]; + tensor var_2160_cast_fp16 = mul(x = x_b_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2160_cast_fp16")]; + tensor rot_b_cast_fp16 = add(x = var_2159_cast_fp16, y = var_2160_cast_fp16)[name = string("rot_b_cast_fp16")]; + bool k_13_interleave_0 = const()[name = string("k_13_interleave_0"), val = bool(false)]; + tensor k_13_cast_fp16 = concat(axis = var_1775, interleave = k_13_interleave_0, values = (rot_a_cast_fp16, rot_b_cast_fp16))[name = string("k_13_cast_fp16")]; + bool var_2165_transpose_x_1 = const()[name = string("op_2165_transpose_x_1"), val = bool(false)]; + bool var_2165_transpose_y_1 = const()[name = string("op_2165_transpose_y_1"), val = bool(true)]; + tensor var_2165_cast_fp16 = matmul(transpose_x = var_2165_transpose_x_1, transpose_y = var_2165_transpose_y_1, x = q_13_cast_fp16, y = k_13_cast_fp16)[name = string("op_2165_cast_fp16")]; + fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_cast_fp16 = mul(x = var_2165_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")]; + tensor input_359_cast_fp16 = sub(x = _inversed_scores_cast_fp16, y = var_530_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_1775, x = input_359_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool var_2174_transpose_x_0 = const()[name = string("op_2174_transpose_x_0"), val = bool(false)]; + bool var_2174_transpose_y_0 = const()[name = string("op_2174_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2110_cast_fp16)[name = string("transpose_21")]; + tensor var_2174_cast_fp16 = matmul(transpose_x = var_2174_transpose_x_0, transpose_y = var_2174_transpose_y_0, x = attn_19_cast_fp16, y = v_13_cast_fp16)[name = string("op_2174_cast_fp16")]; + tensor var_2175_perm_0 = const()[name = string("op_2175_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_41x = const()[name = string("concat_41x"), val = tensor([2, -1, 512])]; + tensor var_2175_cast_fp16 = transpose(perm = var_2175_perm_0, x = var_2174_cast_fp16)[name = string("transpose_20")]; + tensor input_361_cast_fp16 = reshape(shape = concat_41x, x = var_2175_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39632384))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39829056))))[name = string("main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39829248)))]; + tensor linear_33_cast_fp16 = linear(bias = main_blocks_3_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor out_59_perm_0 = const()[name = string("out_59_perm_0"), val = tensor([0, 2, 1])]; + tensor out_59_cast_fp16 = transpose(perm = out_59_perm_0, x = linear_33_cast_fp16)[name = string("transpose_19")]; + tensor var_2183_cast_fp16 = mul(x = out_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_m_13_cast_fp16, y = var_2183_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor input_363_perm_0 = const()[name = string("input_363_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2190_axes_0 = const()[name = string("op_2190_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39830336)))]; + tensor main_blocks_3_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39831424)))]; + tensor input_363_cast_fp16 = transpose(perm = input_363_perm_0, x = x_191_cast_fp16)[name = string("transpose_18")]; + tensor var_2190_cast_fp16 = layer_norm(axes = var_2190_axes_0, beta = main_blocks_3_text_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_text_norm_norm_weight_to_fp16, x = input_363_cast_fp16)[name = string("op_2190_cast_fp16")]; + tensor var_2191_perm_0 = const()[name = string("op_2191_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2191_perm_0, x = var_2190_cast_fp16)[name = string("transpose_17")]; + tensor x_193_cast_fp16 = mul(x = var_2191_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_193_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2204_cast_fp16 = mul(x = x_195_cast_fp16, y = last_1_cast_fp16)[name = string("op_2204_cast_fp16")]; + tensor x_last_47_axes_0 = const()[name = string("x_last_47_axes_0"), val = tensor([-1])]; + bool x_last_47_keep_dims_0 = const()[name = string("x_last_47_keep_dims_0"), val = bool(true)]; + tensor x_last_47_cast_fp16 = reduce_sum(axes = x_last_47_axes_0, keep_dims = x_last_47_keep_dims_0, x = var_2204_cast_fp16)[name = string("x_last_47_cast_fp16")]; + tensor var_2207_cast_fp16 = mul(x = x_195_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2207_cast_fp16")]; + tensor var_2209_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_47_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor input_367_cast_fp16 = add(x = var_2207_cast_fp16, y = var_2209_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_369_mode_0 = const()[name = string("input_369_mode_0"), val = string("replicate")]; + fp16 const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = fp16(0x0p+0)]; + tensor input_369_cast_fp16 = pad(constant_val = const_50_to_fp16, mode = input_369_mode_0, pad = input_369_pad_0, x = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + string h_141_pad_type_0 = const()[name = string("h_141_pad_type_0"), val = string("valid")]; + int32 h_141_groups_0 = const()[name = string("h_141_groups_0"), val = int32(512)]; + tensor h_141_strides_0 = const()[name = string("h_141_strides_0"), val = tensor([1])]; + tensor h_141_pad_0 = const()[name = string("h_141_pad_0"), val = tensor([0, 0])]; + tensor h_141_dilations_0 = const()[name = string("h_141_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39832512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39834496))))[name = string("main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39834688)))]; + tensor h_141_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_141_dilations_0, groups = h_141_groups_0, pad = h_141_pad_0, pad_type = h_141_pad_type_0, strides = h_141_strides_0, weight = main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_palettized, x = input_369_cast_fp16)[name = string("h_141_cast_fp16")]; + tensor x_197_cast_fp16 = mul(x = h_141_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_371_perm_0 = const()[name = string("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2227_axes_0 = const()[name = string("op_2227_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39835776)))]; + tensor main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39836864)))]; + tensor input_371_cast_fp16 = transpose(perm = input_371_perm_0, x = x_197_cast_fp16)[name = string("transpose_16")]; + tensor var_2227_cast_fp16 = layer_norm(axes = var_2227_axes_0, beta = main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16, x = input_371_cast_fp16)[name = string("op_2227_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + string h_143_pad_type_0 = const()[name = string("h_143_pad_type_0"), val = string("valid")]; + tensor h_143_strides_0 = const()[name = string("h_143_strides_0"), val = tensor([1])]; + tensor h_143_pad_0 = const()[name = string("h_143_pad_0"), val = tensor([0, 0])]; + tensor h_143_dilations_0 = const()[name = string("h_143_dilations_0"), val = tensor([1])]; + int32 h_143_groups_0 = const()[name = string("h_143_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39837952))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40624448))))[name = string("main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized")]; + tensor main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40624640)))]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = var_2227_cast_fp16)[name = string("transpose_15")]; + tensor h_143_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_143_dilations_0, groups = h_143_groups_0, pad = h_143_pad_0, pad_type = h_143_pad_type_0, strides = h_143_strides_0, weight = main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = string("h_143_cast_fp16")]; + string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")]; + tensor input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = h_143_cast_fp16)[name = string("input_375_cast_fp16")]; + string h_145_pad_type_0 = const()[name = string("h_145_pad_type_0"), val = string("valid")]; + tensor h_145_strides_0 = const()[name = string("h_145_strides_0"), val = tensor([1])]; + tensor h_145_pad_0 = const()[name = string("h_145_pad_0"), val = tensor([0, 0])]; + tensor h_145_dilations_0 = const()[name = string("h_145_dilations_0"), val = tensor([1])]; + int32 h_145_groups_0 = const()[name = string("h_145_groups_0"), val = int32(1)]; + tensor op_2244_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40628800))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41415296))))[name = string("op_2244_weight_0_to_fp16_palettized")]; + tensor var_2244_bias_0_to_fp16 = const()[name = string("op_2244_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41415488)))]; + tensor var_2244_cast_fp16 = conv(bias = var_2244_bias_0_to_fp16, dilations = h_145_dilations_0, groups = h_145_groups_0, pad = h_145_pad_0, pad_type = h_145_pad_type_0, strides = h_145_strides_0, weight = op_2244_weight_0_to_fp16_palettized, x = input_375_cast_fp16)[name = string("op_2244_cast_fp16")]; + tensor out_61_cast_fp16 = add(x = x_195_cast_fp16, y = var_2244_cast_fp16)[name = string("out_61_cast_fp16")]; + tensor x_199_cast_fp16 = mul(x = out_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor x_m_cast_fp16 = mul(x = x_199_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_cast_fp16")]; + tensor input_377_perm_0 = const()[name = string("input_377_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41416576))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41514944))))[name = string("main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41515136)))]; + tensor input_377_cast_fp16 = transpose(perm = input_377_perm_0, x = x_199_cast_fp16)[name = string("transpose_14")]; + tensor linear_34_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_query_linear_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor concat_42x = const()[name = string("concat_42x"), val = tensor([2, -1, 2, 128])]; + tensor var_2261_cast_fp16 = reshape(shape = concat_42x, x = linear_34_cast_fp16)[name = string("op_2261_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41515712))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41564928))))[name = string("main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41565120)))]; + tensor linear_36_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_value_linear_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_2274 = const()[name = string("op_2274"), val = tensor([2, 50, 2, 128])]; + tensor var_2275_cast_fp16 = reshape(shape = var_2274, x = linear_36_cast_fp16)[name = string("op_2275_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_2279_transpose_x_0 = const()[name = string("op_2279_transpose_x_0"), val = bool(false)]; + bool var_2279_transpose_y_0 = const()[name = string("op_2279_transpose_y_0"), val = bool(false)]; + tensor op_2278_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41565696))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41584960))))[name = string("op_2278_to_fp16_palettized")]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_2261_cast_fp16)[name = string("transpose_13")]; + tensor var_2279_cast_fp16 = matmul(transpose_x = var_2279_transpose_x_0, transpose_y = var_2279_transpose_y_0, x = q_cast_fp16, y = op_2278_to_fp16_palettized)[name = string("op_2279_cast_fp16")]; + fp16 _inversed_input_379_y_0_to_fp16 = const()[name = string("_inversed_input_379_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_379_cast_fp16 = mul(x = var_2279_cast_fp16, y = _inversed_input_379_y_0_to_fp16)[name = string("_inversed_input_379_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_1775, x = _inversed_input_379_cast_fp16)[name = string("attn_21_cast_fp16")]; + tensor attn_cast_fp16 = mul(x = attn_21_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_cast_fp16")]; + bool var_2286_transpose_x_0 = const()[name = string("op_2286_transpose_x_0"), val = bool(false)]; + bool var_2286_transpose_y_0 = const()[name = string("op_2286_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_2275_cast_fp16)[name = string("transpose_12")]; + tensor var_2286_cast_fp16 = matmul(transpose_x = var_2286_transpose_x_0, transpose_y = var_2286_transpose_y_0, x = attn_cast_fp16, y = v_15_cast_fp16)[name = string("op_2286_cast_fp16")]; + tensor var_2287_perm_0 = const()[name = string("op_2287_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_43x = const()[name = string("concat_43x"), val = tensor([2, -1, 256])]; + tensor var_2287_cast_fp16 = transpose(perm = var_2287_perm_0, x = var_2286_cast_fp16)[name = string("transpose_11")]; + tensor input_381_cast_fp16 = reshape(shape = concat_43x, x = var_2287_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41585152))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41683520))))[name = string("main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized")]; + tensor main_blocks_3_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41683712)))]; + tensor linear_37_cast_fp16 = linear(bias = main_blocks_3_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_palettized, x = input_381_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor out_63_perm_0 = const()[name = string("out_63_perm_0"), val = tensor([0, 2, 1])]; + tensor out_63_cast_fp16 = transpose(perm = out_63_perm_0, x = linear_37_cast_fp16)[name = string("transpose_10")]; + tensor var_2295_cast_fp16 = mul(x = out_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2295_cast_fp16")]; + tensor x_201_cast_fp16 = add(x = x_m_cast_fp16, y = var_2295_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_383_perm_0 = const()[name = string("input_383_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2302_axes_0 = const()[name = string("op_2302_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41684800)))]; + tensor main_blocks_3_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41685888)))]; + tensor input_383_cast_fp16 = transpose(perm = input_383_perm_0, x = x_201_cast_fp16)[name = string("transpose_9")]; + tensor var_2302_cast_fp16 = layer_norm(axes = var_2302_axes_0, beta = main_blocks_3_style_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_style_norm_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_perm_0 = const()[name = string("op_2303_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2303_cast_fp16 = transpose(perm = var_2303_perm_0, x = var_2302_cast_fp16)[name = string("transpose_8")]; + tensor x_203_cast_fp16 = mul(x = var_2303_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor x_205_cast_fp16 = mul(x = x_203_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor var_2330_cast_fp16 = mul(x = x_205_cast_fp16, y = last_1_cast_fp16)[name = string("op_2330_cast_fp16")]; + tensor x_last_49_axes_0 = const()[name = string("x_last_49_axes_0"), val = tensor([-1])]; + bool x_last_49_keep_dims_0 = const()[name = string("x_last_49_keep_dims_0"), val = bool(true)]; + tensor x_last_49_cast_fp16 = reduce_sum(axes = x_last_49_axes_0, keep_dims = x_last_49_keep_dims_0, x = var_2330_cast_fp16)[name = string("x_last_49_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = x_205_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor var_2335_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_49_cast_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2335_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_pad_0 = const()[name = string("input_389_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("replicate")]; + fp16 const_53_to_fp16 = const()[name = string("const_53_to_fp16"), val = fp16(0x0p+0)]; + tensor input_389_cast_fp16 = pad(constant_val = const_53_to_fp16, mode = input_389_mode_0, pad = input_389_pad_0, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + string h_147_pad_type_0 = const()[name = string("h_147_pad_type_0"), val = string("valid")]; + int32 h_147_groups_0 = const()[name = string("h_147_groups_0"), val = int32(512)]; + tensor h_147_strides_0 = const()[name = string("h_147_strides_0"), val = tensor([1])]; + tensor h_147_pad_0 = const()[name = string("h_147_pad_0"), val = tensor([0, 0])]; + tensor h_147_dilations_0 = const()[name = string("h_147_dilations_0"), val = tensor([1])]; + tensor last_convnext_0_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41686976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41688960))))[name = string("last_convnext_0_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41689152)))]; + tensor h_147_cast_fp16 = conv(bias = last_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_147_dilations_0, groups = h_147_groups_0, pad = h_147_pad_0, pad_type = h_147_pad_type_0, strides = h_147_strides_0, weight = last_convnext_0_dwconv__conv_weight_to_fp16_palettized, x = input_389_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor x_207_cast_fp16 = mul(x = h_147_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_391_perm_0 = const()[name = string("input_391_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2353_axes_0 = const()[name = string("op_2353_axes_0"), val = tensor([-1])]; + tensor last_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41690240)))]; + tensor last_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41691328)))]; + fp16 var_2307_to_fp16 = const()[name = string("op_2307_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_391_cast_fp16 = transpose(perm = input_391_perm_0, x = x_207_cast_fp16)[name = string("transpose_7")]; + tensor var_2353_cast_fp16 = layer_norm(axes = var_2353_axes_0, beta = last_convnext_0_norm_norm_bias_to_fp16, epsilon = var_2307_to_fp16, gamma = last_convnext_0_norm_norm_weight_to_fp16, x = input_391_cast_fp16)[name = string("op_2353_cast_fp16")]; + tensor input_393_perm_0 = const()[name = string("input_393_perm_0"), val = tensor([0, 2, 1])]; + string h_149_pad_type_0 = const()[name = string("h_149_pad_type_0"), val = string("valid")]; + tensor h_149_strides_0 = const()[name = string("h_149_strides_0"), val = tensor([1])]; + tensor h_149_pad_0 = const()[name = string("h_149_pad_0"), val = tensor([0, 0])]; + tensor h_149_dilations_0 = const()[name = string("h_149_dilations_0"), val = tensor([1])]; + int32 h_149_groups_0 = const()[name = string("h_149_groups_0"), val = int32(1)]; + tensor last_convnext_0_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41692416))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42478912))))[name = string("last_convnext_0_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42479104)))]; + tensor input_393_cast_fp16 = transpose(perm = input_393_perm_0, x = var_2353_cast_fp16)[name = string("transpose_6")]; + tensor h_149_cast_fp16 = conv(bias = last_convnext_0_pwconv1_bias_to_fp16, dilations = h_149_dilations_0, groups = h_149_groups_0, pad = h_149_pad_0, pad_type = h_149_pad_type_0, strides = h_149_strides_0, weight = last_convnext_0_pwconv1_weight_to_fp16_palettized, x = input_393_cast_fp16)[name = string("h_149_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = h_149_cast_fp16)[name = string("input_395_cast_fp16")]; + string h_151_pad_type_0 = const()[name = string("h_151_pad_type_0"), val = string("valid")]; + tensor h_151_strides_0 = const()[name = string("h_151_strides_0"), val = tensor([1])]; + tensor h_151_pad_0 = const()[name = string("h_151_pad_0"), val = tensor([0, 0])]; + tensor h_151_dilations_0 = const()[name = string("h_151_dilations_0"), val = tensor([1])]; + int32 h_151_groups_0 = const()[name = string("h_151_groups_0"), val = int32(1)]; + tensor op_2370_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42483264))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43269760))))[name = string("op_2370_weight_0_to_fp16_palettized")]; + tensor var_2370_bias_0_to_fp16 = const()[name = string("op_2370_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43269952)))]; + tensor var_2370_cast_fp16 = conv(bias = var_2370_bias_0_to_fp16, dilations = h_151_dilations_0, groups = h_151_groups_0, pad = h_151_pad_0, pad_type = h_151_pad_type_0, strides = h_151_strides_0, weight = op_2370_weight_0_to_fp16_palettized, x = input_395_cast_fp16)[name = string("op_2370_cast_fp16")]; + tensor out_65_cast_fp16 = add(x = x_205_cast_fp16, y = var_2370_cast_fp16)[name = string("out_65_cast_fp16")]; + tensor x_209_cast_fp16 = mul(x = out_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor x_211_cast_fp16 = mul(x = x_209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_2398_cast_fp16 = mul(x = x_211_cast_fp16, y = last_1_cast_fp16)[name = string("op_2398_cast_fp16")]; + tensor x_last_51_axes_0 = const()[name = string("x_last_51_axes_0"), val = tensor([-1])]; + bool x_last_51_keep_dims_0 = const()[name = string("x_last_51_keep_dims_0"), val = bool(true)]; + tensor x_last_51_cast_fp16 = reduce_sum(axes = x_last_51_axes_0, keep_dims = x_last_51_keep_dims_0, x = var_2398_cast_fp16)[name = string("x_last_51_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = x_211_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_51_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor input_399_cast_fp16 = add(x = var_2401_cast_fp16, y = var_2403_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor input_401_pad_0 = const()[name = string("input_401_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_401_mode_0 = const()[name = string("input_401_mode_0"), val = string("replicate")]; + fp16 const_55_to_fp16 = const()[name = string("const_55_to_fp16"), val = fp16(0x0p+0)]; + tensor input_401_cast_fp16 = pad(constant_val = const_55_to_fp16, mode = input_401_mode_0, pad = input_401_pad_0, x = input_399_cast_fp16)[name = string("input_401_cast_fp16")]; + string h_153_pad_type_0 = const()[name = string("h_153_pad_type_0"), val = string("valid")]; + int32 h_153_groups_0 = const()[name = string("h_153_groups_0"), val = int32(512)]; + tensor h_153_strides_0 = const()[name = string("h_153_strides_0"), val = tensor([1])]; + tensor h_153_pad_0 = const()[name = string("h_153_pad_0"), val = tensor([0, 0])]; + tensor h_153_dilations_0 = const()[name = string("h_153_dilations_0"), val = tensor([1])]; + tensor last_convnext_1_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43271040))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43273024))))[name = string("last_convnext_1_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43273216)))]; + tensor h_153_cast_fp16 = conv(bias = last_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_153_dilations_0, groups = h_153_groups_0, pad = h_153_pad_0, pad_type = h_153_pad_type_0, strides = h_153_strides_0, weight = last_convnext_1_dwconv__conv_weight_to_fp16_palettized, x = input_401_cast_fp16)[name = string("h_153_cast_fp16")]; + tensor x_213_cast_fp16 = mul(x = h_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_213_cast_fp16")]; + tensor input_403_perm_0 = const()[name = string("input_403_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2421_axes_0 = const()[name = string("op_2421_axes_0"), val = tensor([-1])]; + tensor last_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43274304)))]; + tensor last_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43275392)))]; + fp16 var_2375_to_fp16 = const()[name = string("op_2375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_403_cast_fp16 = transpose(perm = input_403_perm_0, x = x_213_cast_fp16)[name = string("transpose_5")]; + tensor var_2421_cast_fp16 = layer_norm(axes = var_2421_axes_0, beta = last_convnext_1_norm_norm_bias_to_fp16, epsilon = var_2375_to_fp16, gamma = last_convnext_1_norm_norm_weight_to_fp16, x = input_403_cast_fp16)[name = string("op_2421_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string h_155_pad_type_0 = const()[name = string("h_155_pad_type_0"), val = string("valid")]; + tensor h_155_strides_0 = const()[name = string("h_155_strides_0"), val = tensor([1])]; + tensor h_155_pad_0 = const()[name = string("h_155_pad_0"), val = tensor([0, 0])]; + tensor h_155_dilations_0 = const()[name = string("h_155_dilations_0"), val = tensor([1])]; + int32 h_155_groups_0 = const()[name = string("h_155_groups_0"), val = int32(1)]; + tensor last_convnext_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43276480))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44062976))))[name = string("last_convnext_1_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44063168)))]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = var_2421_cast_fp16)[name = string("transpose_4")]; + tensor h_155_cast_fp16 = conv(bias = last_convnext_1_pwconv1_bias_to_fp16, dilations = h_155_dilations_0, groups = h_155_groups_0, pad = h_155_pad_0, pad_type = h_155_pad_type_0, strides = h_155_strides_0, weight = last_convnext_1_pwconv1_weight_to_fp16_palettized, x = input_405_cast_fp16)[name = string("h_155_cast_fp16")]; + string input_407_mode_0 = const()[name = string("input_407_mode_0"), val = string("EXACT")]; + tensor input_407_cast_fp16 = gelu(mode = input_407_mode_0, x = h_155_cast_fp16)[name = string("input_407_cast_fp16")]; + string h_157_pad_type_0 = const()[name = string("h_157_pad_type_0"), val = string("valid")]; + tensor h_157_strides_0 = const()[name = string("h_157_strides_0"), val = tensor([1])]; + tensor h_157_pad_0 = const()[name = string("h_157_pad_0"), val = tensor([0, 0])]; + tensor h_157_dilations_0 = const()[name = string("h_157_dilations_0"), val = tensor([1])]; + int32 h_157_groups_0 = const()[name = string("h_157_groups_0"), val = int32(1)]; + tensor op_2438_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44067328))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44853824))))[name = string("op_2438_weight_0_to_fp16_palettized")]; + tensor var_2438_bias_0_to_fp16 = const()[name = string("op_2438_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44854016)))]; + tensor var_2438_cast_fp16 = conv(bias = var_2438_bias_0_to_fp16, dilations = h_157_dilations_0, groups = h_157_groups_0, pad = h_157_pad_0, pad_type = h_157_pad_type_0, strides = h_157_strides_0, weight = op_2438_weight_0_to_fp16_palettized, x = input_407_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor out_67_cast_fp16 = add(x = x_211_cast_fp16, y = var_2438_cast_fp16)[name = string("out_67_cast_fp16")]; + tensor x_215_cast_fp16 = mul(x = out_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_215_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2466_cast_fp16 = mul(x = x_217_cast_fp16, y = last_1_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor x_last_53_axes_0 = const()[name = string("x_last_53_axes_0"), val = tensor([-1])]; + bool x_last_53_keep_dims_0 = const()[name = string("x_last_53_keep_dims_0"), val = bool(true)]; + tensor x_last_53_cast_fp16 = reduce_sum(axes = x_last_53_axes_0, keep_dims = x_last_53_keep_dims_0, x = var_2466_cast_fp16)[name = string("x_last_53_cast_fp16")]; + tensor var_2469_cast_fp16 = mul(x = x_217_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor var_2471_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_53_cast_fp16)[name = string("op_2471_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = var_2469_cast_fp16, y = var_2471_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("replicate")]; + fp16 const_57_to_fp16 = const()[name = string("const_57_to_fp16"), val = fp16(0x0p+0)]; + tensor input_413_cast_fp16 = pad(constant_val = const_57_to_fp16, mode = input_413_mode_0, pad = input_413_pad_0, x = input_411_cast_fp16)[name = string("input_413_cast_fp16")]; + string h_159_pad_type_0 = const()[name = string("h_159_pad_type_0"), val = string("valid")]; + int32 h_159_groups_0 = const()[name = string("h_159_groups_0"), val = int32(512)]; + tensor h_159_strides_0 = const()[name = string("h_159_strides_0"), val = tensor([1])]; + tensor h_159_pad_0 = const()[name = string("h_159_pad_0"), val = tensor([0, 0])]; + tensor h_159_dilations_0 = const()[name = string("h_159_dilations_0"), val = tensor([1])]; + tensor last_convnext_2_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44855104))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44857088))))[name = string("last_convnext_2_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44857280)))]; + tensor h_159_cast_fp16 = conv(bias = last_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_159_dilations_0, groups = h_159_groups_0, pad = h_159_pad_0, pad_type = h_159_pad_type_0, strides = h_159_strides_0, weight = last_convnext_2_dwconv__conv_weight_to_fp16_palettized, x = input_413_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor x_219_cast_fp16 = mul(x = h_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_415_perm_0 = const()[name = string("input_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2489_axes_0 = const()[name = string("op_2489_axes_0"), val = tensor([-1])]; + tensor last_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44858368)))]; + tensor last_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44859456)))]; + fp16 var_2443_to_fp16 = const()[name = string("op_2443_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_415_cast_fp16 = transpose(perm = input_415_perm_0, x = x_219_cast_fp16)[name = string("transpose_3")]; + tensor var_2489_cast_fp16 = layer_norm(axes = var_2489_axes_0, beta = last_convnext_2_norm_norm_bias_to_fp16, epsilon = var_2443_to_fp16, gamma = last_convnext_2_norm_norm_weight_to_fp16, x = input_415_cast_fp16)[name = string("op_2489_cast_fp16")]; + tensor input_417_perm_0 = const()[name = string("input_417_perm_0"), val = tensor([0, 2, 1])]; + string h_161_pad_type_0 = const()[name = string("h_161_pad_type_0"), val = string("valid")]; + tensor h_161_strides_0 = const()[name = string("h_161_strides_0"), val = tensor([1])]; + tensor h_161_pad_0 = const()[name = string("h_161_pad_0"), val = tensor([0, 0])]; + tensor h_161_dilations_0 = const()[name = string("h_161_dilations_0"), val = tensor([1])]; + int32 h_161_groups_0 = const()[name = string("h_161_groups_0"), val = int32(1)]; + tensor last_convnext_2_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44860544))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45647040))))[name = string("last_convnext_2_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45647232)))]; + tensor input_417_cast_fp16 = transpose(perm = input_417_perm_0, x = var_2489_cast_fp16)[name = string("transpose_2")]; + tensor h_161_cast_fp16 = conv(bias = last_convnext_2_pwconv1_bias_to_fp16, dilations = h_161_dilations_0, groups = h_161_groups_0, pad = h_161_pad_0, pad_type = h_161_pad_type_0, strides = h_161_strides_0, weight = last_convnext_2_pwconv1_weight_to_fp16_palettized, x = input_417_cast_fp16)[name = string("h_161_cast_fp16")]; + string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("EXACT")]; + tensor input_419_cast_fp16 = gelu(mode = input_419_mode_0, x = h_161_cast_fp16)[name = string("input_419_cast_fp16")]; + string h_163_pad_type_0 = const()[name = string("h_163_pad_type_0"), val = string("valid")]; + tensor h_163_strides_0 = const()[name = string("h_163_strides_0"), val = tensor([1])]; + tensor h_163_pad_0 = const()[name = string("h_163_pad_0"), val = tensor([0, 0])]; + tensor h_163_dilations_0 = const()[name = string("h_163_dilations_0"), val = tensor([1])]; + int32 h_163_groups_0 = const()[name = string("h_163_groups_0"), val = int32(1)]; + tensor op_2506_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45651392))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46437888))))[name = string("op_2506_weight_0_to_fp16_palettized")]; + tensor var_2506_bias_0_to_fp16 = const()[name = string("op_2506_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46438080)))]; + tensor var_2506_cast_fp16 = conv(bias = var_2506_bias_0_to_fp16, dilations = h_163_dilations_0, groups = h_163_groups_0, pad = h_163_pad_0, pad_type = h_163_pad_type_0, strides = h_163_strides_0, weight = op_2506_weight_0_to_fp16_palettized, x = input_419_cast_fp16)[name = string("op_2506_cast_fp16")]; + tensor out_69_cast_fp16 = add(x = x_217_cast_fp16, y = var_2506_cast_fp16)[name = string("out_69_cast_fp16")]; + tensor x_221_cast_fp16 = mul(x = out_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor x_223_cast_fp16 = mul(x = x_221_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_2534_cast_fp16 = mul(x = x_223_cast_fp16, y = last_1_cast_fp16)[name = string("op_2534_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_2534_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_2537_cast_fp16 = mul(x = x_223_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2537_cast_fp16")]; + tensor var_2539_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_cast_fp16)[name = string("op_2539_cast_fp16")]; + tensor input_423_cast_fp16 = add(x = var_2537_cast_fp16, y = var_2539_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_425_mode_0 = const()[name = string("input_425_mode_0"), val = string("replicate")]; + fp16 const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = fp16(0x0p+0)]; + tensor input_425_cast_fp16 = pad(constant_val = const_59_to_fp16, mode = input_425_mode_0, pad = input_425_pad_0, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")]; + string h_165_pad_type_0 = const()[name = string("h_165_pad_type_0"), val = string("valid")]; + int32 h_165_groups_0 = const()[name = string("h_165_groups_0"), val = int32(512)]; + tensor h_165_strides_0 = const()[name = string("h_165_strides_0"), val = tensor([1])]; + tensor h_165_pad_0 = const()[name = string("h_165_pad_0"), val = tensor([0, 0])]; + tensor h_165_dilations_0 = const()[name = string("h_165_dilations_0"), val = tensor([1])]; + tensor last_convnext_3_dwconv__conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46439168))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46441152))))[name = string("last_convnext_3_dwconv__conv_weight_to_fp16_palettized")]; + tensor last_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46441344)))]; + tensor h_165_cast_fp16 = conv(bias = last_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_165_dilations_0, groups = h_165_groups_0, pad = h_165_pad_0, pad_type = h_165_pad_type_0, strides = h_165_strides_0, weight = last_convnext_3_dwconv__conv_weight_to_fp16_palettized, x = input_425_cast_fp16)[name = string("h_165_cast_fp16")]; + tensor x_cast_fp16 = mul(x = h_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2557_axes_0 = const()[name = string("op_2557_axes_0"), val = tensor([-1])]; + tensor last_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46442432)))]; + tensor last_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46443520)))]; + fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_cast_fp16)[name = string("transpose_1")]; + tensor var_2557_cast_fp16 = layer_norm(axes = var_2557_axes_0, beta = last_convnext_3_norm_norm_bias_to_fp16, epsilon = var_2511_to_fp16, gamma = last_convnext_3_norm_norm_weight_to_fp16, x = input_427_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor input_429_perm_0 = const()[name = string("input_429_perm_0"), val = tensor([0, 2, 1])]; + string h_167_pad_type_0 = const()[name = string("h_167_pad_type_0"), val = string("valid")]; + tensor h_167_strides_0 = const()[name = string("h_167_strides_0"), val = tensor([1])]; + tensor h_167_pad_0 = const()[name = string("h_167_pad_0"), val = tensor([0, 0])]; + tensor h_167_dilations_0 = const()[name = string("h_167_dilations_0"), val = tensor([1])]; + int32 h_167_groups_0 = const()[name = string("h_167_groups_0"), val = int32(1)]; + tensor last_convnext_3_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46444608))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47231104))))[name = string("last_convnext_3_pwconv1_weight_to_fp16_palettized")]; + tensor last_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47231296)))]; + tensor input_429_cast_fp16 = transpose(perm = input_429_perm_0, x = var_2557_cast_fp16)[name = string("transpose_0")]; + tensor h_167_cast_fp16 = conv(bias = last_convnext_3_pwconv1_bias_to_fp16, dilations = h_167_dilations_0, groups = h_167_groups_0, pad = h_167_pad_0, pad_type = h_167_pad_type_0, strides = h_167_strides_0, weight = last_convnext_3_pwconv1_weight_to_fp16_palettized, x = input_429_cast_fp16)[name = string("h_167_cast_fp16")]; + string input_431_mode_0 = const()[name = string("input_431_mode_0"), val = string("EXACT")]; + tensor input_431_cast_fp16 = gelu(mode = input_431_mode_0, x = h_167_cast_fp16)[name = string("input_431_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor op_2574_weight_0_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47235456))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48021952))))[name = string("op_2574_weight_0_to_fp16_palettized")]; + tensor var_2574_bias_0_to_fp16 = const()[name = string("op_2574_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48022144)))]; + tensor var_2574_cast_fp16 = conv(bias = var_2574_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = op_2574_weight_0_to_fp16_palettized, x = input_431_cast_fp16)[name = string("op_2574_cast_fp16")]; + tensor out_cast_fp16 = add(x = x_223_cast_fp16, y = var_2574_cast_fp16)[name = string("out_cast_fp16")]; + tensor input_cast_fp16 = mul(x = out_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("input_cast_fp16")]; + string var_2587_pad_type_0 = const()[name = string("op_2587_pad_type_0"), val = string("valid")]; + tensor var_2587_strides_0 = const()[name = string("op_2587_strides_0"), val = tensor([1])]; + tensor var_2587_pad_0 = const()[name = string("op_2587_pad_0"), val = tensor([0, 0])]; + tensor var_2587_dilations_0 = const()[name = string("op_2587_dilations_0"), val = tensor([1])]; + int32 var_2587_groups_0 = const()[name = string("op_2587_groups_0"), val = int32(1)]; + tensor proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48023232))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48078592))))[name = string("proj_out_weight_to_fp16_palettized")]; + tensor var_2587_cast_fp16 = conv(dilations = var_2587_dilations_0, groups = var_2587_groups_0, pad = var_2587_pad_0, pad_type = var_2587_pad_type_0, strides = var_2587_strides_0, weight = proj_out_weight_to_fp16_palettized, x = input_cast_fp16)[name = string("op_2587_cast_fp16")]; + tensor v_cast_fp16 = mul(x = var_2587_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("v_cast_fp16")]; + tensor cond_begin_0 = const()[name = string("cond_begin_0"), val = tensor([0, 0, 0])]; + tensor cond_end_0 = const()[name = string("cond_end_0"), val = tensor([1, 144, 0])]; + tensor cond_end_mask_0 = const()[name = string("cond_end_mask_0"), val = tensor([false, true, true])]; + tensor cond_cast_fp16 = slice_by_index(begin = cond_begin_0, end = cond_end_0, end_mask = cond_end_mask_0, x = v_cast_fp16)[name = string("cond_cast_fp16")]; + tensor uncond_begin_0 = const()[name = string("uncond_begin_0"), val = tensor([1, 0, 0])]; + tensor uncond_end_0 = const()[name = string("uncond_end_0"), val = tensor([2, 144, 0])]; + tensor uncond_end_mask_0 = const()[name = string("uncond_end_mask_0"), val = tensor([true, true, true])]; + tensor uncond_cast_fp16 = slice_by_index(begin = uncond_begin_0, end = uncond_end_0, end_mask = uncond_end_mask_0, x = v_cast_fp16)[name = string("uncond_cast_fp16")]; + fp32 var_2597_epsilon_0 = const()[name = string("op_2597_epsilon_0"), val = fp32(0x1.a36e2ep-14)]; + tensor var_2597_cast_fp16 = inverse(epsilon = var_2597_epsilon_0, x = total_step_to_fp16)[name = string("op_2597_cast_fp16")]; + tensor var_2603 = const()[name = string("op_2603"), val = tensor([-1, 1, 1])]; + tensor step_cast_fp16 = reshape(shape = var_2603, x = var_2597_cast_fp16)[name = string("step_cast_fp16")]; + fp16 var_2605_to_fp16 = const()[name = string("op_2605_to_fp16"), val = fp16(0x1p+2)]; + tensor var_2606_cast_fp16 = mul(x = cond_cast_fp16, y = var_2605_to_fp16)[name = string("op_2606_cast_fp16")]; + fp16 var_2607_to_fp16 = const()[name = string("op_2607_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_2608_cast_fp16 = mul(x = uncond_cast_fp16, y = var_2607_to_fp16)[name = string("op_2608_cast_fp16")]; + tensor var_2610_cast_fp16 = sub(x = var_2606_cast_fp16, y = var_2608_cast_fp16)[name = string("op_2610_cast_fp16")]; + tensor var_2611_cast_fp16 = mul(x = step_cast_fp16, y = var_2610_cast_fp16)[name = string("op_2611_cast_fp16")]; + tensor var_2613_cast_fp16 = add(x = noisy_latent_to_fp16, y = var_2611_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor var_2614_cast_fp16 = mul(x = var_2613_cast_fp16, y = latent_mask_to_fp16)[name = string("op_2614_cast_fp16")]; + string var_2614_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2614_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor denoised_latent = cast(dtype = var_2614_cast_fp16_to_fp32_dtype_0, x = var_2614_cast_fp16)[name = string("cast_0")]; + } -> (denoised_latent); +} \ No newline at end of file diff --git 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+1,2269 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor current_step, tensor latent_mask, tensor noisy_latent, tensor style_ttl, tensor text_emb, tensor text_mask, tensor total_step) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"latent_mask", [1, 1, 24]}, {"noisy_latent", [1, 144, 24]}, {"text_emb", [1, 256, 24]}, {"text_mask", [1, 1, 24]}}), ("RangeDims", {{"latent_mask", [[1, 1], [1, 1], [17, 512]]}, {"noisy_latent", [[1, 1], [144, 144], [17, 512]]}, {"text_emb", [[1, 1], [256, 256], [17, 512]]}, {"text_mask", [[1, 1], [1, 1], [17, 512]]}})))] { + string text_emb_to_fp16_dtype_0 = const()[name = string("text_emb_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_emb_to_fp16 = cast(dtype = text_emb_to_fp16_dtype_0, x = text_emb)[name = string("cast_19")]; + tensor var_34_shape_cast_fp16 = shape(x = text_emb_to_fp16)[name = string("op_34_shape_cast_fp16")]; + int32 gather_1_axis_0 = const()[name = string("gather_1_axis_0"), val = int32(0)]; + int32 gather_1_batch_dims_0 = const()[name = string("gather_1_batch_dims_0"), val = int32(0)]; + bool gather_1_validate_indices_0 = const()[name = string("gather_1_validate_indices_0"), val = bool(false)]; + string var_34_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_34_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 gather_1_indices_0_to_uint16 = const()[name = string("gather_1_indices_0_to_uint16"), val = uint16(2)]; + tensor var_34_shape_cast_fp16_to_int16 = cast(dtype = var_34_shape_cast_fp16_to_int16_dtype_0, x = var_34_shape_cast_fp16)[name = string("cast_18")]; + int16 gather_1_cast_uint16 = gather(axis = gather_1_axis_0, batch_dims = gather_1_batch_dims_0, indices = gather_1_indices_0_to_uint16, validate_indices = gather_1_validate_indices_0, x = var_34_shape_cast_fp16_to_int16)[name = string("gather_1_cast_uint16")]; + string gather_1_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_1_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 var_38 = const()[name = string("op_38"), val = int32(-1)]; + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + int32 gather_2 = const()[name = string("gather_2"), val = int32(1)]; + int32 concat_0_axis_0 = const()[name = string("concat_0_axis_0"), val = int32(0)]; + bool concat_0_interleave_0 = const()[name = string("concat_0_interleave_0"), val = bool(false)]; + int32 gather_1_cast_uint16_to_int32 = cast(dtype = gather_1_cast_uint16_to_int32_dtype_0, x = gather_1_cast_uint16)[name = string("cast_17")]; + tensor concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (gather_2, var_38, gather_1_cast_uint16_to_int32))[name = string("concat_0")]; + tensor shape_0 = const()[name = string("shape_0"), val = tensor([1, 256, 1])]; + int32 equal_0_y_0 = const()[name = string("equal_0_y_0"), val = int32(-1)]; + tensor equal_0 = equal(x = concat_0, y = equal_0_y_0)[name = string("equal_0")]; + tensor select_0 = select(a = shape_0, b = concat_0, cond = equal_0)[name = string("select_0")]; + tensor real_div_0 = real_div(x = select_0, y = shape_0)[name = string("real_div_0")]; + tensor uncond_masker_text_special_token_to_fp16 = const()[name = string("uncond_masker_text_special_token_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor text_uncond_cast_fp16 = tile(reps = real_div_0, x = uncond_masker_text_special_token_to_fp16)[name = string("text_uncond_cast_fp16")]; + bool text_emb_interleave_0 = const()[name = string("text_emb_interleave_0"), val = bool(false)]; + tensor text_emb_cast_fp16 = concat(axis = var_39, interleave = text_emb_interleave_0, values = (text_emb_to_fp16, text_uncond_cast_fp16))[name = string("text_emb_cast_fp16")]; + bool input_95_interleave_0 = const()[name = string("input_95_interleave_0"), val = bool(false)]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor uncond_masker_style_value_special_token_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(640))), scale = tensor([[[0x1.bb4p-9]]]))[name = string("uncond_masker_style_value_special_token_to_fp16_quantized")]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_16")]; + tensor input_95_cast_fp16 = concat(axis = var_39, interleave = input_95_interleave_0, values = (style_ttl_to_fp16, uncond_masker_style_value_special_token_to_fp16_quantized))[name = string("input_95_cast_fp16")]; + int32 var_64 = const()[name = string("op_64"), val = int32(0)]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + string noisy_latent_to_fp16_dtype_0 = const()[name = string("noisy_latent_to_fp16_dtype_0"), val = string("fp16")]; + tensor noisy_latent_to_fp16 = cast(dtype = noisy_latent_to_fp16_dtype_0, x = noisy_latent)[name = string("cast_15")]; + tensor input_5_cast_fp16 = concat(axis = var_64, interleave = input_5_interleave_0, values = (noisy_latent_to_fp16, noisy_latent_to_fp16))[name = string("input_5_cast_fp16")]; + int32 var_67 = const()[name = string("op_67"), val = int32(0)]; + bool latent_mask_b_interleave_0 = const()[name = string("latent_mask_b_interleave_0"), val = bool(false)]; + string latent_mask_to_fp16_dtype_0 = const()[name = string("latent_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor latent_mask_to_fp16 = cast(dtype = latent_mask_to_fp16_dtype_0, x = latent_mask)[name = string("cast_14")]; + tensor latent_mask_b_cast_fp16 = concat(axis = var_67, interleave = latent_mask_b_interleave_0, values = (latent_mask_to_fp16, latent_mask_to_fp16))[name = string("latent_mask_b_cast_fp16")]; + int32 var_70 = const()[name = string("op_70"), val = int32(0)]; + bool text_mask_interleave_0 = const()[name = string("text_mask_interleave_0"), val = bool(false)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_13")]; + tensor text_mask_cast_fp16 = concat(axis = var_70, interleave = text_mask_interleave_0, values = (text_mask_to_fp16, text_mask_to_fp16))[name = string("text_mask_cast_fp16")]; + string current_step_to_fp16_dtype_0 = const()[name = string("current_step_to_fp16_dtype_0"), val = string("fp16")]; + string total_step_to_fp16_dtype_0 = const()[name = string("total_step_to_fp16_dtype_0"), val = string("fp16")]; + tensor current_step_to_fp16 = cast(dtype = current_step_to_fp16_dtype_0, x = current_step)[name = string("cast_11")]; + tensor total_step_to_fp16 = cast(dtype = total_step_to_fp16_dtype_0, x = total_step)[name = string("cast_12")]; + tensor t_1_cast_fp16 = real_div(x = current_step_to_fp16, y = total_step_to_fp16)[name = string("t_1_cast_fp16")]; + int32 var_74 = const()[name = string("op_74"), val = int32(0)]; + bool t_3_interleave_0 = const()[name = string("t_3_interleave_0"), val = bool(false)]; + tensor t_3_cast_fp16 = concat(axis = var_74, interleave = t_3_interleave_0, values = (t_1_cast_fp16, t_1_cast_fp16))[name = string("t_3_cast_fp16")]; + int32 var_78 = const()[name = string("op_78"), val = int32(-1)]; + tensor var_85 = const()[name = string("op_85"), val = tensor([-1, 1])]; + tensor var_86_cast_fp16 = reshape(shape = var_85, x = t_3_cast_fp16)[name = string("op_86_cast_fp16")]; + fp16 time_encoder_time_scale_to_fp16 = const()[name = string("time_encoder_time_scale_to_fp16"), val = fp16(0x1.f4p+9)]; + tensor t_scaled_cast_fp16 = mul(x = var_86_cast_fp16, y = time_encoder_time_scale_to_fp16)[name = string("t_scaled_cast_fp16")]; + tensor time_encoder_omegas_to_fp16 = const()[name = string("time_encoder_omegas_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13504)))]; + tensor phase_cast_fp16 = mul(x = t_scaled_cast_fp16, y = time_encoder_omegas_to_fp16)[name = string("phase_cast_fp16")]; + tensor var_89_cast_fp16 = sin(x = phase_cast_fp16)[name = string("op_89_cast_fp16")]; + tensor var_90_cast_fp16 = cos(x = phase_cast_fp16)[name = string("op_90_cast_fp16")]; + bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)]; + tensor input_1_cast_fp16 = concat(axis = var_78, interleave = input_1_interleave_0, values = (var_89_cast_fp16, var_90_cast_fp16))[name = string("input_1_cast_fp16")]; + tensor time_encoder_mlp_0_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30080))))[name = string("time_encoder_mlp_0_linear_weight_to_fp16_quantized")]; + tensor time_encoder_mlp_0_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30656)))]; + tensor linear_0_cast_fp16 = linear(bias = time_encoder_mlp_0_linear_bias_to_fp16, weight = time_encoder_mlp_0_linear_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_97_cast_fp16 = softplus(x = linear_0_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = tanh(x = var_97_cast_fp16)[name = string("op_98_cast_fp16")]; + tensor input_3_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_98_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor time_encoder_mlp_2_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47680))))[name = string("time_encoder_mlp_2_linear_weight_to_fp16_quantized")]; + tensor time_encoder_mlp_2_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47872)))]; + tensor linear_1_cast_fp16 = linear(bias = time_encoder_mlp_2_linear_bias_to_fp16, weight = time_encoder_mlp_2_linear_weight_to_fp16_quantized, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor time_emb_axes_0 = const()[name = string("time_emb_axes_0"), val = tensor([-1])]; + tensor time_emb_cast_fp16 = expand_dims(axes = time_emb_axes_0, x = linear_1_cast_fp16)[name = string("time_emb_cast_fp16")]; + string var_115_pad_type_0 = const()[name = string("op_115_pad_type_0"), val = string("valid")]; + tensor var_115_strides_0 = const()[name = string("op_115_strides_0"), val = tensor([1])]; + tensor var_115_pad_0 = const()[name = string("op_115_pad_0"), val = tensor([0, 0])]; + tensor var_115_dilations_0 = const()[name = string("op_115_dilations_0"), val = tensor([1])]; + int32 var_115_groups_0 = const()[name = string("op_115_groups_0"), val = int32(1)]; + tensor proj_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121856))))[name = string("proj_in_weight_to_fp16_quantized")]; + tensor var_115_cast_fp16 = conv(dilations = var_115_dilations_0, groups = var_115_groups_0, pad = var_115_pad_0, pad_type = var_115_pad_type_0, strides = var_115_strides_0, weight = proj_in_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = var_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_3_cast_fp16")]; + int32 var_134 = const()[name = string("op_134"), val = int32(-1)]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([0, 0, 1])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 0])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([true, true, true])]; + tensor input_7_cast_fp16 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, x = latent_mask_b_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_166_pad_0 = const()[name = string("op_166_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_166_mode_0 = const()[name = string("op_166_mode_0"), val = string("constant")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)]; + tensor var_166_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = var_166_mode_0, pad = var_166_pad_0, x = input_7_cast_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_133_to_fp16 = const()[name = string("op_133_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = sub(x = var_133_to_fp16, y = var_166_cast_fp16)[name = string("op_167_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = latent_mask_b_cast_fp16, y = var_167_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_169_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_172_cast_fp16 = mul(x = x_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor var_173_cast_fp16 = sub(x = var_133_to_fp16, y = latent_mask_b_cast_fp16)[name = string("op_173_cast_fp16")]; + tensor var_174_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_174_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_174_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("replicate")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor input_11_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_11_mode_0, pad = input_11_pad_0, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(512)]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125568))))[name = string("main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126656)))]; + tensor h_3_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, 2, 1])]; + tensor var_192_axes_0 = const()[name = string("op_192_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127744)))]; + tensor main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(128832)))]; + fp16 var_139_to_fp16 = const()[name = string("op_139_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_7_cast_fp16)[name = string("transpose_117")]; + tensor var_192_cast_fp16 = layer_norm(axes = var_192_axes_0, beta = main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16, x = input_13_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, 2, 1])]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1178560))))[name = string("main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1182720)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = var_192_cast_fp16)[name = string("transpose_116")]; + tensor h_5_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("h_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = h_5_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)]; + tensor op_209_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1186880))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2235520))))[name = string("op_209_weight_0_to_fp16_quantized")]; + tensor var_209_bias_0_to_fp16 = const()[name = string("op_209_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2236608)))]; + tensor var_209_cast_fp16 = conv(bias = var_209_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = op_209_weight_0_to_fp16_quantized, x = input_17_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_209_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_223_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_223_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_226_cast_fp16 = mul(x = x_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_226_cast_fp16")]; + tensor var_228_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_228_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_226_cast_fp16, y = var_228_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor input_23_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([2])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(512)]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2237696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2240320))))[name = string("main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2241408)))]; + tensor h_9_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor var_246_axes_0 = const()[name = string("op_246_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2242496)))]; + tensor main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2243584)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_13_cast_fp16)[name = string("transpose_115")]; + tensor var_246_cast_fp16 = layer_norm(axes = var_246_axes_0, beta = main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_246_cast_fp16")]; + tensor input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor([0, 2, 1])]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2244672))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3293312))))[name = string("main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3297472)))]; + tensor input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_246_cast_fp16)[name = string("transpose_114")]; + tensor h_11_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("h_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_11_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)]; + tensor op_263_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3301632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4350272))))[name = string("op_263_weight_0_to_fp16_quantized")]; + tensor var_263_bias_0_to_fp16 = const()[name = string("op_263_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4351360)))]; + tensor var_263_cast_fp16 = conv(bias = var_263_bias_0_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = op_263_weight_0_to_fp16_quantized, x = input_29_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_263_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_277_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_277_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_277_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_280_cast_fp16 = mul(x = x_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_280_cast_fp16")]; + tensor var_282_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_280_cast_fp16, y = var_282_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("replicate")]; + fp16 const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = fp16(0x0p+0)]; + tensor input_35_cast_fp16 = pad(constant_val = const_5_to_fp16, mode = input_35_mode_0, pad = input_35_pad_0, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([4])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(512)]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4352448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4355072))))[name = string("main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4356160)))]; + tensor h_15_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor var_300_axes_0 = const()[name = string("op_300_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4357248)))]; + tensor main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4358336)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_19_cast_fp16)[name = string("transpose_113")]; + tensor var_300_cast_fp16 = layer_norm(axes = var_300_axes_0, beta = main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16, x = input_37_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor([0, 2, 1])]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4359424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5408064))))[name = string("main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5412224)))]; + tensor input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_300_cast_fp16)[name = string("transpose_112")]; + tensor h_17_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("h_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = h_17_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)]; + tensor op_317_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5416384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6465024))))[name = string("op_317_weight_0_to_fp16_quantized")]; + tensor var_317_bias_0_to_fp16 = const()[name = string("op_317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6466112)))]; + tensor var_317_cast_fp16 = conv(bias = var_317_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = op_317_weight_0_to_fp16_quantized, x = input_41_cast_fp16)[name = string("op_317_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_317_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor var_331_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_331_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_331_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = x_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor var_336_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_336_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_334_cast_fp16, y = var_336_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")]; + fp16 const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_7_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([8])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(512)]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6467200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6469824))))[name = string("main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6470912)))]; + tensor h_21_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6472000)))]; + tensor main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6473088)))]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_25_cast_fp16)[name = string("transpose_111")]; + tensor var_354_cast_fp16 = layer_norm(axes = var_354_axes_0, beta = main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_354_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6474176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7522816))))[name = string("main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7526976)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_354_cast_fp16)[name = string("transpose_110")]; + tensor h_23_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("h_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_23_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)]; + tensor op_371_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7531136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579776))))[name = string("op_371_weight_0_to_fp16_quantized")]; + tensor var_371_bias_0_to_fp16 = const()[name = string("op_371_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8580864)))]; + tensor var_371_cast_fp16 = conv(bias = var_371_bias_0_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = op_371_weight_0_to_fp16_quantized, x = input_53_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_371_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor input_55_cast_fp16 = squeeze(axes = input_55_axes_0, x = time_emb_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor main_blocks_0_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8581952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8614784))))[name = string("main_blocks_0_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8615872)))]; + tensor linear_2_cast_fp16 = linear(bias = main_blocks_0_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_0_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor t_5_axes_0 = const()[name = string("t_5_axes_0"), val = tensor([-1])]; + tensor t_5_cast_fp16 = expand_dims(axes = t_5_axes_0, x = linear_2_cast_fp16)[name = string("t_5_cast_fp16")]; + tensor var_381_cast_fp16 = add(x = x_27_cast_fp16, y = t_5_cast_fp16)[name = string("op_381_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_381_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_394_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_394_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_394_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_397_cast_fp16 = mul(x = x_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = var_397_cast_fp16, y = var_399_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("replicate")]; + fp16 const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = fp16(0x0p+0)]; + tensor input_61_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_61_mode_0, pad = input_61_pad_0, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(512)]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8616960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8619584))))[name = string("main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8620672)))]; + tensor h_27_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_61_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417_axes_0 = const()[name = string("op_417_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8621760)))]; + tensor main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8622848)))]; + tensor input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_33_cast_fp16)[name = string("transpose_109")]; + tensor var_417_cast_fp16 = layer_norm(axes = var_417_axes_0, beta = main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16, x = input_63_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8623936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9672576))))[name = string("main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9676736)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = var_417_cast_fp16)[name = string("transpose_108")]; + tensor h_29_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("h_29_cast_fp16")]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("EXACT")]; + tensor input_67_cast_fp16 = gelu(mode = input_67_mode_0, x = h_29_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)]; + tensor op_434_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9680896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10729536))))[name = string("op_434_weight_0_to_fp16_quantized")]; + tensor var_434_bias_0_to_fp16 = const()[name = string("op_434_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10730624)))]; + tensor var_434_cast_fp16 = conv(bias = var_434_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = op_434_weight_0_to_fp16_quantized, x = input_67_cast_fp16)[name = string("op_434_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_434_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_m_1_cast_fp16 = mul(x = x_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_1_cast_fp16")]; + tensor var_444_shape_cast_fp16 = shape(x = x_35_cast_fp16)[name = string("op_444_shape_cast_fp16")]; + int32 gather_4_axis_0 = const()[name = string("gather_4_axis_0"), val = int32(0)]; + int32 gather_4_batch_dims_0 = const()[name = string("gather_4_batch_dims_0"), val = int32(0)]; + bool gather_4_validate_indices_0 = const()[name = string("gather_4_validate_indices_0"), val = bool(false)]; + string var_444_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_444_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 gather_4_indices_0_to_uint16 = const()[name = string("gather_4_indices_0_to_uint16"), val = uint16(2)]; + tensor var_444_shape_cast_fp16_to_int16 = cast(dtype = var_444_shape_cast_fp16_to_int16_dtype_0, x = var_444_shape_cast_fp16)[name = string("cast_10")]; + int16 gather_4_cast_uint16 = gather(axis = gather_4_axis_0, batch_dims = gather_4_batch_dims_0, indices = gather_4_indices_0_to_uint16, validate_indices = gather_4_validate_indices_0, x = var_444_shape_cast_fp16_to_int16)[name = string("gather_4_cast_uint16")]; + string gather_4_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_4_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor var_446_shape_cast_fp16 = shape(x = text_emb_cast_fp16)[name = string("op_446_shape_cast_fp16")]; + int32 gather_5_axis_0 = const()[name = string("gather_5_axis_0"), val = int32(0)]; + int32 gather_5_batch_dims_0 = const()[name = string("gather_5_batch_dims_0"), val = int32(0)]; + bool gather_5_validate_indices_0 = const()[name = string("gather_5_validate_indices_0"), val = bool(false)]; + string var_446_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_446_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_5_indices_0_to_uint16 = const()[name = string("gather_5_indices_0_to_uint16"), val = uint16(2)]; + tensor var_446_shape_cast_fp16_to_uint16 = cast(dtype = var_446_shape_cast_fp16_to_uint16_dtype_0, x = var_446_shape_cast_fp16)[name = string("cast_9")]; + uint16 gather_5_cast_uint16 = gather(axis = gather_5_axis_0, batch_dims = gather_5_batch_dims_0, indices = gather_5_indices_0_to_uint16, validate_indices = gather_5_validate_indices_0, x = var_446_shape_cast_fp16_to_uint16)[name = string("gather_5_cast_uint16")]; + string gather_5_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_5_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10731712))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10993920))))[name = string("main_blocks_0_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10995008)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_35_cast_fp16)[name = string("transpose_107")]; + tensor linear_3_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor concat_4x = const()[name = string("concat_4x"), val = tensor([2, -1, 8, 64])]; + tensor var_453_cast_fp16 = reshape(shape = concat_4x, x = linear_3_cast_fp16)[name = string("op_453_cast_fp16")]; + tensor x_37_perm_0 = const()[name = string("x_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10996096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11127232))))[name = string("main_blocks_0_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11128320)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = text_emb_cast_fp16)[name = string("transpose_106")]; + tensor linear_4_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor concat_5x = const()[name = string("concat_5x"), val = tensor([2, -1, 8, 64])]; + tensor var_461_cast_fp16 = reshape(shape = concat_5x, x = linear_4_cast_fp16)[name = string("op_461_cast_fp16")]; + tensor x_39_perm_0 = const()[name = string("x_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11129408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11260544))))[name = string("main_blocks_0_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11261632)))]; + tensor linear_5_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor concat_6x = const()[name = string("concat_6x"), val = tensor([2, -1, 8, 64])]; + tensor var_469_cast_fp16 = reshape(shape = concat_6x, x = linear_5_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor([0, 2, -3, -1])]; + int32 concat_7_values0_0 = const()[name = string("concat_7_values0_0"), val = int32(1)]; + int32 concat_7_values2_0 = const()[name = string("concat_7_values2_0"), val = int32(1)]; + int32 concat_7_axis_0 = const()[name = string("concat_7_axis_0"), val = int32(0)]; + bool concat_7_interleave_0 = const()[name = string("concat_7_interleave_0"), val = bool(false)]; + int32 gather_4_cast_uint16_to_int32 = cast(dtype = gather_4_cast_uint16_to_int32_dtype_0, x = gather_4_cast_uint16)[name = string("cast_8")]; + tensor concat_7 = concat(axis = concat_7_axis_0, interleave = concat_7_interleave_0, values = (concat_7_values0_0, gather_4_cast_uint16_to_int32, concat_7_values2_0))[name = string("concat_7")]; + tensor var_472_begin_0 = const()[name = string("op_472_begin_0"), val = tensor([0, 0, 0])]; + tensor var_472_end_mask_0 = const()[name = string("op_472_end_mask_0"), val = tensor([true, false, true])]; + tensor main_blocks_0_text_attn_increments_to_fp16 = const()[name = string("main_blocks_0_text_attn_increments_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11262720)))]; + tensor var_472_cast_fp16 = slice_by_index(begin = var_472_begin_0, end = concat_7, end_mask = var_472_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_472_cast_fp16")]; + tensor concat_8 = const()[name = string("concat_8"), val = tensor([2, -1, -1])]; + tensor shape_1_cast_fp16 = shape(x = var_472_cast_fp16)[name = string("shape_1_cast_fp16")]; + tensor equal_1 = const()[name = string("equal_1"), val = tensor([false, true, true])]; + tensor select_1 = select(a = shape_1_cast_fp16, b = concat_8, cond = equal_1)[name = string("select_1")]; + tensor real_div_1 = real_div(x = select_1, y = shape_1_cast_fp16)[name = string("real_div_1")]; + tensor var_475_cast_fp16 = tile(reps = real_div_1, x = var_472_cast_fp16)[name = string("op_475_cast_fp16")]; + int32 concat_9_values0_0 = const()[name = string("concat_9_values0_0"), val = int32(1)]; + int32 concat_9_values2_0 = const()[name = string("concat_9_values2_0"), val = int32(1)]; + int32 concat_9_axis_0 = const()[name = string("concat_9_axis_0"), val = int32(0)]; + bool concat_9_interleave_0 = const()[name = string("concat_9_interleave_0"), val = bool(false)]; + int32 gather_5_cast_uint16_to_int32 = cast(dtype = gather_5_cast_uint16_to_int32_dtype_0, x = gather_5_cast_uint16)[name = string("cast_7")]; + tensor concat_9 = concat(axis = concat_9_axis_0, interleave = concat_9_interleave_0, values = (concat_9_values0_0, gather_5_cast_uint16_to_int32, concat_9_values2_0))[name = string("concat_9")]; + tensor var_478_begin_0 = const()[name = string("op_478_begin_0"), val = tensor([0, 0, 0])]; + tensor var_478_end_mask_0 = const()[name = string("op_478_end_mask_0"), val = tensor([true, false, true])]; + tensor var_478_cast_fp16 = slice_by_index(begin = var_478_begin_0, end = concat_9, end_mask = var_478_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_478_cast_fp16")]; + tensor concat_10 = const()[name = string("concat_10"), val = tensor([2, -1, -1])]; + tensor shape_2_cast_fp16 = shape(x = var_478_cast_fp16)[name = string("shape_2_cast_fp16")]; + tensor equal_2 = const()[name = string("equal_2"), val = tensor([false, true, true])]; + tensor select_2 = select(a = shape_2_cast_fp16, b = concat_10, cond = equal_2)[name = string("select_2")]; + tensor real_div_2 = real_div(x = select_2, y = shape_2_cast_fp16)[name = string("real_div_2")]; + tensor var_481_cast_fp16 = tile(reps = real_div_2, x = var_478_cast_fp16)[name = string("op_481_cast_fp16")]; + tensor divisor_1_axes_0 = const()[name = string("divisor_1_axes_0"), val = tensor([1, 2])]; + bool divisor_1_keep_dims_0 = const()[name = string("divisor_1_keep_dims_0"), val = bool(false)]; + tensor divisor_1_cast_fp16 = reduce_sum(axes = divisor_1_axes_0, keep_dims = divisor_1_keep_dims_0, x = latent_mask_b_cast_fp16)[name = string("divisor_1_cast_fp16")]; + tensor divisor_3_axes_0 = const()[name = string("divisor_3_axes_0"), val = tensor([1, 2])]; + bool divisor_3_keep_dims_0 = const()[name = string("divisor_3_keep_dims_0"), val = bool(false)]; + tensor divisor_3_cast_fp16 = reduce_sum(axes = divisor_3_axes_0, keep_dims = divisor_3_keep_dims_0, x = text_mask_cast_fp16)[name = string("divisor_3_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([-1, 1, 1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = divisor_1_cast_fp16)[name = string("op_488_cast_fp16")]; + tensor scaled_1_cast_fp16 = real_div(x = var_475_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_1_cast_fp16")]; + tensor main_blocks_0_text_attn_theta_to_fp16 = const()[name = string("main_blocks_0_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11264832)))]; + tensor angles_1_cast_fp16 = mul(x = scaled_1_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_1_cast_fp16")]; + tensor var_491_cast_fp16 = cos(x = angles_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = var_491_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor var_493_cast_fp16 = sin(x = angles_1_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = var_493_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor x_a_1_begin_0 = const()[name = string("x_a_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_1_end_0 = const()[name = string("x_a_1_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_1_end_mask_0 = const()[name = string("x_a_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_37_cast_fp16 = transpose(perm = x_37_perm_0, x = var_453_cast_fp16)[name = string("transpose_105")]; + tensor x_a_1_cast_fp16 = slice_by_index(begin = x_a_1_begin_0, end = x_a_1_end_0, end_mask = x_a_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_a_1_cast_fp16")]; + tensor x_b_1_begin_0 = const()[name = string("x_b_1_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_1_end_0 = const()[name = string("x_b_1_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_1_end_mask_0 = const()[name = string("x_b_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_1_cast_fp16 = slice_by_index(begin = x_b_1_begin_0, end = x_b_1_end_0, end_mask = x_b_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_b_1_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = x_a_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor var_498_cast_fp16 = mul(x = x_b_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_498_cast_fp16")]; + tensor rot_a_1_cast_fp16 = sub(x = var_497_cast_fp16, y = var_498_cast_fp16)[name = string("rot_a_1_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = x_a_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = x_b_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor rot_b_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("rot_b_1_cast_fp16")]; + bool q_1_interleave_0 = const()[name = string("q_1_interleave_0"), val = bool(false)]; + tensor q_1_cast_fp16 = concat(axis = var_134, interleave = q_1_interleave_0, values = (rot_a_1_cast_fp16, rot_b_1_cast_fp16))[name = string("q_1_cast_fp16")]; + tensor var_505 = const()[name = string("op_505"), val = tensor([-1, 1, 1])]; + tensor var_506_cast_fp16 = reshape(shape = var_505, x = divisor_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor scaled_3_cast_fp16 = real_div(x = var_481_cast_fp16, y = var_506_cast_fp16)[name = string("scaled_3_cast_fp16")]; + tensor angles_3_cast_fp16 = mul(x = scaled_3_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_3_cast_fp16")]; + tensor var_509_cast_fp16 = cos(x = angles_3_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor cos_3_axes_0 = const()[name = string("cos_3_axes_0"), val = tensor([1])]; + tensor cos_3_cast_fp16 = expand_dims(axes = cos_3_axes_0, x = var_509_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor var_511_cast_fp16 = sin(x = angles_3_cast_fp16)[name = string("op_511_cast_fp16")]; + tensor sin_3_axes_0 = const()[name = string("sin_3_axes_0"), val = tensor([1])]; + tensor sin_3_cast_fp16 = expand_dims(axes = sin_3_axes_0, x = var_511_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor x_a_3_begin_0 = const()[name = string("x_a_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_3_end_0 = const()[name = string("x_a_3_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_3_end_mask_0 = const()[name = string("x_a_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_39_cast_fp16 = transpose(perm = x_39_perm_0, x = var_461_cast_fp16)[name = string("transpose_104")]; + tensor x_a_3_cast_fp16 = slice_by_index(begin = x_a_3_begin_0, end = x_a_3_end_0, end_mask = x_a_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_a_3_cast_fp16")]; + tensor x_b_3_begin_0 = const()[name = string("x_b_3_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_3_end_0 = const()[name = string("x_b_3_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_3_end_mask_0 = const()[name = string("x_b_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_3_cast_fp16 = slice_by_index(begin = x_b_3_begin_0, end = x_b_3_end_0, end_mask = x_b_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_b_3_cast_fp16")]; + tensor var_515_cast_fp16 = mul(x = x_a_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_515_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = x_b_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor rot_a_3_cast_fp16 = sub(x = var_515_cast_fp16, y = var_516_cast_fp16)[name = string("rot_a_3_cast_fp16")]; + tensor var_518_cast_fp16 = mul(x = x_a_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = mul(x = x_b_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor rot_b_3_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = string("rot_b_3_cast_fp16")]; + bool k_1_interleave_0 = const()[name = string("k_1_interleave_0"), val = bool(false)]; + tensor k_1_cast_fp16 = concat(axis = var_134, interleave = k_1_interleave_0, values = (rot_a_3_cast_fp16, rot_b_3_cast_fp16))[name = string("k_1_cast_fp16")]; + bool var_524_transpose_x_1 = const()[name = string("op_524_transpose_x_1"), val = bool(false)]; + bool var_524_transpose_y_1 = const()[name = string("op_524_transpose_y_1"), val = bool(true)]; + tensor var_524_cast_fp16 = matmul(transpose_x = var_524_transpose_x_1, transpose_y = var_524_transpose_y_1, x = q_1_cast_fp16, y = k_1_cast_fp16)[name = string("op_524_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_524_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([2])]; + tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = text_mask_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528_cast_fp16 = sub(x = var_133_to_fp16, y = var_527_cast_fp16)[name = string("op_528_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_530_cast_fp16 = mul(x = var_528_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_75_cast_fp16 = sub(x = _inversed_scores_1_cast_fp16, y = var_530_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_134, x = input_75_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool var_533_transpose_x_0 = const()[name = string("op_533_transpose_x_0"), val = bool(false)]; + bool var_533_transpose_y_0 = const()[name = string("op_533_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_469_cast_fp16)[name = string("transpose_103")]; + tensor var_533_cast_fp16 = matmul(transpose_x = var_533_transpose_x_0, transpose_y = var_533_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("op_533_cast_fp16")]; + tensor var_534_perm_0 = const()[name = string("op_534_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_11x = const()[name = string("concat_11x"), val = tensor([2, -1, 512])]; + tensor var_534_cast_fp16 = transpose(perm = var_534_perm_0, x = var_533_cast_fp16)[name = string("transpose_102")]; + tensor input_77_cast_fp16 = reshape(shape = concat_11x, x = var_534_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11264960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11527168))))[name = string("main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11528256)))]; + tensor linear_6_cast_fp16 = linear(bias = main_blocks_0_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor out_11_perm_0 = const()[name = string("out_11_perm_0"), val = tensor([0, 2, 1])]; + tensor out_11_cast_fp16 = transpose(perm = out_11_perm_0, x = linear_6_cast_fp16)[name = string("transpose_101")]; + tensor var_542_cast_fp16 = mul(x = out_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_542_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = x_m_1_cast_fp16, y = var_542_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor var_549_axes_0 = const()[name = string("op_549_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11529344)))]; + tensor main_blocks_0_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11530432)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_41_cast_fp16)[name = string("transpose_100")]; + tensor var_549_cast_fp16 = layer_norm(axes = var_549_axes_0, beta = main_blocks_0_text_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_text_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor var_550_perm_0 = const()[name = string("op_550_perm_0"), val = tensor([0, 2, 1])]; + tensor var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = var_549_cast_fp16)[name = string("transpose_99")]; + tensor x_43_cast_fp16 = mul(x = var_550_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = x_45_cast_fp16, y = last_1_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor x_last_11_axes_0 = const()[name = string("x_last_11_axes_0"), val = tensor([-1])]; + bool x_last_11_keep_dims_0 = const()[name = string("x_last_11_keep_dims_0"), val = bool(true)]; + tensor x_last_11_cast_fp16 = reduce_sum(axes = x_last_11_axes_0, keep_dims = x_last_11_keep_dims_0, x = var_563_cast_fp16)[name = string("x_last_11_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = x_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor var_568_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_11_cast_fp16)[name = string("op_568_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = var_566_cast_fp16, y = var_568_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_85_mode_0 = const()[name = string("input_85_mode_0"), val = string("replicate")]; + fp16 const_11_to_fp16 = const()[name = string("const_11_to_fp16"), val = fp16(0x0p+0)]; + tensor input_85_cast_fp16 = pad(constant_val = const_11_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(512)]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11531520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11534144))))[name = string("main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11535232)))]; + tensor h_33_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("h_33_cast_fp16")]; + tensor x_47_cast_fp16 = mul(x = h_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11536320)))]; + tensor main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11537408)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_47_cast_fp16)[name = string("transpose_98")]; + tensor var_586_cast_fp16 = layer_norm(axes = var_586_axes_0, beta = main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_586_cast_fp16")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11538496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12587136))))[name = string("main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12591296)))]; + tensor input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = var_586_cast_fp16)[name = string("transpose_97")]; + tensor h_35_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("h_35_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = h_35_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")]; + tensor h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor([1])]; + tensor h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor([0, 0])]; + tensor h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor([1])]; + int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)]; + tensor op_603_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12595456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13644096))))[name = string("op_603_weight_0_to_fp16_quantized")]; + tensor var_603_bias_0_to_fp16 = const()[name = string("op_603_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13645184)))]; + tensor var_603_cast_fp16 = conv(bias = var_603_bias_0_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = op_603_weight_0_to_fp16_quantized, x = input_91_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor out_13_cast_fp16 = add(x = x_45_cast_fp16, y = var_603_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_49_cast_fp16 = mul(x = out_13_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_m_3_cast_fp16 = mul(x = x_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_3_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13646272))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13777408))))[name = string("main_blocks_0_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13777984)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_49_cast_fp16)[name = string("transpose_96")]; + tensor linear_7_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor concat_12x = const()[name = string("concat_12x"), val = tensor([2, -1, 2, 128])]; + tensor var_620_cast_fp16 = reshape(shape = concat_12x, x = linear_7_cast_fp16)[name = string("op_620_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_0_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13778560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13844160))))[name = string("main_blocks_0_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13844736)))]; + tensor linear_9_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_633 = const()[name = string("op_633"), val = tensor([2, 50, 2, 128])]; + tensor var_634_cast_fp16 = reshape(shape = var_633, x = linear_9_cast_fp16)[name = string("op_634_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_638_transpose_x_0 = const()[name = string("op_638_transpose_x_0"), val = bool(false)]; + bool var_638_transpose_y_0 = const()[name = string("op_638_transpose_y_0"), val = bool(false)]; + tensor op_637_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13845312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13870976))))[name = string("op_637_to_fp16_quantized")]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_620_cast_fp16)[name = string("transpose_95")]; + tensor var_638_cast_fp16 = matmul(transpose_x = var_638_transpose_x_0, transpose_y = var_638_transpose_y_0, x = q_3_cast_fp16, y = op_637_to_fp16_quantized)[name = string("op_638_cast_fp16")]; + fp16 _inversed_input_97_y_0_to_fp16 = const()[name = string("_inversed_input_97_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_97_cast_fp16 = mul(x = var_638_cast_fp16, y = _inversed_input_97_y_0_to_fp16)[name = string("_inversed_input_97_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_134, x = _inversed_input_97_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_642_perm_0 = const()[name = string("op_642_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_1_axes_0 = const()[name = string("mask_1_axes_0"), val = tensor([1])]; + tensor var_642_cast_fp16 = transpose(perm = var_642_perm_0, x = latent_mask_b_cast_fp16)[name = string("transpose_94")]; + tensor mask_1_cast_fp16 = expand_dims(axes = mask_1_axes_0, x = var_642_cast_fp16)[name = string("mask_1_cast_fp16")]; + tensor attn_5_cast_fp16 = mul(x = attn_3_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool var_645_transpose_x_0 = const()[name = string("op_645_transpose_x_0"), val = bool(false)]; + bool var_645_transpose_y_0 = const()[name = string("op_645_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_634_cast_fp16)[name = string("transpose_93")]; + tensor var_645_cast_fp16 = matmul(transpose_x = var_645_transpose_x_0, transpose_y = var_645_transpose_y_0, x = attn_5_cast_fp16, y = v_3_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_646_perm_0 = const()[name = string("op_646_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_13x = const()[name = string("concat_13x"), val = tensor([2, -1, 256])]; + tensor var_646_cast_fp16 = transpose(perm = var_646_perm_0, x = var_645_cast_fp16)[name = string("transpose_92")]; + tensor input_99_cast_fp16 = reshape(shape = concat_13x, x = var_646_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13871168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14002304))))[name = string("main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_0_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14003392)))]; + tensor linear_10_cast_fp16 = linear(bias = main_blocks_0_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor out_15_perm_0 = const()[name = string("out_15_perm_0"), val = tensor([0, 2, 1])]; + tensor out_15_cast_fp16 = transpose(perm = out_15_perm_0, x = linear_10_cast_fp16)[name = string("transpose_91")]; + tensor var_654_cast_fp16 = mul(x = out_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor x_51_cast_fp16 = add(x = x_m_3_cast_fp16, y = var_654_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14004480)))]; + tensor main_blocks_0_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14005568)))]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_51_cast_fp16)[name = string("transpose_90")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = main_blocks_0_style_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_style_norm_norm_weight_to_fp16, x = input_101_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_662_perm_0 = const()[name = string("op_662_perm_0"), val = tensor([0, 2, 1])]; + tensor var_662_cast_fp16 = transpose(perm = var_662_perm_0, x = var_661_cast_fp16)[name = string("transpose_89")]; + tensor x_53_cast_fp16 = mul(x = var_662_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_53_cast_fp16")]; + int32 var_681 = const()[name = string("op_681"), val = int32(-1)]; + tensor x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = x_55_cast_fp16, y = last_1_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor x_last_13_axes_0 = const()[name = string("x_last_13_axes_0"), val = tensor([-1])]; + bool x_last_13_keep_dims_0 = const()[name = string("x_last_13_keep_dims_0"), val = bool(true)]; + tensor x_last_13_cast_fp16 = reduce_sum(axes = x_last_13_axes_0, keep_dims = x_last_13_keep_dims_0, x = var_716_cast_fp16)[name = string("x_last_13_cast_fp16")]; + tensor var_719_cast_fp16 = mul(x = x_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_721_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_13_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_719_cast_fp16, y = var_721_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("replicate")]; + fp16 const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(512)]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14006656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14009280))))[name = string("main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14010368)))]; + tensor h_39_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor x_57_cast_fp16 = mul(x = h_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_739_axes_0 = const()[name = string("op_739_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14011456)))]; + tensor main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14012544)))]; + fp16 var_686_to_fp16 = const()[name = string("op_686_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_57_cast_fp16)[name = string("transpose_88")]; + tensor var_739_cast_fp16 = layer_norm(axes = var_739_axes_0, beta = main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_739_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + string h_41_pad_type_0 = const()[name = string("h_41_pad_type_0"), val = string("valid")]; + tensor h_41_strides_0 = const()[name = string("h_41_strides_0"), val = tensor([1])]; + tensor h_41_pad_0 = const()[name = string("h_41_pad_0"), val = tensor([0, 0])]; + tensor h_41_dilations_0 = const()[name = string("h_41_dilations_0"), val = tensor([1])]; + int32 h_41_groups_0 = const()[name = string("h_41_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14013632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15062272))))[name = string("main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15066432)))]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = var_739_cast_fp16)[name = string("transpose_87")]; + tensor h_41_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_41_dilations_0, groups = h_41_groups_0, pad = h_41_pad_0, pad_type = h_41_pad_type_0, strides = h_41_strides_0, weight = main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = h_41_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor op_756_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15070592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16119232))))[name = string("op_756_weight_0_to_fp16_quantized")]; + tensor var_756_bias_0_to_fp16 = const()[name = string("op_756_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16120320)))]; + tensor var_756_cast_fp16 = conv(bias = var_756_bias_0_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = op_756_weight_0_to_fp16_quantized, x = input_113_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor out_17_cast_fp16 = add(x = x_55_cast_fp16, y = var_756_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_59_cast_fp16 = mul(x = out_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor x_61_cast_fp16 = mul(x = x_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_770_cast_fp16 = mul(x = x_61_cast_fp16, y = last_1_cast_fp16)[name = string("op_770_cast_fp16")]; + tensor x_last_15_axes_0 = const()[name = string("x_last_15_axes_0"), val = tensor([-1])]; + bool x_last_15_keep_dims_0 = const()[name = string("x_last_15_keep_dims_0"), val = bool(true)]; + tensor x_last_15_cast_fp16 = reduce_sum(axes = x_last_15_axes_0, keep_dims = x_last_15_keep_dims_0, x = var_770_cast_fp16)[name = string("x_last_15_cast_fp16")]; + tensor var_773_cast_fp16 = mul(x = x_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor var_775_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_15_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = var_773_cast_fp16, y = var_775_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("replicate")]; + fp16 const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = fp16(0x0p+0)]; + tensor input_119_cast_fp16 = pad(constant_val = const_16_to_fp16, mode = input_119_mode_0, pad = input_119_pad_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")]; + string h_45_pad_type_0 = const()[name = string("h_45_pad_type_0"), val = string("valid")]; + tensor h_45_dilations_0 = const()[name = string("h_45_dilations_0"), val = tensor([2])]; + int32 h_45_groups_0 = const()[name = string("h_45_groups_0"), val = int32(512)]; + tensor h_45_strides_0 = const()[name = string("h_45_strides_0"), val = tensor([1])]; + tensor h_45_pad_0 = const()[name = string("h_45_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16121408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16124032))))[name = string("main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16125120)))]; + tensor h_45_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_45_dilations_0, groups = h_45_groups_0, pad = h_45_pad_0, pad_type = h_45_pad_type_0, strides = h_45_strides_0, weight = main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = h_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16126208)))]; + tensor main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16127296)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_63_cast_fp16)[name = string("transpose_86")]; + tensor var_793_cast_fp16 = layer_norm(axes = var_793_axes_0, beta = main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor input_123_perm_0 = const()[name = string("input_123_perm_0"), val = tensor([0, 2, 1])]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16128384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17177024))))[name = string("main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17181184)))]; + tensor input_123_cast_fp16 = transpose(perm = input_123_perm_0, x = var_793_cast_fp16)[name = string("transpose_85")]; + tensor h_47_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_123_cast_fp16)[name = string("h_47_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = h_47_cast_fp16)[name = string("input_125_cast_fp16")]; + string h_49_pad_type_0 = const()[name = string("h_49_pad_type_0"), val = string("valid")]; + tensor h_49_strides_0 = const()[name = string("h_49_strides_0"), val = tensor([1])]; + tensor h_49_pad_0 = const()[name = string("h_49_pad_0"), val = tensor([0, 0])]; + tensor h_49_dilations_0 = const()[name = string("h_49_dilations_0"), val = tensor([1])]; + int32 h_49_groups_0 = const()[name = string("h_49_groups_0"), val = int32(1)]; + tensor op_810_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17185344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18233984))))[name = string("op_810_weight_0_to_fp16_quantized")]; + tensor var_810_bias_0_to_fp16 = const()[name = string("op_810_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18235072)))]; + tensor var_810_cast_fp16 = conv(bias = var_810_bias_0_to_fp16, dilations = h_49_dilations_0, groups = h_49_groups_0, pad = h_49_pad_0, pad_type = h_49_pad_type_0, strides = h_49_strides_0, weight = op_810_weight_0_to_fp16_quantized, x = input_125_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = x_61_cast_fp16, y = var_810_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor x_65_cast_fp16 = mul(x = out_19_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = x_67_cast_fp16, y = last_1_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_last_17_axes_0 = const()[name = string("x_last_17_axes_0"), val = tensor([-1])]; + bool x_last_17_keep_dims_0 = const()[name = string("x_last_17_keep_dims_0"), val = bool(true)]; + tensor x_last_17_cast_fp16 = reduce_sum(axes = x_last_17_axes_0, keep_dims = x_last_17_keep_dims_0, x = var_824_cast_fp16)[name = string("x_last_17_cast_fp16")]; + tensor var_827_cast_fp16 = mul(x = x_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_829_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_17_cast_fp16)[name = string("op_829_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = var_827_cast_fp16, y = var_829_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("replicate")]; + fp16 const_18_to_fp16 = const()[name = string("const_18_to_fp16"), val = fp16(0x0p+0)]; + tensor input_131_cast_fp16 = pad(constant_val = const_18_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + string h_51_pad_type_0 = const()[name = string("h_51_pad_type_0"), val = string("valid")]; + tensor h_51_dilations_0 = const()[name = string("h_51_dilations_0"), val = tensor([4])]; + int32 h_51_groups_0 = const()[name = string("h_51_groups_0"), val = int32(512)]; + tensor h_51_strides_0 = const()[name = string("h_51_strides_0"), val = tensor([1])]; + tensor h_51_pad_0 = const()[name = string("h_51_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18236160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18238784))))[name = string("main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18239872)))]; + tensor h_51_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_51_dilations_0, groups = h_51_groups_0, pad = h_51_pad_0, pad_type = h_51_pad_type_0, strides = h_51_strides_0, weight = main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor x_69_cast_fp16 = mul(x = h_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_847_axes_0 = const()[name = string("op_847_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18240960)))]; + tensor main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18242048)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_69_cast_fp16)[name = string("transpose_84")]; + tensor var_847_cast_fp16 = layer_norm(axes = var_847_axes_0, beta = main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_847_cast_fp16")]; + tensor input_135_perm_0 = const()[name = string("input_135_perm_0"), val = tensor([0, 2, 1])]; + string h_53_pad_type_0 = const()[name = string("h_53_pad_type_0"), val = string("valid")]; + tensor h_53_strides_0 = const()[name = string("h_53_strides_0"), val = tensor([1])]; + tensor h_53_pad_0 = const()[name = string("h_53_pad_0"), val = tensor([0, 0])]; + tensor h_53_dilations_0 = const()[name = string("h_53_dilations_0"), val = tensor([1])]; + int32 h_53_groups_0 = const()[name = string("h_53_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18243136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19291776))))[name = string("main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19295936)))]; + tensor input_135_cast_fp16 = transpose(perm = input_135_perm_0, x = var_847_cast_fp16)[name = string("transpose_83")]; + tensor h_53_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_53_dilations_0, groups = h_53_groups_0, pad = h_53_pad_0, pad_type = h_53_pad_type_0, strides = h_53_strides_0, weight = main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("h_53_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = h_53_cast_fp16)[name = string("input_137_cast_fp16")]; + string h_55_pad_type_0 = const()[name = string("h_55_pad_type_0"), val = string("valid")]; + tensor h_55_strides_0 = const()[name = string("h_55_strides_0"), val = tensor([1])]; + tensor h_55_pad_0 = const()[name = string("h_55_pad_0"), val = tensor([0, 0])]; + tensor h_55_dilations_0 = const()[name = string("h_55_dilations_0"), val = tensor([1])]; + int32 h_55_groups_0 = const()[name = string("h_55_groups_0"), val = int32(1)]; + tensor op_864_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19300096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20348736))))[name = string("op_864_weight_0_to_fp16_quantized")]; + tensor var_864_bias_0_to_fp16 = const()[name = string("op_864_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20349824)))]; + tensor var_864_cast_fp16 = conv(bias = var_864_bias_0_to_fp16, dilations = h_55_dilations_0, groups = h_55_groups_0, pad = h_55_pad_0, pad_type = h_55_pad_type_0, strides = h_55_strides_0, weight = op_864_weight_0_to_fp16_quantized, x = input_137_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor out_21_cast_fp16 = add(x = x_67_cast_fp16, y = var_864_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = out_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor x_73_cast_fp16 = mul(x = x_71_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_878_cast_fp16 = mul(x = x_73_cast_fp16, y = last_1_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor x_last_19_axes_0 = const()[name = string("x_last_19_axes_0"), val = tensor([-1])]; + bool x_last_19_keep_dims_0 = const()[name = string("x_last_19_keep_dims_0"), val = bool(true)]; + tensor x_last_19_cast_fp16 = reduce_sum(axes = x_last_19_axes_0, keep_dims = x_last_19_keep_dims_0, x = var_878_cast_fp16)[name = string("x_last_19_cast_fp16")]; + tensor var_881_cast_fp16 = mul(x = x_73_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_881_cast_fp16")]; + tensor var_883_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_19_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = var_881_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("replicate")]; + fp16 const_20_to_fp16 = const()[name = string("const_20_to_fp16"), val = fp16(0x0p+0)]; + tensor input_143_cast_fp16 = pad(constant_val = const_20_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + string h_57_pad_type_0 = const()[name = string("h_57_pad_type_0"), val = string("valid")]; + tensor h_57_dilations_0 = const()[name = string("h_57_dilations_0"), val = tensor([8])]; + int32 h_57_groups_0 = const()[name = string("h_57_groups_0"), val = int32(512)]; + tensor h_57_strides_0 = const()[name = string("h_57_strides_0"), val = tensor([1])]; + tensor h_57_pad_0 = const()[name = string("h_57_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20350912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20353536))))[name = string("main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20354624)))]; + tensor h_57_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_57_dilations_0, groups = h_57_groups_0, pad = h_57_pad_0, pad_type = h_57_pad_type_0, strides = h_57_strides_0, weight = main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_143_cast_fp16)[name = string("h_57_cast_fp16")]; + tensor x_75_cast_fp16 = mul(x = h_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_901_axes_0 = const()[name = string("op_901_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20355712)))]; + tensor main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20356800)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_75_cast_fp16)[name = string("transpose_82")]; + tensor var_901_cast_fp16 = layer_norm(axes = var_901_axes_0, beta = main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor input_147_perm_0 = const()[name = string("input_147_perm_0"), val = tensor([0, 2, 1])]; + string h_59_pad_type_0 = const()[name = string("h_59_pad_type_0"), val = string("valid")]; + tensor h_59_strides_0 = const()[name = string("h_59_strides_0"), val = tensor([1])]; + tensor h_59_pad_0 = const()[name = string("h_59_pad_0"), val = tensor([0, 0])]; + tensor h_59_dilations_0 = const()[name = string("h_59_dilations_0"), val = tensor([1])]; + int32 h_59_groups_0 = const()[name = string("h_59_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20357888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21406528))))[name = string("main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21410688)))]; + tensor input_147_cast_fp16 = transpose(perm = input_147_perm_0, x = var_901_cast_fp16)[name = string("transpose_81")]; + tensor h_59_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_59_dilations_0, groups = h_59_groups_0, pad = h_59_pad_0, pad_type = h_59_pad_type_0, strides = h_59_strides_0, weight = main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_147_cast_fp16)[name = string("h_59_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = h_59_cast_fp16)[name = string("input_149_cast_fp16")]; + string h_61_pad_type_0 = const()[name = string("h_61_pad_type_0"), val = string("valid")]; + tensor h_61_strides_0 = const()[name = string("h_61_strides_0"), val = tensor([1])]; + tensor h_61_pad_0 = const()[name = string("h_61_pad_0"), val = tensor([0, 0])]; + tensor h_61_dilations_0 = const()[name = string("h_61_dilations_0"), val = tensor([1])]; + int32 h_61_groups_0 = const()[name = string("h_61_groups_0"), val = int32(1)]; + tensor op_918_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21414848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22463488))))[name = string("op_918_weight_0_to_fp16_quantized")]; + tensor var_918_bias_0_to_fp16 = const()[name = string("op_918_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22464576)))]; + tensor var_918_cast_fp16 = conv(bias = var_918_bias_0_to_fp16, dilations = h_61_dilations_0, groups = h_61_groups_0, pad = h_61_pad_0, pad_type = h_61_pad_type_0, strides = h_61_strides_0, weight = op_918_weight_0_to_fp16_quantized, x = input_149_cast_fp16)[name = string("op_918_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = x_73_cast_fp16, y = var_918_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor x_77_cast_fp16 = mul(x = out_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor main_blocks_1_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22465664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22498496))))[name = string("main_blocks_1_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22499584)))]; + tensor linear_11_cast_fp16 = linear(bias = main_blocks_1_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_1_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor t_7_axes_0 = const()[name = string("t_7_axes_0"), val = tensor([-1])]; + tensor t_7_cast_fp16 = expand_dims(axes = t_7_axes_0, x = linear_11_cast_fp16)[name = string("t_7_cast_fp16")]; + tensor var_928_cast_fp16 = add(x = x_77_cast_fp16, y = t_7_cast_fp16)[name = string("op_928_cast_fp16")]; + tensor x_79_cast_fp16 = mul(x = var_928_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor x_81_cast_fp16 = mul(x = x_79_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = x_81_cast_fp16, y = last_1_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor x_last_21_axes_0 = const()[name = string("x_last_21_axes_0"), val = tensor([-1])]; + bool x_last_21_keep_dims_0 = const()[name = string("x_last_21_keep_dims_0"), val = bool(true)]; + tensor x_last_21_cast_fp16 = reduce_sum(axes = x_last_21_axes_0, keep_dims = x_last_21_keep_dims_0, x = var_941_cast_fp16)[name = string("x_last_21_cast_fp16")]; + tensor var_944_cast_fp16 = mul(x = x_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor var_946_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_21_cast_fp16)[name = string("op_946_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = var_944_cast_fp16, y = var_946_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("replicate")]; + fp16 const_22_to_fp16 = const()[name = string("const_22_to_fp16"), val = fp16(0x0p+0)]; + tensor input_157_cast_fp16 = pad(constant_val = const_22_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string h_63_pad_type_0 = const()[name = string("h_63_pad_type_0"), val = string("valid")]; + int32 h_63_groups_0 = const()[name = string("h_63_groups_0"), val = int32(512)]; + tensor h_63_strides_0 = const()[name = string("h_63_strides_0"), val = tensor([1])]; + tensor h_63_pad_0 = const()[name = string("h_63_pad_0"), val = tensor([0, 0])]; + tensor h_63_dilations_0 = const()[name = string("h_63_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22500672))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22503296))))[name = string("main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22504384)))]; + tensor h_63_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_63_dilations_0, groups = h_63_groups_0, pad = h_63_pad_0, pad_type = h_63_pad_type_0, strides = h_63_strides_0, weight = main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_157_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor x_83_cast_fp16 = mul(x = h_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor var_964_axes_0 = const()[name = string("op_964_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22505472)))]; + tensor main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22506560)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_83_cast_fp16)[name = string("transpose_80")]; + tensor var_964_cast_fp16 = layer_norm(axes = var_964_axes_0, beta = main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + string h_65_pad_type_0 = const()[name = string("h_65_pad_type_0"), val = string("valid")]; + tensor h_65_strides_0 = const()[name = string("h_65_strides_0"), val = tensor([1])]; + tensor h_65_pad_0 = const()[name = string("h_65_pad_0"), val = tensor([0, 0])]; + tensor h_65_dilations_0 = const()[name = string("h_65_dilations_0"), val = tensor([1])]; + int32 h_65_groups_0 = const()[name = string("h_65_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22507648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23556288))))[name = string("main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23560448)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = var_964_cast_fp16)[name = string("transpose_79")]; + tensor h_65_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_65_dilations_0, groups = h_65_groups_0, pad = h_65_pad_0, pad_type = h_65_pad_type_0, strides = h_65_strides_0, weight = main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_161_cast_fp16)[name = string("h_65_cast_fp16")]; + string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("EXACT")]; + tensor input_163_cast_fp16 = gelu(mode = input_163_mode_0, x = h_65_cast_fp16)[name = string("input_163_cast_fp16")]; + string h_67_pad_type_0 = const()[name = string("h_67_pad_type_0"), val = string("valid")]; + tensor h_67_strides_0 = const()[name = string("h_67_strides_0"), val = tensor([1])]; + tensor h_67_pad_0 = const()[name = string("h_67_pad_0"), val = tensor([0, 0])]; + tensor h_67_dilations_0 = const()[name = string("h_67_dilations_0"), val = tensor([1])]; + int32 h_67_groups_0 = const()[name = string("h_67_groups_0"), val = int32(1)]; + tensor op_981_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23564608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24613248))))[name = string("op_981_weight_0_to_fp16_quantized")]; + tensor var_981_bias_0_to_fp16 = const()[name = string("op_981_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24614336)))]; + tensor var_981_cast_fp16 = conv(bias = var_981_bias_0_to_fp16, dilations = h_67_dilations_0, groups = h_67_groups_0, pad = h_67_pad_0, pad_type = h_67_pad_type_0, strides = h_67_strides_0, weight = op_981_weight_0_to_fp16_quantized, x = input_163_cast_fp16)[name = string("op_981_cast_fp16")]; + tensor out_25_cast_fp16 = add(x = x_81_cast_fp16, y = var_981_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = out_25_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor x_m_5_cast_fp16 = mul(x = x_85_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_5_cast_fp16")]; + tensor var_991_shape_cast_fp16 = shape(x = x_85_cast_fp16)[name = string("op_991_shape_cast_fp16")]; + int32 gather_9_axis_0 = const()[name = string("gather_9_axis_0"), val = int32(0)]; + int32 gather_9_batch_dims_0 = const()[name = string("gather_9_batch_dims_0"), val = int32(0)]; + bool gather_9_validate_indices_0 = const()[name = string("gather_9_validate_indices_0"), val = bool(false)]; + string var_991_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_991_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_9_indices_0_to_uint16 = const()[name = string("gather_9_indices_0_to_uint16"), val = uint16(2)]; + tensor var_991_shape_cast_fp16_to_uint16 = cast(dtype = var_991_shape_cast_fp16_to_uint16_dtype_0, x = var_991_shape_cast_fp16)[name = string("cast_6")]; + uint16 gather_9_cast_uint16 = gather(axis = gather_9_axis_0, batch_dims = gather_9_batch_dims_0, indices = gather_9_indices_0_to_uint16, validate_indices = gather_9_validate_indices_0, x = var_991_shape_cast_fp16_to_uint16)[name = string("gather_9_cast_uint16")]; + string gather_9_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_9_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24615424))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24877632))))[name = string("main_blocks_1_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24878720)))]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_85_cast_fp16)[name = string("transpose_78")]; + tensor linear_12_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor concat_14x = const()[name = string("concat_14x"), val = tensor([2, -1, 8, 64])]; + tensor var_1000_cast_fp16 = reshape(shape = concat_14x, x = linear_12_cast_fp16)[name = string("op_1000_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24879808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25010944))))[name = string("main_blocks_1_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25012032)))]; + tensor linear_13_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor concat_15x = const()[name = string("concat_15x"), val = tensor([2, -1, 8, 64])]; + tensor var_1008_cast_fp16 = reshape(shape = concat_15x, x = linear_13_cast_fp16)[name = string("op_1008_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25013120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25144256))))[name = string("main_blocks_1_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25145344)))]; + tensor linear_14_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor concat_16x = const()[name = string("concat_16x"), val = tensor([2, -1, 8, 64])]; + tensor var_1016_cast_fp16 = reshape(shape = concat_16x, x = linear_14_cast_fp16)[name = string("op_1016_cast_fp16")]; + tensor v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor([0, 2, -3, -1])]; + int32 concat_17_values0_0 = const()[name = string("concat_17_values0_0"), val = int32(1)]; + int32 concat_17_values2_0 = const()[name = string("concat_17_values2_0"), val = int32(1)]; + int32 concat_17_axis_0 = const()[name = string("concat_17_axis_0"), val = int32(0)]; + bool concat_17_interleave_0 = const()[name = string("concat_17_interleave_0"), val = bool(false)]; + int32 gather_9_cast_uint16_to_int32 = cast(dtype = gather_9_cast_uint16_to_int32_dtype_0, x = gather_9_cast_uint16)[name = string("cast_5")]; + tensor concat_17 = concat(axis = concat_17_axis_0, interleave = concat_17_interleave_0, values = (concat_17_values0_0, gather_9_cast_uint16_to_int32, concat_17_values2_0))[name = string("concat_17")]; + tensor var_1019_begin_0 = const()[name = string("op_1019_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1019_end_mask_0 = const()[name = string("op_1019_end_mask_0"), val = tensor([true, false, true])]; + tensor var_1019_cast_fp16 = slice_by_index(begin = var_1019_begin_0, end = concat_17, end_mask = var_1019_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_1019_cast_fp16")]; + tensor concat_18 = const()[name = string("concat_18"), val = tensor([2, -1, -1])]; + tensor shape_3_cast_fp16 = shape(x = var_1019_cast_fp16)[name = string("shape_3_cast_fp16")]; + tensor equal_3 = const()[name = string("equal_3"), val = tensor([false, true, true])]; + tensor select_3 = select(a = shape_3_cast_fp16, b = concat_18, cond = equal_3)[name = string("select_3")]; + tensor real_div_3 = real_div(x = select_3, y = shape_3_cast_fp16)[name = string("real_div_3")]; + tensor var_1022_cast_fp16 = tile(reps = real_div_3, x = var_1019_cast_fp16)[name = string("op_1022_cast_fp16")]; + tensor scaled_5_cast_fp16 = real_div(x = var_1022_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_5_cast_fp16")]; + tensor main_blocks_1_text_attn_theta_to_fp16 = const()[name = string("main_blocks_1_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25146432)))]; + tensor angles_5_cast_fp16 = mul(x = scaled_5_cast_fp16, y = main_blocks_1_text_attn_theta_to_fp16)[name = string("angles_5_cast_fp16")]; + tensor var_1038_cast_fp16 = cos(x = angles_5_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor cos_5_axes_0 = const()[name = string("cos_5_axes_0"), val = tensor([1])]; + tensor cos_5_cast_fp16 = expand_dims(axes = cos_5_axes_0, x = var_1038_cast_fp16)[name = string("cos_5_cast_fp16")]; + tensor var_1040_cast_fp16 = sin(x = angles_5_cast_fp16)[name = string("op_1040_cast_fp16")]; + tensor sin_5_axes_0 = const()[name = string("sin_5_axes_0"), val = tensor([1])]; + tensor sin_5_cast_fp16 = expand_dims(axes = sin_5_axes_0, x = var_1040_cast_fp16)[name = string("sin_5_cast_fp16")]; + tensor x_a_5_begin_0 = const()[name = string("x_a_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_5_end_0 = const()[name = string("x_a_5_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_5_end_mask_0 = const()[name = string("x_a_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_1000_cast_fp16)[name = string("transpose_77")]; + tensor x_a_5_cast_fp16 = slice_by_index(begin = x_a_5_begin_0, end = x_a_5_end_0, end_mask = x_a_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_a_5_cast_fp16")]; + tensor x_b_5_begin_0 = const()[name = string("x_b_5_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_5_end_0 = const()[name = string("x_b_5_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_5_end_mask_0 = const()[name = string("x_b_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_5_cast_fp16 = slice_by_index(begin = x_b_5_begin_0, end = x_b_5_end_0, end_mask = x_b_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_b_5_cast_fp16")]; + tensor var_1044_cast_fp16 = mul(x = x_a_5_cast_fp16, y = cos_5_cast_fp16)[name = string("op_1044_cast_fp16")]; + tensor var_1045_cast_fp16 = mul(x = x_b_5_cast_fp16, y = sin_5_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor rot_a_5_cast_fp16 = sub(x = var_1044_cast_fp16, y = var_1045_cast_fp16)[name = string("rot_a_5_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_a_5_cast_fp16, y = sin_5_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_cast_fp16 = mul(x = x_b_5_cast_fp16, y = cos_5_cast_fp16)[name = string("op_1048_cast_fp16")]; + tensor rot_b_5_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("rot_b_5_cast_fp16")]; + bool q_5_interleave_0 = const()[name = string("q_5_interleave_0"), val = bool(false)]; + tensor q_5_cast_fp16 = concat(axis = var_681, interleave = q_5_interleave_0, values = (rot_a_5_cast_fp16, rot_b_5_cast_fp16))[name = string("q_5_cast_fp16")]; + tensor x_a_7_begin_0 = const()[name = string("x_a_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_7_end_0 = const()[name = string("x_a_7_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_7_end_mask_0 = const()[name = string("x_a_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_1008_cast_fp16)[name = string("transpose_76")]; + tensor x_a_7_cast_fp16 = slice_by_index(begin = x_a_7_begin_0, end = x_a_7_end_0, end_mask = x_a_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_a_7_cast_fp16")]; + tensor x_b_7_begin_0 = const()[name = string("x_b_7_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_7_end_0 = const()[name = string("x_b_7_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_7_end_mask_0 = const()[name = string("x_b_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_7_cast_fp16 = slice_by_index(begin = x_b_7_begin_0, end = x_b_7_end_0, end_mask = x_b_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_b_7_cast_fp16")]; + tensor var_1062_cast_fp16 = mul(x = x_a_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = x_b_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor rot_a_7_cast_fp16 = sub(x = var_1062_cast_fp16, y = var_1063_cast_fp16)[name = string("rot_a_7_cast_fp16")]; + tensor var_1065_cast_fp16 = mul(x = x_a_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1065_cast_fp16")]; + tensor var_1066_cast_fp16 = mul(x = x_b_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1066_cast_fp16")]; + tensor rot_b_7_cast_fp16 = add(x = var_1065_cast_fp16, y = var_1066_cast_fp16)[name = string("rot_b_7_cast_fp16")]; + bool k_5_interleave_0 = const()[name = string("k_5_interleave_0"), val = bool(false)]; + tensor k_5_cast_fp16 = concat(axis = var_681, interleave = k_5_interleave_0, values = (rot_a_7_cast_fp16, rot_b_7_cast_fp16))[name = string("k_5_cast_fp16")]; + bool var_1071_transpose_x_1 = const()[name = string("op_1071_transpose_x_1"), val = bool(false)]; + bool var_1071_transpose_y_1 = const()[name = string("op_1071_transpose_y_1"), val = bool(true)]; + tensor var_1071_cast_fp16 = matmul(transpose_x = var_1071_transpose_x_1, transpose_y = var_1071_transpose_y_1, x = q_5_cast_fp16, y = k_5_cast_fp16)[name = string("op_1071_cast_fp16")]; + fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_3_cast_fp16 = mul(x = var_1071_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")]; + tensor input_171_cast_fp16 = sub(x = _inversed_scores_3_cast_fp16, y = var_530_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_681, x = input_171_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool var_1080_transpose_x_0 = const()[name = string("op_1080_transpose_x_0"), val = bool(false)]; + bool var_1080_transpose_y_0 = const()[name = string("op_1080_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1016_cast_fp16)[name = string("transpose_75")]; + tensor var_1080_cast_fp16 = matmul(transpose_x = var_1080_transpose_x_0, transpose_y = var_1080_transpose_y_0, x = attn_7_cast_fp16, y = v_5_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_perm_0 = const()[name = string("op_1081_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_21x = const()[name = string("concat_21x"), val = tensor([2, -1, 512])]; + tensor var_1081_cast_fp16 = transpose(perm = var_1081_perm_0, x = var_1080_cast_fp16)[name = string("transpose_74")]; + tensor input_173_cast_fp16 = reshape(shape = concat_21x, x = var_1081_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25146560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25408768))))[name = string("main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25409856)))]; + tensor linear_15_cast_fp16 = linear(bias = main_blocks_1_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_173_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor out_27_perm_0 = const()[name = string("out_27_perm_0"), val = tensor([0, 2, 1])]; + tensor out_27_cast_fp16 = transpose(perm = out_27_perm_0, x = linear_15_cast_fp16)[name = string("transpose_73")]; + tensor var_1089_cast_fp16 = mul(x = out_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor x_91_cast_fp16 = add(x = x_m_5_cast_fp16, y = var_1089_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_175_perm_0 = const()[name = string("input_175_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1096_axes_0 = const()[name = string("op_1096_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25410944)))]; + tensor main_blocks_1_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25412032)))]; + tensor input_175_cast_fp16 = transpose(perm = input_175_perm_0, x = x_91_cast_fp16)[name = string("transpose_72")]; + tensor var_1096_cast_fp16 = layer_norm(axes = var_1096_axes_0, beta = main_blocks_1_text_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_text_norm_norm_weight_to_fp16, x = input_175_cast_fp16)[name = string("op_1096_cast_fp16")]; + tensor var_1097_perm_0 = const()[name = string("op_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1097_cast_fp16 = transpose(perm = var_1097_perm_0, x = var_1096_cast_fp16)[name = string("transpose_71")]; + tensor x_93_cast_fp16 = mul(x = var_1097_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor x_95_cast_fp16 = mul(x = x_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_1110_cast_fp16 = mul(x = x_95_cast_fp16, y = last_1_cast_fp16)[name = string("op_1110_cast_fp16")]; + tensor x_last_23_axes_0 = const()[name = string("x_last_23_axes_0"), val = tensor([-1])]; + bool x_last_23_keep_dims_0 = const()[name = string("x_last_23_keep_dims_0"), val = bool(true)]; + tensor x_last_23_cast_fp16 = reduce_sum(axes = x_last_23_axes_0, keep_dims = x_last_23_keep_dims_0, x = var_1110_cast_fp16)[name = string("x_last_23_cast_fp16")]; + tensor var_1113_cast_fp16 = mul(x = x_95_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1115_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_23_cast_fp16)[name = string("op_1115_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = var_1113_cast_fp16, y = var_1115_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("replicate")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor input_181_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + string h_69_pad_type_0 = const()[name = string("h_69_pad_type_0"), val = string("valid")]; + int32 h_69_groups_0 = const()[name = string("h_69_groups_0"), val = int32(512)]; + tensor h_69_strides_0 = const()[name = string("h_69_strides_0"), val = tensor([1])]; + tensor h_69_pad_0 = const()[name = string("h_69_pad_0"), val = tensor([0, 0])]; + tensor h_69_dilations_0 = const()[name = string("h_69_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25413120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25415744))))[name = string("main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25416832)))]; + tensor h_69_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_69_dilations_0, groups = h_69_groups_0, pad = h_69_pad_0, pad_type = h_69_pad_type_0, strides = h_69_strides_0, weight = main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_181_cast_fp16)[name = string("h_69_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = h_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_183_perm_0 = const()[name = string("input_183_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1133_axes_0 = const()[name = string("op_1133_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25417920)))]; + tensor main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25419008)))]; + tensor input_183_cast_fp16 = transpose(perm = input_183_perm_0, x = x_97_cast_fp16)[name = string("transpose_70")]; + tensor var_1133_cast_fp16 = layer_norm(axes = var_1133_axes_0, beta = main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16, x = input_183_cast_fp16)[name = string("op_1133_cast_fp16")]; + tensor input_185_perm_0 = const()[name = string("input_185_perm_0"), val = tensor([0, 2, 1])]; + string h_71_pad_type_0 = const()[name = string("h_71_pad_type_0"), val = string("valid")]; + tensor h_71_strides_0 = const()[name = string("h_71_strides_0"), val = tensor([1])]; + tensor h_71_pad_0 = const()[name = string("h_71_pad_0"), val = tensor([0, 0])]; + tensor h_71_dilations_0 = const()[name = string("h_71_dilations_0"), val = tensor([1])]; + int32 h_71_groups_0 = const()[name = string("h_71_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25420096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26468736))))[name = string("main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26472896)))]; + tensor input_185_cast_fp16 = transpose(perm = input_185_perm_0, x = var_1133_cast_fp16)[name = string("transpose_69")]; + tensor h_71_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_71_dilations_0, groups = h_71_groups_0, pad = h_71_pad_0, pad_type = h_71_pad_type_0, strides = h_71_strides_0, weight = main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("h_71_cast_fp16")]; + string input_187_mode_0 = const()[name = string("input_187_mode_0"), val = string("EXACT")]; + tensor input_187_cast_fp16 = gelu(mode = input_187_mode_0, x = h_71_cast_fp16)[name = string("input_187_cast_fp16")]; + string h_73_pad_type_0 = const()[name = string("h_73_pad_type_0"), val = string("valid")]; + tensor h_73_strides_0 = const()[name = string("h_73_strides_0"), val = tensor([1])]; + tensor h_73_pad_0 = const()[name = string("h_73_pad_0"), val = tensor([0, 0])]; + tensor h_73_dilations_0 = const()[name = string("h_73_dilations_0"), val = tensor([1])]; + int32 h_73_groups_0 = const()[name = string("h_73_groups_0"), val = int32(1)]; + tensor op_1150_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26477056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27525696))))[name = string("op_1150_weight_0_to_fp16_quantized")]; + tensor var_1150_bias_0_to_fp16 = const()[name = string("op_1150_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27526784)))]; + tensor var_1150_cast_fp16 = conv(bias = var_1150_bias_0_to_fp16, dilations = h_73_dilations_0, groups = h_73_groups_0, pad = h_73_pad_0, pad_type = h_73_pad_type_0, strides = h_73_strides_0, weight = op_1150_weight_0_to_fp16_quantized, x = input_187_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor out_29_cast_fp16 = add(x = x_95_cast_fp16, y = var_1150_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor x_99_cast_fp16 = mul(x = out_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor x_m_7_cast_fp16 = mul(x = x_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_7_cast_fp16")]; + tensor input_189_perm_0 = const()[name = string("input_189_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27527872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27659008))))[name = string("main_blocks_1_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27659584)))]; + tensor input_189_cast_fp16 = transpose(perm = input_189_perm_0, x = x_99_cast_fp16)[name = string("transpose_68")]; + tensor linear_16_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor concat_22x = const()[name = string("concat_22x"), val = tensor([2, -1, 2, 128])]; + tensor var_1167_cast_fp16 = reshape(shape = concat_22x, x = linear_16_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_1_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27660160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27725760))))[name = string("main_blocks_1_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27726336)))]; + tensor linear_18_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_1180 = const()[name = string("op_1180"), val = tensor([2, 50, 2, 128])]; + tensor var_1181_cast_fp16 = reshape(shape = var_1180, x = linear_18_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1185_transpose_x_0 = const()[name = string("op_1185_transpose_x_0"), val = bool(false)]; + bool var_1185_transpose_y_0 = const()[name = string("op_1185_transpose_y_0"), val = bool(false)]; + tensor op_1184_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27726912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27752576))))[name = string("op_1184_to_fp16_quantized")]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1167_cast_fp16)[name = string("transpose_67")]; + tensor var_1185_cast_fp16 = matmul(transpose_x = var_1185_transpose_x_0, transpose_y = var_1185_transpose_y_0, x = q_7_cast_fp16, y = op_1184_to_fp16_quantized)[name = string("op_1185_cast_fp16")]; + fp16 _inversed_input_191_y_0_to_fp16 = const()[name = string("_inversed_input_191_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_191_cast_fp16 = mul(x = var_1185_cast_fp16, y = _inversed_input_191_y_0_to_fp16)[name = string("_inversed_input_191_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_681, x = _inversed_input_191_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor attn_11_cast_fp16 = mul(x = attn_9_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool var_1192_transpose_x_0 = const()[name = string("op_1192_transpose_x_0"), val = bool(false)]; + bool var_1192_transpose_y_0 = const()[name = string("op_1192_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1181_cast_fp16)[name = string("transpose_66")]; + tensor var_1192_cast_fp16 = matmul(transpose_x = var_1192_transpose_x_0, transpose_y = var_1192_transpose_y_0, x = attn_11_cast_fp16, y = v_7_cast_fp16)[name = string("op_1192_cast_fp16")]; + tensor var_1193_perm_0 = const()[name = string("op_1193_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_23x = const()[name = string("concat_23x"), val = tensor([2, -1, 256])]; + tensor var_1193_cast_fp16 = transpose(perm = var_1193_perm_0, x = var_1192_cast_fp16)[name = string("transpose_65")]; + tensor input_193_cast_fp16 = reshape(shape = concat_23x, x = var_1193_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27752768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27883904))))[name = string("main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_1_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27884992)))]; + tensor linear_19_cast_fp16 = linear(bias = main_blocks_1_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_193_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor out_31_perm_0 = const()[name = string("out_31_perm_0"), val = tensor([0, 2, 1])]; + tensor out_31_cast_fp16 = transpose(perm = out_31_perm_0, x = linear_19_cast_fp16)[name = string("transpose_64")]; + tensor var_1201_cast_fp16 = mul(x = out_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1201_cast_fp16")]; + tensor x_101_cast_fp16 = add(x = x_m_7_cast_fp16, y = var_1201_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_195_perm_0 = const()[name = string("input_195_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1208_axes_0 = const()[name = string("op_1208_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27886080)))]; + tensor main_blocks_1_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27887168)))]; + tensor input_195_cast_fp16 = transpose(perm = input_195_perm_0, x = x_101_cast_fp16)[name = string("transpose_63")]; + tensor var_1208_cast_fp16 = layer_norm(axes = var_1208_axes_0, beta = main_blocks_1_style_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_style_norm_norm_weight_to_fp16, x = input_195_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_perm_0 = const()[name = string("op_1209_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1209_cast_fp16 = transpose(perm = var_1209_perm_0, x = var_1208_cast_fp16)[name = string("transpose_62")]; + tensor x_103_cast_fp16 = mul(x = var_1209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_103_cast_fp16")]; + int32 var_1228 = const()[name = string("op_1228"), val = int32(-1)]; + tensor x_105_cast_fp16 = mul(x = x_103_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_1263_cast_fp16 = mul(x = x_105_cast_fp16, y = last_1_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor x_last_25_axes_0 = const()[name = string("x_last_25_axes_0"), val = tensor([-1])]; + bool x_last_25_keep_dims_0 = const()[name = string("x_last_25_keep_dims_0"), val = bool(true)]; + tensor x_last_25_cast_fp16 = reduce_sum(axes = x_last_25_axes_0, keep_dims = x_last_25_keep_dims_0, x = var_1263_cast_fp16)[name = string("x_last_25_cast_fp16")]; + tensor var_1266_cast_fp16 = mul(x = x_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1266_cast_fp16")]; + tensor var_1268_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_25_cast_fp16)[name = string("op_1268_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1266_cast_fp16, y = var_1268_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_201_mode_0 = const()[name = string("input_201_mode_0"), val = string("replicate")]; + fp16 const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = fp16(0x0p+0)]; + tensor input_201_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")]; + string h_75_pad_type_0 = const()[name = string("h_75_pad_type_0"), val = string("valid")]; + int32 h_75_groups_0 = const()[name = string("h_75_groups_0"), val = int32(512)]; + tensor h_75_strides_0 = const()[name = string("h_75_strides_0"), val = tensor([1])]; + tensor h_75_pad_0 = const()[name = string("h_75_pad_0"), val = tensor([0, 0])]; + tensor h_75_dilations_0 = const()[name = string("h_75_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27888256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27890880))))[name = string("main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27891968)))]; + tensor h_75_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_75_dilations_0, groups = h_75_groups_0, pad = h_75_pad_0, pad_type = h_75_pad_type_0, strides = h_75_strides_0, weight = main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_201_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = h_75_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_203_perm_0 = const()[name = string("input_203_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1286_axes_0 = const()[name = string("op_1286_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27893056)))]; + tensor main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894144)))]; + fp16 var_1233_to_fp16 = const()[name = string("op_1233_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_203_cast_fp16 = transpose(perm = input_203_perm_0, x = x_107_cast_fp16)[name = string("transpose_61")]; + tensor var_1286_cast_fp16 = layer_norm(axes = var_1286_axes_0, beta = main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16, x = input_203_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string h_77_pad_type_0 = const()[name = string("h_77_pad_type_0"), val = string("valid")]; + tensor h_77_strides_0 = const()[name = string("h_77_strides_0"), val = tensor([1])]; + tensor h_77_pad_0 = const()[name = string("h_77_pad_0"), val = tensor([0, 0])]; + tensor h_77_dilations_0 = const()[name = string("h_77_dilations_0"), val = tensor([1])]; + int32 h_77_groups_0 = const()[name = string("h_77_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27895232))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28943872))))[name = string("main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28948032)))]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = var_1286_cast_fp16)[name = string("transpose_60")]; + tensor h_77_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_77_dilations_0, groups = h_77_groups_0, pad = h_77_pad_0, pad_type = h_77_pad_type_0, strides = h_77_strides_0, weight = main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("h_77_cast_fp16")]; + string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")]; + tensor input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = h_77_cast_fp16)[name = string("input_207_cast_fp16")]; + string h_79_pad_type_0 = const()[name = string("h_79_pad_type_0"), val = string("valid")]; + tensor h_79_strides_0 = const()[name = string("h_79_strides_0"), val = tensor([1])]; + tensor h_79_pad_0 = const()[name = string("h_79_pad_0"), val = tensor([0, 0])]; + tensor h_79_dilations_0 = const()[name = string("h_79_dilations_0"), val = tensor([1])]; + int32 h_79_groups_0 = const()[name = string("h_79_groups_0"), val = int32(1)]; + tensor op_1303_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28952192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30000832))))[name = string("op_1303_weight_0_to_fp16_quantized")]; + tensor var_1303_bias_0_to_fp16 = const()[name = string("op_1303_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30001920)))]; + tensor var_1303_cast_fp16 = conv(bias = var_1303_bias_0_to_fp16, dilations = h_79_dilations_0, groups = h_79_groups_0, pad = h_79_pad_0, pad_type = h_79_pad_type_0, strides = h_79_strides_0, weight = op_1303_weight_0_to_fp16_quantized, x = input_207_cast_fp16)[name = string("op_1303_cast_fp16")]; + tensor out_33_cast_fp16 = add(x = x_105_cast_fp16, y = var_1303_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor x_109_cast_fp16 = mul(x = out_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor x_111_cast_fp16 = mul(x = x_109_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_1317_cast_fp16 = mul(x = x_111_cast_fp16, y = last_1_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor x_last_27_axes_0 = const()[name = string("x_last_27_axes_0"), val = tensor([-1])]; + bool x_last_27_keep_dims_0 = const()[name = string("x_last_27_keep_dims_0"), val = bool(true)]; + tensor x_last_27_cast_fp16 = reduce_sum(axes = x_last_27_axes_0, keep_dims = x_last_27_keep_dims_0, x = var_1317_cast_fp16)[name = string("x_last_27_cast_fp16")]; + tensor var_1320_cast_fp16 = mul(x = x_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1322_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_27_cast_fp16)[name = string("op_1322_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1320_cast_fp16, y = var_1322_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("replicate")]; + fp16 const_29_to_fp16 = const()[name = string("const_29_to_fp16"), val = fp16(0x0p+0)]; + tensor input_213_cast_fp16 = pad(constant_val = const_29_to_fp16, mode = input_213_mode_0, pad = input_213_pad_0, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + string h_81_pad_type_0 = const()[name = string("h_81_pad_type_0"), val = string("valid")]; + tensor h_81_dilations_0 = const()[name = string("h_81_dilations_0"), val = tensor([2])]; + int32 h_81_groups_0 = const()[name = string("h_81_groups_0"), val = int32(512)]; + tensor h_81_strides_0 = const()[name = string("h_81_strides_0"), val = tensor([1])]; + tensor h_81_pad_0 = const()[name = string("h_81_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30003008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30005632))))[name = string("main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30006720)))]; + tensor h_81_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_81_dilations_0, groups = h_81_groups_0, pad = h_81_pad_0, pad_type = h_81_pad_type_0, strides = h_81_strides_0, weight = main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_213_cast_fp16)[name = string("h_81_cast_fp16")]; + tensor x_113_cast_fp16 = mul(x = h_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor input_215_perm_0 = const()[name = string("input_215_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1340_axes_0 = const()[name = string("op_1340_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30007808)))]; + tensor main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30008896)))]; + tensor input_215_cast_fp16 = transpose(perm = input_215_perm_0, x = x_113_cast_fp16)[name = string("transpose_59")]; + tensor var_1340_cast_fp16 = layer_norm(axes = var_1340_axes_0, beta = main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16, x = input_215_cast_fp16)[name = string("op_1340_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + string h_83_pad_type_0 = const()[name = string("h_83_pad_type_0"), val = string("valid")]; + tensor h_83_strides_0 = const()[name = string("h_83_strides_0"), val = tensor([1])]; + tensor h_83_pad_0 = const()[name = string("h_83_pad_0"), val = tensor([0, 0])]; + tensor h_83_dilations_0 = const()[name = string("h_83_dilations_0"), val = tensor([1])]; + int32 h_83_groups_0 = const()[name = string("h_83_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30009984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31058624))))[name = string("main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31062784)))]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = var_1340_cast_fp16)[name = string("transpose_58")]; + tensor h_83_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_83_dilations_0, groups = h_83_groups_0, pad = h_83_pad_0, pad_type = h_83_pad_type_0, strides = h_83_strides_0, weight = main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_217_cast_fp16)[name = string("h_83_cast_fp16")]; + string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")]; + tensor input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = h_83_cast_fp16)[name = string("input_219_cast_fp16")]; + string h_85_pad_type_0 = const()[name = string("h_85_pad_type_0"), val = string("valid")]; + tensor h_85_strides_0 = const()[name = string("h_85_strides_0"), val = tensor([1])]; + tensor h_85_pad_0 = const()[name = string("h_85_pad_0"), val = tensor([0, 0])]; + tensor h_85_dilations_0 = const()[name = string("h_85_dilations_0"), val = tensor([1])]; + int32 h_85_groups_0 = const()[name = string("h_85_groups_0"), val = int32(1)]; + tensor op_1357_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31066944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32115584))))[name = string("op_1357_weight_0_to_fp16_quantized")]; + tensor var_1357_bias_0_to_fp16 = const()[name = string("op_1357_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32116672)))]; + tensor var_1357_cast_fp16 = conv(bias = var_1357_bias_0_to_fp16, dilations = h_85_dilations_0, groups = h_85_groups_0, pad = h_85_pad_0, pad_type = h_85_pad_type_0, strides = h_85_strides_0, weight = op_1357_weight_0_to_fp16_quantized, x = input_219_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor out_35_cast_fp16 = add(x = x_111_cast_fp16, y = var_1357_cast_fp16)[name = string("out_35_cast_fp16")]; + tensor x_115_cast_fp16 = mul(x = out_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_cast_fp16 = mul(x = x_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1371_cast_fp16 = mul(x = x_117_cast_fp16, y = last_1_cast_fp16)[name = string("op_1371_cast_fp16")]; + tensor x_last_29_axes_0 = const()[name = string("x_last_29_axes_0"), val = tensor([-1])]; + bool x_last_29_keep_dims_0 = const()[name = string("x_last_29_keep_dims_0"), val = bool(true)]; + tensor x_last_29_cast_fp16 = reduce_sum(axes = x_last_29_axes_0, keep_dims = x_last_29_keep_dims_0, x = var_1371_cast_fp16)[name = string("x_last_29_cast_fp16")]; + tensor var_1374_cast_fp16 = mul(x = x_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_29_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1376_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_225_mode_0 = const()[name = string("input_225_mode_0"), val = string("replicate")]; + fp16 const_31_to_fp16 = const()[name = string("const_31_to_fp16"), val = fp16(0x0p+0)]; + tensor input_225_cast_fp16 = pad(constant_val = const_31_to_fp16, mode = input_225_mode_0, pad = input_225_pad_0, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")]; + string h_87_pad_type_0 = const()[name = string("h_87_pad_type_0"), val = string("valid")]; + tensor h_87_dilations_0 = const()[name = string("h_87_dilations_0"), val = tensor([4])]; + int32 h_87_groups_0 = const()[name = string("h_87_groups_0"), val = int32(512)]; + tensor h_87_strides_0 = const()[name = string("h_87_strides_0"), val = tensor([1])]; + tensor h_87_pad_0 = const()[name = string("h_87_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32117760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32120384))))[name = string("main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32121472)))]; + tensor h_87_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_87_dilations_0, groups = h_87_groups_0, pad = h_87_pad_0, pad_type = h_87_pad_type_0, strides = h_87_strides_0, weight = main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor x_119_cast_fp16 = mul(x = h_87_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_227_perm_0 = const()[name = string("input_227_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1394_axes_0 = const()[name = string("op_1394_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32122560)))]; + tensor main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32123648)))]; + tensor input_227_cast_fp16 = transpose(perm = input_227_perm_0, x = x_119_cast_fp16)[name = string("transpose_57")]; + tensor var_1394_cast_fp16 = layer_norm(axes = var_1394_axes_0, beta = main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16, x = input_227_cast_fp16)[name = string("op_1394_cast_fp16")]; + tensor input_229_perm_0 = const()[name = string("input_229_perm_0"), val = tensor([0, 2, 1])]; + string h_89_pad_type_0 = const()[name = string("h_89_pad_type_0"), val = string("valid")]; + tensor h_89_strides_0 = const()[name = string("h_89_strides_0"), val = tensor([1])]; + tensor h_89_pad_0 = const()[name = string("h_89_pad_0"), val = tensor([0, 0])]; + tensor h_89_dilations_0 = const()[name = string("h_89_dilations_0"), val = tensor([1])]; + int32 h_89_groups_0 = const()[name = string("h_89_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32124736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33173376))))[name = string("main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33177536)))]; + tensor input_229_cast_fp16 = transpose(perm = input_229_perm_0, x = var_1394_cast_fp16)[name = string("transpose_56")]; + tensor h_89_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_89_dilations_0, groups = h_89_groups_0, pad = h_89_pad_0, pad_type = h_89_pad_type_0, strides = h_89_strides_0, weight = main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_229_cast_fp16)[name = string("h_89_cast_fp16")]; + string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")]; + tensor input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = h_89_cast_fp16)[name = string("input_231_cast_fp16")]; + string h_91_pad_type_0 = const()[name = string("h_91_pad_type_0"), val = string("valid")]; + tensor h_91_strides_0 = const()[name = string("h_91_strides_0"), val = tensor([1])]; + tensor h_91_pad_0 = const()[name = string("h_91_pad_0"), val = tensor([0, 0])]; + tensor h_91_dilations_0 = const()[name = string("h_91_dilations_0"), val = tensor([1])]; + int32 h_91_groups_0 = const()[name = string("h_91_groups_0"), val = int32(1)]; + tensor op_1411_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33181696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34230336))))[name = string("op_1411_weight_0_to_fp16_quantized")]; + tensor var_1411_bias_0_to_fp16 = const()[name = string("op_1411_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34231424)))]; + tensor var_1411_cast_fp16 = conv(bias = var_1411_bias_0_to_fp16, dilations = h_91_dilations_0, groups = h_91_groups_0, pad = h_91_pad_0, pad_type = h_91_pad_type_0, strides = h_91_strides_0, weight = op_1411_weight_0_to_fp16_quantized, x = input_231_cast_fp16)[name = string("op_1411_cast_fp16")]; + tensor out_37_cast_fp16 = add(x = x_117_cast_fp16, y = var_1411_cast_fp16)[name = string("out_37_cast_fp16")]; + tensor x_121_cast_fp16 = mul(x = out_37_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_121_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1425_cast_fp16 = mul(x = x_123_cast_fp16, y = last_1_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor x_last_31_axes_0 = const()[name = string("x_last_31_axes_0"), val = tensor([-1])]; + bool x_last_31_keep_dims_0 = const()[name = string("x_last_31_keep_dims_0"), val = bool(true)]; + tensor x_last_31_cast_fp16 = reduce_sum(axes = x_last_31_axes_0, keep_dims = x_last_31_keep_dims_0, x = var_1425_cast_fp16)[name = string("x_last_31_cast_fp16")]; + tensor var_1428_cast_fp16 = mul(x = x_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_31_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = var_1428_cast_fp16, y = var_1430_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("replicate")]; + fp16 const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = fp16(0x0p+0)]; + tensor input_237_cast_fp16 = pad(constant_val = const_33_to_fp16, mode = input_237_mode_0, pad = input_237_pad_0, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + string h_93_pad_type_0 = const()[name = string("h_93_pad_type_0"), val = string("valid")]; + tensor h_93_dilations_0 = const()[name = string("h_93_dilations_0"), val = tensor([8])]; + int32 h_93_groups_0 = const()[name = string("h_93_groups_0"), val = int32(512)]; + tensor h_93_strides_0 = const()[name = string("h_93_strides_0"), val = tensor([1])]; + tensor h_93_pad_0 = const()[name = string("h_93_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34232512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34235136))))[name = string("main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34236224)))]; + tensor h_93_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_93_dilations_0, groups = h_93_groups_0, pad = h_93_pad_0, pad_type = h_93_pad_type_0, strides = h_93_strides_0, weight = main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_237_cast_fp16)[name = string("h_93_cast_fp16")]; + tensor x_125_cast_fp16 = mul(x = h_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_239_perm_0 = const()[name = string("input_239_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1448_axes_0 = const()[name = string("op_1448_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34237312)))]; + tensor main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34238400)))]; + tensor input_239_cast_fp16 = transpose(perm = input_239_perm_0, x = x_125_cast_fp16)[name = string("transpose_55")]; + tensor var_1448_cast_fp16 = layer_norm(axes = var_1448_axes_0, beta = main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16, x = input_239_cast_fp16)[name = string("op_1448_cast_fp16")]; + tensor input_241_perm_0 = const()[name = string("input_241_perm_0"), val = tensor([0, 2, 1])]; + string h_95_pad_type_0 = const()[name = string("h_95_pad_type_0"), val = string("valid")]; + tensor h_95_strides_0 = const()[name = string("h_95_strides_0"), val = tensor([1])]; + tensor h_95_pad_0 = const()[name = string("h_95_pad_0"), val = tensor([0, 0])]; + tensor h_95_dilations_0 = const()[name = string("h_95_dilations_0"), val = tensor([1])]; + int32 h_95_groups_0 = const()[name = string("h_95_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34239488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35288128))))[name = string("main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35292288)))]; + tensor input_241_cast_fp16 = transpose(perm = input_241_perm_0, x = var_1448_cast_fp16)[name = string("transpose_54")]; + tensor h_95_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_95_dilations_0, groups = h_95_groups_0, pad = h_95_pad_0, pad_type = h_95_pad_type_0, strides = h_95_strides_0, weight = main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_241_cast_fp16)[name = string("h_95_cast_fp16")]; + string input_243_mode_0 = const()[name = string("input_243_mode_0"), val = string("EXACT")]; + tensor input_243_cast_fp16 = gelu(mode = input_243_mode_0, x = h_95_cast_fp16)[name = string("input_243_cast_fp16")]; + string h_97_pad_type_0 = const()[name = string("h_97_pad_type_0"), val = string("valid")]; + tensor h_97_strides_0 = const()[name = string("h_97_strides_0"), val = tensor([1])]; + tensor h_97_pad_0 = const()[name = string("h_97_pad_0"), val = tensor([0, 0])]; + tensor h_97_dilations_0 = const()[name = string("h_97_dilations_0"), val = tensor([1])]; + int32 h_97_groups_0 = const()[name = string("h_97_groups_0"), val = int32(1)]; + tensor op_1465_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35296448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36345088))))[name = string("op_1465_weight_0_to_fp16_quantized")]; + tensor var_1465_bias_0_to_fp16 = const()[name = string("op_1465_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36346176)))]; + tensor var_1465_cast_fp16 = conv(bias = var_1465_bias_0_to_fp16, dilations = h_97_dilations_0, groups = h_97_groups_0, pad = h_97_pad_0, pad_type = h_97_pad_type_0, strides = h_97_strides_0, weight = op_1465_weight_0_to_fp16_quantized, x = input_243_cast_fp16)[name = string("op_1465_cast_fp16")]; + tensor out_39_cast_fp16 = add(x = x_123_cast_fp16, y = var_1465_cast_fp16)[name = string("out_39_cast_fp16")]; + tensor x_127_cast_fp16 = mul(x = out_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor main_blocks_2_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36347264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36380096))))[name = string("main_blocks_2_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36381184)))]; + tensor linear_20_cast_fp16 = linear(bias = main_blocks_2_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_2_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor t_9_axes_0 = const()[name = string("t_9_axes_0"), val = tensor([-1])]; + tensor t_9_cast_fp16 = expand_dims(axes = t_9_axes_0, x = linear_20_cast_fp16)[name = string("t_9_cast_fp16")]; + tensor var_1475_cast_fp16 = add(x = x_127_cast_fp16, y = t_9_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = var_1475_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor x_131_cast_fp16 = mul(x = x_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor var_1488_cast_fp16 = mul(x = x_131_cast_fp16, y = last_1_cast_fp16)[name = string("op_1488_cast_fp16")]; + tensor x_last_33_axes_0 = const()[name = string("x_last_33_axes_0"), val = tensor([-1])]; + bool x_last_33_keep_dims_0 = const()[name = string("x_last_33_keep_dims_0"), val = bool(true)]; + tensor x_last_33_cast_fp16 = reduce_sum(axes = x_last_33_axes_0, keep_dims = x_last_33_keep_dims_0, x = var_1488_cast_fp16)[name = string("x_last_33_cast_fp16")]; + tensor var_1491_cast_fp16 = mul(x = x_131_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1491_cast_fp16")]; + tensor var_1493_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_33_cast_fp16)[name = string("op_1493_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = var_1491_cast_fp16, y = var_1493_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_251_mode_0 = const()[name = string("input_251_mode_0"), val = string("replicate")]; + fp16 const_35_to_fp16 = const()[name = string("const_35_to_fp16"), val = fp16(0x0p+0)]; + tensor input_251_cast_fp16 = pad(constant_val = const_35_to_fp16, mode = input_251_mode_0, pad = input_251_pad_0, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + string h_99_pad_type_0 = const()[name = string("h_99_pad_type_0"), val = string("valid")]; + int32 h_99_groups_0 = const()[name = string("h_99_groups_0"), val = int32(512)]; + tensor h_99_strides_0 = const()[name = string("h_99_strides_0"), val = tensor([1])]; + tensor h_99_pad_0 = const()[name = string("h_99_pad_0"), val = tensor([0, 0])]; + tensor h_99_dilations_0 = const()[name = string("h_99_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36382272))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36384896))))[name = string("main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36385984)))]; + tensor h_99_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_99_dilations_0, groups = h_99_groups_0, pad = h_99_pad_0, pad_type = h_99_pad_type_0, strides = h_99_strides_0, weight = main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor x_133_cast_fp16 = mul(x = h_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_253_perm_0 = const()[name = string("input_253_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1511_axes_0 = const()[name = string("op_1511_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36387072)))]; + tensor main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36388160)))]; + tensor input_253_cast_fp16 = transpose(perm = input_253_perm_0, x = x_133_cast_fp16)[name = string("transpose_53")]; + tensor var_1511_cast_fp16 = layer_norm(axes = var_1511_axes_0, beta = main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16, x = input_253_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor input_255_perm_0 = const()[name = string("input_255_perm_0"), val = tensor([0, 2, 1])]; + string h_101_pad_type_0 = const()[name = string("h_101_pad_type_0"), val = string("valid")]; + tensor h_101_strides_0 = const()[name = string("h_101_strides_0"), val = tensor([1])]; + tensor h_101_pad_0 = const()[name = string("h_101_pad_0"), val = tensor([0, 0])]; + tensor h_101_dilations_0 = const()[name = string("h_101_dilations_0"), val = tensor([1])]; + int32 h_101_groups_0 = const()[name = string("h_101_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36389248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37437888))))[name = string("main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37442048)))]; + tensor input_255_cast_fp16 = transpose(perm = input_255_perm_0, x = var_1511_cast_fp16)[name = string("transpose_52")]; + tensor h_101_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_101_dilations_0, groups = h_101_groups_0, pad = h_101_pad_0, pad_type = h_101_pad_type_0, strides = h_101_strides_0, weight = main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("h_101_cast_fp16")]; + string input_257_mode_0 = const()[name = string("input_257_mode_0"), val = string("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = h_101_cast_fp16)[name = string("input_257_cast_fp16")]; + string h_103_pad_type_0 = const()[name = string("h_103_pad_type_0"), val = string("valid")]; + tensor h_103_strides_0 = const()[name = string("h_103_strides_0"), val = tensor([1])]; + tensor h_103_pad_0 = const()[name = string("h_103_pad_0"), val = tensor([0, 0])]; + tensor h_103_dilations_0 = const()[name = string("h_103_dilations_0"), val = tensor([1])]; + int32 h_103_groups_0 = const()[name = string("h_103_groups_0"), val = int32(1)]; + tensor op_1528_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37446208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38494848))))[name = string("op_1528_weight_0_to_fp16_quantized")]; + tensor var_1528_bias_0_to_fp16 = const()[name = string("op_1528_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38495936)))]; + tensor var_1528_cast_fp16 = conv(bias = var_1528_bias_0_to_fp16, dilations = h_103_dilations_0, groups = h_103_groups_0, pad = h_103_pad_0, pad_type = h_103_pad_type_0, strides = h_103_strides_0, weight = op_1528_weight_0_to_fp16_quantized, x = input_257_cast_fp16)[name = string("op_1528_cast_fp16")]; + tensor out_41_cast_fp16 = add(x = x_131_cast_fp16, y = var_1528_cast_fp16)[name = string("out_41_cast_fp16")]; + tensor x_135_cast_fp16 = mul(x = out_41_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor x_m_9_cast_fp16 = mul(x = x_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_9_cast_fp16")]; + tensor var_1538_shape_cast_fp16 = shape(x = x_135_cast_fp16)[name = string("op_1538_shape_cast_fp16")]; + int32 gather_14_axis_0 = const()[name = string("gather_14_axis_0"), val = int32(0)]; + int32 gather_14_batch_dims_0 = const()[name = string("gather_14_batch_dims_0"), val = int32(0)]; + bool gather_14_validate_indices_0 = const()[name = string("gather_14_validate_indices_0"), val = bool(false)]; + string var_1538_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_1538_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_14_indices_0_to_uint16 = const()[name = string("gather_14_indices_0_to_uint16"), val = uint16(2)]; + tensor var_1538_shape_cast_fp16_to_uint16 = cast(dtype = var_1538_shape_cast_fp16_to_uint16_dtype_0, x = var_1538_shape_cast_fp16)[name = string("cast_4")]; + uint16 gather_14_cast_uint16 = gather(axis = gather_14_axis_0, batch_dims = gather_14_batch_dims_0, indices = gather_14_indices_0_to_uint16, validate_indices = gather_14_validate_indices_0, x = var_1538_shape_cast_fp16_to_uint16)[name = string("gather_14_cast_uint16")]; + string gather_14_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_14_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_259_perm_0 = const()[name = string("input_259_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38497024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38759232))))[name = string("main_blocks_2_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38760320)))]; + tensor input_259_cast_fp16 = transpose(perm = input_259_perm_0, x = x_135_cast_fp16)[name = string("transpose_51")]; + tensor linear_21_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_259_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor concat_24x = const()[name = string("concat_24x"), val = tensor([2, -1, 8, 64])]; + tensor var_1547_cast_fp16 = reshape(shape = concat_24x, x = linear_21_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor x_137_perm_0 = const()[name = string("x_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38761408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38892544))))[name = string("main_blocks_2_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38893632)))]; + tensor linear_22_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor concat_25x = const()[name = string("concat_25x"), val = tensor([2, -1, 8, 64])]; + tensor var_1555_cast_fp16 = reshape(shape = concat_25x, x = linear_22_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_139_perm_0 = const()[name = string("x_139_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38894720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39025856))))[name = string("main_blocks_2_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39026944)))]; + tensor linear_23_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor concat_26x = const()[name = string("concat_26x"), val = tensor([2, -1, 8, 64])]; + tensor var_1563_cast_fp16 = reshape(shape = concat_26x, x = linear_23_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, -3, -1])]; + int32 concat_27_values0_0 = const()[name = string("concat_27_values0_0"), val = int32(1)]; + int32 concat_27_values2_0 = const()[name = string("concat_27_values2_0"), val = int32(1)]; + int32 concat_27_axis_0 = const()[name = string("concat_27_axis_0"), val = int32(0)]; + bool concat_27_interleave_0 = const()[name = string("concat_27_interleave_0"), val = bool(false)]; + int32 gather_14_cast_uint16_to_int32 = cast(dtype = gather_14_cast_uint16_to_int32_dtype_0, x = gather_14_cast_uint16)[name = string("cast_3")]; + tensor concat_27 = concat(axis = concat_27_axis_0, interleave = concat_27_interleave_0, values = (concat_27_values0_0, gather_14_cast_uint16_to_int32, concat_27_values2_0))[name = string("concat_27")]; + tensor var_1566_begin_0 = const()[name = string("op_1566_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1566_end_mask_0 = const()[name = string("op_1566_end_mask_0"), val = tensor([true, false, true])]; + tensor var_1566_cast_fp16 = slice_by_index(begin = var_1566_begin_0, end = concat_27, end_mask = var_1566_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_1566_cast_fp16")]; + tensor concat_28 = const()[name = string("concat_28"), val = tensor([2, -1, -1])]; + tensor shape_5_cast_fp16 = shape(x = var_1566_cast_fp16)[name = string("shape_5_cast_fp16")]; + tensor equal_5 = const()[name = string("equal_5"), val = tensor([false, true, true])]; + tensor select_5 = select(a = shape_5_cast_fp16, b = concat_28, cond = equal_5)[name = string("select_5")]; + tensor real_div_5 = real_div(x = select_5, y = shape_5_cast_fp16)[name = string("real_div_5")]; + tensor var_1569_cast_fp16 = tile(reps = real_div_5, x = var_1566_cast_fp16)[name = string("op_1569_cast_fp16")]; + tensor scaled_9_cast_fp16 = real_div(x = var_1569_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_9_cast_fp16")]; + tensor main_blocks_2_text_attn_theta_to_fp16 = const()[name = string("main_blocks_2_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39028032)))]; + tensor angles_9_cast_fp16 = mul(x = scaled_9_cast_fp16, y = main_blocks_2_text_attn_theta_to_fp16)[name = string("angles_9_cast_fp16")]; + tensor var_1585_cast_fp16 = cos(x = angles_9_cast_fp16)[name = string("op_1585_cast_fp16")]; + tensor cos_9_axes_0 = const()[name = string("cos_9_axes_0"), val = tensor([1])]; + tensor cos_9_cast_fp16 = expand_dims(axes = cos_9_axes_0, x = var_1585_cast_fp16)[name = string("cos_9_cast_fp16")]; + tensor var_1587_cast_fp16 = sin(x = angles_9_cast_fp16)[name = string("op_1587_cast_fp16")]; + tensor sin_9_axes_0 = const()[name = string("sin_9_axes_0"), val = tensor([1])]; + tensor sin_9_cast_fp16 = expand_dims(axes = sin_9_axes_0, x = var_1587_cast_fp16)[name = string("sin_9_cast_fp16")]; + tensor x_a_9_begin_0 = const()[name = string("x_a_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_9_end_0 = const()[name = string("x_a_9_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_9_end_mask_0 = const()[name = string("x_a_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_137_cast_fp16 = transpose(perm = x_137_perm_0, x = var_1547_cast_fp16)[name = string("transpose_50")]; + tensor x_a_9_cast_fp16 = slice_by_index(begin = x_a_9_begin_0, end = x_a_9_end_0, end_mask = x_a_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_a_9_cast_fp16")]; + tensor x_b_9_begin_0 = const()[name = string("x_b_9_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_9_end_0 = const()[name = string("x_b_9_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_9_end_mask_0 = const()[name = string("x_b_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_9_cast_fp16 = slice_by_index(begin = x_b_9_begin_0, end = x_b_9_end_0, end_mask = x_b_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_b_9_cast_fp16")]; + tensor var_1591_cast_fp16 = mul(x = x_a_9_cast_fp16, y = cos_9_cast_fp16)[name = string("op_1591_cast_fp16")]; + tensor var_1592_cast_fp16 = mul(x = x_b_9_cast_fp16, y = sin_9_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor rot_a_9_cast_fp16 = sub(x = var_1591_cast_fp16, y = var_1592_cast_fp16)[name = string("rot_a_9_cast_fp16")]; + tensor var_1594_cast_fp16 = mul(x = x_a_9_cast_fp16, y = sin_9_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595_cast_fp16 = mul(x = x_b_9_cast_fp16, y = cos_9_cast_fp16)[name = string("op_1595_cast_fp16")]; + tensor rot_b_9_cast_fp16 = add(x = var_1594_cast_fp16, y = var_1595_cast_fp16)[name = string("rot_b_9_cast_fp16")]; + bool q_9_interleave_0 = const()[name = string("q_9_interleave_0"), val = bool(false)]; + tensor q_9_cast_fp16 = concat(axis = var_1228, interleave = q_9_interleave_0, values = (rot_a_9_cast_fp16, rot_b_9_cast_fp16))[name = string("q_9_cast_fp16")]; + tensor x_a_11_begin_0 = const()[name = string("x_a_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_11_end_0 = const()[name = string("x_a_11_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_11_end_mask_0 = const()[name = string("x_a_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_139_cast_fp16 = transpose(perm = x_139_perm_0, x = var_1555_cast_fp16)[name = string("transpose_49")]; + tensor x_a_11_cast_fp16 = slice_by_index(begin = x_a_11_begin_0, end = x_a_11_end_0, end_mask = x_a_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_a_11_cast_fp16")]; + tensor x_b_11_begin_0 = const()[name = string("x_b_11_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_11_end_0 = const()[name = string("x_b_11_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_11_end_mask_0 = const()[name = string("x_b_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_11_cast_fp16 = slice_by_index(begin = x_b_11_begin_0, end = x_b_11_end_0, end_mask = x_b_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_b_11_cast_fp16")]; + tensor var_1609_cast_fp16 = mul(x = x_a_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor var_1610_cast_fp16 = mul(x = x_b_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor rot_a_11_cast_fp16 = sub(x = var_1609_cast_fp16, y = var_1610_cast_fp16)[name = string("rot_a_11_cast_fp16")]; + tensor var_1612_cast_fp16 = mul(x = x_a_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1612_cast_fp16")]; + tensor var_1613_cast_fp16 = mul(x = x_b_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1613_cast_fp16")]; + tensor rot_b_11_cast_fp16 = add(x = var_1612_cast_fp16, y = var_1613_cast_fp16)[name = string("rot_b_11_cast_fp16")]; + bool k_9_interleave_0 = const()[name = string("k_9_interleave_0"), val = bool(false)]; + tensor k_9_cast_fp16 = concat(axis = var_1228, interleave = k_9_interleave_0, values = (rot_a_11_cast_fp16, rot_b_11_cast_fp16))[name = string("k_9_cast_fp16")]; + bool var_1618_transpose_x_1 = const()[name = string("op_1618_transpose_x_1"), val = bool(false)]; + bool var_1618_transpose_y_1 = const()[name = string("op_1618_transpose_y_1"), val = bool(true)]; + tensor var_1618_cast_fp16 = matmul(transpose_x = var_1618_transpose_x_1, transpose_y = var_1618_transpose_y_1, x = q_9_cast_fp16, y = k_9_cast_fp16)[name = string("op_1618_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_1618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor input_265_cast_fp16 = sub(x = _inversed_scores_5_cast_fp16, y = var_530_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1228, x = input_265_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool var_1627_transpose_x_0 = const()[name = string("op_1627_transpose_x_0"), val = bool(false)]; + bool var_1627_transpose_y_0 = const()[name = string("op_1627_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1563_cast_fp16)[name = string("transpose_48")]; + tensor var_1627_cast_fp16 = matmul(transpose_x = var_1627_transpose_x_0, transpose_y = var_1627_transpose_y_0, x = attn_13_cast_fp16, y = v_9_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor var_1628_perm_0 = const()[name = string("op_1628_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_31x = const()[name = string("concat_31x"), val = tensor([2, -1, 512])]; + tensor var_1628_cast_fp16 = transpose(perm = var_1628_perm_0, x = var_1627_cast_fp16)[name = string("transpose_47")]; + tensor input_267_cast_fp16 = reshape(shape = concat_31x, x = var_1628_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39028160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39290368))))[name = string("main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39291456)))]; + tensor linear_24_cast_fp16 = linear(bias = main_blocks_2_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_267_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor out_43_perm_0 = const()[name = string("out_43_perm_0"), val = tensor([0, 2, 1])]; + tensor out_43_cast_fp16 = transpose(perm = out_43_perm_0, x = linear_24_cast_fp16)[name = string("transpose_46")]; + tensor var_1636_cast_fp16 = mul(x = out_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1636_cast_fp16")]; + tensor x_141_cast_fp16 = add(x = x_m_9_cast_fp16, y = var_1636_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1643_axes_0 = const()[name = string("op_1643_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39292544)))]; + tensor main_blocks_2_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39293632)))]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_141_cast_fp16)[name = string("transpose_45")]; + tensor var_1643_cast_fp16 = layer_norm(axes = var_1643_axes_0, beta = main_blocks_2_text_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_text_norm_norm_weight_to_fp16, x = input_269_cast_fp16)[name = string("op_1643_cast_fp16")]; + tensor var_1644_perm_0 = const()[name = string("op_1644_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1644_cast_fp16 = transpose(perm = var_1644_perm_0, x = var_1643_cast_fp16)[name = string("transpose_44")]; + tensor x_143_cast_fp16 = mul(x = var_1644_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor x_145_cast_fp16 = mul(x = x_143_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1657_cast_fp16 = mul(x = x_145_cast_fp16, y = last_1_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor x_last_35_axes_0 = const()[name = string("x_last_35_axes_0"), val = tensor([-1])]; + bool x_last_35_keep_dims_0 = const()[name = string("x_last_35_keep_dims_0"), val = bool(true)]; + tensor x_last_35_cast_fp16 = reduce_sum(axes = x_last_35_axes_0, keep_dims = x_last_35_keep_dims_0, x = var_1657_cast_fp16)[name = string("x_last_35_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = x_145_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1662_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_35_cast_fp16)[name = string("op_1662_cast_fp16")]; + tensor input_273_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1662_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("replicate")]; + fp16 const_37_to_fp16 = const()[name = string("const_37_to_fp16"), val = fp16(0x0p+0)]; + tensor input_275_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_275_mode_0, pad = input_275_pad_0, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")]; + string h_105_pad_type_0 = const()[name = string("h_105_pad_type_0"), val = string("valid")]; + int32 h_105_groups_0 = const()[name = string("h_105_groups_0"), val = int32(512)]; + tensor h_105_strides_0 = const()[name = string("h_105_strides_0"), val = tensor([1])]; + tensor h_105_pad_0 = const()[name = string("h_105_pad_0"), val = tensor([0, 0])]; + tensor h_105_dilations_0 = const()[name = string("h_105_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39294720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39297344))))[name = string("main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39298432)))]; + tensor h_105_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_105_dilations_0, groups = h_105_groups_0, pad = h_105_pad_0, pad_type = h_105_pad_type_0, strides = h_105_strides_0, weight = main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("h_105_cast_fp16")]; + tensor x_147_cast_fp16 = mul(x = h_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_277_perm_0 = const()[name = string("input_277_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1680_axes_0 = const()[name = string("op_1680_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39299520)))]; + tensor main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39300608)))]; + tensor input_277_cast_fp16 = transpose(perm = input_277_perm_0, x = x_147_cast_fp16)[name = string("transpose_43")]; + tensor var_1680_cast_fp16 = layer_norm(axes = var_1680_axes_0, beta = main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16, x = input_277_cast_fp16)[name = string("op_1680_cast_fp16")]; + tensor input_279_perm_0 = const()[name = string("input_279_perm_0"), val = tensor([0, 2, 1])]; + string h_107_pad_type_0 = const()[name = string("h_107_pad_type_0"), val = string("valid")]; + tensor h_107_strides_0 = const()[name = string("h_107_strides_0"), val = tensor([1])]; + tensor h_107_pad_0 = const()[name = string("h_107_pad_0"), val = tensor([0, 0])]; + tensor h_107_dilations_0 = const()[name = string("h_107_dilations_0"), val = tensor([1])]; + int32 h_107_groups_0 = const()[name = string("h_107_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39301696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40350336))))[name = string("main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40354496)))]; + tensor input_279_cast_fp16 = transpose(perm = input_279_perm_0, x = var_1680_cast_fp16)[name = string("transpose_42")]; + tensor h_107_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_107_dilations_0, groups = h_107_groups_0, pad = h_107_pad_0, pad_type = h_107_pad_type_0, strides = h_107_strides_0, weight = main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_279_cast_fp16)[name = string("h_107_cast_fp16")]; + string input_281_mode_0 = const()[name = string("input_281_mode_0"), val = string("EXACT")]; + tensor input_281_cast_fp16 = gelu(mode = input_281_mode_0, x = h_107_cast_fp16)[name = string("input_281_cast_fp16")]; + string h_109_pad_type_0 = const()[name = string("h_109_pad_type_0"), val = string("valid")]; + tensor h_109_strides_0 = const()[name = string("h_109_strides_0"), val = tensor([1])]; + tensor h_109_pad_0 = const()[name = string("h_109_pad_0"), val = tensor([0, 0])]; + tensor h_109_dilations_0 = const()[name = string("h_109_dilations_0"), val = tensor([1])]; + int32 h_109_groups_0 = const()[name = string("h_109_groups_0"), val = int32(1)]; + tensor op_1697_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40358656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41407296))))[name = string("op_1697_weight_0_to_fp16_quantized")]; + tensor var_1697_bias_0_to_fp16 = const()[name = string("op_1697_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41408384)))]; + tensor var_1697_cast_fp16 = conv(bias = var_1697_bias_0_to_fp16, dilations = h_109_dilations_0, groups = h_109_groups_0, pad = h_109_pad_0, pad_type = h_109_pad_type_0, strides = h_109_strides_0, weight = op_1697_weight_0_to_fp16_quantized, x = input_281_cast_fp16)[name = string("op_1697_cast_fp16")]; + tensor out_45_cast_fp16 = add(x = x_145_cast_fp16, y = var_1697_cast_fp16)[name = string("out_45_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = out_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor x_m_11_cast_fp16 = mul(x = x_149_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_11_cast_fp16")]; + tensor input_283_perm_0 = const()[name = string("input_283_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41409472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41540608))))[name = string("main_blocks_2_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41541184)))]; + tensor input_283_cast_fp16 = transpose(perm = input_283_perm_0, x = x_149_cast_fp16)[name = string("transpose_41")]; + tensor linear_25_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_283_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor concat_32x = const()[name = string("concat_32x"), val = tensor([2, -1, 2, 128])]; + tensor var_1714_cast_fp16 = reshape(shape = concat_32x, x = linear_25_cast_fp16)[name = string("op_1714_cast_fp16")]; + tensor q_11_perm_0 = const()[name = string("q_11_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_2_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41541760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41607360))))[name = string("main_blocks_2_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41607936)))]; + tensor linear_27_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1727 = const()[name = string("op_1727"), val = tensor([2, 50, 2, 128])]; + tensor var_1728_cast_fp16 = reshape(shape = var_1727, x = linear_27_cast_fp16)[name = string("op_1728_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_1732_transpose_x_0 = const()[name = string("op_1732_transpose_x_0"), val = bool(false)]; + bool var_1732_transpose_y_0 = const()[name = string("op_1732_transpose_y_0"), val = bool(false)]; + tensor op_1731_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41608512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41634176))))[name = string("op_1731_to_fp16_quantized")]; + tensor q_11_cast_fp16 = transpose(perm = q_11_perm_0, x = var_1714_cast_fp16)[name = string("transpose_40")]; + tensor var_1732_cast_fp16 = matmul(transpose_x = var_1732_transpose_x_0, transpose_y = var_1732_transpose_y_0, x = q_11_cast_fp16, y = op_1731_to_fp16_quantized)[name = string("op_1732_cast_fp16")]; + fp16 _inversed_input_285_y_0_to_fp16 = const()[name = string("_inversed_input_285_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_285_cast_fp16 = mul(x = var_1732_cast_fp16, y = _inversed_input_285_y_0_to_fp16)[name = string("_inversed_input_285_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1228, x = _inversed_input_285_cast_fp16)[name = string("attn_15_cast_fp16")]; + tensor attn_17_cast_fp16 = mul(x = attn_15_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool var_1739_transpose_x_0 = const()[name = string("op_1739_transpose_x_0"), val = bool(false)]; + bool var_1739_transpose_y_0 = const()[name = string("op_1739_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_1728_cast_fp16)[name = string("transpose_39")]; + tensor var_1739_cast_fp16 = matmul(transpose_x = var_1739_transpose_x_0, transpose_y = var_1739_transpose_y_0, x = attn_17_cast_fp16, y = v_11_cast_fp16)[name = string("op_1739_cast_fp16")]; + tensor var_1740_perm_0 = const()[name = string("op_1740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_33x = const()[name = string("concat_33x"), val = tensor([2, -1, 256])]; + tensor var_1740_cast_fp16 = transpose(perm = var_1740_perm_0, x = var_1739_cast_fp16)[name = string("transpose_38")]; + tensor input_287_cast_fp16 = reshape(shape = concat_33x, x = var_1740_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41634368))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41765504))))[name = string("main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_2_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41766592)))]; + tensor linear_28_cast_fp16 = linear(bias = main_blocks_2_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_287_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor out_47_perm_0 = const()[name = string("out_47_perm_0"), val = tensor([0, 2, 1])]; + tensor out_47_cast_fp16 = transpose(perm = out_47_perm_0, x = linear_28_cast_fp16)[name = string("transpose_37")]; + tensor var_1748_cast_fp16 = mul(x = out_47_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1748_cast_fp16")]; + tensor x_151_cast_fp16 = add(x = x_m_11_cast_fp16, y = var_1748_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_289_perm_0 = const()[name = string("input_289_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1755_axes_0 = const()[name = string("op_1755_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41767680)))]; + tensor main_blocks_2_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41768768)))]; + tensor input_289_cast_fp16 = transpose(perm = input_289_perm_0, x = x_151_cast_fp16)[name = string("transpose_36")]; + tensor var_1755_cast_fp16 = layer_norm(axes = var_1755_axes_0, beta = main_blocks_2_style_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_style_norm_norm_weight_to_fp16, x = input_289_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor var_1756_perm_0 = const()[name = string("op_1756_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1756_cast_fp16 = transpose(perm = var_1756_perm_0, x = var_1755_cast_fp16)[name = string("transpose_35")]; + tensor x_153_cast_fp16 = mul(x = var_1756_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_153_cast_fp16")]; + int32 var_1775 = const()[name = string("op_1775"), val = int32(-1)]; + tensor x_155_cast_fp16 = mul(x = x_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor var_1810_cast_fp16 = mul(x = x_155_cast_fp16, y = last_1_cast_fp16)[name = string("op_1810_cast_fp16")]; + tensor x_last_37_axes_0 = const()[name = string("x_last_37_axes_0"), val = tensor([-1])]; + bool x_last_37_keep_dims_0 = const()[name = string("x_last_37_keep_dims_0"), val = bool(true)]; + tensor x_last_37_cast_fp16 = reduce_sum(axes = x_last_37_axes_0, keep_dims = x_last_37_keep_dims_0, x = var_1810_cast_fp16)[name = string("x_last_37_cast_fp16")]; + tensor var_1813_cast_fp16 = mul(x = x_155_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1815_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_37_cast_fp16)[name = string("op_1815_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = var_1813_cast_fp16, y = var_1815_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_295_mode_0 = const()[name = string("input_295_mode_0"), val = string("replicate")]; + fp16 const_40_to_fp16 = const()[name = string("const_40_to_fp16"), val = fp16(0x0p+0)]; + tensor input_295_cast_fp16 = pad(constant_val = const_40_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")]; + string h_111_pad_type_0 = const()[name = string("h_111_pad_type_0"), val = string("valid")]; + int32 h_111_groups_0 = const()[name = string("h_111_groups_0"), val = int32(512)]; + tensor h_111_strides_0 = const()[name = string("h_111_strides_0"), val = tensor([1])]; + tensor h_111_pad_0 = const()[name = string("h_111_pad_0"), val = tensor([0, 0])]; + tensor h_111_dilations_0 = const()[name = string("h_111_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41769856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41772480))))[name = string("main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41773568)))]; + tensor h_111_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_111_dilations_0, groups = h_111_groups_0, pad = h_111_pad_0, pad_type = h_111_pad_type_0, strides = h_111_strides_0, weight = main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16_quantized, x = input_295_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor x_157_cast_fp16 = mul(x = h_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor input_297_perm_0 = const()[name = string("input_297_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1833_axes_0 = const()[name = string("op_1833_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41774656)))]; + tensor main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41775744)))]; + fp16 var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_297_cast_fp16 = transpose(perm = input_297_perm_0, x = x_157_cast_fp16)[name = string("transpose_34")]; + tensor var_1833_cast_fp16 = layer_norm(axes = var_1833_axes_0, beta = main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16, x = input_297_cast_fp16)[name = string("op_1833_cast_fp16")]; + tensor input_299_perm_0 = const()[name = string("input_299_perm_0"), val = tensor([0, 2, 1])]; + string h_113_pad_type_0 = const()[name = string("h_113_pad_type_0"), val = string("valid")]; + tensor h_113_strides_0 = const()[name = string("h_113_strides_0"), val = tensor([1])]; + tensor h_113_pad_0 = const()[name = string("h_113_pad_0"), val = tensor([0, 0])]; + tensor h_113_dilations_0 = const()[name = string("h_113_dilations_0"), val = tensor([1])]; + int32 h_113_groups_0 = const()[name = string("h_113_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41776832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42825472))))[name = string("main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42829632)))]; + tensor input_299_cast_fp16 = transpose(perm = input_299_perm_0, x = var_1833_cast_fp16)[name = string("transpose_33")]; + tensor h_113_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_113_dilations_0, groups = h_113_groups_0, pad = h_113_pad_0, pad_type = h_113_pad_type_0, strides = h_113_strides_0, weight = main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16_quantized, x = input_299_cast_fp16)[name = string("h_113_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = h_113_cast_fp16)[name = string("input_301_cast_fp16")]; + string h_115_pad_type_0 = const()[name = string("h_115_pad_type_0"), val = string("valid")]; + tensor h_115_strides_0 = const()[name = string("h_115_strides_0"), val = tensor([1])]; + tensor h_115_pad_0 = const()[name = string("h_115_pad_0"), val = tensor([0, 0])]; + tensor h_115_dilations_0 = const()[name = string("h_115_dilations_0"), val = tensor([1])]; + int32 h_115_groups_0 = const()[name = string("h_115_groups_0"), val = int32(1)]; + tensor op_1850_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42833792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43882432))))[name = string("op_1850_weight_0_to_fp16_quantized")]; + tensor var_1850_bias_0_to_fp16 = const()[name = string("op_1850_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43883520)))]; + tensor var_1850_cast_fp16 = conv(bias = var_1850_bias_0_to_fp16, dilations = h_115_dilations_0, groups = h_115_groups_0, pad = h_115_pad_0, pad_type = h_115_pad_type_0, strides = h_115_strides_0, weight = op_1850_weight_0_to_fp16_quantized, x = input_301_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor out_49_cast_fp16 = add(x = x_155_cast_fp16, y = var_1850_cast_fp16)[name = string("out_49_cast_fp16")]; + tensor x_159_cast_fp16 = mul(x = out_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor x_161_cast_fp16 = mul(x = x_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor var_1864_cast_fp16 = mul(x = x_161_cast_fp16, y = last_1_cast_fp16)[name = string("op_1864_cast_fp16")]; + tensor x_last_39_axes_0 = const()[name = string("x_last_39_axes_0"), val = tensor([-1])]; + bool x_last_39_keep_dims_0 = const()[name = string("x_last_39_keep_dims_0"), val = bool(true)]; + tensor x_last_39_cast_fp16 = reduce_sum(axes = x_last_39_axes_0, keep_dims = x_last_39_keep_dims_0, x = var_1864_cast_fp16)[name = string("x_last_39_cast_fp16")]; + tensor var_1867_cast_fp16 = mul(x = x_161_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_39_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor input_305_cast_fp16 = add(x = var_1867_cast_fp16, y = var_1869_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("replicate")]; + fp16 const_42_to_fp16 = const()[name = string("const_42_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_42_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string h_117_pad_type_0 = const()[name = string("h_117_pad_type_0"), val = string("valid")]; + tensor h_117_dilations_0 = const()[name = string("h_117_dilations_0"), val = tensor([2])]; + int32 h_117_groups_0 = const()[name = string("h_117_groups_0"), val = int32(512)]; + tensor h_117_strides_0 = const()[name = string("h_117_strides_0"), val = tensor([1])]; + tensor h_117_pad_0 = const()[name = string("h_117_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43884608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43887232))))[name = string("main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43888320)))]; + tensor h_117_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_117_dilations_0, groups = h_117_groups_0, pad = h_117_pad_0, pad_type = h_117_pad_type_0, strides = h_117_strides_0, weight = main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16_quantized, x = input_307_cast_fp16)[name = string("h_117_cast_fp16")]; + tensor x_163_cast_fp16 = mul(x = h_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1887_axes_0 = const()[name = string("op_1887_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43889408)))]; + tensor main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43890496)))]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_163_cast_fp16)[name = string("transpose_32")]; + tensor var_1887_cast_fp16 = layer_norm(axes = var_1887_axes_0, beta = main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16, x = input_309_cast_fp16)[name = string("op_1887_cast_fp16")]; + tensor input_311_perm_0 = const()[name = string("input_311_perm_0"), val = tensor([0, 2, 1])]; + string h_119_pad_type_0 = const()[name = string("h_119_pad_type_0"), val = string("valid")]; + tensor h_119_strides_0 = const()[name = string("h_119_strides_0"), val = tensor([1])]; + tensor h_119_pad_0 = const()[name = string("h_119_pad_0"), val = tensor([0, 0])]; + tensor h_119_dilations_0 = const()[name = string("h_119_dilations_0"), val = tensor([1])]; + int32 h_119_groups_0 = const()[name = string("h_119_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43891584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44940224))))[name = string("main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44944384)))]; + tensor input_311_cast_fp16 = transpose(perm = input_311_perm_0, x = var_1887_cast_fp16)[name = string("transpose_31")]; + tensor h_119_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_119_dilations_0, groups = h_119_groups_0, pad = h_119_pad_0, pad_type = h_119_pad_type_0, strides = h_119_strides_0, weight = main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16_quantized, x = input_311_cast_fp16)[name = string("h_119_cast_fp16")]; + string input_313_mode_0 = const()[name = string("input_313_mode_0"), val = string("EXACT")]; + tensor input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = h_119_cast_fp16)[name = string("input_313_cast_fp16")]; + string h_121_pad_type_0 = const()[name = string("h_121_pad_type_0"), val = string("valid")]; + tensor h_121_strides_0 = const()[name = string("h_121_strides_0"), val = tensor([1])]; + tensor h_121_pad_0 = const()[name = string("h_121_pad_0"), val = tensor([0, 0])]; + tensor h_121_dilations_0 = const()[name = string("h_121_dilations_0"), val = tensor([1])]; + int32 h_121_groups_0 = const()[name = string("h_121_groups_0"), val = int32(1)]; + tensor op_1904_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44948544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45997184))))[name = string("op_1904_weight_0_to_fp16_quantized")]; + tensor var_1904_bias_0_to_fp16 = const()[name = string("op_1904_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45998272)))]; + tensor var_1904_cast_fp16 = conv(bias = var_1904_bias_0_to_fp16, dilations = h_121_dilations_0, groups = h_121_groups_0, pad = h_121_pad_0, pad_type = h_121_pad_type_0, strides = h_121_strides_0, weight = op_1904_weight_0_to_fp16_quantized, x = input_313_cast_fp16)[name = string("op_1904_cast_fp16")]; + tensor out_51_cast_fp16 = add(x = x_161_cast_fp16, y = var_1904_cast_fp16)[name = string("out_51_cast_fp16")]; + tensor x_165_cast_fp16 = mul(x = out_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor x_167_cast_fp16 = mul(x = x_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1918_cast_fp16 = mul(x = x_167_cast_fp16, y = last_1_cast_fp16)[name = string("op_1918_cast_fp16")]; + tensor x_last_41_axes_0 = const()[name = string("x_last_41_axes_0"), val = tensor([-1])]; + bool x_last_41_keep_dims_0 = const()[name = string("x_last_41_keep_dims_0"), val = bool(true)]; + tensor x_last_41_cast_fp16 = reduce_sum(axes = x_last_41_axes_0, keep_dims = x_last_41_keep_dims_0, x = var_1918_cast_fp16)[name = string("x_last_41_cast_fp16")]; + tensor var_1921_cast_fp16 = mul(x = x_167_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1921_cast_fp16")]; + tensor var_1923_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_41_cast_fp16)[name = string("op_1923_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = var_1921_cast_fp16, y = var_1923_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("replicate")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_319_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = input_319_mode_0, pad = input_319_pad_0, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string h_123_pad_type_0 = const()[name = string("h_123_pad_type_0"), val = string("valid")]; + tensor h_123_dilations_0 = const()[name = string("h_123_dilations_0"), val = tensor([4])]; + int32 h_123_groups_0 = const()[name = string("h_123_groups_0"), val = int32(512)]; + tensor h_123_strides_0 = const()[name = string("h_123_strides_0"), val = tensor([1])]; + tensor h_123_pad_0 = const()[name = string("h_123_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45999360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46001984))))[name = string("main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46003072)))]; + tensor h_123_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_123_dilations_0, groups = h_123_groups_0, pad = h_123_pad_0, pad_type = h_123_pad_type_0, strides = h_123_strides_0, weight = main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16_quantized, x = input_319_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor x_169_cast_fp16 = mul(x = h_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1941_axes_0 = const()[name = string("op_1941_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46004160)))]; + tensor main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46005248)))]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_169_cast_fp16)[name = string("transpose_30")]; + tensor var_1941_cast_fp16 = layer_norm(axes = var_1941_axes_0, beta = main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16, x = input_321_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor([0, 2, 1])]; + string h_125_pad_type_0 = const()[name = string("h_125_pad_type_0"), val = string("valid")]; + tensor h_125_strides_0 = const()[name = string("h_125_strides_0"), val = tensor([1])]; + tensor h_125_pad_0 = const()[name = string("h_125_pad_0"), val = tensor([0, 0])]; + tensor h_125_dilations_0 = const()[name = string("h_125_dilations_0"), val = tensor([1])]; + int32 h_125_groups_0 = const()[name = string("h_125_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46006336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47054976))))[name = string("main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47059136)))]; + tensor input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = var_1941_cast_fp16)[name = string("transpose_29")]; + tensor h_125_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_125_dilations_0, groups = h_125_groups_0, pad = h_125_pad_0, pad_type = h_125_pad_type_0, strides = h_125_strides_0, weight = main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16_quantized, x = input_323_cast_fp16)[name = string("h_125_cast_fp16")]; + string input_325_mode_0 = const()[name = string("input_325_mode_0"), val = string("EXACT")]; + tensor input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = h_125_cast_fp16)[name = string("input_325_cast_fp16")]; + string h_127_pad_type_0 = const()[name = string("h_127_pad_type_0"), val = string("valid")]; + tensor h_127_strides_0 = const()[name = string("h_127_strides_0"), val = tensor([1])]; + tensor h_127_pad_0 = const()[name = string("h_127_pad_0"), val = tensor([0, 0])]; + tensor h_127_dilations_0 = const()[name = string("h_127_dilations_0"), val = tensor([1])]; + int32 h_127_groups_0 = const()[name = string("h_127_groups_0"), val = int32(1)]; + tensor op_1958_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47063296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48111936))))[name = string("op_1958_weight_0_to_fp16_quantized")]; + tensor var_1958_bias_0_to_fp16 = const()[name = string("op_1958_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48113024)))]; + tensor var_1958_cast_fp16 = conv(bias = var_1958_bias_0_to_fp16, dilations = h_127_dilations_0, groups = h_127_groups_0, pad = h_127_pad_0, pad_type = h_127_pad_type_0, strides = h_127_strides_0, weight = op_1958_weight_0_to_fp16_quantized, x = input_325_cast_fp16)[name = string("op_1958_cast_fp16")]; + tensor out_53_cast_fp16 = add(x = x_167_cast_fp16, y = var_1958_cast_fp16)[name = string("out_53_cast_fp16")]; + tensor x_171_cast_fp16 = mul(x = out_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_171_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor var_1972_cast_fp16 = mul(x = x_173_cast_fp16, y = last_1_cast_fp16)[name = string("op_1972_cast_fp16")]; + tensor x_last_43_axes_0 = const()[name = string("x_last_43_axes_0"), val = tensor([-1])]; + bool x_last_43_keep_dims_0 = const()[name = string("x_last_43_keep_dims_0"), val = bool(true)]; + tensor x_last_43_cast_fp16 = reduce_sum(axes = x_last_43_axes_0, keep_dims = x_last_43_keep_dims_0, x = var_1972_cast_fp16)[name = string("x_last_43_cast_fp16")]; + tensor var_1975_cast_fp16 = mul(x = x_173_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1975_cast_fp16")]; + tensor var_1977_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_43_cast_fp16)[name = string("op_1977_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1977_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("replicate")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor input_331_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = input_331_mode_0, pad = input_331_pad_0, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + string h_129_pad_type_0 = const()[name = string("h_129_pad_type_0"), val = string("valid")]; + tensor h_129_dilations_0 = const()[name = string("h_129_dilations_0"), val = tensor([8])]; + int32 h_129_groups_0 = const()[name = string("h_129_groups_0"), val = int32(512)]; + tensor h_129_strides_0 = const()[name = string("h_129_strides_0"), val = tensor([1])]; + tensor h_129_pad_0 = const()[name = string("h_129_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48114112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48116736))))[name = string("main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48117824)))]; + tensor h_129_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_129_dilations_0, groups = h_129_groups_0, pad = h_129_pad_0, pad_type = h_129_pad_type_0, strides = h_129_strides_0, weight = main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16_quantized, x = input_331_cast_fp16)[name = string("h_129_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = h_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_333_perm_0 = const()[name = string("input_333_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1995_axes_0 = const()[name = string("op_1995_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48118912)))]; + tensor main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48120000)))]; + tensor input_333_cast_fp16 = transpose(perm = input_333_perm_0, x = x_175_cast_fp16)[name = string("transpose_28")]; + tensor var_1995_cast_fp16 = layer_norm(axes = var_1995_axes_0, beta = main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16, x = input_333_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor input_335_perm_0 = const()[name = string("input_335_perm_0"), val = tensor([0, 2, 1])]; + string h_131_pad_type_0 = const()[name = string("h_131_pad_type_0"), val = string("valid")]; + tensor h_131_strides_0 = const()[name = string("h_131_strides_0"), val = tensor([1])]; + tensor h_131_pad_0 = const()[name = string("h_131_pad_0"), val = tensor([0, 0])]; + tensor h_131_dilations_0 = const()[name = string("h_131_dilations_0"), val = tensor([1])]; + int32 h_131_groups_0 = const()[name = string("h_131_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48121088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49169728))))[name = string("main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49173888)))]; + tensor input_335_cast_fp16 = transpose(perm = input_335_perm_0, x = var_1995_cast_fp16)[name = string("transpose_27")]; + tensor h_131_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_131_dilations_0, groups = h_131_groups_0, pad = h_131_pad_0, pad_type = h_131_pad_type_0, strides = h_131_strides_0, weight = main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16_quantized, x = input_335_cast_fp16)[name = string("h_131_cast_fp16")]; + string input_337_mode_0 = const()[name = string("input_337_mode_0"), val = string("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = h_131_cast_fp16)[name = string("input_337_cast_fp16")]; + string h_133_pad_type_0 = const()[name = string("h_133_pad_type_0"), val = string("valid")]; + tensor h_133_strides_0 = const()[name = string("h_133_strides_0"), val = tensor([1])]; + tensor h_133_pad_0 = const()[name = string("h_133_pad_0"), val = tensor([0, 0])]; + tensor h_133_dilations_0 = const()[name = string("h_133_dilations_0"), val = tensor([1])]; + int32 h_133_groups_0 = const()[name = string("h_133_groups_0"), val = int32(1)]; + tensor op_2012_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49178048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50226688))))[name = string("op_2012_weight_0_to_fp16_quantized")]; + tensor var_2012_bias_0_to_fp16 = const()[name = string("op_2012_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50227776)))]; + tensor var_2012_cast_fp16 = conv(bias = var_2012_bias_0_to_fp16, dilations = h_133_dilations_0, groups = h_133_groups_0, pad = h_133_pad_0, pad_type = h_133_pad_type_0, strides = h_133_strides_0, weight = op_2012_weight_0_to_fp16_quantized, x = input_337_cast_fp16)[name = string("op_2012_cast_fp16")]; + tensor out_55_cast_fp16 = add(x = x_173_cast_fp16, y = var_2012_cast_fp16)[name = string("out_55_cast_fp16")]; + tensor x_177_cast_fp16 = mul(x = out_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor main_blocks_3_time_cond_linear_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50228864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50261696))))[name = string("main_blocks_3_time_cond_linear_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50262784)))]; + tensor linear_29_cast_fp16 = linear(bias = main_blocks_3_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_3_time_cond_linear_linear_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor t_axes_0 = const()[name = string("t_axes_0"), val = tensor([-1])]; + tensor t_cast_fp16 = expand_dims(axes = t_axes_0, x = linear_29_cast_fp16)[name = string("t_cast_fp16")]; + tensor var_2022_cast_fp16 = add(x = x_177_cast_fp16, y = t_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor x_179_cast_fp16 = mul(x = var_2022_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor x_181_cast_fp16 = mul(x = x_179_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor var_2035_cast_fp16 = mul(x = x_181_cast_fp16, y = last_1_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor x_last_45_axes_0 = const()[name = string("x_last_45_axes_0"), val = tensor([-1])]; + bool x_last_45_keep_dims_0 = const()[name = string("x_last_45_keep_dims_0"), val = bool(true)]; + tensor x_last_45_cast_fp16 = reduce_sum(axes = x_last_45_axes_0, keep_dims = x_last_45_keep_dims_0, x = var_2035_cast_fp16)[name = string("x_last_45_cast_fp16")]; + tensor var_2038_cast_fp16 = mul(x = x_181_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2038_cast_fp16")]; + tensor var_2040_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_45_cast_fp16)[name = string("op_2040_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = var_2038_cast_fp16, y = var_2040_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_345_mode_0 = const()[name = string("input_345_mode_0"), val = string("replicate")]; + fp16 const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = fp16(0x0p+0)]; + tensor input_345_cast_fp16 = pad(constant_val = const_48_to_fp16, mode = input_345_mode_0, pad = input_345_pad_0, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + string h_135_pad_type_0 = const()[name = string("h_135_pad_type_0"), val = string("valid")]; + int32 h_135_groups_0 = const()[name = string("h_135_groups_0"), val = int32(512)]; + tensor h_135_strides_0 = const()[name = string("h_135_strides_0"), val = tensor([1])]; + tensor h_135_pad_0 = const()[name = string("h_135_pad_0"), val = tensor([0, 0])]; + tensor h_135_dilations_0 = const()[name = string("h_135_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50263872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50266496))))[name = string("main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50267584)))]; + tensor h_135_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_135_dilations_0, groups = h_135_groups_0, pad = h_135_pad_0, pad_type = h_135_pad_type_0, strides = h_135_strides_0, weight = main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16_quantized, x = input_345_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor x_183_cast_fp16 = mul(x = h_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_183_cast_fp16")]; + tensor input_347_perm_0 = const()[name = string("input_347_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2058_axes_0 = const()[name = string("op_2058_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50268672)))]; + tensor main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50269760)))]; + tensor input_347_cast_fp16 = transpose(perm = input_347_perm_0, x = x_183_cast_fp16)[name = string("transpose_26")]; + tensor var_2058_cast_fp16 = layer_norm(axes = var_2058_axes_0, beta = main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16, x = input_347_cast_fp16)[name = string("op_2058_cast_fp16")]; + tensor input_349_perm_0 = const()[name = string("input_349_perm_0"), val = tensor([0, 2, 1])]; + string h_137_pad_type_0 = const()[name = string("h_137_pad_type_0"), val = string("valid")]; + tensor h_137_strides_0 = const()[name = string("h_137_strides_0"), val = tensor([1])]; + tensor h_137_pad_0 = const()[name = string("h_137_pad_0"), val = tensor([0, 0])]; + tensor h_137_dilations_0 = const()[name = string("h_137_dilations_0"), val = tensor([1])]; + int32 h_137_groups_0 = const()[name = string("h_137_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50270848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51319488))))[name = string("main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51323648)))]; + tensor input_349_cast_fp16 = transpose(perm = input_349_perm_0, x = var_2058_cast_fp16)[name = string("transpose_25")]; + tensor h_137_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_137_dilations_0, groups = h_137_groups_0, pad = h_137_pad_0, pad_type = h_137_pad_type_0, strides = h_137_strides_0, weight = main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16_quantized, x = input_349_cast_fp16)[name = string("h_137_cast_fp16")]; + string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")]; + tensor input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = h_137_cast_fp16)[name = string("input_351_cast_fp16")]; + string h_139_pad_type_0 = const()[name = string("h_139_pad_type_0"), val = string("valid")]; + tensor h_139_strides_0 = const()[name = string("h_139_strides_0"), val = tensor([1])]; + tensor h_139_pad_0 = const()[name = string("h_139_pad_0"), val = tensor([0, 0])]; + tensor h_139_dilations_0 = const()[name = string("h_139_dilations_0"), val = tensor([1])]; + int32 h_139_groups_0 = const()[name = string("h_139_groups_0"), val = int32(1)]; + tensor op_2075_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51327808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52376448))))[name = string("op_2075_weight_0_to_fp16_quantized")]; + tensor var_2075_bias_0_to_fp16 = const()[name = string("op_2075_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52377536)))]; + tensor var_2075_cast_fp16 = conv(bias = var_2075_bias_0_to_fp16, dilations = h_139_dilations_0, groups = h_139_groups_0, pad = h_139_pad_0, pad_type = h_139_pad_type_0, strides = h_139_strides_0, weight = op_2075_weight_0_to_fp16_quantized, x = input_351_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor out_57_cast_fp16 = add(x = x_181_cast_fp16, y = var_2075_cast_fp16)[name = string("out_57_cast_fp16")]; + tensor x_185_cast_fp16 = mul(x = out_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor x_m_13_cast_fp16 = mul(x = x_185_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_13_cast_fp16")]; + tensor var_2085_shape_cast_fp16 = shape(x = x_185_cast_fp16)[name = string("op_2085_shape_cast_fp16")]; + int32 gather_19_axis_0 = const()[name = string("gather_19_axis_0"), val = int32(0)]; + int32 gather_19_batch_dims_0 = const()[name = string("gather_19_batch_dims_0"), val = int32(0)]; + bool gather_19_validate_indices_0 = const()[name = string("gather_19_validate_indices_0"), val = bool(false)]; + string var_2085_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_2085_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_19_indices_0_to_uint16 = const()[name = string("gather_19_indices_0_to_uint16"), val = uint16(2)]; + tensor var_2085_shape_cast_fp16_to_uint16 = cast(dtype = var_2085_shape_cast_fp16_to_uint16_dtype_0, x = var_2085_shape_cast_fp16)[name = string("cast_2")]; + uint16 gather_19_cast_uint16 = gather(axis = gather_19_axis_0, batch_dims = gather_19_batch_dims_0, indices = gather_19_indices_0_to_uint16, validate_indices = gather_19_validate_indices_0, x = var_2085_shape_cast_fp16_to_uint16)[name = string("gather_19_cast_uint16")]; + string gather_19_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_19_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_text_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52378624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52640832))))[name = string("main_blocks_3_text_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52641920)))]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_185_cast_fp16)[name = string("transpose_24")]; + tensor linear_30_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_query_linear_weight_to_fp16_quantized, x = input_353_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor concat_34x = const()[name = string("concat_34x"), val = tensor([2, -1, 8, 64])]; + tensor var_2094_cast_fp16 = reshape(shape = concat_34x, x = linear_30_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor x_187_perm_0 = const()[name = string("x_187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_key_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52643008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52774144))))[name = string("main_blocks_3_text_attn_W_key_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52775232)))]; + tensor linear_31_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_key_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor concat_35x = const()[name = string("concat_35x"), val = tensor([2, -1, 8, 64])]; + tensor var_2102_cast_fp16 = reshape(shape = concat_35x, x = linear_31_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor x_189_perm_0 = const()[name = string("x_189_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52776320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52907456))))[name = string("main_blocks_3_text_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52908544)))]; + tensor linear_32_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_value_linear_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor concat_36x = const()[name = string("concat_36x"), val = tensor([2, -1, 8, 64])]; + tensor var_2110_cast_fp16 = reshape(shape = concat_36x, x = linear_32_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor([0, 2, -3, -1])]; + int32 concat_37_values0_0 = const()[name = string("concat_37_values0_0"), val = int32(1)]; + int32 concat_37_values2_0 = const()[name = string("concat_37_values2_0"), val = int32(1)]; + int32 concat_37_axis_0 = const()[name = string("concat_37_axis_0"), val = int32(0)]; + bool concat_37_interleave_0 = const()[name = string("concat_37_interleave_0"), val = bool(false)]; + int32 gather_19_cast_uint16_to_int32 = cast(dtype = gather_19_cast_uint16_to_int32_dtype_0, x = gather_19_cast_uint16)[name = string("cast_1")]; + tensor concat_37 = concat(axis = concat_37_axis_0, interleave = concat_37_interleave_0, values = (concat_37_values0_0, gather_19_cast_uint16_to_int32, concat_37_values2_0))[name = string("concat_37")]; + tensor var_2113_begin_0 = const()[name = string("op_2113_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2113_end_mask_0 = const()[name = string("op_2113_end_mask_0"), val = tensor([true, false, true])]; + tensor var_2113_cast_fp16 = slice_by_index(begin = var_2113_begin_0, end = concat_37, end_mask = var_2113_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_2113_cast_fp16")]; + tensor concat_38 = const()[name = string("concat_38"), val = tensor([2, -1, -1])]; + tensor shape_7_cast_fp16 = shape(x = var_2113_cast_fp16)[name = string("shape_7_cast_fp16")]; + tensor equal_7 = const()[name = string("equal_7"), val = tensor([false, true, true])]; + tensor select_7 = select(a = shape_7_cast_fp16, b = concat_38, cond = equal_7)[name = string("select_7")]; + tensor real_div_7 = real_div(x = select_7, y = shape_7_cast_fp16)[name = string("real_div_7")]; + tensor var_2116_cast_fp16 = tile(reps = real_div_7, x = var_2113_cast_fp16)[name = string("op_2116_cast_fp16")]; + tensor scaled_13_cast_fp16 = real_div(x = var_2116_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_13_cast_fp16")]; + tensor main_blocks_3_text_attn_theta_to_fp16 = const()[name = string("main_blocks_3_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52909632)))]; + tensor angles_13_cast_fp16 = mul(x = scaled_13_cast_fp16, y = main_blocks_3_text_attn_theta_to_fp16)[name = string("angles_13_cast_fp16")]; + tensor var_2132_cast_fp16 = cos(x = angles_13_cast_fp16)[name = string("op_2132_cast_fp16")]; + tensor cos_13_axes_0 = const()[name = string("cos_13_axes_0"), val = tensor([1])]; + tensor cos_13_cast_fp16 = expand_dims(axes = cos_13_axes_0, x = var_2132_cast_fp16)[name = string("cos_13_cast_fp16")]; + tensor var_2134_cast_fp16 = sin(x = angles_13_cast_fp16)[name = string("op_2134_cast_fp16")]; + tensor sin_13_axes_0 = const()[name = string("sin_13_axes_0"), val = tensor([1])]; + tensor sin_13_cast_fp16 = expand_dims(axes = sin_13_axes_0, x = var_2134_cast_fp16)[name = string("sin_13_cast_fp16")]; + tensor x_a_13_begin_0 = const()[name = string("x_a_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_13_end_0 = const()[name = string("x_a_13_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_13_end_mask_0 = const()[name = string("x_a_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_187_cast_fp16 = transpose(perm = x_187_perm_0, x = var_2094_cast_fp16)[name = string("transpose_23")]; + tensor x_a_13_cast_fp16 = slice_by_index(begin = x_a_13_begin_0, end = x_a_13_end_0, end_mask = x_a_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_a_13_cast_fp16")]; + tensor x_b_13_begin_0 = const()[name = string("x_b_13_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_13_end_0 = const()[name = string("x_b_13_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_13_end_mask_0 = const()[name = string("x_b_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_13_cast_fp16 = slice_by_index(begin = x_b_13_begin_0, end = x_b_13_end_0, end_mask = x_b_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_b_13_cast_fp16")]; + tensor var_2138_cast_fp16 = mul(x = x_a_13_cast_fp16, y = cos_13_cast_fp16)[name = string("op_2138_cast_fp16")]; + tensor var_2139_cast_fp16 = mul(x = x_b_13_cast_fp16, y = sin_13_cast_fp16)[name = string("op_2139_cast_fp16")]; + tensor rot_a_13_cast_fp16 = sub(x = var_2138_cast_fp16, y = var_2139_cast_fp16)[name = string("rot_a_13_cast_fp16")]; + tensor var_2141_cast_fp16 = mul(x = x_a_13_cast_fp16, y = sin_13_cast_fp16)[name = string("op_2141_cast_fp16")]; + tensor var_2142_cast_fp16 = mul(x = x_b_13_cast_fp16, y = cos_13_cast_fp16)[name = string("op_2142_cast_fp16")]; + tensor rot_b_13_cast_fp16 = add(x = var_2141_cast_fp16, y = var_2142_cast_fp16)[name = string("rot_b_13_cast_fp16")]; + bool q_13_interleave_0 = const()[name = string("q_13_interleave_0"), val = bool(false)]; + tensor q_13_cast_fp16 = concat(axis = var_1775, interleave = q_13_interleave_0, values = (rot_a_13_cast_fp16, rot_b_13_cast_fp16))[name = string("q_13_cast_fp16")]; + tensor x_a_begin_0 = const()[name = string("x_a_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_end_0 = const()[name = string("x_a_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_end_mask_0 = const()[name = string("x_a_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_189_cast_fp16 = transpose(perm = x_189_perm_0, x = var_2102_cast_fp16)[name = string("transpose_22")]; + tensor x_a_cast_fp16 = slice_by_index(begin = x_a_begin_0, end = x_a_end_0, end_mask = x_a_end_mask_0, x = x_189_cast_fp16)[name = string("x_a_cast_fp16")]; + tensor x_b_begin_0 = const()[name = string("x_b_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_end_0 = const()[name = string("x_b_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_end_mask_0 = const()[name = string("x_b_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_cast_fp16 = slice_by_index(begin = x_b_begin_0, end = x_b_end_0, end_mask = x_b_end_mask_0, x = x_189_cast_fp16)[name = string("x_b_cast_fp16")]; + tensor var_2156_cast_fp16 = mul(x = x_a_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2156_cast_fp16")]; + tensor var_2157_cast_fp16 = mul(x = x_b_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2157_cast_fp16")]; + tensor rot_a_cast_fp16 = sub(x = var_2156_cast_fp16, y = var_2157_cast_fp16)[name = string("rot_a_cast_fp16")]; + tensor var_2159_cast_fp16 = mul(x = x_a_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2159_cast_fp16")]; + tensor var_2160_cast_fp16 = mul(x = x_b_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2160_cast_fp16")]; + tensor rot_b_cast_fp16 = add(x = var_2159_cast_fp16, y = var_2160_cast_fp16)[name = string("rot_b_cast_fp16")]; + bool k_13_interleave_0 = const()[name = string("k_13_interleave_0"), val = bool(false)]; + tensor k_13_cast_fp16 = concat(axis = var_1775, interleave = k_13_interleave_0, values = (rot_a_cast_fp16, rot_b_cast_fp16))[name = string("k_13_cast_fp16")]; + bool var_2165_transpose_x_1 = const()[name = string("op_2165_transpose_x_1"), val = bool(false)]; + bool var_2165_transpose_y_1 = const()[name = string("op_2165_transpose_y_1"), val = bool(true)]; + tensor var_2165_cast_fp16 = matmul(transpose_x = var_2165_transpose_x_1, transpose_y = var_2165_transpose_y_1, x = q_13_cast_fp16, y = k_13_cast_fp16)[name = string("op_2165_cast_fp16")]; + fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_cast_fp16 = mul(x = var_2165_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")]; + tensor input_359_cast_fp16 = sub(x = _inversed_scores_cast_fp16, y = var_530_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_1775, x = input_359_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool var_2174_transpose_x_0 = const()[name = string("op_2174_transpose_x_0"), val = bool(false)]; + bool var_2174_transpose_y_0 = const()[name = string("op_2174_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2110_cast_fp16)[name = string("transpose_21")]; + tensor var_2174_cast_fp16 = matmul(transpose_x = var_2174_transpose_x_0, transpose_y = var_2174_transpose_y_0, x = attn_19_cast_fp16, y = v_13_cast_fp16)[name = string("op_2174_cast_fp16")]; + tensor var_2175_perm_0 = const()[name = string("op_2175_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_41x = const()[name = string("concat_41x"), val = tensor([2, -1, 512])]; + tensor var_2175_cast_fp16 = transpose(perm = var_2175_perm_0, x = var_2174_cast_fp16)[name = string("transpose_20")]; + tensor input_361_cast_fp16 = reshape(shape = concat_41x, x = var_2175_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52909760))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53171968))))[name = string("main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53173056)))]; + tensor linear_33_cast_fp16 = linear(bias = main_blocks_3_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_text_attn_out_fc_linear_weight_to_fp16_quantized, x = input_361_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor out_59_perm_0 = const()[name = string("out_59_perm_0"), val = tensor([0, 2, 1])]; + tensor out_59_cast_fp16 = transpose(perm = out_59_perm_0, x = linear_33_cast_fp16)[name = string("transpose_19")]; + tensor var_2183_cast_fp16 = mul(x = out_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_m_13_cast_fp16, y = var_2183_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor input_363_perm_0 = const()[name = string("input_363_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2190_axes_0 = const()[name = string("op_2190_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53174144)))]; + tensor main_blocks_3_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53175232)))]; + tensor input_363_cast_fp16 = transpose(perm = input_363_perm_0, x = x_191_cast_fp16)[name = string("transpose_18")]; + tensor var_2190_cast_fp16 = layer_norm(axes = var_2190_axes_0, beta = main_blocks_3_text_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_text_norm_norm_weight_to_fp16, x = input_363_cast_fp16)[name = string("op_2190_cast_fp16")]; + tensor var_2191_perm_0 = const()[name = string("op_2191_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2191_perm_0, x = var_2190_cast_fp16)[name = string("transpose_17")]; + tensor x_193_cast_fp16 = mul(x = var_2191_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_193_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2204_cast_fp16 = mul(x = x_195_cast_fp16, y = last_1_cast_fp16)[name = string("op_2204_cast_fp16")]; + tensor x_last_47_axes_0 = const()[name = string("x_last_47_axes_0"), val = tensor([-1])]; + bool x_last_47_keep_dims_0 = const()[name = string("x_last_47_keep_dims_0"), val = bool(true)]; + tensor x_last_47_cast_fp16 = reduce_sum(axes = x_last_47_axes_0, keep_dims = x_last_47_keep_dims_0, x = var_2204_cast_fp16)[name = string("x_last_47_cast_fp16")]; + tensor var_2207_cast_fp16 = mul(x = x_195_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2207_cast_fp16")]; + tensor var_2209_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_47_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor input_367_cast_fp16 = add(x = var_2207_cast_fp16, y = var_2209_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_369_mode_0 = const()[name = string("input_369_mode_0"), val = string("replicate")]; + fp16 const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = fp16(0x0p+0)]; + tensor input_369_cast_fp16 = pad(constant_val = const_50_to_fp16, mode = input_369_mode_0, pad = input_369_pad_0, x = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + string h_141_pad_type_0 = const()[name = string("h_141_pad_type_0"), val = string("valid")]; + int32 h_141_groups_0 = const()[name = string("h_141_groups_0"), val = int32(512)]; + tensor h_141_strides_0 = const()[name = string("h_141_strides_0"), val = tensor([1])]; + tensor h_141_pad_0 = const()[name = string("h_141_pad_0"), val = tensor([0, 0])]; + tensor h_141_dilations_0 = const()[name = string("h_141_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53176320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53178944))))[name = string("main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53180032)))]; + tensor h_141_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_141_dilations_0, groups = h_141_groups_0, pad = h_141_pad_0, pad_type = h_141_pad_type_0, strides = h_141_strides_0, weight = main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16_quantized, x = input_369_cast_fp16)[name = string("h_141_cast_fp16")]; + tensor x_197_cast_fp16 = mul(x = h_141_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_371_perm_0 = const()[name = string("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2227_axes_0 = const()[name = string("op_2227_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53181120)))]; + tensor main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53182208)))]; + tensor input_371_cast_fp16 = transpose(perm = input_371_perm_0, x = x_197_cast_fp16)[name = string("transpose_16")]; + tensor var_2227_cast_fp16 = layer_norm(axes = var_2227_axes_0, beta = main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16, x = input_371_cast_fp16)[name = string("op_2227_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + string h_143_pad_type_0 = const()[name = string("h_143_pad_type_0"), val = string("valid")]; + tensor h_143_strides_0 = const()[name = string("h_143_strides_0"), val = tensor([1])]; + tensor h_143_pad_0 = const()[name = string("h_143_pad_0"), val = tensor([0, 0])]; + tensor h_143_dilations_0 = const()[name = string("h_143_dilations_0"), val = tensor([1])]; + int32 h_143_groups_0 = const()[name = string("h_143_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53183296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54231936))))[name = string("main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_quantized")]; + tensor main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54236096)))]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = var_2227_cast_fp16)[name = string("transpose_15")]; + tensor h_143_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_143_dilations_0, groups = h_143_groups_0, pad = h_143_pad_0, pad_type = h_143_pad_type_0, strides = h_143_strides_0, weight = main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16_quantized, x = input_373_cast_fp16)[name = string("h_143_cast_fp16")]; + string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")]; + tensor input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = h_143_cast_fp16)[name = string("input_375_cast_fp16")]; + string h_145_pad_type_0 = const()[name = string("h_145_pad_type_0"), val = string("valid")]; + tensor h_145_strides_0 = const()[name = string("h_145_strides_0"), val = tensor([1])]; + tensor h_145_pad_0 = const()[name = string("h_145_pad_0"), val = tensor([0, 0])]; + tensor h_145_dilations_0 = const()[name = string("h_145_dilations_0"), val = tensor([1])]; + int32 h_145_groups_0 = const()[name = string("h_145_groups_0"), val = int32(1)]; + tensor op_2244_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54240256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55288896))))[name = string("op_2244_weight_0_to_fp16_quantized")]; + tensor var_2244_bias_0_to_fp16 = const()[name = string("op_2244_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55289984)))]; + tensor var_2244_cast_fp16 = conv(bias = var_2244_bias_0_to_fp16, dilations = h_145_dilations_0, groups = h_145_groups_0, pad = h_145_pad_0, pad_type = h_145_pad_type_0, strides = h_145_strides_0, weight = op_2244_weight_0_to_fp16_quantized, x = input_375_cast_fp16)[name = string("op_2244_cast_fp16")]; + tensor out_61_cast_fp16 = add(x = x_195_cast_fp16, y = var_2244_cast_fp16)[name = string("out_61_cast_fp16")]; + tensor x_199_cast_fp16 = mul(x = out_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor x_m_cast_fp16 = mul(x = x_199_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_cast_fp16")]; + tensor input_377_perm_0 = const()[name = string("input_377_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_style_attn_W_query_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55291072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55422208))))[name = string("main_blocks_3_style_attn_W_query_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55422784)))]; + tensor input_377_cast_fp16 = transpose(perm = input_377_perm_0, x = x_199_cast_fp16)[name = string("transpose_14")]; + tensor linear_34_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_query_linear_weight_to_fp16_quantized, x = input_377_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor concat_42x = const()[name = string("concat_42x"), val = tensor([2, -1, 2, 128])]; + tensor var_2261_cast_fp16 = reshape(shape = concat_42x, x = linear_34_cast_fp16)[name = string("op_2261_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor main_blocks_3_style_attn_W_value_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55423360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55488960))))[name = string("main_blocks_3_style_attn_W_value_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55489536)))]; + tensor linear_36_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_value_linear_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_2274 = const()[name = string("op_2274"), val = tensor([2, 50, 2, 128])]; + tensor var_2275_cast_fp16 = reshape(shape = var_2274, x = linear_36_cast_fp16)[name = string("op_2275_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_2279_transpose_x_0 = const()[name = string("op_2279_transpose_x_0"), val = bool(false)]; + bool var_2279_transpose_y_0 = const()[name = string("op_2279_transpose_y_0"), val = bool(false)]; + tensor op_2278_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55490112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55515776))))[name = string("op_2278_to_fp16_quantized")]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_2261_cast_fp16)[name = string("transpose_13")]; + tensor var_2279_cast_fp16 = matmul(transpose_x = var_2279_transpose_x_0, transpose_y = var_2279_transpose_y_0, x = q_cast_fp16, y = op_2278_to_fp16_quantized)[name = string("op_2279_cast_fp16")]; + fp16 _inversed_input_379_y_0_to_fp16 = const()[name = string("_inversed_input_379_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_379_cast_fp16 = mul(x = var_2279_cast_fp16, y = _inversed_input_379_y_0_to_fp16)[name = string("_inversed_input_379_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_1775, x = _inversed_input_379_cast_fp16)[name = string("attn_21_cast_fp16")]; + tensor attn_cast_fp16 = mul(x = attn_21_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_cast_fp16")]; + bool var_2286_transpose_x_0 = const()[name = string("op_2286_transpose_x_0"), val = bool(false)]; + bool var_2286_transpose_y_0 = const()[name = string("op_2286_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_2275_cast_fp16)[name = string("transpose_12")]; + tensor var_2286_cast_fp16 = matmul(transpose_x = var_2286_transpose_x_0, transpose_y = var_2286_transpose_y_0, x = attn_cast_fp16, y = v_15_cast_fp16)[name = string("op_2286_cast_fp16")]; + tensor var_2287_perm_0 = const()[name = string("op_2287_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_43x = const()[name = string("concat_43x"), val = tensor([2, -1, 256])]; + tensor var_2287_cast_fp16 = transpose(perm = var_2287_perm_0, x = var_2286_cast_fp16)[name = string("transpose_11")]; + tensor input_381_cast_fp16 = reshape(shape = concat_43x, x = var_2287_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55515968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55647104))))[name = string("main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_quantized")]; + tensor main_blocks_3_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55648192)))]; + tensor linear_37_cast_fp16 = linear(bias = main_blocks_3_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_style_attn_out_fc_linear_weight_to_fp16_quantized, x = input_381_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor out_63_perm_0 = const()[name = string("out_63_perm_0"), val = tensor([0, 2, 1])]; + tensor out_63_cast_fp16 = transpose(perm = out_63_perm_0, x = linear_37_cast_fp16)[name = string("transpose_10")]; + tensor var_2295_cast_fp16 = mul(x = out_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2295_cast_fp16")]; + tensor x_201_cast_fp16 = add(x = x_m_cast_fp16, y = var_2295_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_383_perm_0 = const()[name = string("input_383_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2302_axes_0 = const()[name = string("op_2302_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55649280)))]; + tensor main_blocks_3_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55650368)))]; + tensor input_383_cast_fp16 = transpose(perm = input_383_perm_0, x = x_201_cast_fp16)[name = string("transpose_9")]; + tensor var_2302_cast_fp16 = layer_norm(axes = var_2302_axes_0, beta = main_blocks_3_style_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_style_norm_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_perm_0 = const()[name = string("op_2303_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2303_cast_fp16 = transpose(perm = var_2303_perm_0, x = var_2302_cast_fp16)[name = string("transpose_8")]; + tensor x_203_cast_fp16 = mul(x = var_2303_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor x_205_cast_fp16 = mul(x = x_203_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor var_2330_cast_fp16 = mul(x = x_205_cast_fp16, y = last_1_cast_fp16)[name = string("op_2330_cast_fp16")]; + tensor x_last_49_axes_0 = const()[name = string("x_last_49_axes_0"), val = tensor([-1])]; + bool x_last_49_keep_dims_0 = const()[name = string("x_last_49_keep_dims_0"), val = bool(true)]; + tensor x_last_49_cast_fp16 = reduce_sum(axes = x_last_49_axes_0, keep_dims = x_last_49_keep_dims_0, x = var_2330_cast_fp16)[name = string("x_last_49_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = x_205_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor var_2335_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_49_cast_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2335_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_pad_0 = const()[name = string("input_389_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("replicate")]; + fp16 const_53_to_fp16 = const()[name = string("const_53_to_fp16"), val = fp16(0x0p+0)]; + tensor input_389_cast_fp16 = pad(constant_val = const_53_to_fp16, mode = input_389_mode_0, pad = input_389_pad_0, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + string h_147_pad_type_0 = const()[name = string("h_147_pad_type_0"), val = string("valid")]; + int32 h_147_groups_0 = const()[name = string("h_147_groups_0"), val = int32(512)]; + tensor h_147_strides_0 = const()[name = string("h_147_strides_0"), val = tensor([1])]; + tensor h_147_pad_0 = const()[name = string("h_147_pad_0"), val = tensor([0, 0])]; + tensor h_147_dilations_0 = const()[name = string("h_147_dilations_0"), val = tensor([1])]; + tensor last_convnext_0_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55651456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55654080))))[name = string("last_convnext_0_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55655168)))]; + tensor h_147_cast_fp16 = conv(bias = last_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_147_dilations_0, groups = h_147_groups_0, pad = h_147_pad_0, pad_type = h_147_pad_type_0, strides = h_147_strides_0, weight = last_convnext_0_dwconv__conv_weight_to_fp16_quantized, x = input_389_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor x_207_cast_fp16 = mul(x = h_147_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_391_perm_0 = const()[name = string("input_391_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2353_axes_0 = const()[name = string("op_2353_axes_0"), val = tensor([-1])]; + tensor last_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55656256)))]; + tensor last_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55657344)))]; + fp16 var_2307_to_fp16 = const()[name = string("op_2307_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_391_cast_fp16 = transpose(perm = input_391_perm_0, x = x_207_cast_fp16)[name = string("transpose_7")]; + tensor var_2353_cast_fp16 = layer_norm(axes = var_2353_axes_0, beta = last_convnext_0_norm_norm_bias_to_fp16, epsilon = var_2307_to_fp16, gamma = last_convnext_0_norm_norm_weight_to_fp16, x = input_391_cast_fp16)[name = string("op_2353_cast_fp16")]; + tensor input_393_perm_0 = const()[name = string("input_393_perm_0"), val = tensor([0, 2, 1])]; + string h_149_pad_type_0 = const()[name = string("h_149_pad_type_0"), val = string("valid")]; + tensor h_149_strides_0 = const()[name = string("h_149_strides_0"), val = tensor([1])]; + tensor h_149_pad_0 = const()[name = string("h_149_pad_0"), val = tensor([0, 0])]; + tensor h_149_dilations_0 = const()[name = string("h_149_dilations_0"), val = tensor([1])]; + int32 h_149_groups_0 = const()[name = string("h_149_groups_0"), val = int32(1)]; + tensor last_convnext_0_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55658432))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56707072))))[name = string("last_convnext_0_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56711232)))]; + tensor input_393_cast_fp16 = transpose(perm = input_393_perm_0, x = var_2353_cast_fp16)[name = string("transpose_6")]; + tensor h_149_cast_fp16 = conv(bias = last_convnext_0_pwconv1_bias_to_fp16, dilations = h_149_dilations_0, groups = h_149_groups_0, pad = h_149_pad_0, pad_type = h_149_pad_type_0, strides = h_149_strides_0, weight = last_convnext_0_pwconv1_weight_to_fp16_quantized, x = input_393_cast_fp16)[name = string("h_149_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = h_149_cast_fp16)[name = string("input_395_cast_fp16")]; + string h_151_pad_type_0 = const()[name = string("h_151_pad_type_0"), val = string("valid")]; + tensor h_151_strides_0 = const()[name = string("h_151_strides_0"), val = tensor([1])]; + tensor h_151_pad_0 = const()[name = string("h_151_pad_0"), val = tensor([0, 0])]; + tensor h_151_dilations_0 = const()[name = string("h_151_dilations_0"), val = tensor([1])]; + int32 h_151_groups_0 = const()[name = string("h_151_groups_0"), val = int32(1)]; + tensor op_2370_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56715392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57764032))))[name = string("op_2370_weight_0_to_fp16_quantized")]; + tensor var_2370_bias_0_to_fp16 = const()[name = string("op_2370_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57765120)))]; + tensor var_2370_cast_fp16 = conv(bias = var_2370_bias_0_to_fp16, dilations = h_151_dilations_0, groups = h_151_groups_0, pad = h_151_pad_0, pad_type = h_151_pad_type_0, strides = h_151_strides_0, weight = op_2370_weight_0_to_fp16_quantized, x = input_395_cast_fp16)[name = string("op_2370_cast_fp16")]; + tensor out_65_cast_fp16 = add(x = x_205_cast_fp16, y = var_2370_cast_fp16)[name = string("out_65_cast_fp16")]; + tensor x_209_cast_fp16 = mul(x = out_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor x_211_cast_fp16 = mul(x = x_209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_2398_cast_fp16 = mul(x = x_211_cast_fp16, y = last_1_cast_fp16)[name = string("op_2398_cast_fp16")]; + tensor x_last_51_axes_0 = const()[name = string("x_last_51_axes_0"), val = tensor([-1])]; + bool x_last_51_keep_dims_0 = const()[name = string("x_last_51_keep_dims_0"), val = bool(true)]; + tensor x_last_51_cast_fp16 = reduce_sum(axes = x_last_51_axes_0, keep_dims = x_last_51_keep_dims_0, x = var_2398_cast_fp16)[name = string("x_last_51_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = x_211_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_51_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor input_399_cast_fp16 = add(x = var_2401_cast_fp16, y = var_2403_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor input_401_pad_0 = const()[name = string("input_401_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_401_mode_0 = const()[name = string("input_401_mode_0"), val = string("replicate")]; + fp16 const_55_to_fp16 = const()[name = string("const_55_to_fp16"), val = fp16(0x0p+0)]; + tensor input_401_cast_fp16 = pad(constant_val = const_55_to_fp16, mode = input_401_mode_0, pad = input_401_pad_0, x = input_399_cast_fp16)[name = string("input_401_cast_fp16")]; + string h_153_pad_type_0 = const()[name = string("h_153_pad_type_0"), val = string("valid")]; + int32 h_153_groups_0 = const()[name = string("h_153_groups_0"), val = int32(512)]; + tensor h_153_strides_0 = const()[name = string("h_153_strides_0"), val = tensor([1])]; + tensor h_153_pad_0 = const()[name = string("h_153_pad_0"), val = tensor([0, 0])]; + tensor h_153_dilations_0 = const()[name = string("h_153_dilations_0"), val = tensor([1])]; + tensor last_convnext_1_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57766208))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57768832))))[name = string("last_convnext_1_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57769920)))]; + tensor h_153_cast_fp16 = conv(bias = last_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_153_dilations_0, groups = h_153_groups_0, pad = h_153_pad_0, pad_type = h_153_pad_type_0, strides = h_153_strides_0, weight = last_convnext_1_dwconv__conv_weight_to_fp16_quantized, x = input_401_cast_fp16)[name = string("h_153_cast_fp16")]; + tensor x_213_cast_fp16 = mul(x = h_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_213_cast_fp16")]; + tensor input_403_perm_0 = const()[name = string("input_403_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2421_axes_0 = const()[name = string("op_2421_axes_0"), val = tensor([-1])]; + tensor last_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57771008)))]; + tensor last_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57772096)))]; + fp16 var_2375_to_fp16 = const()[name = string("op_2375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_403_cast_fp16 = transpose(perm = input_403_perm_0, x = x_213_cast_fp16)[name = string("transpose_5")]; + tensor var_2421_cast_fp16 = layer_norm(axes = var_2421_axes_0, beta = last_convnext_1_norm_norm_bias_to_fp16, epsilon = var_2375_to_fp16, gamma = last_convnext_1_norm_norm_weight_to_fp16, x = input_403_cast_fp16)[name = string("op_2421_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string h_155_pad_type_0 = const()[name = string("h_155_pad_type_0"), val = string("valid")]; + tensor h_155_strides_0 = const()[name = string("h_155_strides_0"), val = tensor([1])]; + tensor h_155_pad_0 = const()[name = string("h_155_pad_0"), val = tensor([0, 0])]; + tensor h_155_dilations_0 = const()[name = string("h_155_dilations_0"), val = tensor([1])]; + int32 h_155_groups_0 = const()[name = string("h_155_groups_0"), val = int32(1)]; + tensor last_convnext_1_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57773184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58821824))))[name = string("last_convnext_1_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58825984)))]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = var_2421_cast_fp16)[name = string("transpose_4")]; + tensor h_155_cast_fp16 = conv(bias = last_convnext_1_pwconv1_bias_to_fp16, dilations = h_155_dilations_0, groups = h_155_groups_0, pad = h_155_pad_0, pad_type = h_155_pad_type_0, strides = h_155_strides_0, weight = last_convnext_1_pwconv1_weight_to_fp16_quantized, x = input_405_cast_fp16)[name = string("h_155_cast_fp16")]; + string input_407_mode_0 = const()[name = string("input_407_mode_0"), val = string("EXACT")]; + tensor input_407_cast_fp16 = gelu(mode = input_407_mode_0, x = h_155_cast_fp16)[name = string("input_407_cast_fp16")]; + string h_157_pad_type_0 = const()[name = string("h_157_pad_type_0"), val = string("valid")]; + tensor h_157_strides_0 = const()[name = string("h_157_strides_0"), val = tensor([1])]; + tensor h_157_pad_0 = const()[name = string("h_157_pad_0"), val = tensor([0, 0])]; + tensor h_157_dilations_0 = const()[name = string("h_157_dilations_0"), val = tensor([1])]; + int32 h_157_groups_0 = const()[name = string("h_157_groups_0"), val = int32(1)]; + tensor op_2438_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58830144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59878784))))[name = string("op_2438_weight_0_to_fp16_quantized")]; + tensor var_2438_bias_0_to_fp16 = const()[name = string("op_2438_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59879872)))]; + tensor var_2438_cast_fp16 = conv(bias = var_2438_bias_0_to_fp16, dilations = h_157_dilations_0, groups = h_157_groups_0, pad = h_157_pad_0, pad_type = h_157_pad_type_0, strides = h_157_strides_0, weight = op_2438_weight_0_to_fp16_quantized, x = input_407_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor out_67_cast_fp16 = add(x = x_211_cast_fp16, y = var_2438_cast_fp16)[name = string("out_67_cast_fp16")]; + tensor x_215_cast_fp16 = mul(x = out_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_215_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2466_cast_fp16 = mul(x = x_217_cast_fp16, y = last_1_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor x_last_53_axes_0 = const()[name = string("x_last_53_axes_0"), val = tensor([-1])]; + bool x_last_53_keep_dims_0 = const()[name = string("x_last_53_keep_dims_0"), val = bool(true)]; + tensor x_last_53_cast_fp16 = reduce_sum(axes = x_last_53_axes_0, keep_dims = x_last_53_keep_dims_0, x = var_2466_cast_fp16)[name = string("x_last_53_cast_fp16")]; + tensor var_2469_cast_fp16 = mul(x = x_217_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor var_2471_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_53_cast_fp16)[name = string("op_2471_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = var_2469_cast_fp16, y = var_2471_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("replicate")]; + fp16 const_57_to_fp16 = const()[name = string("const_57_to_fp16"), val = fp16(0x0p+0)]; + tensor input_413_cast_fp16 = pad(constant_val = const_57_to_fp16, mode = input_413_mode_0, pad = input_413_pad_0, x = input_411_cast_fp16)[name = string("input_413_cast_fp16")]; + string h_159_pad_type_0 = const()[name = string("h_159_pad_type_0"), val = string("valid")]; + int32 h_159_groups_0 = const()[name = string("h_159_groups_0"), val = int32(512)]; + tensor h_159_strides_0 = const()[name = string("h_159_strides_0"), val = tensor([1])]; + tensor h_159_pad_0 = const()[name = string("h_159_pad_0"), val = tensor([0, 0])]; + tensor h_159_dilations_0 = const()[name = string("h_159_dilations_0"), val = tensor([1])]; + tensor last_convnext_2_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59880960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59883584))))[name = string("last_convnext_2_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59884672)))]; + tensor h_159_cast_fp16 = conv(bias = last_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_159_dilations_0, groups = h_159_groups_0, pad = h_159_pad_0, pad_type = h_159_pad_type_0, strides = h_159_strides_0, weight = last_convnext_2_dwconv__conv_weight_to_fp16_quantized, x = input_413_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor x_219_cast_fp16 = mul(x = h_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_415_perm_0 = const()[name = string("input_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2489_axes_0 = const()[name = string("op_2489_axes_0"), val = tensor([-1])]; + tensor last_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59885760)))]; + tensor last_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59886848)))]; + fp16 var_2443_to_fp16 = const()[name = string("op_2443_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_415_cast_fp16 = transpose(perm = input_415_perm_0, x = x_219_cast_fp16)[name = string("transpose_3")]; + tensor var_2489_cast_fp16 = layer_norm(axes = var_2489_axes_0, beta = last_convnext_2_norm_norm_bias_to_fp16, epsilon = var_2443_to_fp16, gamma = last_convnext_2_norm_norm_weight_to_fp16, x = input_415_cast_fp16)[name = string("op_2489_cast_fp16")]; + tensor input_417_perm_0 = const()[name = string("input_417_perm_0"), val = tensor([0, 2, 1])]; + string h_161_pad_type_0 = const()[name = string("h_161_pad_type_0"), val = string("valid")]; + tensor h_161_strides_0 = const()[name = string("h_161_strides_0"), val = tensor([1])]; + tensor h_161_pad_0 = const()[name = string("h_161_pad_0"), val = tensor([0, 0])]; + tensor h_161_dilations_0 = const()[name = string("h_161_dilations_0"), val = tensor([1])]; + int32 h_161_groups_0 = const()[name = string("h_161_groups_0"), val = int32(1)]; + tensor last_convnext_2_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59887936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60936576))))[name = string("last_convnext_2_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60940736)))]; + tensor input_417_cast_fp16 = transpose(perm = input_417_perm_0, x = var_2489_cast_fp16)[name = string("transpose_2")]; + tensor h_161_cast_fp16 = conv(bias = last_convnext_2_pwconv1_bias_to_fp16, dilations = h_161_dilations_0, groups = h_161_groups_0, pad = h_161_pad_0, pad_type = h_161_pad_type_0, strides = h_161_strides_0, weight = last_convnext_2_pwconv1_weight_to_fp16_quantized, x = input_417_cast_fp16)[name = string("h_161_cast_fp16")]; + string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("EXACT")]; + tensor input_419_cast_fp16 = gelu(mode = input_419_mode_0, x = h_161_cast_fp16)[name = string("input_419_cast_fp16")]; + string h_163_pad_type_0 = const()[name = string("h_163_pad_type_0"), val = string("valid")]; + tensor h_163_strides_0 = const()[name = string("h_163_strides_0"), val = tensor([1])]; + tensor h_163_pad_0 = const()[name = string("h_163_pad_0"), val = tensor([0, 0])]; + tensor h_163_dilations_0 = const()[name = string("h_163_dilations_0"), val = tensor([1])]; + int32 h_163_groups_0 = const()[name = string("h_163_groups_0"), val = int32(1)]; + tensor op_2506_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60944896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61993536))))[name = string("op_2506_weight_0_to_fp16_quantized")]; + tensor var_2506_bias_0_to_fp16 = const()[name = string("op_2506_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61994624)))]; + tensor var_2506_cast_fp16 = conv(bias = var_2506_bias_0_to_fp16, dilations = h_163_dilations_0, groups = h_163_groups_0, pad = h_163_pad_0, pad_type = h_163_pad_type_0, strides = h_163_strides_0, weight = op_2506_weight_0_to_fp16_quantized, x = input_419_cast_fp16)[name = string("op_2506_cast_fp16")]; + tensor out_69_cast_fp16 = add(x = x_217_cast_fp16, y = var_2506_cast_fp16)[name = string("out_69_cast_fp16")]; + tensor x_221_cast_fp16 = mul(x = out_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor x_223_cast_fp16 = mul(x = x_221_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_2534_cast_fp16 = mul(x = x_223_cast_fp16, y = last_1_cast_fp16)[name = string("op_2534_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_2534_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_2537_cast_fp16 = mul(x = x_223_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2537_cast_fp16")]; + tensor var_2539_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_cast_fp16)[name = string("op_2539_cast_fp16")]; + tensor input_423_cast_fp16 = add(x = var_2537_cast_fp16, y = var_2539_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_425_mode_0 = const()[name = string("input_425_mode_0"), val = string("replicate")]; + fp16 const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = fp16(0x0p+0)]; + tensor input_425_cast_fp16 = pad(constant_val = const_59_to_fp16, mode = input_425_mode_0, pad = input_425_pad_0, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")]; + string h_165_pad_type_0 = const()[name = string("h_165_pad_type_0"), val = string("valid")]; + int32 h_165_groups_0 = const()[name = string("h_165_groups_0"), val = int32(512)]; + tensor h_165_strides_0 = const()[name = string("h_165_strides_0"), val = tensor([1])]; + tensor h_165_pad_0 = const()[name = string("h_165_pad_0"), val = tensor([0, 0])]; + tensor h_165_dilations_0 = const()[name = string("h_165_dilations_0"), val = tensor([1])]; + tensor last_convnext_3_dwconv__conv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61995712))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61998336))))[name = string("last_convnext_3_dwconv__conv_weight_to_fp16_quantized")]; + tensor last_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61999424)))]; + tensor h_165_cast_fp16 = conv(bias = last_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_165_dilations_0, groups = h_165_groups_0, pad = h_165_pad_0, pad_type = h_165_pad_type_0, strides = h_165_strides_0, weight = last_convnext_3_dwconv__conv_weight_to_fp16_quantized, x = input_425_cast_fp16)[name = string("h_165_cast_fp16")]; + tensor x_cast_fp16 = mul(x = h_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2557_axes_0 = const()[name = string("op_2557_axes_0"), val = tensor([-1])]; + tensor last_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62000512)))]; + tensor last_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62001600)))]; + fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_cast_fp16)[name = string("transpose_1")]; + tensor var_2557_cast_fp16 = layer_norm(axes = var_2557_axes_0, beta = last_convnext_3_norm_norm_bias_to_fp16, epsilon = var_2511_to_fp16, gamma = last_convnext_3_norm_norm_weight_to_fp16, x = input_427_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor input_429_perm_0 = const()[name = string("input_429_perm_0"), val = tensor([0, 2, 1])]; + string h_167_pad_type_0 = const()[name = string("h_167_pad_type_0"), val = string("valid")]; + tensor h_167_strides_0 = const()[name = string("h_167_strides_0"), val = tensor([1])]; + tensor h_167_pad_0 = const()[name = string("h_167_pad_0"), val = tensor([0, 0])]; + tensor h_167_dilations_0 = const()[name = string("h_167_dilations_0"), val = tensor([1])]; + int32 h_167_groups_0 = const()[name = string("h_167_groups_0"), val = int32(1)]; + tensor last_convnext_3_pwconv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62002688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63051328))))[name = string("last_convnext_3_pwconv1_weight_to_fp16_quantized")]; + tensor last_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63055488)))]; + tensor input_429_cast_fp16 = transpose(perm = input_429_perm_0, x = var_2557_cast_fp16)[name = string("transpose_0")]; + tensor h_167_cast_fp16 = conv(bias = last_convnext_3_pwconv1_bias_to_fp16, dilations = h_167_dilations_0, groups = h_167_groups_0, pad = h_167_pad_0, pad_type = h_167_pad_type_0, strides = h_167_strides_0, weight = last_convnext_3_pwconv1_weight_to_fp16_quantized, x = input_429_cast_fp16)[name = string("h_167_cast_fp16")]; + string input_431_mode_0 = const()[name = string("input_431_mode_0"), val = string("EXACT")]; + tensor input_431_cast_fp16 = gelu(mode = input_431_mode_0, x = h_167_cast_fp16)[name = string("input_431_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor op_2574_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63059648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64108288))))[name = string("op_2574_weight_0_to_fp16_quantized")]; + tensor var_2574_bias_0_to_fp16 = const()[name = string("op_2574_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64109376)))]; + tensor var_2574_cast_fp16 = conv(bias = var_2574_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = op_2574_weight_0_to_fp16_quantized, x = input_431_cast_fp16)[name = string("op_2574_cast_fp16")]; + tensor out_cast_fp16 = add(x = x_223_cast_fp16, y = var_2574_cast_fp16)[name = string("out_cast_fp16")]; + tensor input_cast_fp16 = mul(x = out_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("input_cast_fp16")]; + string var_2587_pad_type_0 = const()[name = string("op_2587_pad_type_0"), val = string("valid")]; + tensor var_2587_strides_0 = const()[name = string("op_2587_strides_0"), val = tensor([1])]; + tensor var_2587_pad_0 = const()[name = string("op_2587_pad_0"), val = tensor([0, 0])]; + tensor var_2587_dilations_0 = const()[name = string("op_2587_dilations_0"), val = tensor([1])]; + int32 var_2587_groups_0 = const()[name = string("op_2587_groups_0"), val = int32(1)]; + tensor proj_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64110464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64184256))))[name = string("proj_out_weight_to_fp16_quantized")]; + tensor var_2587_cast_fp16 = conv(dilations = var_2587_dilations_0, groups = var_2587_groups_0, pad = var_2587_pad_0, pad_type = var_2587_pad_type_0, strides = var_2587_strides_0, weight = proj_out_weight_to_fp16_quantized, x = input_cast_fp16)[name = string("op_2587_cast_fp16")]; + tensor v_cast_fp16 = mul(x = var_2587_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("v_cast_fp16")]; + tensor cond_begin_0 = const()[name = string("cond_begin_0"), val = tensor([0, 0, 0])]; + tensor cond_end_0 = const()[name = string("cond_end_0"), val = tensor([1, 144, 0])]; + tensor cond_end_mask_0 = const()[name = string("cond_end_mask_0"), val = tensor([false, true, true])]; + tensor cond_cast_fp16 = slice_by_index(begin = cond_begin_0, end = cond_end_0, end_mask = cond_end_mask_0, x = v_cast_fp16)[name = string("cond_cast_fp16")]; + tensor uncond_begin_0 = const()[name = string("uncond_begin_0"), val = tensor([1, 0, 0])]; + tensor uncond_end_0 = const()[name = string("uncond_end_0"), val = tensor([2, 144, 0])]; + tensor uncond_end_mask_0 = const()[name = string("uncond_end_mask_0"), val = tensor([true, true, true])]; + tensor uncond_cast_fp16 = slice_by_index(begin = uncond_begin_0, end = uncond_end_0, end_mask = uncond_end_mask_0, x = v_cast_fp16)[name = string("uncond_cast_fp16")]; + fp32 var_2597_epsilon_0 = const()[name = string("op_2597_epsilon_0"), val = fp32(0x1.a36e2ep-14)]; + tensor var_2597_cast_fp16 = inverse(epsilon = var_2597_epsilon_0, x = total_step_to_fp16)[name = string("op_2597_cast_fp16")]; + tensor var_2603 = const()[name = string("op_2603"), val = tensor([-1, 1, 1])]; + tensor step_cast_fp16 = reshape(shape = var_2603, x = var_2597_cast_fp16)[name = string("step_cast_fp16")]; + fp16 var_2605_to_fp16 = const()[name = string("op_2605_to_fp16"), val = fp16(0x1p+2)]; + tensor var_2606_cast_fp16 = mul(x = cond_cast_fp16, y = var_2605_to_fp16)[name = string("op_2606_cast_fp16")]; + fp16 var_2607_to_fp16 = const()[name = string("op_2607_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_2608_cast_fp16 = mul(x = uncond_cast_fp16, y = var_2607_to_fp16)[name = string("op_2608_cast_fp16")]; + tensor var_2610_cast_fp16 = sub(x = var_2606_cast_fp16, y = var_2608_cast_fp16)[name = string("op_2610_cast_fp16")]; + tensor var_2611_cast_fp16 = mul(x = step_cast_fp16, y = var_2610_cast_fp16)[name = string("op_2611_cast_fp16")]; + tensor var_2613_cast_fp16 = add(x = noisy_latent_to_fp16, y = var_2611_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor var_2614_cast_fp16 = mul(x = var_2613_cast_fp16, y = latent_mask_to_fp16)[name = string("op_2614_cast_fp16")]; + string var_2614_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2614_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor denoised_latent = cast(dtype = var_2614_cast_fp16_to_fp32_dtype_0, x = var_2614_cast_fp16)[name = string("cast_0")]; + } -> (denoised_latent); +} \ No newline at end of file diff --git a/VectorEstimatorVariants/VectorEstimator_v2_int8.mlmodelc/weights/weight.bin b/VectorEstimatorVariants/VectorEstimator_v2_int8.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..ac95b620695b767c0c20c7e3564cb2cabcefb88d --- /dev/null +++ b/VectorEstimatorVariants/VectorEstimator_v2_int8.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e637c3a1725b5e1b3df09cc162d1822e8fe80fa92ba01c180dde2ca29c12523 +size 64184608 diff --git a/VectorEstimatorVariants/VectorEstimator_v2_int8.mlpackage/Data/com.apple.CoreML/model.mlmodel b/VectorEstimatorVariants/VectorEstimator_v2_int8.mlpackage/Data/com.apple.CoreML/model.mlmodel new file mode 100644 index 0000000000000000000000000000000000000000..bc51ecf2a646cac149a28f60e2cf48dbf6f3c5be --- 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0000000000000000000000000000000000000000..a5306ac3e4c2233e1ac8c98d8253496946397ed0 --- /dev/null +++ b/VectorEstimator_v2.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:436efdac46acc82cc15611e5601911d6d6f2d9a3a8e5986e79483800c15fcf37 +size 243 diff --git a/VectorEstimator_v2.mlmodelc/coremldata.bin b/VectorEstimator_v2.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..73982c06bc09bcdb1bc7f734ecb79811119567a5 --- /dev/null +++ b/VectorEstimator_v2.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:46c39b731027dff10eeb42b6e2f68da3da82e794099678d99de63f6773bf6ca8 +size 634 diff --git a/VectorEstimator_v2.mlmodelc/model.mil b/VectorEstimator_v2.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..05b8d72e46d1618e731462570bc282dd40e9be63 --- /dev/null +++ b/VectorEstimator_v2.mlmodelc/model.mil @@ -0,0 +1,2269 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor current_step, tensor latent_mask, tensor noisy_latent, tensor style_ttl, tensor text_emb, tensor text_mask, tensor total_step) [FlexibleShapeInformation = tuple>>, tuple, ?>>>>((("DefaultShapes", {{"latent_mask", [1, 1, 24]}, {"noisy_latent", [1, 144, 24]}, {"text_emb", [1, 256, 24]}, {"text_mask", [1, 1, 24]}}), ("RangeDims", {{"latent_mask", [[1, 1], [1, 1], [17, 512]]}, {"noisy_latent", [[1, 1], [144, 144], [17, 512]]}, {"text_emb", [[1, 1], [256, 256], [17, 512]]}, {"text_mask", [[1, 1], [1, 1], [17, 512]]}})))] { + string text_emb_to_fp16_dtype_0 = const()[name = string("text_emb_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_emb_to_fp16 = cast(dtype = text_emb_to_fp16_dtype_0, x = text_emb)[name = string("cast_104")]; + tensor var_34_shape_cast_fp16 = shape(x = text_emb_to_fp16)[name = string("op_34_shape_cast_fp16")]; + int32 gather_1_axis_0 = const()[name = string("gather_1_axis_0"), val = int32(0)]; + int32 gather_1_batch_dims_0 = const()[name = string("gather_1_batch_dims_0"), val = int32(0)]; + bool gather_1_validate_indices_0 = const()[name = string("gather_1_validate_indices_0"), val = bool(false)]; + string var_34_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_34_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 gather_1_indices_0_to_uint16 = const()[name = string("gather_1_indices_0_to_uint16"), val = uint16(2)]; + tensor var_34_shape_cast_fp16_to_int16 = cast(dtype = var_34_shape_cast_fp16_to_int16_dtype_0, x = var_34_shape_cast_fp16)[name = string("cast_103")]; + int16 gather_1_cast_uint16 = gather(axis = gather_1_axis_0, batch_dims = gather_1_batch_dims_0, indices = gather_1_indices_0_to_uint16, validate_indices = gather_1_validate_indices_0, x = var_34_shape_cast_fp16_to_int16)[name = string("gather_1_cast_uint16")]; + string gather_1_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_1_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + int32 var_38 = const()[name = string("op_38"), val = int32(-1)]; + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + int32 gather_2 = const()[name = string("gather_2"), val = int32(1)]; + int32 concat_0_axis_0 = const()[name = string("concat_0_axis_0"), val = int32(0)]; + bool concat_0_interleave_0 = const()[name = string("concat_0_interleave_0"), val = bool(false)]; + int32 gather_1_cast_uint16_to_int32 = cast(dtype = gather_1_cast_uint16_to_int32_dtype_0, x = gather_1_cast_uint16)[name = string("cast_102")]; + tensor concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (gather_2, var_38, gather_1_cast_uint16_to_int32))[name = string("concat_0")]; + tensor shape_0 = const()[name = string("shape_0"), val = tensor([1, 256, 1])]; + int32 equal_0_y_0 = const()[name = string("equal_0_y_0"), val = int32(-1)]; + tensor equal_0 = equal(x = concat_0, y = equal_0_y_0)[name = string("equal_0")]; + tensor select_0 = select(a = shape_0, b = concat_0, cond = equal_0)[name = string("select_0")]; + tensor real_div_0 = real_div(x = select_0, y = shape_0)[name = string("real_div_0")]; + tensor uncond_masker_text_special_token_to_fp16 = const()[name = string("uncond_masker_text_special_token_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; + tensor text_uncond_cast_fp16 = tile(reps = real_div_0, x = uncond_masker_text_special_token_to_fp16)[name = string("text_uncond_cast_fp16")]; + bool text_emb_interleave_0 = const()[name = string("text_emb_interleave_0"), val = bool(false)]; + tensor text_emb_cast_fp16 = concat(axis = var_39, interleave = text_emb_interleave_0, values = (text_emb_to_fp16, text_uncond_cast_fp16))[name = string("text_emb_cast_fp16")]; + bool input_95_interleave_0 = const()[name = string("input_95_interleave_0"), val = bool(false)]; + string style_ttl_to_fp16_dtype_0 = const()[name = string("style_ttl_to_fp16_dtype_0"), val = string("fp16")]; + tensor uncond_masker_style_value_special_token_to_fp16 = const()[name = string("uncond_masker_style_value_special_token_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(640)))]; + tensor style_ttl_to_fp16 = cast(dtype = style_ttl_to_fp16_dtype_0, x = style_ttl)[name = string("cast_101")]; + tensor input_95_cast_fp16 = concat(axis = var_39, interleave = input_95_interleave_0, values = (style_ttl_to_fp16, uncond_masker_style_value_special_token_to_fp16))[name = string("input_95_cast_fp16")]; + int32 var_64 = const()[name = string("op_64"), val = int32(0)]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + string noisy_latent_to_fp16_dtype_0 = const()[name = string("noisy_latent_to_fp16_dtype_0"), val = string("fp16")]; + tensor noisy_latent_to_fp16 = cast(dtype = noisy_latent_to_fp16_dtype_0, x = noisy_latent)[name = string("cast_100")]; + tensor input_5_cast_fp16 = concat(axis = var_64, interleave = input_5_interleave_0, values = (noisy_latent_to_fp16, noisy_latent_to_fp16))[name = string("input_5_cast_fp16")]; + int32 var_67 = const()[name = string("op_67"), val = int32(0)]; + bool latent_mask_b_interleave_0 = const()[name = string("latent_mask_b_interleave_0"), val = bool(false)]; + string latent_mask_to_fp16_dtype_0 = const()[name = string("latent_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor latent_mask_to_fp16 = cast(dtype = latent_mask_to_fp16_dtype_0, x = latent_mask)[name = string("cast_99")]; + tensor latent_mask_b_cast_fp16 = concat(axis = var_67, interleave = latent_mask_b_interleave_0, values = (latent_mask_to_fp16, latent_mask_to_fp16))[name = string("latent_mask_b_cast_fp16")]; + int32 var_70 = const()[name = string("op_70"), val = int32(0)]; + bool text_mask_interleave_0 = const()[name = string("text_mask_interleave_0"), val = bool(false)]; + string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")]; + tensor text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_98")]; + tensor text_mask_cast_fp16 = concat(axis = var_70, interleave = text_mask_interleave_0, values = (text_mask_to_fp16, text_mask_to_fp16))[name = string("text_mask_cast_fp16")]; + string current_step_to_fp16_dtype_0 = const()[name = string("current_step_to_fp16_dtype_0"), val = string("fp16")]; + string total_step_to_fp16_dtype_0 = const()[name = string("total_step_to_fp16_dtype_0"), val = string("fp16")]; + tensor total_step_to_fp16 = cast(dtype = total_step_to_fp16_dtype_0, x = total_step)[name = string("cast_96")]; + tensor current_step_to_fp16 = cast(dtype = current_step_to_fp16_dtype_0, x = current_step)[name = string("cast_97")]; + tensor t_1_cast_fp16 = real_div(x = current_step_to_fp16, y = total_step_to_fp16)[name = string("t_1_cast_fp16")]; + int32 var_74 = const()[name = string("op_74"), val = int32(0)]; + bool t_3_interleave_0 = const()[name = string("t_3_interleave_0"), val = bool(false)]; + tensor t_3_cast_fp16 = concat(axis = var_74, interleave = t_3_interleave_0, values = (t_1_cast_fp16, t_1_cast_fp16))[name = string("t_3_cast_fp16")]; + int32 var_78 = const()[name = string("op_78"), val = int32(-1)]; + tensor var_85 = const()[name = string("op_85"), val = tensor([-1, 1])]; + tensor var_86_cast_fp16 = reshape(shape = var_85, x = t_3_cast_fp16)[name = string("op_86_cast_fp16")]; + fp16 time_encoder_time_scale_to_fp16 = const()[name = string("time_encoder_time_scale_to_fp16"), val = fp16(0x1.f4p+9)]; + tensor t_scaled_cast_fp16 = mul(x = var_86_cast_fp16, y = time_encoder_time_scale_to_fp16)[name = string("t_scaled_cast_fp16")]; + tensor time_encoder_omegas_to_fp16 = const()[name = string("time_encoder_omegas_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26304)))]; + tensor phase_cast_fp16 = mul(x = t_scaled_cast_fp16, y = time_encoder_omegas_to_fp16)[name = string("phase_cast_fp16")]; + tensor var_89_cast_fp16 = sin(x = phase_cast_fp16)[name = string("op_89_cast_fp16")]; + tensor var_90_cast_fp16 = cos(x = phase_cast_fp16)[name = string("op_90_cast_fp16")]; + bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)]; + tensor input_1_cast_fp16 = concat(axis = var_78, interleave = input_1_interleave_0, values = (var_89_cast_fp16, var_90_cast_fp16))[name = string("input_1_cast_fp16")]; + tensor time_encoder_mlp_0_linear_weight_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26432)))]; + tensor time_encoder_mlp_0_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_0_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264)))]; + tensor linear_0_cast_fp16 = linear(bias = time_encoder_mlp_0_linear_bias_to_fp16, weight = time_encoder_mlp_0_linear_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_97_cast_fp16 = softplus(x = linear_0_cast_fp16)[name = string("op_97_cast_fp16")]; + tensor var_98_cast_fp16 = tanh(x = var_97_cast_fp16)[name = string("op_98_cast_fp16")]; + tensor input_3_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_98_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor time_encoder_mlp_2_linear_weight_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59840)))]; + tensor time_encoder_mlp_2_linear_bias_to_fp16 = const()[name = string("time_encoder_mlp_2_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92672)))]; + tensor linear_1_cast_fp16 = linear(bias = time_encoder_mlp_2_linear_bias_to_fp16, weight = time_encoder_mlp_2_linear_weight_to_fp16, x = input_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor time_emb_axes_0 = const()[name = string("time_emb_axes_0"), val = tensor([-1])]; + tensor time_emb_cast_fp16 = expand_dims(axes = time_emb_axes_0, x = linear_1_cast_fp16)[name = string("time_emb_cast_fp16")]; + string var_115_pad_type_0 = const()[name = string("op_115_pad_type_0"), val = string("valid")]; + tensor var_115_strides_0 = const()[name = string("op_115_strides_0"), val = tensor([1])]; + tensor var_115_pad_0 = const()[name = string("op_115_pad_0"), val = tensor([0, 0])]; + tensor var_115_dilations_0 = const()[name = string("op_115_dilations_0"), val = tensor([1])]; + int32 var_115_groups_0 = const()[name = string("op_115_groups_0"), val = int32(1)]; + tensor proj_in_weight_to_fp16 = const()[name = string("proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92864)))]; + tensor var_115_cast_fp16 = conv(dilations = var_115_dilations_0, groups = var_115_groups_0, pad = var_115_pad_0, pad_type = var_115_pad_type_0, strides = var_115_strides_0, weight = proj_in_weight_to_fp16, x = input_5_cast_fp16)[name = string("op_115_cast_fp16")]; + tensor x_3_cast_fp16 = mul(x = var_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_3_cast_fp16")]; + int32 var_134 = const()[name = string("op_134"), val = int32(-1)]; + tensor x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_7_begin_0 = const()[name = string("input_7_begin_0"), val = tensor([0, 0, 1])]; + tensor input_7_end_0 = const()[name = string("input_7_end_0"), val = tensor([2, 1, 0])]; + tensor input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor([true, true, true])]; + tensor input_7_cast_fp16 = slice_by_index(begin = input_7_begin_0, end = input_7_end_0, end_mask = input_7_end_mask_0, x = latent_mask_b_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_166_pad_0 = const()[name = string("op_166_pad_0"), val = tensor([0, 0, 0, 0, 0, 1])]; + string var_166_mode_0 = const()[name = string("op_166_mode_0"), val = string("constant")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)]; + tensor var_166_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = var_166_mode_0, pad = var_166_pad_0, x = input_7_cast_fp16)[name = string("op_166_cast_fp16")]; + fp16 var_133_to_fp16 = const()[name = string("op_133_to_fp16"), val = fp16(0x1p+0)]; + tensor var_167_cast_fp16 = sub(x = var_133_to_fp16, y = var_166_cast_fp16)[name = string("op_167_cast_fp16")]; + tensor last_1_cast_fp16 = mul(x = latent_mask_b_cast_fp16, y = var_167_cast_fp16)[name = string("last_1_cast_fp16")]; + tensor var_169_cast_fp16 = mul(x = x_5_cast_fp16, y = last_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor([-1])]; + bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)]; + tensor x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_169_cast_fp16)[name = string("x_last_1_cast_fp16")]; + tensor var_172_cast_fp16 = mul(x = x_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_172_cast_fp16")]; + tensor var_173_cast_fp16 = sub(x = var_133_to_fp16, y = latent_mask_b_cast_fp16)[name = string("op_173_cast_fp16")]; + tensor var_174_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_174_cast_fp16")]; + tensor input_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_174_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("replicate")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor input_11_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_11_mode_0, pad = input_11_pad_0, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")]; + string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")]; + int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(512)]; + tensor h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor([1])]; + tensor h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor([0, 0])]; + tensor h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240384)))]; + tensor main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245568)))]; + tensor h_3_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = main_blocks_0_convnext_0_0_dwconv__conv_weight_to_fp16, x = input_11_cast_fp16)[name = string("h_3_cast_fp16")]; + tensor x_7_cast_fp16 = mul(x = h_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor([0, 2, 1])]; + tensor var_192_axes_0 = const()[name = string("op_192_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246656)))]; + tensor main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247744)))]; + fp16 var_139_to_fp16 = const()[name = string("op_139_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_13_cast_fp16 = transpose(perm = input_13_perm_0, x = x_7_cast_fp16)[name = string("transpose_117")]; + tensor var_192_cast_fp16 = layer_norm(axes = var_192_axes_0, beta = main_blocks_0_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_0_norm_norm_weight_to_fp16, x = input_13_cast_fp16)[name = string("op_192_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([0, 2, 1])]; + string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")]; + tensor h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor([1])]; + tensor h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor([0, 0])]; + tensor h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor([1])]; + int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248832)))]; + tensor main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2346048)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = var_192_cast_fp16)[name = string("transpose_116")]; + tensor h_5_cast_fp16 = conv(bias = main_blocks_0_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = main_blocks_0_convnext_0_0_pwconv1_weight_to_fp16, x = input_15_cast_fp16)[name = string("h_5_cast_fp16")]; + string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = h_5_cast_fp16)[name = string("input_17_cast_fp16")]; + string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")]; + tensor h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor([1])]; + tensor h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor([0, 0])]; + tensor h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor([1])]; + int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)]; + tensor var_209_weight_0_to_fp16 = const()[name = string("op_209_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2350208)))]; + tensor var_209_bias_0_to_fp16 = const()[name = string("op_209_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4447424)))]; + tensor var_209_cast_fp16 = conv(bias = var_209_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = var_209_weight_0_to_fp16, x = input_17_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor out_1_cast_fp16 = add(x = x_5_cast_fp16, y = var_209_cast_fp16)[name = string("out_1_cast_fp16")]; + tensor x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor x_11_cast_fp16 = mul(x = x_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_223_cast_fp16 = mul(x = x_11_cast_fp16, y = last_1_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor([-1])]; + bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)]; + tensor x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_223_cast_fp16)[name = string("x_last_3_cast_fp16")]; + tensor var_226_cast_fp16 = mul(x = x_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_226_cast_fp16")]; + tensor var_228_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_228_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = var_226_cast_fp16, y = var_228_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor input_23_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")]; + string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")]; + tensor h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor([2])]; + int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(512)]; + tensor h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor([1])]; + tensor h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4448512)))]; + tensor main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4453696)))]; + tensor h_9_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = main_blocks_0_convnext_0_1_dwconv__conv_weight_to_fp16, x = input_23_cast_fp16)[name = string("h_9_cast_fp16")]; + tensor x_13_cast_fp16 = mul(x = h_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor var_246_axes_0 = const()[name = string("op_246_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4454784)))]; + tensor main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4455872)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_13_cast_fp16)[name = string("transpose_115")]; + tensor var_246_cast_fp16 = layer_norm(axes = var_246_axes_0, beta = main_blocks_0_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_1_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_246_cast_fp16")]; + tensor input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor([0, 2, 1])]; + string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")]; + tensor h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor([1])]; + tensor h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor([0, 0])]; + tensor h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor([1])]; + int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4456960)))]; + tensor main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6554176)))]; + tensor input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_246_cast_fp16)[name = string("transpose_114")]; + tensor h_11_cast_fp16 = conv(bias = main_blocks_0_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = main_blocks_0_convnext_0_1_pwconv1_weight_to_fp16, x = input_27_cast_fp16)[name = string("h_11_cast_fp16")]; + string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")]; + tensor input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_11_cast_fp16)[name = string("input_29_cast_fp16")]; + string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")]; + tensor h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor([1])]; + tensor h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor([0, 0])]; + tensor h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor([1])]; + int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)]; + tensor var_263_weight_0_to_fp16 = const()[name = string("op_263_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6558336)))]; + tensor var_263_bias_0_to_fp16 = const()[name = string("op_263_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8655552)))]; + tensor var_263_cast_fp16 = conv(bias = var_263_bias_0_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = var_263_weight_0_to_fp16, x = input_29_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor out_3_cast_fp16 = add(x = x_11_cast_fp16, y = var_263_cast_fp16)[name = string("out_3_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = out_3_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor x_17_cast_fp16 = mul(x = x_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_277_cast_fp16 = mul(x = x_17_cast_fp16, y = last_1_cast_fp16)[name = string("op_277_cast_fp16")]; + tensor x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor([-1])]; + bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)]; + tensor x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_277_cast_fp16)[name = string("x_last_5_cast_fp16")]; + tensor var_280_cast_fp16 = mul(x = x_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_280_cast_fp16")]; + tensor var_282_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = var_280_cast_fp16, y = var_282_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("replicate")]; + fp16 const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = fp16(0x0p+0)]; + tensor input_35_cast_fp16 = pad(constant_val = const_5_to_fp16, mode = input_35_mode_0, pad = input_35_pad_0, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")]; + tensor h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor([4])]; + int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(512)]; + tensor h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor([1])]; + tensor h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8656640)))]; + tensor main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8661824)))]; + tensor h_15_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = main_blocks_0_convnext_0_2_dwconv__conv_weight_to_fp16, x = input_35_cast_fp16)[name = string("h_15_cast_fp16")]; + tensor x_19_cast_fp16 = mul(x = h_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor var_300_axes_0 = const()[name = string("op_300_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8662912)))]; + tensor main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8664000)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_19_cast_fp16)[name = string("transpose_113")]; + tensor var_300_cast_fp16 = layer_norm(axes = var_300_axes_0, beta = main_blocks_0_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_2_norm_norm_weight_to_fp16, x = input_37_cast_fp16)[name = string("op_300_cast_fp16")]; + tensor input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor([0, 2, 1])]; + string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")]; + tensor h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor([1])]; + tensor h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor([0, 0])]; + tensor h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor([1])]; + int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8665088)))]; + tensor main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10762304)))]; + tensor input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_300_cast_fp16)[name = string("transpose_112")]; + tensor h_17_cast_fp16 = conv(bias = main_blocks_0_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = main_blocks_0_convnext_0_2_pwconv1_weight_to_fp16, x = input_39_cast_fp16)[name = string("h_17_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = h_17_cast_fp16)[name = string("input_41_cast_fp16")]; + string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")]; + tensor h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor([1])]; + tensor h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor([0, 0])]; + tensor h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor([1])]; + int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)]; + tensor var_317_weight_0_to_fp16 = const()[name = string("op_317_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10766464)))]; + tensor var_317_bias_0_to_fp16 = const()[name = string("op_317_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12863680)))]; + tensor var_317_cast_fp16 = conv(bias = var_317_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = var_317_weight_0_to_fp16, x = input_41_cast_fp16)[name = string("op_317_cast_fp16")]; + tensor out_5_cast_fp16 = add(x = x_17_cast_fp16, y = var_317_cast_fp16)[name = string("out_5_cast_fp16")]; + tensor x_21_cast_fp16 = mul(x = out_5_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor var_331_cast_fp16 = mul(x = x_23_cast_fp16, y = last_1_cast_fp16)[name = string("op_331_cast_fp16")]; + tensor x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor([-1])]; + bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)]; + tensor x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_331_cast_fp16)[name = string("x_last_7_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = x_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor var_336_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_336_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_334_cast_fp16, y = var_336_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")]; + fp16 const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = fp16(0x0p+0)]; + tensor input_47_cast_fp16 = pad(constant_val = const_7_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")]; + tensor h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor([8])]; + int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(512)]; + tensor h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor([1])]; + tensor h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12864768)))]; + tensor main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12869952)))]; + tensor h_21_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = main_blocks_0_convnext_0_3_dwconv__conv_weight_to_fp16, x = input_47_cast_fp16)[name = string("h_21_cast_fp16")]; + tensor x_25_cast_fp16 = mul(x = h_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12871040)))]; + tensor main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12872128)))]; + tensor input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_25_cast_fp16)[name = string("transpose_111")]; + tensor var_354_cast_fp16 = layer_norm(axes = var_354_axes_0, beta = main_blocks_0_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_0_3_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_354_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")]; + tensor h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor([1])]; + tensor h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor([0, 0])]; + tensor h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor([1])]; + int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12873216)))]; + tensor main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14970432)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_354_cast_fp16)[name = string("transpose_110")]; + tensor h_23_cast_fp16 = conv(bias = main_blocks_0_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = main_blocks_0_convnext_0_3_pwconv1_weight_to_fp16, x = input_51_cast_fp16)[name = string("h_23_cast_fp16")]; + string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")]; + tensor input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_23_cast_fp16)[name = string("input_53_cast_fp16")]; + string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")]; + tensor h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor([1])]; + tensor h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor([0, 0])]; + tensor h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor([1])]; + int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)]; + tensor var_371_weight_0_to_fp16 = const()[name = string("op_371_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14974592)))]; + tensor var_371_bias_0_to_fp16 = const()[name = string("op_371_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17071808)))]; + tensor var_371_cast_fp16 = conv(bias = var_371_bias_0_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = var_371_weight_0_to_fp16, x = input_53_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor out_7_cast_fp16 = add(x = x_23_cast_fp16, y = var_371_cast_fp16)[name = string("out_7_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = out_7_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor input_55_cast_fp16 = squeeze(axes = input_55_axes_0, x = time_emb_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor main_blocks_0_time_cond_linear_linear_weight_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17072896)))]; + tensor main_blocks_0_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_0_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17138496)))]; + tensor linear_2_cast_fp16 = linear(bias = main_blocks_0_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_0_time_cond_linear_linear_weight_to_fp16, x = input_55_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor t_5_axes_0 = const()[name = string("t_5_axes_0"), val = tensor([-1])]; + tensor t_5_cast_fp16 = expand_dims(axes = t_5_axes_0, x = linear_2_cast_fp16)[name = string("t_5_cast_fp16")]; + tensor var_381_cast_fp16 = add(x = x_27_cast_fp16, y = t_5_cast_fp16)[name = string("op_381_cast_fp16")]; + tensor x_29_cast_fp16 = mul(x = var_381_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_394_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_394_cast_fp16")]; + tensor x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor([-1])]; + bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)]; + tensor x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_394_cast_fp16)[name = string("x_last_9_cast_fp16")]; + tensor var_397_cast_fp16 = mul(x = x_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor input_59_cast_fp16 = add(x = var_397_cast_fp16, y = var_399_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_61_mode_0 = const()[name = string("input_61_mode_0"), val = string("replicate")]; + fp16 const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = fp16(0x0p+0)]; + tensor input_61_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_61_mode_0, pad = input_61_pad_0, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")]; + int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(512)]; + tensor h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor([1])]; + tensor h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor([0, 0])]; + tensor h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17139584)))]; + tensor main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17144768)))]; + tensor h_27_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = main_blocks_0_convnext_1_0_dwconv__conv_weight_to_fp16, x = input_61_cast_fp16)[name = string("h_27_cast_fp16")]; + tensor x_33_cast_fp16 = mul(x = h_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor([0, 2, 1])]; + tensor var_417_axes_0 = const()[name = string("op_417_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17145856)))]; + tensor main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17146944)))]; + tensor input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_33_cast_fp16)[name = string("transpose_109")]; + tensor var_417_cast_fp16 = layer_norm(axes = var_417_axes_0, beta = main_blocks_0_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_1_0_norm_norm_weight_to_fp16, x = input_63_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")]; + tensor h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor([1])]; + tensor h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor([0, 0])]; + tensor h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor([1])]; + int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17148032)))]; + tensor main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19245248)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = var_417_cast_fp16)[name = string("transpose_108")]; + tensor h_29_cast_fp16 = conv(bias = main_blocks_0_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = main_blocks_0_convnext_1_0_pwconv1_weight_to_fp16, x = input_65_cast_fp16)[name = string("h_29_cast_fp16")]; + string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("EXACT")]; + tensor input_67_cast_fp16 = gelu(mode = input_67_mode_0, x = h_29_cast_fp16)[name = string("input_67_cast_fp16")]; + string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")]; + tensor h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor([1])]; + tensor h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor([0, 0])]; + tensor h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor([1])]; + int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)]; + tensor var_434_weight_0_to_fp16 = const()[name = string("op_434_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19249408)))]; + tensor var_434_bias_0_to_fp16 = const()[name = string("op_434_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21346624)))]; + tensor var_434_cast_fp16 = conv(bias = var_434_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = var_434_weight_0_to_fp16, x = input_67_cast_fp16)[name = string("op_434_cast_fp16")]; + tensor out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_434_cast_fp16)[name = string("out_9_cast_fp16")]; + tensor x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor x_m_1_cast_fp16 = mul(x = x_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_1_cast_fp16")]; + tensor var_444_shape_cast_fp16 = shape(x = x_35_cast_fp16)[name = string("op_444_shape_cast_fp16")]; + int32 gather_4_axis_0 = const()[name = string("gather_4_axis_0"), val = int32(0)]; + int32 gather_4_batch_dims_0 = const()[name = string("gather_4_batch_dims_0"), val = int32(0)]; + bool gather_4_validate_indices_0 = const()[name = string("gather_4_validate_indices_0"), val = bool(false)]; + string var_444_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_444_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")]; + uint16 gather_4_indices_0_to_uint16 = const()[name = string("gather_4_indices_0_to_uint16"), val = uint16(2)]; + tensor var_444_shape_cast_fp16_to_int16 = cast(dtype = var_444_shape_cast_fp16_to_int16_dtype_0, x = var_444_shape_cast_fp16)[name = string("cast_95")]; + int16 gather_4_cast_uint16 = gather(axis = gather_4_axis_0, batch_dims = gather_4_batch_dims_0, indices = gather_4_indices_0_to_uint16, validate_indices = gather_4_validate_indices_0, x = var_444_shape_cast_fp16_to_int16)[name = string("gather_4_cast_uint16")]; + string gather_4_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_4_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor var_446_shape_cast_fp16 = shape(x = text_emb_cast_fp16)[name = string("op_446_shape_cast_fp16")]; + int32 gather_5_axis_0 = const()[name = string("gather_5_axis_0"), val = int32(0)]; + int32 gather_5_batch_dims_0 = const()[name = string("gather_5_batch_dims_0"), val = int32(0)]; + bool gather_5_validate_indices_0 = const()[name = string("gather_5_validate_indices_0"), val = bool(false)]; + string var_446_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_446_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_5_indices_0_to_uint16 = const()[name = string("gather_5_indices_0_to_uint16"), val = uint16(2)]; + tensor var_446_shape_cast_fp16_to_uint16 = cast(dtype = var_446_shape_cast_fp16_to_uint16_dtype_0, x = var_446_shape_cast_fp16)[name = string("cast_93")]; + uint16 gather_5_cast_uint16 = gather(axis = gather_5_axis_0, batch_dims = gather_5_batch_dims_0, indices = gather_5_indices_0_to_uint16, validate_indices = gather_5_validate_indices_0, x = var_446_shape_cast_fp16_to_uint16)[name = string("gather_5_cast_uint16")]; + string gather_5_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_5_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_query_linear_weight_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21347712)))]; + tensor main_blocks_0_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21872064)))]; + tensor input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_35_cast_fp16)[name = string("transpose_107")]; + tensor linear_3_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_query_linear_weight_to_fp16, x = input_69_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor concat_4x = const()[name = string("concat_4x"), val = tensor([2, -1, 8, 64])]; + tensor var_453_cast_fp16 = reshape(shape = concat_4x, x = linear_3_cast_fp16)[name = string("op_453_cast_fp16")]; + tensor x_37_perm_0 = const()[name = string("x_37_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_text_attn_W_key_linear_weight_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21873152)))]; + tensor main_blocks_0_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22135360)))]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = text_emb_cast_fp16)[name = string("transpose_105")]; + tensor linear_4_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_key_linear_weight_to_fp16, x = input_71_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor concat_5x = const()[name = string("concat_5x"), val = tensor([2, -1, 8, 64])]; + tensor var_461_cast_fp16 = reshape(shape = concat_5x, x = linear_4_cast_fp16)[name = string("op_461_cast_fp16")]; + tensor x_39_perm_0 = const()[name = string("x_39_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_text_attn_W_value_linear_weight_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22136448)))]; + tensor main_blocks_0_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22398656)))]; + tensor linear_5_cast_fp16 = linear(bias = main_blocks_0_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_text_attn_W_value_linear_weight_to_fp16, x = input_71_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor concat_6x = const()[name = string("concat_6x"), val = tensor([2, -1, 8, 64])]; + tensor var_469_cast_fp16 = reshape(shape = concat_6x, x = linear_5_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor([0, 2, 1, 3])]; + int32 concat_7_values0_0 = const()[name = string("concat_7_values0_0"), val = int32(1)]; + int32 concat_7_values2_0 = const()[name = string("concat_7_values2_0"), val = int32(1)]; + int32 concat_7_axis_0 = const()[name = string("concat_7_axis_0"), val = int32(0)]; + bool concat_7_interleave_0 = const()[name = string("concat_7_interleave_0"), val = bool(false)]; + int32 gather_4_cast_uint16_to_int32 = cast(dtype = gather_4_cast_uint16_to_int32_dtype_0, x = gather_4_cast_uint16)[name = string("cast_94")]; + tensor concat_7 = concat(axis = concat_7_axis_0, interleave = concat_7_interleave_0, values = (concat_7_values0_0, gather_4_cast_uint16_to_int32, concat_7_values2_0))[name = string("concat_7")]; + tensor var_472_begin_0 = const()[name = string("op_472_begin_0"), val = tensor([0, 0, 0])]; + tensor var_472_end_mask_0 = const()[name = string("op_472_end_mask_0"), val = tensor([true, false, true])]; + tensor main_blocks_0_text_attn_increments_to_fp16 = const()[name = string("main_blocks_0_text_attn_increments_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22399744)))]; + tensor var_472_cast_fp16 = slice_by_index(begin = var_472_begin_0, end = concat_7, end_mask = var_472_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_472_cast_fp16")]; + tensor concat_8 = const()[name = string("concat_8"), val = tensor([2, -1, -1])]; + tensor shape_1_cast_fp16 = shape(x = var_472_cast_fp16)[name = string("shape_1_cast_fp16")]; + tensor equal_1 = const()[name = string("equal_1"), val = tensor([false, true, true])]; + tensor select_1 = select(a = shape_1_cast_fp16, b = concat_8, cond = equal_1)[name = string("select_1")]; + tensor real_div_1 = real_div(x = select_1, y = shape_1_cast_fp16)[name = string("real_div_1")]; + tensor var_475_cast_fp16 = tile(reps = real_div_1, x = var_472_cast_fp16)[name = string("op_475_cast_fp16")]; + int32 concat_9_values0_0 = const()[name = string("concat_9_values0_0"), val = int32(1)]; + int32 concat_9_values2_0 = const()[name = string("concat_9_values2_0"), val = int32(1)]; + int32 concat_9_axis_0 = const()[name = string("concat_9_axis_0"), val = int32(0)]; + bool concat_9_interleave_0 = const()[name = string("concat_9_interleave_0"), val = bool(false)]; + int32 gather_5_cast_uint16_to_int32 = cast(dtype = gather_5_cast_uint16_to_int32_dtype_0, x = gather_5_cast_uint16)[name = string("cast_92")]; + tensor concat_9 = concat(axis = concat_9_axis_0, interleave = concat_9_interleave_0, values = (concat_9_values0_0, gather_5_cast_uint16_to_int32, concat_9_values2_0))[name = string("concat_9")]; + tensor var_478_begin_0 = const()[name = string("op_478_begin_0"), val = tensor([0, 0, 0])]; + tensor var_478_end_mask_0 = const()[name = string("op_478_end_mask_0"), val = tensor([true, false, true])]; + tensor var_478_cast_fp16 = slice_by_index(begin = var_478_begin_0, end = concat_9, end_mask = var_478_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_478_cast_fp16")]; + tensor concat_10 = const()[name = string("concat_10"), val = tensor([2, -1, -1])]; + tensor shape_2_cast_fp16 = shape(x = var_478_cast_fp16)[name = string("shape_2_cast_fp16")]; + tensor equal_2 = const()[name = string("equal_2"), val = tensor([false, true, true])]; + tensor select_2 = select(a = shape_2_cast_fp16, b = concat_10, cond = equal_2)[name = string("select_2")]; + tensor real_div_2 = real_div(x = select_2, y = shape_2_cast_fp16)[name = string("real_div_2")]; + tensor var_481_cast_fp16 = tile(reps = real_div_2, x = var_478_cast_fp16)[name = string("op_481_cast_fp16")]; + tensor divisor_1_axes_0 = const()[name = string("divisor_1_axes_0"), val = tensor([1, 2])]; + bool divisor_1_keep_dims_0 = const()[name = string("divisor_1_keep_dims_0"), val = bool(false)]; + tensor divisor_1_cast_fp16 = reduce_sum(axes = divisor_1_axes_0, keep_dims = divisor_1_keep_dims_0, x = latent_mask_b_cast_fp16)[name = string("divisor_1_cast_fp16")]; + tensor divisor_3_axes_0 = const()[name = string("divisor_3_axes_0"), val = tensor([1, 2])]; + bool divisor_3_keep_dims_0 = const()[name = string("divisor_3_keep_dims_0"), val = bool(false)]; + tensor divisor_3_cast_fp16 = reduce_sum(axes = divisor_3_axes_0, keep_dims = divisor_3_keep_dims_0, x = text_mask_cast_fp16)[name = string("divisor_3_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([-1, 1, 1])]; + tensor var_488_cast_fp16 = reshape(shape = var_487, x = divisor_1_cast_fp16)[name = string("op_488_cast_fp16")]; + tensor scaled_1_cast_fp16 = real_div(x = var_475_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_1_cast_fp16")]; + tensor main_blocks_0_text_attn_theta_to_fp16 = const()[name = string("main_blocks_0_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22401856)))]; + tensor angles_1_cast_fp16 = mul(x = scaled_1_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_1_cast_fp16")]; + tensor var_491_cast_fp16 = cos(x = angles_1_cast_fp16)[name = string("op_491_cast_fp16")]; + tensor cos_1_axes_0 = const()[name = string("cos_1_axes_0"), val = tensor([1])]; + tensor cos_1_cast_fp16 = expand_dims(axes = cos_1_axes_0, x = var_491_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor var_493_cast_fp16 = sin(x = angles_1_cast_fp16)[name = string("op_493_cast_fp16")]; + tensor sin_1_axes_0 = const()[name = string("sin_1_axes_0"), val = tensor([1])]; + tensor sin_1_cast_fp16 = expand_dims(axes = sin_1_axes_0, x = var_493_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor x_a_1_begin_0 = const()[name = string("x_a_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_1_end_0 = const()[name = string("x_a_1_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_1_end_mask_0 = const()[name = string("x_a_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_37_cast_fp16 = transpose(perm = x_37_perm_0, x = var_453_cast_fp16)[name = string("transpose_106")]; + tensor x_a_1_cast_fp16 = slice_by_index(begin = x_a_1_begin_0, end = x_a_1_end_0, end_mask = x_a_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_a_1_cast_fp16")]; + tensor x_b_1_begin_0 = const()[name = string("x_b_1_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_1_end_0 = const()[name = string("x_b_1_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_1_end_mask_0 = const()[name = string("x_b_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_1_cast_fp16 = slice_by_index(begin = x_b_1_begin_0, end = x_b_1_end_0, end_mask = x_b_1_end_mask_0, x = x_37_cast_fp16)[name = string("x_b_1_cast_fp16")]; + tensor var_497_cast_fp16 = mul(x = x_a_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_497_cast_fp16")]; + tensor var_498_cast_fp16 = mul(x = x_b_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_498_cast_fp16")]; + tensor rot_a_1_cast_fp16 = sub(x = var_497_cast_fp16, y = var_498_cast_fp16)[name = string("rot_a_1_cast_fp16")]; + tensor var_500_cast_fp16 = mul(x = x_a_1_cast_fp16, y = sin_1_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor var_501_cast_fp16 = mul(x = x_b_1_cast_fp16, y = cos_1_cast_fp16)[name = string("op_501_cast_fp16")]; + tensor rot_b_1_cast_fp16 = add(x = var_500_cast_fp16, y = var_501_cast_fp16)[name = string("rot_b_1_cast_fp16")]; + bool q_1_interleave_0 = const()[name = string("q_1_interleave_0"), val = bool(false)]; + tensor q_1_cast_fp16 = concat(axis = var_134, interleave = q_1_interleave_0, values = (rot_a_1_cast_fp16, rot_b_1_cast_fp16))[name = string("q_1_cast_fp16")]; + tensor var_505 = const()[name = string("op_505"), val = tensor([-1, 1, 1])]; + tensor var_506_cast_fp16 = reshape(shape = var_505, x = divisor_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor scaled_3_cast_fp16 = real_div(x = var_481_cast_fp16, y = var_506_cast_fp16)[name = string("scaled_3_cast_fp16")]; + tensor angles_3_cast_fp16 = mul(x = scaled_3_cast_fp16, y = main_blocks_0_text_attn_theta_to_fp16)[name = string("angles_3_cast_fp16")]; + tensor var_509_cast_fp16 = cos(x = angles_3_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor cos_3_axes_0 = const()[name = string("cos_3_axes_0"), val = tensor([1])]; + tensor cos_3_cast_fp16 = expand_dims(axes = cos_3_axes_0, x = var_509_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor var_511_cast_fp16 = sin(x = angles_3_cast_fp16)[name = string("op_511_cast_fp16")]; + tensor sin_3_axes_0 = const()[name = string("sin_3_axes_0"), val = tensor([1])]; + tensor sin_3_cast_fp16 = expand_dims(axes = sin_3_axes_0, x = var_511_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor x_a_3_begin_0 = const()[name = string("x_a_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_3_end_0 = const()[name = string("x_a_3_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_3_end_mask_0 = const()[name = string("x_a_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_39_cast_fp16 = transpose(perm = x_39_perm_0, x = var_461_cast_fp16)[name = string("transpose_104")]; + tensor x_a_3_cast_fp16 = slice_by_index(begin = x_a_3_begin_0, end = x_a_3_end_0, end_mask = x_a_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_a_3_cast_fp16")]; + tensor x_b_3_begin_0 = const()[name = string("x_b_3_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_3_end_0 = const()[name = string("x_b_3_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_3_end_mask_0 = const()[name = string("x_b_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_3_cast_fp16 = slice_by_index(begin = x_b_3_begin_0, end = x_b_3_end_0, end_mask = x_b_3_end_mask_0, x = x_39_cast_fp16)[name = string("x_b_3_cast_fp16")]; + tensor var_515_cast_fp16 = mul(x = x_a_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_515_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = x_b_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor rot_a_3_cast_fp16 = sub(x = var_515_cast_fp16, y = var_516_cast_fp16)[name = string("rot_a_3_cast_fp16")]; + tensor var_518_cast_fp16 = mul(x = x_a_3_cast_fp16, y = sin_3_cast_fp16)[name = string("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = mul(x = x_b_3_cast_fp16, y = cos_3_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor rot_b_3_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = string("rot_b_3_cast_fp16")]; + bool k_1_interleave_0 = const()[name = string("k_1_interleave_0"), val = bool(false)]; + tensor k_1_cast_fp16 = concat(axis = var_134, interleave = k_1_interleave_0, values = (rot_a_3_cast_fp16, rot_b_3_cast_fp16))[name = string("k_1_cast_fp16")]; + bool var_524_transpose_x_1 = const()[name = string("op_524_transpose_x_1"), val = bool(false)]; + bool var_524_transpose_y_1 = const()[name = string("op_524_transpose_y_1"), val = bool(true)]; + tensor var_524_cast_fp16 = matmul(transpose_x = var_524_transpose_x_1, transpose_y = var_524_transpose_y_1, x = q_1_cast_fp16, y = k_1_cast_fp16)[name = string("op_524_cast_fp16")]; + fp16 _inversed_scores_1_y_0_to_fp16 = const()[name = string("_inversed_scores_1_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_1_cast_fp16 = mul(x = var_524_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")]; + tensor var_527_axes_0 = const()[name = string("op_527_axes_0"), val = tensor([2])]; + tensor var_527_cast_fp16 = expand_dims(axes = var_527_axes_0, x = text_mask_cast_fp16)[name = string("op_527_cast_fp16")]; + tensor var_528_cast_fp16 = sub(x = var_133_to_fp16, y = var_527_cast_fp16)[name = string("op_528_cast_fp16")]; + fp16 var_529_to_fp16 = const()[name = string("op_529_to_fp16"), val = fp16(0x1.388p+13)]; + tensor var_530_cast_fp16 = mul(x = var_528_cast_fp16, y = var_529_to_fp16)[name = string("op_530_cast_fp16")]; + tensor input_75_cast_fp16 = sub(x = _inversed_scores_1_cast_fp16, y = var_530_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor attn_1_cast_fp16 = softmax(axis = var_134, x = input_75_cast_fp16)[name = string("attn_1_cast_fp16")]; + bool var_533_transpose_x_0 = const()[name = string("op_533_transpose_x_0"), val = bool(false)]; + bool var_533_transpose_y_0 = const()[name = string("op_533_transpose_y_0"), val = bool(false)]; + tensor v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_469_cast_fp16)[name = string("transpose_103")]; + tensor var_533_cast_fp16 = matmul(transpose_x = var_533_transpose_x_0, transpose_y = var_533_transpose_y_0, x = attn_1_cast_fp16, y = v_1_cast_fp16)[name = string("op_533_cast_fp16")]; + tensor var_534_perm_0 = const()[name = string("op_534_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_11x = const()[name = string("concat_11x"), val = tensor([2, -1, 512])]; + tensor var_534_cast_fp16 = transpose(perm = var_534_perm_0, x = var_533_cast_fp16)[name = string("transpose_102")]; + tensor input_77_cast_fp16 = reshape(shape = concat_11x, x = var_534_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor main_blocks_0_text_attn_out_fc_linear_weight_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22401984)))]; + tensor main_blocks_0_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22926336)))]; + tensor linear_6_cast_fp16 = linear(bias = main_blocks_0_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_text_attn_out_fc_linear_weight_to_fp16, x = input_77_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor out_11_perm_0 = const()[name = string("out_11_perm_0"), val = tensor([0, 2, 1])]; + tensor out_11_cast_fp16 = transpose(perm = out_11_perm_0, x = linear_6_cast_fp16)[name = string("transpose_101")]; + tensor var_542_cast_fp16 = mul(x = out_11_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_542_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = x_m_1_cast_fp16, y = var_542_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor var_549_axes_0 = const()[name = string("op_549_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22927424)))]; + tensor main_blocks_0_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22928512)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_41_cast_fp16)[name = string("transpose_100")]; + tensor var_549_cast_fp16 = layer_norm(axes = var_549_axes_0, beta = main_blocks_0_text_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_text_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor var_550_perm_0 = const()[name = string("op_550_perm_0"), val = tensor([0, 2, 1])]; + tensor var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = var_549_cast_fp16)[name = string("transpose_99")]; + tensor x_43_cast_fp16 = mul(x = var_550_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor x_45_cast_fp16 = mul(x = x_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = x_45_cast_fp16, y = last_1_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor x_last_11_axes_0 = const()[name = string("x_last_11_axes_0"), val = tensor([-1])]; + bool x_last_11_keep_dims_0 = const()[name = string("x_last_11_keep_dims_0"), val = bool(true)]; + tensor x_last_11_cast_fp16 = reduce_sum(axes = x_last_11_axes_0, keep_dims = x_last_11_keep_dims_0, x = var_563_cast_fp16)[name = string("x_last_11_cast_fp16")]; + tensor var_566_cast_fp16 = mul(x = x_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_566_cast_fp16")]; + tensor var_568_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_11_cast_fp16)[name = string("op_568_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = var_566_cast_fp16, y = var_568_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_85_mode_0 = const()[name = string("input_85_mode_0"), val = string("replicate")]; + fp16 const_11_to_fp16 = const()[name = string("const_11_to_fp16"), val = fp16(0x0p+0)]; + tensor input_85_cast_fp16 = pad(constant_val = const_11_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")]; + string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")]; + int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(512)]; + tensor h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor([1])]; + tensor h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor([0, 0])]; + tensor h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor([1])]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22929600)))]; + tensor main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22934784)))]; + tensor h_33_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = main_blocks_0_convnext_2_0_dwconv__conv_weight_to_fp16, x = input_85_cast_fp16)[name = string("h_33_cast_fp16")]; + tensor x_47_cast_fp16 = mul(x = h_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22935872)))]; + tensor main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22936960)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_47_cast_fp16)[name = string("transpose_98")]; + tensor var_586_cast_fp16 = layer_norm(axes = var_586_axes_0, beta = main_blocks_0_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_convnext_2_0_norm_norm_weight_to_fp16, x = input_87_cast_fp16)[name = string("op_586_cast_fp16")]; + tensor input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor([0, 2, 1])]; + string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")]; + tensor h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor([1])]; + tensor h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor([0, 0])]; + tensor h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor([1])]; + int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)]; + tensor main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22938048)))]; + tensor main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25035264)))]; + tensor input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = var_586_cast_fp16)[name = string("transpose_97")]; + tensor h_35_cast_fp16 = conv(bias = main_blocks_0_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = main_blocks_0_convnext_2_0_pwconv1_weight_to_fp16, x = input_89_cast_fp16)[name = string("h_35_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = h_35_cast_fp16)[name = string("input_91_cast_fp16")]; + string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")]; + tensor h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor([1])]; + tensor h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor([0, 0])]; + tensor h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor([1])]; + int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)]; + tensor var_603_weight_0_to_fp16 = const()[name = string("op_603_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25039424)))]; + tensor var_603_bias_0_to_fp16 = const()[name = string("op_603_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27136640)))]; + tensor var_603_cast_fp16 = conv(bias = var_603_bias_0_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = var_603_weight_0_to_fp16, x = input_91_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor out_13_cast_fp16 = add(x = x_45_cast_fp16, y = var_603_cast_fp16)[name = string("out_13_cast_fp16")]; + tensor x_49_cast_fp16 = mul(x = out_13_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_m_3_cast_fp16 = mul(x = x_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_3_cast_fp16")]; + tensor input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_0_style_attn_W_query_linear_weight_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27137728)))]; + tensor main_blocks_0_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27399936)))]; + tensor input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_49_cast_fp16)[name = string("transpose_96")]; + tensor linear_7_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_query_linear_weight_to_fp16, x = input_93_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor concat_12x = const()[name = string("concat_12x"), val = tensor([2, -1, 2, 128])]; + tensor var_620_cast_fp16 = reshape(shape = concat_12x, x = linear_7_cast_fp16)[name = string("op_620_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_0_style_attn_W_value_linear_weight_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27400512)))]; + tensor main_blocks_0_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27531648)))]; + tensor linear_9_cast_fp16 = linear(bias = main_blocks_0_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_0_style_attn_W_value_linear_weight_to_fp16, x = input_95_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_633 = const()[name = string("op_633"), val = tensor([2, 50, 2, 128])]; + tensor var_634_cast_fp16 = reshape(shape = var_633, x = linear_9_cast_fp16)[name = string("op_634_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, 1, 3])]; + bool var_638_transpose_x_0 = const()[name = string("op_638_transpose_x_0"), val = bool(false)]; + bool var_638_transpose_y_0 = const()[name = string("op_638_transpose_y_0"), val = bool(false)]; + tensor var_637_to_fp16 = const()[name = string("op_637_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27532224)))]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_620_cast_fp16)[name = string("transpose_95")]; + tensor var_638_cast_fp16 = matmul(transpose_x = var_638_transpose_x_0, transpose_y = var_638_transpose_y_0, x = q_3_cast_fp16, y = var_637_to_fp16)[name = string("op_638_cast_fp16")]; + fp16 _inversed_input_97_y_0_to_fp16 = const()[name = string("_inversed_input_97_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_97_cast_fp16 = mul(x = var_638_cast_fp16, y = _inversed_input_97_y_0_to_fp16)[name = string("_inversed_input_97_cast_fp16")]; + tensor attn_3_cast_fp16 = softmax(axis = var_134, x = _inversed_input_97_cast_fp16)[name = string("attn_3_cast_fp16")]; + tensor var_642_perm_0 = const()[name = string("op_642_perm_0"), val = tensor([0, 2, 1])]; + tensor mask_1_axes_0 = const()[name = string("mask_1_axes_0"), val = tensor([1])]; + tensor var_642_cast_fp16 = transpose(perm = var_642_perm_0, x = latent_mask_b_cast_fp16)[name = string("transpose_93")]; + tensor mask_1_cast_fp16 = expand_dims(axes = mask_1_axes_0, x = var_642_cast_fp16)[name = string("mask_1_cast_fp16")]; + tensor attn_5_cast_fp16 = mul(x = attn_3_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_5_cast_fp16")]; + bool var_645_transpose_x_0 = const()[name = string("op_645_transpose_x_0"), val = bool(false)]; + bool var_645_transpose_y_0 = const()[name = string("op_645_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_634_cast_fp16)[name = string("transpose_94")]; + tensor var_645_cast_fp16 = matmul(transpose_x = var_645_transpose_x_0, transpose_y = var_645_transpose_y_0, x = attn_5_cast_fp16, y = v_3_cast_fp16)[name = string("op_645_cast_fp16")]; + tensor var_646_perm_0 = const()[name = string("op_646_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_13x = const()[name = string("concat_13x"), val = tensor([2, -1, 256])]; + tensor var_646_cast_fp16 = transpose(perm = var_646_perm_0, x = var_645_cast_fp16)[name = string("transpose_92")]; + tensor input_99_cast_fp16 = reshape(shape = concat_13x, x = var_646_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor main_blocks_0_style_attn_out_fc_linear_weight_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27583488)))]; + tensor main_blocks_0_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_0_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27845696)))]; + tensor linear_10_cast_fp16 = linear(bias = main_blocks_0_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_0_style_attn_out_fc_linear_weight_to_fp16, x = input_99_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor out_15_perm_0 = const()[name = string("out_15_perm_0"), val = tensor([0, 2, 1])]; + tensor out_15_cast_fp16 = transpose(perm = out_15_perm_0, x = linear_10_cast_fp16)[name = string("transpose_91")]; + tensor var_654_cast_fp16 = mul(x = out_15_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_654_cast_fp16")]; + tensor x_51_cast_fp16 = add(x = x_m_3_cast_fp16, y = var_654_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor main_blocks_0_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27846784)))]; + tensor main_blocks_0_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_0_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27847872)))]; + tensor input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_51_cast_fp16)[name = string("transpose_90")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = main_blocks_0_style_norm_norm_bias_to_fp16, epsilon = var_139_to_fp16, gamma = main_blocks_0_style_norm_norm_weight_to_fp16, x = input_101_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor var_662_perm_0 = const()[name = string("op_662_perm_0"), val = tensor([0, 2, 1])]; + tensor var_662_cast_fp16 = transpose(perm = var_662_perm_0, x = var_661_cast_fp16)[name = string("transpose_89")]; + tensor x_53_cast_fp16 = mul(x = var_662_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_53_cast_fp16")]; + int32 var_681 = const()[name = string("op_681"), val = int32(-1)]; + tensor x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = x_55_cast_fp16, y = last_1_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor x_last_13_axes_0 = const()[name = string("x_last_13_axes_0"), val = tensor([-1])]; + bool x_last_13_keep_dims_0 = const()[name = string("x_last_13_keep_dims_0"), val = bool(true)]; + tensor x_last_13_cast_fp16 = reduce_sum(axes = x_last_13_axes_0, keep_dims = x_last_13_keep_dims_0, x = var_716_cast_fp16)[name = string("x_last_13_cast_fp16")]; + tensor var_719_cast_fp16 = mul(x = x_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_721_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_13_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_719_cast_fp16, y = var_721_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("replicate")]; + fp16 const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = fp16(0x0p+0)]; + tensor input_107_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")]; + string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")]; + int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(512)]; + tensor h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor([1])]; + tensor h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor([0, 0])]; + tensor h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27848960)))]; + tensor main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27854144)))]; + tensor h_39_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = main_blocks_1_convnext_0_0_dwconv__conv_weight_to_fp16, x = input_107_cast_fp16)[name = string("h_39_cast_fp16")]; + tensor x_57_cast_fp16 = mul(x = h_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_109_perm_0 = const()[name = string("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor var_739_axes_0 = const()[name = string("op_739_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27855232)))]; + tensor main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27856320)))]; + fp16 var_686_to_fp16 = const()[name = string("op_686_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_57_cast_fp16)[name = string("transpose_88")]; + tensor var_739_cast_fp16 = layer_norm(axes = var_739_axes_0, beta = main_blocks_1_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_0_norm_norm_weight_to_fp16, x = input_109_cast_fp16)[name = string("op_739_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + string h_41_pad_type_0 = const()[name = string("h_41_pad_type_0"), val = string("valid")]; + tensor h_41_strides_0 = const()[name = string("h_41_strides_0"), val = tensor([1])]; + tensor h_41_pad_0 = const()[name = string("h_41_pad_0"), val = tensor([0, 0])]; + tensor h_41_dilations_0 = const()[name = string("h_41_dilations_0"), val = tensor([1])]; + int32 h_41_groups_0 = const()[name = string("h_41_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27857408)))]; + tensor main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29954624)))]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = var_739_cast_fp16)[name = string("transpose_87")]; + tensor h_41_cast_fp16 = conv(bias = main_blocks_1_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_41_dilations_0, groups = h_41_groups_0, pad = h_41_pad_0, pad_type = h_41_pad_type_0, strides = h_41_strides_0, weight = main_blocks_1_convnext_0_0_pwconv1_weight_to_fp16, x = input_111_cast_fp16)[name = string("h_41_cast_fp16")]; + string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("EXACT")]; + tensor input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = h_41_cast_fp16)[name = string("input_113_cast_fp16")]; + string h_43_pad_type_0 = const()[name = string("h_43_pad_type_0"), val = string("valid")]; + tensor h_43_strides_0 = const()[name = string("h_43_strides_0"), val = tensor([1])]; + tensor h_43_pad_0 = const()[name = string("h_43_pad_0"), val = tensor([0, 0])]; + tensor h_43_dilations_0 = const()[name = string("h_43_dilations_0"), val = tensor([1])]; + int32 h_43_groups_0 = const()[name = string("h_43_groups_0"), val = int32(1)]; + tensor var_756_weight_0_to_fp16 = const()[name = string("op_756_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29958784)))]; + tensor var_756_bias_0_to_fp16 = const()[name = string("op_756_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32056000)))]; + tensor var_756_cast_fp16 = conv(bias = var_756_bias_0_to_fp16, dilations = h_43_dilations_0, groups = h_43_groups_0, pad = h_43_pad_0, pad_type = h_43_pad_type_0, strides = h_43_strides_0, weight = var_756_weight_0_to_fp16, x = input_113_cast_fp16)[name = string("op_756_cast_fp16")]; + tensor out_17_cast_fp16 = add(x = x_55_cast_fp16, y = var_756_cast_fp16)[name = string("out_17_cast_fp16")]; + tensor x_59_cast_fp16 = mul(x = out_17_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor x_61_cast_fp16 = mul(x = x_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_770_cast_fp16 = mul(x = x_61_cast_fp16, y = last_1_cast_fp16)[name = string("op_770_cast_fp16")]; + tensor x_last_15_axes_0 = const()[name = string("x_last_15_axes_0"), val = tensor([-1])]; + bool x_last_15_keep_dims_0 = const()[name = string("x_last_15_keep_dims_0"), val = bool(true)]; + tensor x_last_15_cast_fp16 = reduce_sum(axes = x_last_15_axes_0, keep_dims = x_last_15_keep_dims_0, x = var_770_cast_fp16)[name = string("x_last_15_cast_fp16")]; + tensor var_773_cast_fp16 = mul(x = x_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_773_cast_fp16")]; + tensor var_775_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_15_cast_fp16)[name = string("op_775_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = var_773_cast_fp16, y = var_775_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("replicate")]; + fp16 const_16_to_fp16 = const()[name = string("const_16_to_fp16"), val = fp16(0x0p+0)]; + tensor input_119_cast_fp16 = pad(constant_val = const_16_to_fp16, mode = input_119_mode_0, pad = input_119_pad_0, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")]; + string h_45_pad_type_0 = const()[name = string("h_45_pad_type_0"), val = string("valid")]; + tensor h_45_dilations_0 = const()[name = string("h_45_dilations_0"), val = tensor([2])]; + int32 h_45_groups_0 = const()[name = string("h_45_groups_0"), val = int32(512)]; + tensor h_45_strides_0 = const()[name = string("h_45_strides_0"), val = tensor([1])]; + tensor h_45_pad_0 = const()[name = string("h_45_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32057088)))]; + tensor main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32062272)))]; + tensor h_45_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_45_dilations_0, groups = h_45_groups_0, pad = h_45_pad_0, pad_type = h_45_pad_type_0, strides = h_45_strides_0, weight = main_blocks_1_convnext_0_1_dwconv__conv_weight_to_fp16, x = input_119_cast_fp16)[name = string("h_45_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = h_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32063360)))]; + tensor main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32064448)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_63_cast_fp16)[name = string("transpose_86")]; + tensor var_793_cast_fp16 = layer_norm(axes = var_793_axes_0, beta = main_blocks_1_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_1_norm_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_793_cast_fp16")]; + tensor input_123_perm_0 = const()[name = string("input_123_perm_0"), val = tensor([0, 2, 1])]; + string h_47_pad_type_0 = const()[name = string("h_47_pad_type_0"), val = string("valid")]; + tensor h_47_strides_0 = const()[name = string("h_47_strides_0"), val = tensor([1])]; + tensor h_47_pad_0 = const()[name = string("h_47_pad_0"), val = tensor([0, 0])]; + tensor h_47_dilations_0 = const()[name = string("h_47_dilations_0"), val = tensor([1])]; + int32 h_47_groups_0 = const()[name = string("h_47_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32065536)))]; + tensor main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34162752)))]; + tensor input_123_cast_fp16 = transpose(perm = input_123_perm_0, x = var_793_cast_fp16)[name = string("transpose_85")]; + tensor h_47_cast_fp16 = conv(bias = main_blocks_1_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_47_dilations_0, groups = h_47_groups_0, pad = h_47_pad_0, pad_type = h_47_pad_type_0, strides = h_47_strides_0, weight = main_blocks_1_convnext_0_1_pwconv1_weight_to_fp16, x = input_123_cast_fp16)[name = string("h_47_cast_fp16")]; + string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("EXACT")]; + tensor input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = h_47_cast_fp16)[name = string("input_125_cast_fp16")]; + string h_49_pad_type_0 = const()[name = string("h_49_pad_type_0"), val = string("valid")]; + tensor h_49_strides_0 = const()[name = string("h_49_strides_0"), val = tensor([1])]; + tensor h_49_pad_0 = const()[name = string("h_49_pad_0"), val = tensor([0, 0])]; + tensor h_49_dilations_0 = const()[name = string("h_49_dilations_0"), val = tensor([1])]; + int32 h_49_groups_0 = const()[name = string("h_49_groups_0"), val = int32(1)]; + tensor var_810_weight_0_to_fp16 = const()[name = string("op_810_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34166912)))]; + tensor var_810_bias_0_to_fp16 = const()[name = string("op_810_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36264128)))]; + tensor var_810_cast_fp16 = conv(bias = var_810_bias_0_to_fp16, dilations = h_49_dilations_0, groups = h_49_groups_0, pad = h_49_pad_0, pad_type = h_49_pad_type_0, strides = h_49_strides_0, weight = var_810_weight_0_to_fp16, x = input_125_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor out_19_cast_fp16 = add(x = x_61_cast_fp16, y = var_810_cast_fp16)[name = string("out_19_cast_fp16")]; + tensor x_65_cast_fp16 = mul(x = out_19_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = x_67_cast_fp16, y = last_1_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_last_17_axes_0 = const()[name = string("x_last_17_axes_0"), val = tensor([-1])]; + bool x_last_17_keep_dims_0 = const()[name = string("x_last_17_keep_dims_0"), val = bool(true)]; + tensor x_last_17_cast_fp16 = reduce_sum(axes = x_last_17_axes_0, keep_dims = x_last_17_keep_dims_0, x = var_824_cast_fp16)[name = string("x_last_17_cast_fp16")]; + tensor var_827_cast_fp16 = mul(x = x_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_827_cast_fp16")]; + tensor var_829_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_17_cast_fp16)[name = string("op_829_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = var_827_cast_fp16, y = var_829_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("replicate")]; + fp16 const_18_to_fp16 = const()[name = string("const_18_to_fp16"), val = fp16(0x0p+0)]; + tensor input_131_cast_fp16 = pad(constant_val = const_18_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")]; + string h_51_pad_type_0 = const()[name = string("h_51_pad_type_0"), val = string("valid")]; + tensor h_51_dilations_0 = const()[name = string("h_51_dilations_0"), val = tensor([4])]; + int32 h_51_groups_0 = const()[name = string("h_51_groups_0"), val = int32(512)]; + tensor h_51_strides_0 = const()[name = string("h_51_strides_0"), val = tensor([1])]; + tensor h_51_pad_0 = const()[name = string("h_51_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36265216)))]; + tensor main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36270400)))]; + tensor h_51_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_51_dilations_0, groups = h_51_groups_0, pad = h_51_pad_0, pad_type = h_51_pad_type_0, strides = h_51_strides_0, weight = main_blocks_1_convnext_0_2_dwconv__conv_weight_to_fp16, x = input_131_cast_fp16)[name = string("h_51_cast_fp16")]; + tensor x_69_cast_fp16 = mul(x = h_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_847_axes_0 = const()[name = string("op_847_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36271488)))]; + tensor main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36272576)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_69_cast_fp16)[name = string("transpose_84")]; + tensor var_847_cast_fp16 = layer_norm(axes = var_847_axes_0, beta = main_blocks_1_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_2_norm_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_847_cast_fp16")]; + tensor input_135_perm_0 = const()[name = string("input_135_perm_0"), val = tensor([0, 2, 1])]; + string h_53_pad_type_0 = const()[name = string("h_53_pad_type_0"), val = string("valid")]; + tensor h_53_strides_0 = const()[name = string("h_53_strides_0"), val = tensor([1])]; + tensor h_53_pad_0 = const()[name = string("h_53_pad_0"), val = tensor([0, 0])]; + tensor h_53_dilations_0 = const()[name = string("h_53_dilations_0"), val = tensor([1])]; + int32 h_53_groups_0 = const()[name = string("h_53_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36273664)))]; + tensor main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38370880)))]; + tensor input_135_cast_fp16 = transpose(perm = input_135_perm_0, x = var_847_cast_fp16)[name = string("transpose_83")]; + tensor h_53_cast_fp16 = conv(bias = main_blocks_1_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_53_dilations_0, groups = h_53_groups_0, pad = h_53_pad_0, pad_type = h_53_pad_type_0, strides = h_53_strides_0, weight = main_blocks_1_convnext_0_2_pwconv1_weight_to_fp16, x = input_135_cast_fp16)[name = string("h_53_cast_fp16")]; + string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = h_53_cast_fp16)[name = string("input_137_cast_fp16")]; + string h_55_pad_type_0 = const()[name = string("h_55_pad_type_0"), val = string("valid")]; + tensor h_55_strides_0 = const()[name = string("h_55_strides_0"), val = tensor([1])]; + tensor h_55_pad_0 = const()[name = string("h_55_pad_0"), val = tensor([0, 0])]; + tensor h_55_dilations_0 = const()[name = string("h_55_dilations_0"), val = tensor([1])]; + int32 h_55_groups_0 = const()[name = string("h_55_groups_0"), val = int32(1)]; + tensor var_864_weight_0_to_fp16 = const()[name = string("op_864_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38375040)))]; + tensor var_864_bias_0_to_fp16 = const()[name = string("op_864_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40472256)))]; + tensor var_864_cast_fp16 = conv(bias = var_864_bias_0_to_fp16, dilations = h_55_dilations_0, groups = h_55_groups_0, pad = h_55_pad_0, pad_type = h_55_pad_type_0, strides = h_55_strides_0, weight = var_864_weight_0_to_fp16, x = input_137_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor out_21_cast_fp16 = add(x = x_67_cast_fp16, y = var_864_cast_fp16)[name = string("out_21_cast_fp16")]; + tensor x_71_cast_fp16 = mul(x = out_21_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor x_73_cast_fp16 = mul(x = x_71_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_878_cast_fp16 = mul(x = x_73_cast_fp16, y = last_1_cast_fp16)[name = string("op_878_cast_fp16")]; + tensor x_last_19_axes_0 = const()[name = string("x_last_19_axes_0"), val = tensor([-1])]; + bool x_last_19_keep_dims_0 = const()[name = string("x_last_19_keep_dims_0"), val = bool(true)]; + tensor x_last_19_cast_fp16 = reduce_sum(axes = x_last_19_axes_0, keep_dims = x_last_19_keep_dims_0, x = var_878_cast_fp16)[name = string("x_last_19_cast_fp16")]; + tensor var_881_cast_fp16 = mul(x = x_73_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_881_cast_fp16")]; + tensor var_883_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_19_cast_fp16)[name = string("op_883_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = var_881_cast_fp16, y = var_883_cast_fp16)[name = string("input_141_cast_fp16")]; + tensor input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("replicate")]; + fp16 const_20_to_fp16 = const()[name = string("const_20_to_fp16"), val = fp16(0x0p+0)]; + tensor input_143_cast_fp16 = pad(constant_val = const_20_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141_cast_fp16)[name = string("input_143_cast_fp16")]; + string h_57_pad_type_0 = const()[name = string("h_57_pad_type_0"), val = string("valid")]; + tensor h_57_dilations_0 = const()[name = string("h_57_dilations_0"), val = tensor([8])]; + int32 h_57_groups_0 = const()[name = string("h_57_groups_0"), val = int32(512)]; + tensor h_57_strides_0 = const()[name = string("h_57_strides_0"), val = tensor([1])]; + tensor h_57_pad_0 = const()[name = string("h_57_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40473344)))]; + tensor main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40478528)))]; + tensor h_57_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_57_dilations_0, groups = h_57_groups_0, pad = h_57_pad_0, pad_type = h_57_pad_type_0, strides = h_57_strides_0, weight = main_blocks_1_convnext_0_3_dwconv__conv_weight_to_fp16, x = input_143_cast_fp16)[name = string("h_57_cast_fp16")]; + tensor x_75_cast_fp16 = mul(x = h_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_901_axes_0 = const()[name = string("op_901_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40479616)))]; + tensor main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40480704)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_75_cast_fp16)[name = string("transpose_82")]; + tensor var_901_cast_fp16 = layer_norm(axes = var_901_axes_0, beta = main_blocks_1_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_0_3_norm_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor input_147_perm_0 = const()[name = string("input_147_perm_0"), val = tensor([0, 2, 1])]; + string h_59_pad_type_0 = const()[name = string("h_59_pad_type_0"), val = string("valid")]; + tensor h_59_strides_0 = const()[name = string("h_59_strides_0"), val = tensor([1])]; + tensor h_59_pad_0 = const()[name = string("h_59_pad_0"), val = tensor([0, 0])]; + tensor h_59_dilations_0 = const()[name = string("h_59_dilations_0"), val = tensor([1])]; + int32 h_59_groups_0 = const()[name = string("h_59_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40481792)))]; + tensor main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42579008)))]; + tensor input_147_cast_fp16 = transpose(perm = input_147_perm_0, x = var_901_cast_fp16)[name = string("transpose_81")]; + tensor h_59_cast_fp16 = conv(bias = main_blocks_1_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_59_dilations_0, groups = h_59_groups_0, pad = h_59_pad_0, pad_type = h_59_pad_type_0, strides = h_59_strides_0, weight = main_blocks_1_convnext_0_3_pwconv1_weight_to_fp16, x = input_147_cast_fp16)[name = string("h_59_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = h_59_cast_fp16)[name = string("input_149_cast_fp16")]; + string h_61_pad_type_0 = const()[name = string("h_61_pad_type_0"), val = string("valid")]; + tensor h_61_strides_0 = const()[name = string("h_61_strides_0"), val = tensor([1])]; + tensor h_61_pad_0 = const()[name = string("h_61_pad_0"), val = tensor([0, 0])]; + tensor h_61_dilations_0 = const()[name = string("h_61_dilations_0"), val = tensor([1])]; + int32 h_61_groups_0 = const()[name = string("h_61_groups_0"), val = int32(1)]; + tensor var_918_weight_0_to_fp16 = const()[name = string("op_918_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42583168)))]; + tensor var_918_bias_0_to_fp16 = const()[name = string("op_918_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44680384)))]; + tensor var_918_cast_fp16 = conv(bias = var_918_bias_0_to_fp16, dilations = h_61_dilations_0, groups = h_61_groups_0, pad = h_61_pad_0, pad_type = h_61_pad_type_0, strides = h_61_strides_0, weight = var_918_weight_0_to_fp16, x = input_149_cast_fp16)[name = string("op_918_cast_fp16")]; + tensor out_23_cast_fp16 = add(x = x_73_cast_fp16, y = var_918_cast_fp16)[name = string("out_23_cast_fp16")]; + tensor x_77_cast_fp16 = mul(x = out_23_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor main_blocks_1_time_cond_linear_linear_weight_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44681472)))]; + tensor main_blocks_1_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_1_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44747072)))]; + tensor linear_11_cast_fp16 = linear(bias = main_blocks_1_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_1_time_cond_linear_linear_weight_to_fp16, x = input_55_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor t_7_axes_0 = const()[name = string("t_7_axes_0"), val = tensor([-1])]; + tensor t_7_cast_fp16 = expand_dims(axes = t_7_axes_0, x = linear_11_cast_fp16)[name = string("t_7_cast_fp16")]; + tensor var_928_cast_fp16 = add(x = x_77_cast_fp16, y = t_7_cast_fp16)[name = string("op_928_cast_fp16")]; + tensor x_79_cast_fp16 = mul(x = var_928_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor x_81_cast_fp16 = mul(x = x_79_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = x_81_cast_fp16, y = last_1_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor x_last_21_axes_0 = const()[name = string("x_last_21_axes_0"), val = tensor([-1])]; + bool x_last_21_keep_dims_0 = const()[name = string("x_last_21_keep_dims_0"), val = bool(true)]; + tensor x_last_21_cast_fp16 = reduce_sum(axes = x_last_21_axes_0, keep_dims = x_last_21_keep_dims_0, x = var_941_cast_fp16)[name = string("x_last_21_cast_fp16")]; + tensor var_944_cast_fp16 = mul(x = x_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor var_946_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_21_cast_fp16)[name = string("op_946_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = var_944_cast_fp16, y = var_946_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("replicate")]; + fp16 const_22_to_fp16 = const()[name = string("const_22_to_fp16"), val = fp16(0x0p+0)]; + tensor input_157_cast_fp16 = pad(constant_val = const_22_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = input_155_cast_fp16)[name = string("input_157_cast_fp16")]; + string h_63_pad_type_0 = const()[name = string("h_63_pad_type_0"), val = string("valid")]; + int32 h_63_groups_0 = const()[name = string("h_63_groups_0"), val = int32(512)]; + tensor h_63_strides_0 = const()[name = string("h_63_strides_0"), val = tensor([1])]; + tensor h_63_pad_0 = const()[name = string("h_63_pad_0"), val = tensor([0, 0])]; + tensor h_63_dilations_0 = const()[name = string("h_63_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44748160)))]; + tensor main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44753344)))]; + tensor h_63_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_63_dilations_0, groups = h_63_groups_0, pad = h_63_pad_0, pad_type = h_63_pad_type_0, strides = h_63_strides_0, weight = main_blocks_1_convnext_1_0_dwconv__conv_weight_to_fp16, x = input_157_cast_fp16)[name = string("h_63_cast_fp16")]; + tensor x_83_cast_fp16 = mul(x = h_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor input_159_perm_0 = const()[name = string("input_159_perm_0"), val = tensor([0, 2, 1])]; + tensor var_964_axes_0 = const()[name = string("op_964_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44754432)))]; + tensor main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44755520)))]; + tensor input_159_cast_fp16 = transpose(perm = input_159_perm_0, x = x_83_cast_fp16)[name = string("transpose_80")]; + tensor var_964_cast_fp16 = layer_norm(axes = var_964_axes_0, beta = main_blocks_1_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_1_0_norm_norm_weight_to_fp16, x = input_159_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + string h_65_pad_type_0 = const()[name = string("h_65_pad_type_0"), val = string("valid")]; + tensor h_65_strides_0 = const()[name = string("h_65_strides_0"), val = tensor([1])]; + tensor h_65_pad_0 = const()[name = string("h_65_pad_0"), val = tensor([0, 0])]; + tensor h_65_dilations_0 = const()[name = string("h_65_dilations_0"), val = tensor([1])]; + int32 h_65_groups_0 = const()[name = string("h_65_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44756608)))]; + tensor main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46853824)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = var_964_cast_fp16)[name = string("transpose_79")]; + tensor h_65_cast_fp16 = conv(bias = main_blocks_1_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_65_dilations_0, groups = h_65_groups_0, pad = h_65_pad_0, pad_type = h_65_pad_type_0, strides = h_65_strides_0, weight = main_blocks_1_convnext_1_0_pwconv1_weight_to_fp16, x = input_161_cast_fp16)[name = string("h_65_cast_fp16")]; + string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("EXACT")]; + tensor input_163_cast_fp16 = gelu(mode = input_163_mode_0, x = h_65_cast_fp16)[name = string("input_163_cast_fp16")]; + string h_67_pad_type_0 = const()[name = string("h_67_pad_type_0"), val = string("valid")]; + tensor h_67_strides_0 = const()[name = string("h_67_strides_0"), val = tensor([1])]; + tensor h_67_pad_0 = const()[name = string("h_67_pad_0"), val = tensor([0, 0])]; + tensor h_67_dilations_0 = const()[name = string("h_67_dilations_0"), val = tensor([1])]; + int32 h_67_groups_0 = const()[name = string("h_67_groups_0"), val = int32(1)]; + tensor var_981_weight_0_to_fp16 = const()[name = string("op_981_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46857984)))]; + tensor var_981_bias_0_to_fp16 = const()[name = string("op_981_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48955200)))]; + tensor var_981_cast_fp16 = conv(bias = var_981_bias_0_to_fp16, dilations = h_67_dilations_0, groups = h_67_groups_0, pad = h_67_pad_0, pad_type = h_67_pad_type_0, strides = h_67_strides_0, weight = var_981_weight_0_to_fp16, x = input_163_cast_fp16)[name = string("op_981_cast_fp16")]; + tensor out_25_cast_fp16 = add(x = x_81_cast_fp16, y = var_981_cast_fp16)[name = string("out_25_cast_fp16")]; + tensor x_85_cast_fp16 = mul(x = out_25_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor x_m_5_cast_fp16 = mul(x = x_85_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_5_cast_fp16")]; + tensor var_991_shape_cast_fp16 = shape(x = x_85_cast_fp16)[name = string("op_991_shape_cast_fp16")]; + int32 gather_9_axis_0 = const()[name = string("gather_9_axis_0"), val = int32(0)]; + int32 gather_9_batch_dims_0 = const()[name = string("gather_9_batch_dims_0"), val = int32(0)]; + bool gather_9_validate_indices_0 = const()[name = string("gather_9_validate_indices_0"), val = bool(false)]; + string var_991_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_991_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_9_indices_0_to_uint16 = const()[name = string("gather_9_indices_0_to_uint16"), val = uint16(2)]; + tensor var_991_shape_cast_fp16_to_uint16 = cast(dtype = var_991_shape_cast_fp16_to_uint16_dtype_0, x = var_991_shape_cast_fp16)[name = string("cast_91")]; + uint16 gather_9_cast_uint16 = gather(axis = gather_9_axis_0, batch_dims = gather_9_batch_dims_0, indices = gather_9_indices_0_to_uint16, validate_indices = gather_9_validate_indices_0, x = var_991_shape_cast_fp16_to_uint16)[name = string("gather_9_cast_uint16")]; + string gather_9_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_9_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_165_perm_0 = const()[name = string("input_165_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_text_attn_W_query_linear_weight_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48956288)))]; + tensor main_blocks_1_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49480640)))]; + tensor input_165_cast_fp16 = transpose(perm = input_165_perm_0, x = x_85_cast_fp16)[name = string("transpose_78")]; + tensor linear_12_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_query_linear_weight_to_fp16, x = input_165_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor concat_14x = const()[name = string("concat_14x"), val = tensor([2, -1, 8, 64])]; + tensor var_1000_cast_fp16 = reshape(shape = concat_14x, x = linear_12_cast_fp16)[name = string("op_1000_cast_fp16")]; + tensor x_87_perm_0 = const()[name = string("x_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_key_linear_weight_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49481728)))]; + tensor main_blocks_1_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49743936)))]; + tensor linear_13_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_key_linear_weight_to_fp16, x = input_71_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor concat_15x = const()[name = string("concat_15x"), val = tensor([2, -1, 8, 64])]; + tensor var_1008_cast_fp16 = reshape(shape = concat_15x, x = linear_13_cast_fp16)[name = string("op_1008_cast_fp16")]; + tensor x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_text_attn_W_value_linear_weight_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49745024)))]; + tensor main_blocks_1_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50007232)))]; + tensor linear_14_cast_fp16 = linear(bias = main_blocks_1_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_text_attn_W_value_linear_weight_to_fp16, x = input_71_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor concat_16x = const()[name = string("concat_16x"), val = tensor([2, -1, 8, 64])]; + tensor var_1016_cast_fp16 = reshape(shape = concat_16x, x = linear_14_cast_fp16)[name = string("op_1016_cast_fp16")]; + tensor v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor([0, 2, 1, 3])]; + int32 concat_17_values0_0 = const()[name = string("concat_17_values0_0"), val = int32(1)]; + int32 concat_17_values2_0 = const()[name = string("concat_17_values2_0"), val = int32(1)]; + int32 concat_17_axis_0 = const()[name = string("concat_17_axis_0"), val = int32(0)]; + bool concat_17_interleave_0 = const()[name = string("concat_17_interleave_0"), val = bool(false)]; + int32 gather_9_cast_uint16_to_int32 = cast(dtype = gather_9_cast_uint16_to_int32_dtype_0, x = gather_9_cast_uint16)[name = string("cast_90")]; + tensor concat_17 = concat(axis = concat_17_axis_0, interleave = concat_17_interleave_0, values = (concat_17_values0_0, gather_9_cast_uint16_to_int32, concat_17_values2_0))[name = string("concat_17")]; + tensor var_1019_begin_0 = const()[name = string("op_1019_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1019_end_mask_0 = const()[name = string("op_1019_end_mask_0"), val = tensor([true, false, true])]; + tensor var_1019_cast_fp16 = slice_by_index(begin = var_1019_begin_0, end = concat_17, end_mask = var_1019_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_1019_cast_fp16")]; + tensor concat_18 = const()[name = string("concat_18"), val = tensor([2, -1, -1])]; + tensor shape_3_cast_fp16 = shape(x = var_1019_cast_fp16)[name = string("shape_3_cast_fp16")]; + tensor equal_3 = const()[name = string("equal_3"), val = tensor([false, true, true])]; + tensor select_3 = select(a = shape_3_cast_fp16, b = concat_18, cond = equal_3)[name = string("select_3")]; + tensor real_div_3 = real_div(x = select_3, y = shape_3_cast_fp16)[name = string("real_div_3")]; + tensor var_1022_cast_fp16 = tile(reps = real_div_3, x = var_1019_cast_fp16)[name = string("op_1022_cast_fp16")]; + tensor scaled_5_cast_fp16 = real_div(x = var_1022_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_5_cast_fp16")]; + tensor main_blocks_1_text_attn_theta_to_fp16 = const()[name = string("main_blocks_1_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50008320)))]; + tensor angles_5_cast_fp16 = mul(x = scaled_5_cast_fp16, y = main_blocks_1_text_attn_theta_to_fp16)[name = string("angles_5_cast_fp16")]; + tensor var_1038_cast_fp16 = cos(x = angles_5_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor cos_5_axes_0 = const()[name = string("cos_5_axes_0"), val = tensor([1])]; + tensor cos_5_cast_fp16 = expand_dims(axes = cos_5_axes_0, x = var_1038_cast_fp16)[name = string("cos_5_cast_fp16")]; + tensor var_1040_cast_fp16 = sin(x = angles_5_cast_fp16)[name = string("op_1040_cast_fp16")]; + tensor sin_5_axes_0 = const()[name = string("sin_5_axes_0"), val = tensor([1])]; + tensor sin_5_cast_fp16 = expand_dims(axes = sin_5_axes_0, x = var_1040_cast_fp16)[name = string("sin_5_cast_fp16")]; + tensor x_a_5_begin_0 = const()[name = string("x_a_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_5_end_0 = const()[name = string("x_a_5_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_5_end_mask_0 = const()[name = string("x_a_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_87_cast_fp16 = transpose(perm = x_87_perm_0, x = var_1000_cast_fp16)[name = string("transpose_77")]; + tensor x_a_5_cast_fp16 = slice_by_index(begin = x_a_5_begin_0, end = x_a_5_end_0, end_mask = x_a_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_a_5_cast_fp16")]; + tensor x_b_5_begin_0 = const()[name = string("x_b_5_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_5_end_0 = const()[name = string("x_b_5_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_5_end_mask_0 = const()[name = string("x_b_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_5_cast_fp16 = slice_by_index(begin = x_b_5_begin_0, end = x_b_5_end_0, end_mask = x_b_5_end_mask_0, x = x_87_cast_fp16)[name = string("x_b_5_cast_fp16")]; + tensor var_1044_cast_fp16 = mul(x = x_a_5_cast_fp16, y = cos_5_cast_fp16)[name = string("op_1044_cast_fp16")]; + tensor var_1045_cast_fp16 = mul(x = x_b_5_cast_fp16, y = sin_5_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor rot_a_5_cast_fp16 = sub(x = var_1044_cast_fp16, y = var_1045_cast_fp16)[name = string("rot_a_5_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_a_5_cast_fp16, y = sin_5_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_cast_fp16 = mul(x = x_b_5_cast_fp16, y = cos_5_cast_fp16)[name = string("op_1048_cast_fp16")]; + tensor rot_b_5_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("rot_b_5_cast_fp16")]; + bool q_5_interleave_0 = const()[name = string("q_5_interleave_0"), val = bool(false)]; + tensor q_5_cast_fp16 = concat(axis = var_681, interleave = q_5_interleave_0, values = (rot_a_5_cast_fp16, rot_b_5_cast_fp16))[name = string("q_5_cast_fp16")]; + tensor x_a_7_begin_0 = const()[name = string("x_a_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_7_end_0 = const()[name = string("x_a_7_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_7_end_mask_0 = const()[name = string("x_a_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_1008_cast_fp16)[name = string("transpose_76")]; + tensor x_a_7_cast_fp16 = slice_by_index(begin = x_a_7_begin_0, end = x_a_7_end_0, end_mask = x_a_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_a_7_cast_fp16")]; + tensor x_b_7_begin_0 = const()[name = string("x_b_7_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_7_end_0 = const()[name = string("x_b_7_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_7_end_mask_0 = const()[name = string("x_b_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_7_cast_fp16 = slice_by_index(begin = x_b_7_begin_0, end = x_b_7_end_0, end_mask = x_b_7_end_mask_0, x = x_89_cast_fp16)[name = string("x_b_7_cast_fp16")]; + tensor var_1062_cast_fp16 = mul(x = x_a_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = x_b_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor rot_a_7_cast_fp16 = sub(x = var_1062_cast_fp16, y = var_1063_cast_fp16)[name = string("rot_a_7_cast_fp16")]; + tensor var_1065_cast_fp16 = mul(x = x_a_7_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1065_cast_fp16")]; + tensor var_1066_cast_fp16 = mul(x = x_b_7_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1066_cast_fp16")]; + tensor rot_b_7_cast_fp16 = add(x = var_1065_cast_fp16, y = var_1066_cast_fp16)[name = string("rot_b_7_cast_fp16")]; + bool k_5_interleave_0 = const()[name = string("k_5_interleave_0"), val = bool(false)]; + tensor k_5_cast_fp16 = concat(axis = var_681, interleave = k_5_interleave_0, values = (rot_a_7_cast_fp16, rot_b_7_cast_fp16))[name = string("k_5_cast_fp16")]; + bool var_1071_transpose_x_1 = const()[name = string("op_1071_transpose_x_1"), val = bool(false)]; + bool var_1071_transpose_y_1 = const()[name = string("op_1071_transpose_y_1"), val = bool(true)]; + tensor var_1071_cast_fp16 = matmul(transpose_x = var_1071_transpose_x_1, transpose_y = var_1071_transpose_y_1, x = q_5_cast_fp16, y = k_5_cast_fp16)[name = string("op_1071_cast_fp16")]; + fp16 _inversed_scores_3_y_0_to_fp16 = const()[name = string("_inversed_scores_3_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_3_cast_fp16 = mul(x = var_1071_cast_fp16, y = _inversed_scores_3_y_0_to_fp16)[name = string("_inversed_scores_3_cast_fp16")]; + tensor input_171_cast_fp16 = sub(x = _inversed_scores_3_cast_fp16, y = var_530_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor attn_7_cast_fp16 = softmax(axis = var_681, x = input_171_cast_fp16)[name = string("attn_7_cast_fp16")]; + bool var_1080_transpose_x_0 = const()[name = string("op_1080_transpose_x_0"), val = bool(false)]; + bool var_1080_transpose_y_0 = const()[name = string("op_1080_transpose_y_0"), val = bool(false)]; + tensor v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1016_cast_fp16)[name = string("transpose_75")]; + tensor var_1080_cast_fp16 = matmul(transpose_x = var_1080_transpose_x_0, transpose_y = var_1080_transpose_y_0, x = attn_7_cast_fp16, y = v_5_cast_fp16)[name = string("op_1080_cast_fp16")]; + tensor var_1081_perm_0 = const()[name = string("op_1081_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_21x = const()[name = string("concat_21x"), val = tensor([2, -1, 512])]; + tensor var_1081_cast_fp16 = transpose(perm = var_1081_perm_0, x = var_1080_cast_fp16)[name = string("transpose_74")]; + tensor input_173_cast_fp16 = reshape(shape = concat_21x, x = var_1081_cast_fp16)[name = string("input_173_cast_fp16")]; + tensor main_blocks_1_text_attn_out_fc_linear_weight_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50008448)))]; + tensor main_blocks_1_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50532800)))]; + tensor linear_15_cast_fp16 = linear(bias = main_blocks_1_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_text_attn_out_fc_linear_weight_to_fp16, x = input_173_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor out_27_perm_0 = const()[name = string("out_27_perm_0"), val = tensor([0, 2, 1])]; + tensor out_27_cast_fp16 = transpose(perm = out_27_perm_0, x = linear_15_cast_fp16)[name = string("transpose_73")]; + tensor var_1089_cast_fp16 = mul(x = out_27_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1089_cast_fp16")]; + tensor x_91_cast_fp16 = add(x = x_m_5_cast_fp16, y = var_1089_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_175_perm_0 = const()[name = string("input_175_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1096_axes_0 = const()[name = string("op_1096_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50533888)))]; + tensor main_blocks_1_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50534976)))]; + tensor input_175_cast_fp16 = transpose(perm = input_175_perm_0, x = x_91_cast_fp16)[name = string("transpose_72")]; + tensor var_1096_cast_fp16 = layer_norm(axes = var_1096_axes_0, beta = main_blocks_1_text_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_text_norm_norm_weight_to_fp16, x = input_175_cast_fp16)[name = string("op_1096_cast_fp16")]; + tensor var_1097_perm_0 = const()[name = string("op_1097_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1097_cast_fp16 = transpose(perm = var_1097_perm_0, x = var_1096_cast_fp16)[name = string("transpose_71")]; + tensor x_93_cast_fp16 = mul(x = var_1097_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_93_cast_fp16")]; + tensor x_95_cast_fp16 = mul(x = x_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_1110_cast_fp16 = mul(x = x_95_cast_fp16, y = last_1_cast_fp16)[name = string("op_1110_cast_fp16")]; + tensor x_last_23_axes_0 = const()[name = string("x_last_23_axes_0"), val = tensor([-1])]; + bool x_last_23_keep_dims_0 = const()[name = string("x_last_23_keep_dims_0"), val = bool(true)]; + tensor x_last_23_cast_fp16 = reduce_sum(axes = x_last_23_axes_0, keep_dims = x_last_23_keep_dims_0, x = var_1110_cast_fp16)[name = string("x_last_23_cast_fp16")]; + tensor var_1113_cast_fp16 = mul(x = x_95_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1115_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_23_cast_fp16)[name = string("op_1115_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = var_1113_cast_fp16, y = var_1115_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("replicate")]; + fp16 const_24_to_fp16 = const()[name = string("const_24_to_fp16"), val = fp16(0x0p+0)]; + tensor input_181_cast_fp16 = pad(constant_val = const_24_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = input_179_cast_fp16)[name = string("input_181_cast_fp16")]; + string h_69_pad_type_0 = const()[name = string("h_69_pad_type_0"), val = string("valid")]; + int32 h_69_groups_0 = const()[name = string("h_69_groups_0"), val = int32(512)]; + tensor h_69_strides_0 = const()[name = string("h_69_strides_0"), val = tensor([1])]; + tensor h_69_pad_0 = const()[name = string("h_69_pad_0"), val = tensor([0, 0])]; + tensor h_69_dilations_0 = const()[name = string("h_69_dilations_0"), val = tensor([1])]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50536064)))]; + tensor main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50541248)))]; + tensor h_69_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_69_dilations_0, groups = h_69_groups_0, pad = h_69_pad_0, pad_type = h_69_pad_type_0, strides = h_69_strides_0, weight = main_blocks_1_convnext_2_0_dwconv__conv_weight_to_fp16, x = input_181_cast_fp16)[name = string("h_69_cast_fp16")]; + tensor x_97_cast_fp16 = mul(x = h_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor input_183_perm_0 = const()[name = string("input_183_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1133_axes_0 = const()[name = string("op_1133_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50542336)))]; + tensor main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50543424)))]; + tensor input_183_cast_fp16 = transpose(perm = input_183_perm_0, x = x_97_cast_fp16)[name = string("transpose_70")]; + tensor var_1133_cast_fp16 = layer_norm(axes = var_1133_axes_0, beta = main_blocks_1_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_convnext_2_0_norm_norm_weight_to_fp16, x = input_183_cast_fp16)[name = string("op_1133_cast_fp16")]; + tensor input_185_perm_0 = const()[name = string("input_185_perm_0"), val = tensor([0, 2, 1])]; + string h_71_pad_type_0 = const()[name = string("h_71_pad_type_0"), val = string("valid")]; + tensor h_71_strides_0 = const()[name = string("h_71_strides_0"), val = tensor([1])]; + tensor h_71_pad_0 = const()[name = string("h_71_pad_0"), val = tensor([0, 0])]; + tensor h_71_dilations_0 = const()[name = string("h_71_dilations_0"), val = tensor([1])]; + int32 h_71_groups_0 = const()[name = string("h_71_groups_0"), val = int32(1)]; + tensor main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50544512)))]; + tensor main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52641728)))]; + tensor input_185_cast_fp16 = transpose(perm = input_185_perm_0, x = var_1133_cast_fp16)[name = string("transpose_69")]; + tensor h_71_cast_fp16 = conv(bias = main_blocks_1_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_71_dilations_0, groups = h_71_groups_0, pad = h_71_pad_0, pad_type = h_71_pad_type_0, strides = h_71_strides_0, weight = main_blocks_1_convnext_2_0_pwconv1_weight_to_fp16, x = input_185_cast_fp16)[name = string("h_71_cast_fp16")]; + string input_187_mode_0 = const()[name = string("input_187_mode_0"), val = string("EXACT")]; + tensor input_187_cast_fp16 = gelu(mode = input_187_mode_0, x = h_71_cast_fp16)[name = string("input_187_cast_fp16")]; + string h_73_pad_type_0 = const()[name = string("h_73_pad_type_0"), val = string("valid")]; + tensor h_73_strides_0 = const()[name = string("h_73_strides_0"), val = tensor([1])]; + tensor h_73_pad_0 = const()[name = string("h_73_pad_0"), val = tensor([0, 0])]; + tensor h_73_dilations_0 = const()[name = string("h_73_dilations_0"), val = tensor([1])]; + int32 h_73_groups_0 = const()[name = string("h_73_groups_0"), val = int32(1)]; + tensor var_1150_weight_0_to_fp16 = const()[name = string("op_1150_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52645888)))]; + tensor var_1150_bias_0_to_fp16 = const()[name = string("op_1150_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54743104)))]; + tensor var_1150_cast_fp16 = conv(bias = var_1150_bias_0_to_fp16, dilations = h_73_dilations_0, groups = h_73_groups_0, pad = h_73_pad_0, pad_type = h_73_pad_type_0, strides = h_73_strides_0, weight = var_1150_weight_0_to_fp16, x = input_187_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor out_29_cast_fp16 = add(x = x_95_cast_fp16, y = var_1150_cast_fp16)[name = string("out_29_cast_fp16")]; + tensor x_99_cast_fp16 = mul(x = out_29_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor x_m_7_cast_fp16 = mul(x = x_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_7_cast_fp16")]; + tensor input_189_perm_0 = const()[name = string("input_189_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_1_style_attn_W_query_linear_weight_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54744192)))]; + tensor main_blocks_1_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55006400)))]; + tensor input_189_cast_fp16 = transpose(perm = input_189_perm_0, x = x_99_cast_fp16)[name = string("transpose_68")]; + tensor linear_16_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_query_linear_weight_to_fp16, x = input_189_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor concat_22x = const()[name = string("concat_22x"), val = tensor([2, -1, 2, 128])]; + tensor var_1167_cast_fp16 = reshape(shape = concat_22x, x = linear_16_cast_fp16)[name = string("op_1167_cast_fp16")]; + tensor q_7_perm_0 = const()[name = string("q_7_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_1_style_attn_W_value_linear_weight_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55006976)))]; + tensor main_blocks_1_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55138112)))]; + tensor linear_18_cast_fp16 = linear(bias = main_blocks_1_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_1_style_attn_W_value_linear_weight_to_fp16, x = input_95_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_1180 = const()[name = string("op_1180"), val = tensor([2, 50, 2, 128])]; + tensor var_1181_cast_fp16 = reshape(shape = var_1180, x = linear_18_cast_fp16)[name = string("op_1181_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, 1, 3])]; + bool var_1185_transpose_x_0 = const()[name = string("op_1185_transpose_x_0"), val = bool(false)]; + bool var_1185_transpose_y_0 = const()[name = string("op_1185_transpose_y_0"), val = bool(false)]; + tensor var_1184_to_fp16 = const()[name = string("op_1184_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55138688)))]; + tensor q_7_cast_fp16 = transpose(perm = q_7_perm_0, x = var_1167_cast_fp16)[name = string("transpose_67")]; + tensor var_1185_cast_fp16 = matmul(transpose_x = var_1185_transpose_x_0, transpose_y = var_1185_transpose_y_0, x = q_7_cast_fp16, y = var_1184_to_fp16)[name = string("op_1185_cast_fp16")]; + fp16 _inversed_input_191_y_0_to_fp16 = const()[name = string("_inversed_input_191_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_191_cast_fp16 = mul(x = var_1185_cast_fp16, y = _inversed_input_191_y_0_to_fp16)[name = string("_inversed_input_191_cast_fp16")]; + tensor attn_9_cast_fp16 = softmax(axis = var_681, x = _inversed_input_191_cast_fp16)[name = string("attn_9_cast_fp16")]; + tensor attn_11_cast_fp16 = mul(x = attn_9_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_11_cast_fp16")]; + bool var_1192_transpose_x_0 = const()[name = string("op_1192_transpose_x_0"), val = bool(false)]; + bool var_1192_transpose_y_0 = const()[name = string("op_1192_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1181_cast_fp16)[name = string("transpose_66")]; + tensor var_1192_cast_fp16 = matmul(transpose_x = var_1192_transpose_x_0, transpose_y = var_1192_transpose_y_0, x = attn_11_cast_fp16, y = v_7_cast_fp16)[name = string("op_1192_cast_fp16")]; + tensor var_1193_perm_0 = const()[name = string("op_1193_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_23x = const()[name = string("concat_23x"), val = tensor([2, -1, 256])]; + tensor var_1193_cast_fp16 = transpose(perm = var_1193_perm_0, x = var_1192_cast_fp16)[name = string("transpose_65")]; + tensor input_193_cast_fp16 = reshape(shape = concat_23x, x = var_1193_cast_fp16)[name = string("input_193_cast_fp16")]; + tensor main_blocks_1_style_attn_out_fc_linear_weight_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55189952)))]; + tensor main_blocks_1_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_1_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55452160)))]; + tensor linear_19_cast_fp16 = linear(bias = main_blocks_1_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_1_style_attn_out_fc_linear_weight_to_fp16, x = input_193_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor out_31_perm_0 = const()[name = string("out_31_perm_0"), val = tensor([0, 2, 1])]; + tensor out_31_cast_fp16 = transpose(perm = out_31_perm_0, x = linear_19_cast_fp16)[name = string("transpose_64")]; + tensor var_1201_cast_fp16 = mul(x = out_31_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1201_cast_fp16")]; + tensor x_101_cast_fp16 = add(x = x_m_7_cast_fp16, y = var_1201_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_195_perm_0 = const()[name = string("input_195_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1208_axes_0 = const()[name = string("op_1208_axes_0"), val = tensor([-1])]; + tensor main_blocks_1_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55453248)))]; + tensor main_blocks_1_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_1_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55454336)))]; + tensor input_195_cast_fp16 = transpose(perm = input_195_perm_0, x = x_101_cast_fp16)[name = string("transpose_63")]; + tensor var_1208_cast_fp16 = layer_norm(axes = var_1208_axes_0, beta = main_blocks_1_style_norm_norm_bias_to_fp16, epsilon = var_686_to_fp16, gamma = main_blocks_1_style_norm_norm_weight_to_fp16, x = input_195_cast_fp16)[name = string("op_1208_cast_fp16")]; + tensor var_1209_perm_0 = const()[name = string("op_1209_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1209_cast_fp16 = transpose(perm = var_1209_perm_0, x = var_1208_cast_fp16)[name = string("transpose_62")]; + tensor x_103_cast_fp16 = mul(x = var_1209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_103_cast_fp16")]; + int32 var_1228 = const()[name = string("op_1228"), val = int32(-1)]; + tensor x_105_cast_fp16 = mul(x = x_103_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_105_cast_fp16")]; + tensor var_1263_cast_fp16 = mul(x = x_105_cast_fp16, y = last_1_cast_fp16)[name = string("op_1263_cast_fp16")]; + tensor x_last_25_axes_0 = const()[name = string("x_last_25_axes_0"), val = tensor([-1])]; + bool x_last_25_keep_dims_0 = const()[name = string("x_last_25_keep_dims_0"), val = bool(true)]; + tensor x_last_25_cast_fp16 = reduce_sum(axes = x_last_25_axes_0, keep_dims = x_last_25_keep_dims_0, x = var_1263_cast_fp16)[name = string("x_last_25_cast_fp16")]; + tensor var_1266_cast_fp16 = mul(x = x_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1266_cast_fp16")]; + tensor var_1268_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_25_cast_fp16)[name = string("op_1268_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1266_cast_fp16, y = var_1268_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_201_mode_0 = const()[name = string("input_201_mode_0"), val = string("replicate")]; + fp16 const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = fp16(0x0p+0)]; + tensor input_201_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = input_201_mode_0, pad = input_201_pad_0, x = input_199_cast_fp16)[name = string("input_201_cast_fp16")]; + string h_75_pad_type_0 = const()[name = string("h_75_pad_type_0"), val = string("valid")]; + int32 h_75_groups_0 = const()[name = string("h_75_groups_0"), val = int32(512)]; + tensor h_75_strides_0 = const()[name = string("h_75_strides_0"), val = tensor([1])]; + tensor h_75_pad_0 = const()[name = string("h_75_pad_0"), val = tensor([0, 0])]; + tensor h_75_dilations_0 = const()[name = string("h_75_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55455424)))]; + tensor main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55460608)))]; + tensor h_75_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_75_dilations_0, groups = h_75_groups_0, pad = h_75_pad_0, pad_type = h_75_pad_type_0, strides = h_75_strides_0, weight = main_blocks_2_convnext_0_0_dwconv__conv_weight_to_fp16, x = input_201_cast_fp16)[name = string("h_75_cast_fp16")]; + tensor x_107_cast_fp16 = mul(x = h_75_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor input_203_perm_0 = const()[name = string("input_203_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1286_axes_0 = const()[name = string("op_1286_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55461696)))]; + tensor main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55462784)))]; + fp16 var_1233_to_fp16 = const()[name = string("op_1233_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_203_cast_fp16 = transpose(perm = input_203_perm_0, x = x_107_cast_fp16)[name = string("transpose_61")]; + tensor var_1286_cast_fp16 = layer_norm(axes = var_1286_axes_0, beta = main_blocks_2_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_0_norm_norm_weight_to_fp16, x = input_203_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor([0, 2, 1])]; + string h_77_pad_type_0 = const()[name = string("h_77_pad_type_0"), val = string("valid")]; + tensor h_77_strides_0 = const()[name = string("h_77_strides_0"), val = tensor([1])]; + tensor h_77_pad_0 = const()[name = string("h_77_pad_0"), val = tensor([0, 0])]; + tensor h_77_dilations_0 = const()[name = string("h_77_dilations_0"), val = tensor([1])]; + int32 h_77_groups_0 = const()[name = string("h_77_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55463872)))]; + tensor main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57561088)))]; + tensor input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = var_1286_cast_fp16)[name = string("transpose_60")]; + tensor h_77_cast_fp16 = conv(bias = main_blocks_2_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_77_dilations_0, groups = h_77_groups_0, pad = h_77_pad_0, pad_type = h_77_pad_type_0, strides = h_77_strides_0, weight = main_blocks_2_convnext_0_0_pwconv1_weight_to_fp16, x = input_205_cast_fp16)[name = string("h_77_cast_fp16")]; + string input_207_mode_0 = const()[name = string("input_207_mode_0"), val = string("EXACT")]; + tensor input_207_cast_fp16 = gelu(mode = input_207_mode_0, x = h_77_cast_fp16)[name = string("input_207_cast_fp16")]; + string h_79_pad_type_0 = const()[name = string("h_79_pad_type_0"), val = string("valid")]; + tensor h_79_strides_0 = const()[name = string("h_79_strides_0"), val = tensor([1])]; + tensor h_79_pad_0 = const()[name = string("h_79_pad_0"), val = tensor([0, 0])]; + tensor h_79_dilations_0 = const()[name = string("h_79_dilations_0"), val = tensor([1])]; + int32 h_79_groups_0 = const()[name = string("h_79_groups_0"), val = int32(1)]; + tensor var_1303_weight_0_to_fp16 = const()[name = string("op_1303_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57565248)))]; + tensor var_1303_bias_0_to_fp16 = const()[name = string("op_1303_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59662464)))]; + tensor var_1303_cast_fp16 = conv(bias = var_1303_bias_0_to_fp16, dilations = h_79_dilations_0, groups = h_79_groups_0, pad = h_79_pad_0, pad_type = h_79_pad_type_0, strides = h_79_strides_0, weight = var_1303_weight_0_to_fp16, x = input_207_cast_fp16)[name = string("op_1303_cast_fp16")]; + tensor out_33_cast_fp16 = add(x = x_105_cast_fp16, y = var_1303_cast_fp16)[name = string("out_33_cast_fp16")]; + tensor x_109_cast_fp16 = mul(x = out_33_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor x_111_cast_fp16 = mul(x = x_109_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor var_1317_cast_fp16 = mul(x = x_111_cast_fp16, y = last_1_cast_fp16)[name = string("op_1317_cast_fp16")]; + tensor x_last_27_axes_0 = const()[name = string("x_last_27_axes_0"), val = tensor([-1])]; + bool x_last_27_keep_dims_0 = const()[name = string("x_last_27_keep_dims_0"), val = bool(true)]; + tensor x_last_27_cast_fp16 = reduce_sum(axes = x_last_27_axes_0, keep_dims = x_last_27_keep_dims_0, x = var_1317_cast_fp16)[name = string("x_last_27_cast_fp16")]; + tensor var_1320_cast_fp16 = mul(x = x_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1322_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_27_cast_fp16)[name = string("op_1322_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1320_cast_fp16, y = var_1322_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_213_mode_0 = const()[name = string("input_213_mode_0"), val = string("replicate")]; + fp16 const_29_to_fp16 = const()[name = string("const_29_to_fp16"), val = fp16(0x0p+0)]; + tensor input_213_cast_fp16 = pad(constant_val = const_29_to_fp16, mode = input_213_mode_0, pad = input_213_pad_0, x = input_211_cast_fp16)[name = string("input_213_cast_fp16")]; + string h_81_pad_type_0 = const()[name = string("h_81_pad_type_0"), val = string("valid")]; + tensor h_81_dilations_0 = const()[name = string("h_81_dilations_0"), val = tensor([2])]; + int32 h_81_groups_0 = const()[name = string("h_81_groups_0"), val = int32(512)]; + tensor h_81_strides_0 = const()[name = string("h_81_strides_0"), val = tensor([1])]; + tensor h_81_pad_0 = const()[name = string("h_81_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59663552)))]; + tensor main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59668736)))]; + tensor h_81_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_81_dilations_0, groups = h_81_groups_0, pad = h_81_pad_0, pad_type = h_81_pad_type_0, strides = h_81_strides_0, weight = main_blocks_2_convnext_0_1_dwconv__conv_weight_to_fp16, x = input_213_cast_fp16)[name = string("h_81_cast_fp16")]; + tensor x_113_cast_fp16 = mul(x = h_81_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_113_cast_fp16")]; + tensor input_215_perm_0 = const()[name = string("input_215_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1340_axes_0 = const()[name = string("op_1340_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59669824)))]; + tensor main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59670912)))]; + tensor input_215_cast_fp16 = transpose(perm = input_215_perm_0, x = x_113_cast_fp16)[name = string("transpose_59")]; + tensor var_1340_cast_fp16 = layer_norm(axes = var_1340_axes_0, beta = main_blocks_2_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_1_norm_norm_weight_to_fp16, x = input_215_cast_fp16)[name = string("op_1340_cast_fp16")]; + tensor input_217_perm_0 = const()[name = string("input_217_perm_0"), val = tensor([0, 2, 1])]; + string h_83_pad_type_0 = const()[name = string("h_83_pad_type_0"), val = string("valid")]; + tensor h_83_strides_0 = const()[name = string("h_83_strides_0"), val = tensor([1])]; + tensor h_83_pad_0 = const()[name = string("h_83_pad_0"), val = tensor([0, 0])]; + tensor h_83_dilations_0 = const()[name = string("h_83_dilations_0"), val = tensor([1])]; + int32 h_83_groups_0 = const()[name = string("h_83_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59672000)))]; + tensor main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61769216)))]; + tensor input_217_cast_fp16 = transpose(perm = input_217_perm_0, x = var_1340_cast_fp16)[name = string("transpose_58")]; + tensor h_83_cast_fp16 = conv(bias = main_blocks_2_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_83_dilations_0, groups = h_83_groups_0, pad = h_83_pad_0, pad_type = h_83_pad_type_0, strides = h_83_strides_0, weight = main_blocks_2_convnext_0_1_pwconv1_weight_to_fp16, x = input_217_cast_fp16)[name = string("h_83_cast_fp16")]; + string input_219_mode_0 = const()[name = string("input_219_mode_0"), val = string("EXACT")]; + tensor input_219_cast_fp16 = gelu(mode = input_219_mode_0, x = h_83_cast_fp16)[name = string("input_219_cast_fp16")]; + string h_85_pad_type_0 = const()[name = string("h_85_pad_type_0"), val = string("valid")]; + tensor h_85_strides_0 = const()[name = string("h_85_strides_0"), val = tensor([1])]; + tensor h_85_pad_0 = const()[name = string("h_85_pad_0"), val = tensor([0, 0])]; + tensor h_85_dilations_0 = const()[name = string("h_85_dilations_0"), val = tensor([1])]; + int32 h_85_groups_0 = const()[name = string("h_85_groups_0"), val = int32(1)]; + tensor var_1357_weight_0_to_fp16 = const()[name = string("op_1357_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61773376)))]; + tensor var_1357_bias_0_to_fp16 = const()[name = string("op_1357_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63870592)))]; + tensor var_1357_cast_fp16 = conv(bias = var_1357_bias_0_to_fp16, dilations = h_85_dilations_0, groups = h_85_groups_0, pad = h_85_pad_0, pad_type = h_85_pad_type_0, strides = h_85_strides_0, weight = var_1357_weight_0_to_fp16, x = input_219_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor out_35_cast_fp16 = add(x = x_111_cast_fp16, y = var_1357_cast_fp16)[name = string("out_35_cast_fp16")]; + tensor x_115_cast_fp16 = mul(x = out_35_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_115_cast_fp16")]; + tensor x_117_cast_fp16 = mul(x = x_115_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_117_cast_fp16")]; + tensor var_1371_cast_fp16 = mul(x = x_117_cast_fp16, y = last_1_cast_fp16)[name = string("op_1371_cast_fp16")]; + tensor x_last_29_axes_0 = const()[name = string("x_last_29_axes_0"), val = tensor([-1])]; + bool x_last_29_keep_dims_0 = const()[name = string("x_last_29_keep_dims_0"), val = bool(true)]; + tensor x_last_29_cast_fp16 = reduce_sum(axes = x_last_29_axes_0, keep_dims = x_last_29_keep_dims_0, x = var_1371_cast_fp16)[name = string("x_last_29_cast_fp16")]; + tensor var_1374_cast_fp16 = mul(x = x_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1374_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_29_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = var_1374_cast_fp16, y = var_1376_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor input_225_pad_0 = const()[name = string("input_225_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_225_mode_0 = const()[name = string("input_225_mode_0"), val = string("replicate")]; + fp16 const_31_to_fp16 = const()[name = string("const_31_to_fp16"), val = fp16(0x0p+0)]; + tensor input_225_cast_fp16 = pad(constant_val = const_31_to_fp16, mode = input_225_mode_0, pad = input_225_pad_0, x = input_223_cast_fp16)[name = string("input_225_cast_fp16")]; + string h_87_pad_type_0 = const()[name = string("h_87_pad_type_0"), val = string("valid")]; + tensor h_87_dilations_0 = const()[name = string("h_87_dilations_0"), val = tensor([4])]; + int32 h_87_groups_0 = const()[name = string("h_87_groups_0"), val = int32(512)]; + tensor h_87_strides_0 = const()[name = string("h_87_strides_0"), val = tensor([1])]; + tensor h_87_pad_0 = const()[name = string("h_87_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63871680)))]; + tensor main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63876864)))]; + tensor h_87_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_87_dilations_0, groups = h_87_groups_0, pad = h_87_pad_0, pad_type = h_87_pad_type_0, strides = h_87_strides_0, weight = main_blocks_2_convnext_0_2_dwconv__conv_weight_to_fp16, x = input_225_cast_fp16)[name = string("h_87_cast_fp16")]; + tensor x_119_cast_fp16 = mul(x = h_87_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_227_perm_0 = const()[name = string("input_227_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1394_axes_0 = const()[name = string("op_1394_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63877952)))]; + tensor main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63879040)))]; + tensor input_227_cast_fp16 = transpose(perm = input_227_perm_0, x = x_119_cast_fp16)[name = string("transpose_57")]; + tensor var_1394_cast_fp16 = layer_norm(axes = var_1394_axes_0, beta = main_blocks_2_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_2_norm_norm_weight_to_fp16, x = input_227_cast_fp16)[name = string("op_1394_cast_fp16")]; + tensor input_229_perm_0 = const()[name = string("input_229_perm_0"), val = tensor([0, 2, 1])]; + string h_89_pad_type_0 = const()[name = string("h_89_pad_type_0"), val = string("valid")]; + tensor h_89_strides_0 = const()[name = string("h_89_strides_0"), val = tensor([1])]; + tensor h_89_pad_0 = const()[name = string("h_89_pad_0"), val = tensor([0, 0])]; + tensor h_89_dilations_0 = const()[name = string("h_89_dilations_0"), val = tensor([1])]; + int32 h_89_groups_0 = const()[name = string("h_89_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63880128)))]; + tensor main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65977344)))]; + tensor input_229_cast_fp16 = transpose(perm = input_229_perm_0, x = var_1394_cast_fp16)[name = string("transpose_56")]; + tensor h_89_cast_fp16 = conv(bias = main_blocks_2_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_89_dilations_0, groups = h_89_groups_0, pad = h_89_pad_0, pad_type = h_89_pad_type_0, strides = h_89_strides_0, weight = main_blocks_2_convnext_0_2_pwconv1_weight_to_fp16, x = input_229_cast_fp16)[name = string("h_89_cast_fp16")]; + string input_231_mode_0 = const()[name = string("input_231_mode_0"), val = string("EXACT")]; + tensor input_231_cast_fp16 = gelu(mode = input_231_mode_0, x = h_89_cast_fp16)[name = string("input_231_cast_fp16")]; + string h_91_pad_type_0 = const()[name = string("h_91_pad_type_0"), val = string("valid")]; + tensor h_91_strides_0 = const()[name = string("h_91_strides_0"), val = tensor([1])]; + tensor h_91_pad_0 = const()[name = string("h_91_pad_0"), val = tensor([0, 0])]; + tensor h_91_dilations_0 = const()[name = string("h_91_dilations_0"), val = tensor([1])]; + int32 h_91_groups_0 = const()[name = string("h_91_groups_0"), val = int32(1)]; + tensor var_1411_weight_0_to_fp16 = const()[name = string("op_1411_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65981504)))]; + tensor var_1411_bias_0_to_fp16 = const()[name = string("op_1411_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68078720)))]; + tensor var_1411_cast_fp16 = conv(bias = var_1411_bias_0_to_fp16, dilations = h_91_dilations_0, groups = h_91_groups_0, pad = h_91_pad_0, pad_type = h_91_pad_type_0, strides = h_91_strides_0, weight = var_1411_weight_0_to_fp16, x = input_231_cast_fp16)[name = string("op_1411_cast_fp16")]; + tensor out_37_cast_fp16 = add(x = x_117_cast_fp16, y = var_1411_cast_fp16)[name = string("out_37_cast_fp16")]; + tensor x_121_cast_fp16 = mul(x = out_37_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor x_123_cast_fp16 = mul(x = x_121_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_123_cast_fp16")]; + tensor var_1425_cast_fp16 = mul(x = x_123_cast_fp16, y = last_1_cast_fp16)[name = string("op_1425_cast_fp16")]; + tensor x_last_31_axes_0 = const()[name = string("x_last_31_axes_0"), val = tensor([-1])]; + bool x_last_31_keep_dims_0 = const()[name = string("x_last_31_keep_dims_0"), val = bool(true)]; + tensor x_last_31_cast_fp16 = reduce_sum(axes = x_last_31_axes_0, keep_dims = x_last_31_keep_dims_0, x = var_1425_cast_fp16)[name = string("x_last_31_cast_fp16")]; + tensor var_1428_cast_fp16 = mul(x = x_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1428_cast_fp16")]; + tensor var_1430_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_31_cast_fp16)[name = string("op_1430_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = var_1428_cast_fp16, y = var_1430_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor input_237_pad_0 = const()[name = string("input_237_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_237_mode_0 = const()[name = string("input_237_mode_0"), val = string("replicate")]; + fp16 const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = fp16(0x0p+0)]; + tensor input_237_cast_fp16 = pad(constant_val = const_33_to_fp16, mode = input_237_mode_0, pad = input_237_pad_0, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")]; + string h_93_pad_type_0 = const()[name = string("h_93_pad_type_0"), val = string("valid")]; + tensor h_93_dilations_0 = const()[name = string("h_93_dilations_0"), val = tensor([8])]; + int32 h_93_groups_0 = const()[name = string("h_93_groups_0"), val = int32(512)]; + tensor h_93_strides_0 = const()[name = string("h_93_strides_0"), val = tensor([1])]; + tensor h_93_pad_0 = const()[name = string("h_93_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68079808)))]; + tensor main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68084992)))]; + tensor h_93_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_93_dilations_0, groups = h_93_groups_0, pad = h_93_pad_0, pad_type = h_93_pad_type_0, strides = h_93_strides_0, weight = main_blocks_2_convnext_0_3_dwconv__conv_weight_to_fp16, x = input_237_cast_fp16)[name = string("h_93_cast_fp16")]; + tensor x_125_cast_fp16 = mul(x = h_93_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_125_cast_fp16")]; + tensor input_239_perm_0 = const()[name = string("input_239_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1448_axes_0 = const()[name = string("op_1448_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68086080)))]; + tensor main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68087168)))]; + tensor input_239_cast_fp16 = transpose(perm = input_239_perm_0, x = x_125_cast_fp16)[name = string("transpose_55")]; + tensor var_1448_cast_fp16 = layer_norm(axes = var_1448_axes_0, beta = main_blocks_2_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_0_3_norm_norm_weight_to_fp16, x = input_239_cast_fp16)[name = string("op_1448_cast_fp16")]; + tensor input_241_perm_0 = const()[name = string("input_241_perm_0"), val = tensor([0, 2, 1])]; + string h_95_pad_type_0 = const()[name = string("h_95_pad_type_0"), val = string("valid")]; + tensor h_95_strides_0 = const()[name = string("h_95_strides_0"), val = tensor([1])]; + tensor h_95_pad_0 = const()[name = string("h_95_pad_0"), val = tensor([0, 0])]; + tensor h_95_dilations_0 = const()[name = string("h_95_dilations_0"), val = tensor([1])]; + int32 h_95_groups_0 = const()[name = string("h_95_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68088256)))]; + tensor main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70185472)))]; + tensor input_241_cast_fp16 = transpose(perm = input_241_perm_0, x = var_1448_cast_fp16)[name = string("transpose_54")]; + tensor h_95_cast_fp16 = conv(bias = main_blocks_2_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_95_dilations_0, groups = h_95_groups_0, pad = h_95_pad_0, pad_type = h_95_pad_type_0, strides = h_95_strides_0, weight = main_blocks_2_convnext_0_3_pwconv1_weight_to_fp16, x = input_241_cast_fp16)[name = string("h_95_cast_fp16")]; + string input_243_mode_0 = const()[name = string("input_243_mode_0"), val = string("EXACT")]; + tensor input_243_cast_fp16 = gelu(mode = input_243_mode_0, x = h_95_cast_fp16)[name = string("input_243_cast_fp16")]; + string h_97_pad_type_0 = const()[name = string("h_97_pad_type_0"), val = string("valid")]; + tensor h_97_strides_0 = const()[name = string("h_97_strides_0"), val = tensor([1])]; + tensor h_97_pad_0 = const()[name = string("h_97_pad_0"), val = tensor([0, 0])]; + tensor h_97_dilations_0 = const()[name = string("h_97_dilations_0"), val = tensor([1])]; + int32 h_97_groups_0 = const()[name = string("h_97_groups_0"), val = int32(1)]; + tensor var_1465_weight_0_to_fp16 = const()[name = string("op_1465_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70189632)))]; + tensor var_1465_bias_0_to_fp16 = const()[name = string("op_1465_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72286848)))]; + tensor var_1465_cast_fp16 = conv(bias = var_1465_bias_0_to_fp16, dilations = h_97_dilations_0, groups = h_97_groups_0, pad = h_97_pad_0, pad_type = h_97_pad_type_0, strides = h_97_strides_0, weight = var_1465_weight_0_to_fp16, x = input_243_cast_fp16)[name = string("op_1465_cast_fp16")]; + tensor out_39_cast_fp16 = add(x = x_123_cast_fp16, y = var_1465_cast_fp16)[name = string("out_39_cast_fp16")]; + tensor x_127_cast_fp16 = mul(x = out_39_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_127_cast_fp16")]; + tensor main_blocks_2_time_cond_linear_linear_weight_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72287936)))]; + tensor main_blocks_2_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_2_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72353536)))]; + tensor linear_20_cast_fp16 = linear(bias = main_blocks_2_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_2_time_cond_linear_linear_weight_to_fp16, x = input_55_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor t_9_axes_0 = const()[name = string("t_9_axes_0"), val = tensor([-1])]; + tensor t_9_cast_fp16 = expand_dims(axes = t_9_axes_0, x = linear_20_cast_fp16)[name = string("t_9_cast_fp16")]; + tensor var_1475_cast_fp16 = add(x = x_127_cast_fp16, y = t_9_cast_fp16)[name = string("op_1475_cast_fp16")]; + tensor x_129_cast_fp16 = mul(x = var_1475_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor x_131_cast_fp16 = mul(x = x_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor var_1488_cast_fp16 = mul(x = x_131_cast_fp16, y = last_1_cast_fp16)[name = string("op_1488_cast_fp16")]; + tensor x_last_33_axes_0 = const()[name = string("x_last_33_axes_0"), val = tensor([-1])]; + bool x_last_33_keep_dims_0 = const()[name = string("x_last_33_keep_dims_0"), val = bool(true)]; + tensor x_last_33_cast_fp16 = reduce_sum(axes = x_last_33_axes_0, keep_dims = x_last_33_keep_dims_0, x = var_1488_cast_fp16)[name = string("x_last_33_cast_fp16")]; + tensor var_1491_cast_fp16 = mul(x = x_131_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1491_cast_fp16")]; + tensor var_1493_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_33_cast_fp16)[name = string("op_1493_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = var_1491_cast_fp16, y = var_1493_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor input_251_pad_0 = const()[name = string("input_251_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_251_mode_0 = const()[name = string("input_251_mode_0"), val = string("replicate")]; + fp16 const_35_to_fp16 = const()[name = string("const_35_to_fp16"), val = fp16(0x0p+0)]; + tensor input_251_cast_fp16 = pad(constant_val = const_35_to_fp16, mode = input_251_mode_0, pad = input_251_pad_0, x = input_249_cast_fp16)[name = string("input_251_cast_fp16")]; + string h_99_pad_type_0 = const()[name = string("h_99_pad_type_0"), val = string("valid")]; + int32 h_99_groups_0 = const()[name = string("h_99_groups_0"), val = int32(512)]; + tensor h_99_strides_0 = const()[name = string("h_99_strides_0"), val = tensor([1])]; + tensor h_99_pad_0 = const()[name = string("h_99_pad_0"), val = tensor([0, 0])]; + tensor h_99_dilations_0 = const()[name = string("h_99_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72354624)))]; + tensor main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72359808)))]; + tensor h_99_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_99_dilations_0, groups = h_99_groups_0, pad = h_99_pad_0, pad_type = h_99_pad_type_0, strides = h_99_strides_0, weight = main_blocks_2_convnext_1_0_dwconv__conv_weight_to_fp16, x = input_251_cast_fp16)[name = string("h_99_cast_fp16")]; + tensor x_133_cast_fp16 = mul(x = h_99_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_133_cast_fp16")]; + tensor input_253_perm_0 = const()[name = string("input_253_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1511_axes_0 = const()[name = string("op_1511_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72360896)))]; + tensor main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72361984)))]; + tensor input_253_cast_fp16 = transpose(perm = input_253_perm_0, x = x_133_cast_fp16)[name = string("transpose_53")]; + tensor var_1511_cast_fp16 = layer_norm(axes = var_1511_axes_0, beta = main_blocks_2_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_1_0_norm_norm_weight_to_fp16, x = input_253_cast_fp16)[name = string("op_1511_cast_fp16")]; + tensor input_255_perm_0 = const()[name = string("input_255_perm_0"), val = tensor([0, 2, 1])]; + string h_101_pad_type_0 = const()[name = string("h_101_pad_type_0"), val = string("valid")]; + tensor h_101_strides_0 = const()[name = string("h_101_strides_0"), val = tensor([1])]; + tensor h_101_pad_0 = const()[name = string("h_101_pad_0"), val = tensor([0, 0])]; + tensor h_101_dilations_0 = const()[name = string("h_101_dilations_0"), val = tensor([1])]; + int32 h_101_groups_0 = const()[name = string("h_101_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72363072)))]; + tensor main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74460288)))]; + tensor input_255_cast_fp16 = transpose(perm = input_255_perm_0, x = var_1511_cast_fp16)[name = string("transpose_52")]; + tensor h_101_cast_fp16 = conv(bias = main_blocks_2_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_101_dilations_0, groups = h_101_groups_0, pad = h_101_pad_0, pad_type = h_101_pad_type_0, strides = h_101_strides_0, weight = main_blocks_2_convnext_1_0_pwconv1_weight_to_fp16, x = input_255_cast_fp16)[name = string("h_101_cast_fp16")]; + string input_257_mode_0 = const()[name = string("input_257_mode_0"), val = string("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = h_101_cast_fp16)[name = string("input_257_cast_fp16")]; + string h_103_pad_type_0 = const()[name = string("h_103_pad_type_0"), val = string("valid")]; + tensor h_103_strides_0 = const()[name = string("h_103_strides_0"), val = tensor([1])]; + tensor h_103_pad_0 = const()[name = string("h_103_pad_0"), val = tensor([0, 0])]; + tensor h_103_dilations_0 = const()[name = string("h_103_dilations_0"), val = tensor([1])]; + int32 h_103_groups_0 = const()[name = string("h_103_groups_0"), val = int32(1)]; + tensor var_1528_weight_0_to_fp16 = const()[name = string("op_1528_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74464448)))]; + tensor var_1528_bias_0_to_fp16 = const()[name = string("op_1528_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76561664)))]; + tensor var_1528_cast_fp16 = conv(bias = var_1528_bias_0_to_fp16, dilations = h_103_dilations_0, groups = h_103_groups_0, pad = h_103_pad_0, pad_type = h_103_pad_type_0, strides = h_103_strides_0, weight = var_1528_weight_0_to_fp16, x = input_257_cast_fp16)[name = string("op_1528_cast_fp16")]; + tensor out_41_cast_fp16 = add(x = x_131_cast_fp16, y = var_1528_cast_fp16)[name = string("out_41_cast_fp16")]; + tensor x_135_cast_fp16 = mul(x = out_41_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_135_cast_fp16")]; + tensor x_m_9_cast_fp16 = mul(x = x_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_9_cast_fp16")]; + tensor var_1538_shape_cast_fp16 = shape(x = x_135_cast_fp16)[name = string("op_1538_shape_cast_fp16")]; + int32 gather_14_axis_0 = const()[name = string("gather_14_axis_0"), val = int32(0)]; + int32 gather_14_batch_dims_0 = const()[name = string("gather_14_batch_dims_0"), val = int32(0)]; + bool gather_14_validate_indices_0 = const()[name = string("gather_14_validate_indices_0"), val = bool(false)]; + string var_1538_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_1538_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_14_indices_0_to_uint16 = const()[name = string("gather_14_indices_0_to_uint16"), val = uint16(2)]; + tensor var_1538_shape_cast_fp16_to_uint16 = cast(dtype = var_1538_shape_cast_fp16_to_uint16_dtype_0, x = var_1538_shape_cast_fp16)[name = string("cast_89")]; + uint16 gather_14_cast_uint16 = gather(axis = gather_14_axis_0, batch_dims = gather_14_batch_dims_0, indices = gather_14_indices_0_to_uint16, validate_indices = gather_14_validate_indices_0, x = var_1538_shape_cast_fp16_to_uint16)[name = string("gather_14_cast_uint16")]; + string gather_14_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_14_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_259_perm_0 = const()[name = string("input_259_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_text_attn_W_query_linear_weight_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76562752)))]; + tensor main_blocks_2_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77087104)))]; + tensor input_259_cast_fp16 = transpose(perm = input_259_perm_0, x = x_135_cast_fp16)[name = string("transpose_51")]; + tensor linear_21_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_query_linear_weight_to_fp16, x = input_259_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor concat_24x = const()[name = string("concat_24x"), val = tensor([2, -1, 8, 64])]; + tensor var_1547_cast_fp16 = reshape(shape = concat_24x, x = linear_21_cast_fp16)[name = string("op_1547_cast_fp16")]; + tensor x_137_perm_0 = const()[name = string("x_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_key_linear_weight_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77088192)))]; + tensor main_blocks_2_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77350400)))]; + tensor linear_22_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_key_linear_weight_to_fp16, x = input_71_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor concat_25x = const()[name = string("concat_25x"), val = tensor([2, -1, 8, 64])]; + tensor var_1555_cast_fp16 = reshape(shape = concat_25x, x = linear_22_cast_fp16)[name = string("op_1555_cast_fp16")]; + tensor x_139_perm_0 = const()[name = string("x_139_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_text_attn_W_value_linear_weight_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77351488)))]; + tensor main_blocks_2_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77613696)))]; + tensor linear_23_cast_fp16 = linear(bias = main_blocks_2_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_text_attn_W_value_linear_weight_to_fp16, x = input_71_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor concat_26x = const()[name = string("concat_26x"), val = tensor([2, -1, 8, 64])]; + tensor var_1563_cast_fp16 = reshape(shape = concat_26x, x = linear_23_cast_fp16)[name = string("op_1563_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([0, 2, 1, 3])]; + int32 concat_27_values0_0 = const()[name = string("concat_27_values0_0"), val = int32(1)]; + int32 concat_27_values2_0 = const()[name = string("concat_27_values2_0"), val = int32(1)]; + int32 concat_27_axis_0 = const()[name = string("concat_27_axis_0"), val = int32(0)]; + bool concat_27_interleave_0 = const()[name = string("concat_27_interleave_0"), val = bool(false)]; + int32 gather_14_cast_uint16_to_int32 = cast(dtype = gather_14_cast_uint16_to_int32_dtype_0, x = gather_14_cast_uint16)[name = string("cast_88")]; + tensor concat_27 = concat(axis = concat_27_axis_0, interleave = concat_27_interleave_0, values = (concat_27_values0_0, gather_14_cast_uint16_to_int32, concat_27_values2_0))[name = string("concat_27")]; + tensor var_1566_begin_0 = const()[name = string("op_1566_begin_0"), val = tensor([0, 0, 0])]; + tensor var_1566_end_mask_0 = const()[name = string("op_1566_end_mask_0"), val = tensor([true, false, true])]; + tensor var_1566_cast_fp16 = slice_by_index(begin = var_1566_begin_0, end = concat_27, end_mask = var_1566_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_1566_cast_fp16")]; + tensor concat_28 = const()[name = string("concat_28"), val = tensor([2, -1, -1])]; + tensor shape_5_cast_fp16 = shape(x = var_1566_cast_fp16)[name = string("shape_5_cast_fp16")]; + tensor equal_5 = const()[name = string("equal_5"), val = tensor([false, true, true])]; + tensor select_5 = select(a = shape_5_cast_fp16, b = concat_28, cond = equal_5)[name = string("select_5")]; + tensor real_div_5 = real_div(x = select_5, y = shape_5_cast_fp16)[name = string("real_div_5")]; + tensor var_1569_cast_fp16 = tile(reps = real_div_5, x = var_1566_cast_fp16)[name = string("op_1569_cast_fp16")]; + tensor scaled_9_cast_fp16 = real_div(x = var_1569_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_9_cast_fp16")]; + tensor main_blocks_2_text_attn_theta_to_fp16 = const()[name = string("main_blocks_2_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77614784)))]; + tensor angles_9_cast_fp16 = mul(x = scaled_9_cast_fp16, y = main_blocks_2_text_attn_theta_to_fp16)[name = string("angles_9_cast_fp16")]; + tensor var_1585_cast_fp16 = cos(x = angles_9_cast_fp16)[name = string("op_1585_cast_fp16")]; + tensor cos_9_axes_0 = const()[name = string("cos_9_axes_0"), val = tensor([1])]; + tensor cos_9_cast_fp16 = expand_dims(axes = cos_9_axes_0, x = var_1585_cast_fp16)[name = string("cos_9_cast_fp16")]; + tensor var_1587_cast_fp16 = sin(x = angles_9_cast_fp16)[name = string("op_1587_cast_fp16")]; + tensor sin_9_axes_0 = const()[name = string("sin_9_axes_0"), val = tensor([1])]; + tensor sin_9_cast_fp16 = expand_dims(axes = sin_9_axes_0, x = var_1587_cast_fp16)[name = string("sin_9_cast_fp16")]; + tensor x_a_9_begin_0 = const()[name = string("x_a_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_9_end_0 = const()[name = string("x_a_9_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_9_end_mask_0 = const()[name = string("x_a_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_137_cast_fp16 = transpose(perm = x_137_perm_0, x = var_1547_cast_fp16)[name = string("transpose_50")]; + tensor x_a_9_cast_fp16 = slice_by_index(begin = x_a_9_begin_0, end = x_a_9_end_0, end_mask = x_a_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_a_9_cast_fp16")]; + tensor x_b_9_begin_0 = const()[name = string("x_b_9_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_9_end_0 = const()[name = string("x_b_9_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_9_end_mask_0 = const()[name = string("x_b_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_9_cast_fp16 = slice_by_index(begin = x_b_9_begin_0, end = x_b_9_end_0, end_mask = x_b_9_end_mask_0, x = x_137_cast_fp16)[name = string("x_b_9_cast_fp16")]; + tensor var_1591_cast_fp16 = mul(x = x_a_9_cast_fp16, y = cos_9_cast_fp16)[name = string("op_1591_cast_fp16")]; + tensor var_1592_cast_fp16 = mul(x = x_b_9_cast_fp16, y = sin_9_cast_fp16)[name = string("op_1592_cast_fp16")]; + tensor rot_a_9_cast_fp16 = sub(x = var_1591_cast_fp16, y = var_1592_cast_fp16)[name = string("rot_a_9_cast_fp16")]; + tensor var_1594_cast_fp16 = mul(x = x_a_9_cast_fp16, y = sin_9_cast_fp16)[name = string("op_1594_cast_fp16")]; + tensor var_1595_cast_fp16 = mul(x = x_b_9_cast_fp16, y = cos_9_cast_fp16)[name = string("op_1595_cast_fp16")]; + tensor rot_b_9_cast_fp16 = add(x = var_1594_cast_fp16, y = var_1595_cast_fp16)[name = string("rot_b_9_cast_fp16")]; + bool q_9_interleave_0 = const()[name = string("q_9_interleave_0"), val = bool(false)]; + tensor q_9_cast_fp16 = concat(axis = var_1228, interleave = q_9_interleave_0, values = (rot_a_9_cast_fp16, rot_b_9_cast_fp16))[name = string("q_9_cast_fp16")]; + tensor x_a_11_begin_0 = const()[name = string("x_a_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_11_end_0 = const()[name = string("x_a_11_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_11_end_mask_0 = const()[name = string("x_a_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_139_cast_fp16 = transpose(perm = x_139_perm_0, x = var_1555_cast_fp16)[name = string("transpose_49")]; + tensor x_a_11_cast_fp16 = slice_by_index(begin = x_a_11_begin_0, end = x_a_11_end_0, end_mask = x_a_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_a_11_cast_fp16")]; + tensor x_b_11_begin_0 = const()[name = string("x_b_11_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_11_end_0 = const()[name = string("x_b_11_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_11_end_mask_0 = const()[name = string("x_b_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_11_cast_fp16 = slice_by_index(begin = x_b_11_begin_0, end = x_b_11_end_0, end_mask = x_b_11_end_mask_0, x = x_139_cast_fp16)[name = string("x_b_11_cast_fp16")]; + tensor var_1609_cast_fp16 = mul(x = x_a_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1609_cast_fp16")]; + tensor var_1610_cast_fp16 = mul(x = x_b_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1610_cast_fp16")]; + tensor rot_a_11_cast_fp16 = sub(x = var_1609_cast_fp16, y = var_1610_cast_fp16)[name = string("rot_a_11_cast_fp16")]; + tensor var_1612_cast_fp16 = mul(x = x_a_11_cast_fp16, y = sin_3_cast_fp16)[name = string("op_1612_cast_fp16")]; + tensor var_1613_cast_fp16 = mul(x = x_b_11_cast_fp16, y = cos_3_cast_fp16)[name = string("op_1613_cast_fp16")]; + tensor rot_b_11_cast_fp16 = add(x = var_1612_cast_fp16, y = var_1613_cast_fp16)[name = string("rot_b_11_cast_fp16")]; + bool k_9_interleave_0 = const()[name = string("k_9_interleave_0"), val = bool(false)]; + tensor k_9_cast_fp16 = concat(axis = var_1228, interleave = k_9_interleave_0, values = (rot_a_11_cast_fp16, rot_b_11_cast_fp16))[name = string("k_9_cast_fp16")]; + bool var_1618_transpose_x_1 = const()[name = string("op_1618_transpose_x_1"), val = bool(false)]; + bool var_1618_transpose_y_1 = const()[name = string("op_1618_transpose_y_1"), val = bool(true)]; + tensor var_1618_cast_fp16 = matmul(transpose_x = var_1618_transpose_x_1, transpose_y = var_1618_transpose_y_1, x = q_9_cast_fp16, y = k_9_cast_fp16)[name = string("op_1618_cast_fp16")]; + fp16 _inversed_scores_5_y_0_to_fp16 = const()[name = string("_inversed_scores_5_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_5_cast_fp16 = mul(x = var_1618_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")]; + tensor input_265_cast_fp16 = sub(x = _inversed_scores_5_cast_fp16, y = var_530_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor attn_13_cast_fp16 = softmax(axis = var_1228, x = input_265_cast_fp16)[name = string("attn_13_cast_fp16")]; + bool var_1627_transpose_x_0 = const()[name = string("op_1627_transpose_x_0"), val = bool(false)]; + bool var_1627_transpose_y_0 = const()[name = string("op_1627_transpose_y_0"), val = bool(false)]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1563_cast_fp16)[name = string("transpose_48")]; + tensor var_1627_cast_fp16 = matmul(transpose_x = var_1627_transpose_x_0, transpose_y = var_1627_transpose_y_0, x = attn_13_cast_fp16, y = v_9_cast_fp16)[name = string("op_1627_cast_fp16")]; + tensor var_1628_perm_0 = const()[name = string("op_1628_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_31x = const()[name = string("concat_31x"), val = tensor([2, -1, 512])]; + tensor var_1628_cast_fp16 = transpose(perm = var_1628_perm_0, x = var_1627_cast_fp16)[name = string("transpose_47")]; + tensor input_267_cast_fp16 = reshape(shape = concat_31x, x = var_1628_cast_fp16)[name = string("input_267_cast_fp16")]; + tensor main_blocks_2_text_attn_out_fc_linear_weight_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77614912)))]; + tensor main_blocks_2_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78139264)))]; + tensor linear_24_cast_fp16 = linear(bias = main_blocks_2_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_text_attn_out_fc_linear_weight_to_fp16, x = input_267_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor out_43_perm_0 = const()[name = string("out_43_perm_0"), val = tensor([0, 2, 1])]; + tensor out_43_cast_fp16 = transpose(perm = out_43_perm_0, x = linear_24_cast_fp16)[name = string("transpose_46")]; + tensor var_1636_cast_fp16 = mul(x = out_43_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1636_cast_fp16")]; + tensor x_141_cast_fp16 = add(x = x_m_9_cast_fp16, y = var_1636_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_269_perm_0 = const()[name = string("input_269_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1643_axes_0 = const()[name = string("op_1643_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78140352)))]; + tensor main_blocks_2_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78141440)))]; + tensor input_269_cast_fp16 = transpose(perm = input_269_perm_0, x = x_141_cast_fp16)[name = string("transpose_45")]; + tensor var_1643_cast_fp16 = layer_norm(axes = var_1643_axes_0, beta = main_blocks_2_text_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_text_norm_norm_weight_to_fp16, x = input_269_cast_fp16)[name = string("op_1643_cast_fp16")]; + tensor var_1644_perm_0 = const()[name = string("op_1644_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1644_cast_fp16 = transpose(perm = var_1644_perm_0, x = var_1643_cast_fp16)[name = string("transpose_44")]; + tensor x_143_cast_fp16 = mul(x = var_1644_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_143_cast_fp16")]; + tensor x_145_cast_fp16 = mul(x = x_143_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_145_cast_fp16")]; + tensor var_1657_cast_fp16 = mul(x = x_145_cast_fp16, y = last_1_cast_fp16)[name = string("op_1657_cast_fp16")]; + tensor x_last_35_axes_0 = const()[name = string("x_last_35_axes_0"), val = tensor([-1])]; + bool x_last_35_keep_dims_0 = const()[name = string("x_last_35_keep_dims_0"), val = bool(true)]; + tensor x_last_35_cast_fp16 = reduce_sum(axes = x_last_35_axes_0, keep_dims = x_last_35_keep_dims_0, x = var_1657_cast_fp16)[name = string("x_last_35_cast_fp16")]; + tensor var_1660_cast_fp16 = mul(x = x_145_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1660_cast_fp16")]; + tensor var_1662_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_35_cast_fp16)[name = string("op_1662_cast_fp16")]; + tensor input_273_cast_fp16 = add(x = var_1660_cast_fp16, y = var_1662_cast_fp16)[name = string("input_273_cast_fp16")]; + tensor input_275_pad_0 = const()[name = string("input_275_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("replicate")]; + fp16 const_37_to_fp16 = const()[name = string("const_37_to_fp16"), val = fp16(0x0p+0)]; + tensor input_275_cast_fp16 = pad(constant_val = const_37_to_fp16, mode = input_275_mode_0, pad = input_275_pad_0, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")]; + string h_105_pad_type_0 = const()[name = string("h_105_pad_type_0"), val = string("valid")]; + int32 h_105_groups_0 = const()[name = string("h_105_groups_0"), val = int32(512)]; + tensor h_105_strides_0 = const()[name = string("h_105_strides_0"), val = tensor([1])]; + tensor h_105_pad_0 = const()[name = string("h_105_pad_0"), val = tensor([0, 0])]; + tensor h_105_dilations_0 = const()[name = string("h_105_dilations_0"), val = tensor([1])]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78142528)))]; + tensor main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78147712)))]; + tensor h_105_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_105_dilations_0, groups = h_105_groups_0, pad = h_105_pad_0, pad_type = h_105_pad_type_0, strides = h_105_strides_0, weight = main_blocks_2_convnext_2_0_dwconv__conv_weight_to_fp16, x = input_275_cast_fp16)[name = string("h_105_cast_fp16")]; + tensor x_147_cast_fp16 = mul(x = h_105_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_147_cast_fp16")]; + tensor input_277_perm_0 = const()[name = string("input_277_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1680_axes_0 = const()[name = string("op_1680_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78148800)))]; + tensor main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78149888)))]; + tensor input_277_cast_fp16 = transpose(perm = input_277_perm_0, x = x_147_cast_fp16)[name = string("transpose_43")]; + tensor var_1680_cast_fp16 = layer_norm(axes = var_1680_axes_0, beta = main_blocks_2_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_convnext_2_0_norm_norm_weight_to_fp16, x = input_277_cast_fp16)[name = string("op_1680_cast_fp16")]; + tensor input_279_perm_0 = const()[name = string("input_279_perm_0"), val = tensor([0, 2, 1])]; + string h_107_pad_type_0 = const()[name = string("h_107_pad_type_0"), val = string("valid")]; + tensor h_107_strides_0 = const()[name = string("h_107_strides_0"), val = tensor([1])]; + tensor h_107_pad_0 = const()[name = string("h_107_pad_0"), val = tensor([0, 0])]; + tensor h_107_dilations_0 = const()[name = string("h_107_dilations_0"), val = tensor([1])]; + int32 h_107_groups_0 = const()[name = string("h_107_groups_0"), val = int32(1)]; + tensor main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78150976)))]; + tensor main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80248192)))]; + tensor input_279_cast_fp16 = transpose(perm = input_279_perm_0, x = var_1680_cast_fp16)[name = string("transpose_42")]; + tensor h_107_cast_fp16 = conv(bias = main_blocks_2_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_107_dilations_0, groups = h_107_groups_0, pad = h_107_pad_0, pad_type = h_107_pad_type_0, strides = h_107_strides_0, weight = main_blocks_2_convnext_2_0_pwconv1_weight_to_fp16, x = input_279_cast_fp16)[name = string("h_107_cast_fp16")]; + string input_281_mode_0 = const()[name = string("input_281_mode_0"), val = string("EXACT")]; + tensor input_281_cast_fp16 = gelu(mode = input_281_mode_0, x = h_107_cast_fp16)[name = string("input_281_cast_fp16")]; + string h_109_pad_type_0 = const()[name = string("h_109_pad_type_0"), val = string("valid")]; + tensor h_109_strides_0 = const()[name = string("h_109_strides_0"), val = tensor([1])]; + tensor h_109_pad_0 = const()[name = string("h_109_pad_0"), val = tensor([0, 0])]; + tensor h_109_dilations_0 = const()[name = string("h_109_dilations_0"), val = tensor([1])]; + int32 h_109_groups_0 = const()[name = string("h_109_groups_0"), val = int32(1)]; + tensor var_1697_weight_0_to_fp16 = const()[name = string("op_1697_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80252352)))]; + tensor var_1697_bias_0_to_fp16 = const()[name = string("op_1697_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82349568)))]; + tensor var_1697_cast_fp16 = conv(bias = var_1697_bias_0_to_fp16, dilations = h_109_dilations_0, groups = h_109_groups_0, pad = h_109_pad_0, pad_type = h_109_pad_type_0, strides = h_109_strides_0, weight = var_1697_weight_0_to_fp16, x = input_281_cast_fp16)[name = string("op_1697_cast_fp16")]; + tensor out_45_cast_fp16 = add(x = x_145_cast_fp16, y = var_1697_cast_fp16)[name = string("out_45_cast_fp16")]; + tensor x_149_cast_fp16 = mul(x = out_45_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor x_m_11_cast_fp16 = mul(x = x_149_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_11_cast_fp16")]; + tensor input_283_perm_0 = const()[name = string("input_283_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_2_style_attn_W_query_linear_weight_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82350656)))]; + tensor main_blocks_2_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82612864)))]; + tensor input_283_cast_fp16 = transpose(perm = input_283_perm_0, x = x_149_cast_fp16)[name = string("transpose_41")]; + tensor linear_25_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_query_linear_weight_to_fp16, x = input_283_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor concat_32x = const()[name = string("concat_32x"), val = tensor([2, -1, 2, 128])]; + tensor var_1714_cast_fp16 = reshape(shape = concat_32x, x = linear_25_cast_fp16)[name = string("op_1714_cast_fp16")]; + tensor q_11_perm_0 = const()[name = string("q_11_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_2_style_attn_W_value_linear_weight_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82613440)))]; + tensor main_blocks_2_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82744576)))]; + tensor linear_27_cast_fp16 = linear(bias = main_blocks_2_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_2_style_attn_W_value_linear_weight_to_fp16, x = input_95_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_1727 = const()[name = string("op_1727"), val = tensor([2, 50, 2, 128])]; + tensor var_1728_cast_fp16 = reshape(shape = var_1727, x = linear_27_cast_fp16)[name = string("op_1728_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, 1, 3])]; + bool var_1732_transpose_x_0 = const()[name = string("op_1732_transpose_x_0"), val = bool(false)]; + bool var_1732_transpose_y_0 = const()[name = string("op_1732_transpose_y_0"), val = bool(false)]; + tensor var_1731_to_fp16 = const()[name = string("op_1731_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82745152)))]; + tensor q_11_cast_fp16 = transpose(perm = q_11_perm_0, x = var_1714_cast_fp16)[name = string("transpose_40")]; + tensor var_1732_cast_fp16 = matmul(transpose_x = var_1732_transpose_x_0, transpose_y = var_1732_transpose_y_0, x = q_11_cast_fp16, y = var_1731_to_fp16)[name = string("op_1732_cast_fp16")]; + fp16 _inversed_input_285_y_0_to_fp16 = const()[name = string("_inversed_input_285_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_285_cast_fp16 = mul(x = var_1732_cast_fp16, y = _inversed_input_285_y_0_to_fp16)[name = string("_inversed_input_285_cast_fp16")]; + tensor attn_15_cast_fp16 = softmax(axis = var_1228, x = _inversed_input_285_cast_fp16)[name = string("attn_15_cast_fp16")]; + tensor attn_17_cast_fp16 = mul(x = attn_15_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_17_cast_fp16")]; + bool var_1739_transpose_x_0 = const()[name = string("op_1739_transpose_x_0"), val = bool(false)]; + bool var_1739_transpose_y_0 = const()[name = string("op_1739_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_1728_cast_fp16)[name = string("transpose_39")]; + tensor var_1739_cast_fp16 = matmul(transpose_x = var_1739_transpose_x_0, transpose_y = var_1739_transpose_y_0, x = attn_17_cast_fp16, y = v_11_cast_fp16)[name = string("op_1739_cast_fp16")]; + tensor var_1740_perm_0 = const()[name = string("op_1740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_33x = const()[name = string("concat_33x"), val = tensor([2, -1, 256])]; + tensor var_1740_cast_fp16 = transpose(perm = var_1740_perm_0, x = var_1739_cast_fp16)[name = string("transpose_38")]; + tensor input_287_cast_fp16 = reshape(shape = concat_33x, x = var_1740_cast_fp16)[name = string("input_287_cast_fp16")]; + tensor main_blocks_2_style_attn_out_fc_linear_weight_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82796416)))]; + tensor main_blocks_2_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_2_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83058624)))]; + tensor linear_28_cast_fp16 = linear(bias = main_blocks_2_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_2_style_attn_out_fc_linear_weight_to_fp16, x = input_287_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor out_47_perm_0 = const()[name = string("out_47_perm_0"), val = tensor([0, 2, 1])]; + tensor out_47_cast_fp16 = transpose(perm = out_47_perm_0, x = linear_28_cast_fp16)[name = string("transpose_37")]; + tensor var_1748_cast_fp16 = mul(x = out_47_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1748_cast_fp16")]; + tensor x_151_cast_fp16 = add(x = x_m_11_cast_fp16, y = var_1748_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_289_perm_0 = const()[name = string("input_289_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1755_axes_0 = const()[name = string("op_1755_axes_0"), val = tensor([-1])]; + tensor main_blocks_2_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83059712)))]; + tensor main_blocks_2_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_2_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83060800)))]; + tensor input_289_cast_fp16 = transpose(perm = input_289_perm_0, x = x_151_cast_fp16)[name = string("transpose_36")]; + tensor var_1755_cast_fp16 = layer_norm(axes = var_1755_axes_0, beta = main_blocks_2_style_norm_norm_bias_to_fp16, epsilon = var_1233_to_fp16, gamma = main_blocks_2_style_norm_norm_weight_to_fp16, x = input_289_cast_fp16)[name = string("op_1755_cast_fp16")]; + tensor var_1756_perm_0 = const()[name = string("op_1756_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1756_cast_fp16 = transpose(perm = var_1756_perm_0, x = var_1755_cast_fp16)[name = string("transpose_35")]; + tensor x_153_cast_fp16 = mul(x = var_1756_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_153_cast_fp16")]; + int32 var_1775 = const()[name = string("op_1775"), val = int32(-1)]; + tensor x_155_cast_fp16 = mul(x = x_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_155_cast_fp16")]; + tensor var_1810_cast_fp16 = mul(x = x_155_cast_fp16, y = last_1_cast_fp16)[name = string("op_1810_cast_fp16")]; + tensor x_last_37_axes_0 = const()[name = string("x_last_37_axes_0"), val = tensor([-1])]; + bool x_last_37_keep_dims_0 = const()[name = string("x_last_37_keep_dims_0"), val = bool(true)]; + tensor x_last_37_cast_fp16 = reduce_sum(axes = x_last_37_axes_0, keep_dims = x_last_37_keep_dims_0, x = var_1810_cast_fp16)[name = string("x_last_37_cast_fp16")]; + tensor var_1813_cast_fp16 = mul(x = x_155_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1813_cast_fp16")]; + tensor var_1815_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_37_cast_fp16)[name = string("op_1815_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = var_1813_cast_fp16, y = var_1815_cast_fp16)[name = string("input_293_cast_fp16")]; + tensor input_295_pad_0 = const()[name = string("input_295_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_295_mode_0 = const()[name = string("input_295_mode_0"), val = string("replicate")]; + fp16 const_40_to_fp16 = const()[name = string("const_40_to_fp16"), val = fp16(0x0p+0)]; + tensor input_295_cast_fp16 = pad(constant_val = const_40_to_fp16, mode = input_295_mode_0, pad = input_295_pad_0, x = input_293_cast_fp16)[name = string("input_295_cast_fp16")]; + string h_111_pad_type_0 = const()[name = string("h_111_pad_type_0"), val = string("valid")]; + int32 h_111_groups_0 = const()[name = string("h_111_groups_0"), val = int32(512)]; + tensor h_111_strides_0 = const()[name = string("h_111_strides_0"), val = tensor([1])]; + tensor h_111_pad_0 = const()[name = string("h_111_pad_0"), val = tensor([0, 0])]; + tensor h_111_dilations_0 = const()[name = string("h_111_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83061888)))]; + tensor main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83067072)))]; + tensor h_111_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_dwconv__conv_bias_to_fp16, dilations = h_111_dilations_0, groups = h_111_groups_0, pad = h_111_pad_0, pad_type = h_111_pad_type_0, strides = h_111_strides_0, weight = main_blocks_3_convnext_0_0_dwconv__conv_weight_to_fp16, x = input_295_cast_fp16)[name = string("h_111_cast_fp16")]; + tensor x_157_cast_fp16 = mul(x = h_111_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_157_cast_fp16")]; + tensor input_297_perm_0 = const()[name = string("input_297_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1833_axes_0 = const()[name = string("op_1833_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83068160)))]; + tensor main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83069248)))]; + fp16 var_1780_to_fp16 = const()[name = string("op_1780_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_297_cast_fp16 = transpose(perm = input_297_perm_0, x = x_157_cast_fp16)[name = string("transpose_34")]; + tensor var_1833_cast_fp16 = layer_norm(axes = var_1833_axes_0, beta = main_blocks_3_convnext_0_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_0_norm_norm_weight_to_fp16, x = input_297_cast_fp16)[name = string("op_1833_cast_fp16")]; + tensor input_299_perm_0 = const()[name = string("input_299_perm_0"), val = tensor([0, 2, 1])]; + string h_113_pad_type_0 = const()[name = string("h_113_pad_type_0"), val = string("valid")]; + tensor h_113_strides_0 = const()[name = string("h_113_strides_0"), val = tensor([1])]; + tensor h_113_pad_0 = const()[name = string("h_113_pad_0"), val = tensor([0, 0])]; + tensor h_113_dilations_0 = const()[name = string("h_113_dilations_0"), val = tensor([1])]; + int32 h_113_groups_0 = const()[name = string("h_113_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83070336)))]; + tensor main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85167552)))]; + tensor input_299_cast_fp16 = transpose(perm = input_299_perm_0, x = var_1833_cast_fp16)[name = string("transpose_33")]; + tensor h_113_cast_fp16 = conv(bias = main_blocks_3_convnext_0_0_pwconv1_bias_to_fp16, dilations = h_113_dilations_0, groups = h_113_groups_0, pad = h_113_pad_0, pad_type = h_113_pad_type_0, strides = h_113_strides_0, weight = main_blocks_3_convnext_0_0_pwconv1_weight_to_fp16, x = input_299_cast_fp16)[name = string("h_113_cast_fp16")]; + string input_301_mode_0 = const()[name = string("input_301_mode_0"), val = string("EXACT")]; + tensor input_301_cast_fp16 = gelu(mode = input_301_mode_0, x = h_113_cast_fp16)[name = string("input_301_cast_fp16")]; + string h_115_pad_type_0 = const()[name = string("h_115_pad_type_0"), val = string("valid")]; + tensor h_115_strides_0 = const()[name = string("h_115_strides_0"), val = tensor([1])]; + tensor h_115_pad_0 = const()[name = string("h_115_pad_0"), val = tensor([0, 0])]; + tensor h_115_dilations_0 = const()[name = string("h_115_dilations_0"), val = tensor([1])]; + int32 h_115_groups_0 = const()[name = string("h_115_groups_0"), val = int32(1)]; + tensor var_1850_weight_0_to_fp16 = const()[name = string("op_1850_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85171712)))]; + tensor var_1850_bias_0_to_fp16 = const()[name = string("op_1850_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87268928)))]; + tensor var_1850_cast_fp16 = conv(bias = var_1850_bias_0_to_fp16, dilations = h_115_dilations_0, groups = h_115_groups_0, pad = h_115_pad_0, pad_type = h_115_pad_type_0, strides = h_115_strides_0, weight = var_1850_weight_0_to_fp16, x = input_301_cast_fp16)[name = string("op_1850_cast_fp16")]; + tensor out_49_cast_fp16 = add(x = x_155_cast_fp16, y = var_1850_cast_fp16)[name = string("out_49_cast_fp16")]; + tensor x_159_cast_fp16 = mul(x = out_49_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor x_161_cast_fp16 = mul(x = x_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor var_1864_cast_fp16 = mul(x = x_161_cast_fp16, y = last_1_cast_fp16)[name = string("op_1864_cast_fp16")]; + tensor x_last_39_axes_0 = const()[name = string("x_last_39_axes_0"), val = tensor([-1])]; + bool x_last_39_keep_dims_0 = const()[name = string("x_last_39_keep_dims_0"), val = bool(true)]; + tensor x_last_39_cast_fp16 = reduce_sum(axes = x_last_39_axes_0, keep_dims = x_last_39_keep_dims_0, x = var_1864_cast_fp16)[name = string("x_last_39_cast_fp16")]; + tensor var_1867_cast_fp16 = mul(x = x_161_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1867_cast_fp16")]; + tensor var_1869_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_39_cast_fp16)[name = string("op_1869_cast_fp16")]; + tensor input_305_cast_fp16 = add(x = var_1867_cast_fp16, y = var_1869_cast_fp16)[name = string("input_305_cast_fp16")]; + tensor input_307_pad_0 = const()[name = string("input_307_pad_0"), val = tensor([0, 0, 0, 0, 4, 4])]; + string input_307_mode_0 = const()[name = string("input_307_mode_0"), val = string("replicate")]; + fp16 const_42_to_fp16 = const()[name = string("const_42_to_fp16"), val = fp16(0x0p+0)]; + tensor input_307_cast_fp16 = pad(constant_val = const_42_to_fp16, mode = input_307_mode_0, pad = input_307_pad_0, x = input_305_cast_fp16)[name = string("input_307_cast_fp16")]; + string h_117_pad_type_0 = const()[name = string("h_117_pad_type_0"), val = string("valid")]; + tensor h_117_dilations_0 = const()[name = string("h_117_dilations_0"), val = tensor([2])]; + int32 h_117_groups_0 = const()[name = string("h_117_groups_0"), val = int32(512)]; + tensor h_117_strides_0 = const()[name = string("h_117_strides_0"), val = tensor([1])]; + tensor h_117_pad_0 = const()[name = string("h_117_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87270016)))]; + tensor main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87275200)))]; + tensor h_117_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_dwconv__conv_bias_to_fp16, dilations = h_117_dilations_0, groups = h_117_groups_0, pad = h_117_pad_0, pad_type = h_117_pad_type_0, strides = h_117_strides_0, weight = main_blocks_3_convnext_0_1_dwconv__conv_weight_to_fp16, x = input_307_cast_fp16)[name = string("h_117_cast_fp16")]; + tensor x_163_cast_fp16 = mul(x = h_117_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_163_cast_fp16")]; + tensor input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1887_axes_0 = const()[name = string("op_1887_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87276288)))]; + tensor main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87277376)))]; + tensor input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_163_cast_fp16)[name = string("transpose_32")]; + tensor var_1887_cast_fp16 = layer_norm(axes = var_1887_axes_0, beta = main_blocks_3_convnext_0_1_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_1_norm_norm_weight_to_fp16, x = input_309_cast_fp16)[name = string("op_1887_cast_fp16")]; + tensor input_311_perm_0 = const()[name = string("input_311_perm_0"), val = tensor([0, 2, 1])]; + string h_119_pad_type_0 = const()[name = string("h_119_pad_type_0"), val = string("valid")]; + tensor h_119_strides_0 = const()[name = string("h_119_strides_0"), val = tensor([1])]; + tensor h_119_pad_0 = const()[name = string("h_119_pad_0"), val = tensor([0, 0])]; + tensor h_119_dilations_0 = const()[name = string("h_119_dilations_0"), val = tensor([1])]; + int32 h_119_groups_0 = const()[name = string("h_119_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87278464)))]; + tensor main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89375680)))]; + tensor input_311_cast_fp16 = transpose(perm = input_311_perm_0, x = var_1887_cast_fp16)[name = string("transpose_31")]; + tensor h_119_cast_fp16 = conv(bias = main_blocks_3_convnext_0_1_pwconv1_bias_to_fp16, dilations = h_119_dilations_0, groups = h_119_groups_0, pad = h_119_pad_0, pad_type = h_119_pad_type_0, strides = h_119_strides_0, weight = main_blocks_3_convnext_0_1_pwconv1_weight_to_fp16, x = input_311_cast_fp16)[name = string("h_119_cast_fp16")]; + string input_313_mode_0 = const()[name = string("input_313_mode_0"), val = string("EXACT")]; + tensor input_313_cast_fp16 = gelu(mode = input_313_mode_0, x = h_119_cast_fp16)[name = string("input_313_cast_fp16")]; + string h_121_pad_type_0 = const()[name = string("h_121_pad_type_0"), val = string("valid")]; + tensor h_121_strides_0 = const()[name = string("h_121_strides_0"), val = tensor([1])]; + tensor h_121_pad_0 = const()[name = string("h_121_pad_0"), val = tensor([0, 0])]; + tensor h_121_dilations_0 = const()[name = string("h_121_dilations_0"), val = tensor([1])]; + int32 h_121_groups_0 = const()[name = string("h_121_groups_0"), val = int32(1)]; + tensor var_1904_weight_0_to_fp16 = const()[name = string("op_1904_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89379840)))]; + tensor var_1904_bias_0_to_fp16 = const()[name = string("op_1904_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91477056)))]; + tensor var_1904_cast_fp16 = conv(bias = var_1904_bias_0_to_fp16, dilations = h_121_dilations_0, groups = h_121_groups_0, pad = h_121_pad_0, pad_type = h_121_pad_type_0, strides = h_121_strides_0, weight = var_1904_weight_0_to_fp16, x = input_313_cast_fp16)[name = string("op_1904_cast_fp16")]; + tensor out_51_cast_fp16 = add(x = x_161_cast_fp16, y = var_1904_cast_fp16)[name = string("out_51_cast_fp16")]; + tensor x_165_cast_fp16 = mul(x = out_51_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_165_cast_fp16")]; + tensor x_167_cast_fp16 = mul(x = x_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_167_cast_fp16")]; + tensor var_1918_cast_fp16 = mul(x = x_167_cast_fp16, y = last_1_cast_fp16)[name = string("op_1918_cast_fp16")]; + tensor x_last_41_axes_0 = const()[name = string("x_last_41_axes_0"), val = tensor([-1])]; + bool x_last_41_keep_dims_0 = const()[name = string("x_last_41_keep_dims_0"), val = bool(true)]; + tensor x_last_41_cast_fp16 = reduce_sum(axes = x_last_41_axes_0, keep_dims = x_last_41_keep_dims_0, x = var_1918_cast_fp16)[name = string("x_last_41_cast_fp16")]; + tensor var_1921_cast_fp16 = mul(x = x_167_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1921_cast_fp16")]; + tensor var_1923_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_41_cast_fp16)[name = string("op_1923_cast_fp16")]; + tensor input_317_cast_fp16 = add(x = var_1921_cast_fp16, y = var_1923_cast_fp16)[name = string("input_317_cast_fp16")]; + tensor input_319_pad_0 = const()[name = string("input_319_pad_0"), val = tensor([0, 0, 0, 0, 8, 8])]; + string input_319_mode_0 = const()[name = string("input_319_mode_0"), val = string("replicate")]; + fp16 const_44_to_fp16 = const()[name = string("const_44_to_fp16"), val = fp16(0x0p+0)]; + tensor input_319_cast_fp16 = pad(constant_val = const_44_to_fp16, mode = input_319_mode_0, pad = input_319_pad_0, x = input_317_cast_fp16)[name = string("input_319_cast_fp16")]; + string h_123_pad_type_0 = const()[name = string("h_123_pad_type_0"), val = string("valid")]; + tensor h_123_dilations_0 = const()[name = string("h_123_dilations_0"), val = tensor([4])]; + int32 h_123_groups_0 = const()[name = string("h_123_groups_0"), val = int32(512)]; + tensor h_123_strides_0 = const()[name = string("h_123_strides_0"), val = tensor([1])]; + tensor h_123_pad_0 = const()[name = string("h_123_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91478144)))]; + tensor main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91483328)))]; + tensor h_123_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_dwconv__conv_bias_to_fp16, dilations = h_123_dilations_0, groups = h_123_groups_0, pad = h_123_pad_0, pad_type = h_123_pad_type_0, strides = h_123_strides_0, weight = main_blocks_3_convnext_0_2_dwconv__conv_weight_to_fp16, x = input_319_cast_fp16)[name = string("h_123_cast_fp16")]; + tensor x_169_cast_fp16 = mul(x = h_123_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor input_321_perm_0 = const()[name = string("input_321_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1941_axes_0 = const()[name = string("op_1941_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91484416)))]; + tensor main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91485504)))]; + tensor input_321_cast_fp16 = transpose(perm = input_321_perm_0, x = x_169_cast_fp16)[name = string("transpose_30")]; + tensor var_1941_cast_fp16 = layer_norm(axes = var_1941_axes_0, beta = main_blocks_3_convnext_0_2_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_2_norm_norm_weight_to_fp16, x = input_321_cast_fp16)[name = string("op_1941_cast_fp16")]; + tensor input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor([0, 2, 1])]; + string h_125_pad_type_0 = const()[name = string("h_125_pad_type_0"), val = string("valid")]; + tensor h_125_strides_0 = const()[name = string("h_125_strides_0"), val = tensor([1])]; + tensor h_125_pad_0 = const()[name = string("h_125_pad_0"), val = tensor([0, 0])]; + tensor h_125_dilations_0 = const()[name = string("h_125_dilations_0"), val = tensor([1])]; + int32 h_125_groups_0 = const()[name = string("h_125_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91486592)))]; + tensor main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93583808)))]; + tensor input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = var_1941_cast_fp16)[name = string("transpose_29")]; + tensor h_125_cast_fp16 = conv(bias = main_blocks_3_convnext_0_2_pwconv1_bias_to_fp16, dilations = h_125_dilations_0, groups = h_125_groups_0, pad = h_125_pad_0, pad_type = h_125_pad_type_0, strides = h_125_strides_0, weight = main_blocks_3_convnext_0_2_pwconv1_weight_to_fp16, x = input_323_cast_fp16)[name = string("h_125_cast_fp16")]; + string input_325_mode_0 = const()[name = string("input_325_mode_0"), val = string("EXACT")]; + tensor input_325_cast_fp16 = gelu(mode = input_325_mode_0, x = h_125_cast_fp16)[name = string("input_325_cast_fp16")]; + string h_127_pad_type_0 = const()[name = string("h_127_pad_type_0"), val = string("valid")]; + tensor h_127_strides_0 = const()[name = string("h_127_strides_0"), val = tensor([1])]; + tensor h_127_pad_0 = const()[name = string("h_127_pad_0"), val = tensor([0, 0])]; + tensor h_127_dilations_0 = const()[name = string("h_127_dilations_0"), val = tensor([1])]; + int32 h_127_groups_0 = const()[name = string("h_127_groups_0"), val = int32(1)]; + tensor var_1958_weight_0_to_fp16 = const()[name = string("op_1958_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93587968)))]; + tensor var_1958_bias_0_to_fp16 = const()[name = string("op_1958_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95685184)))]; + tensor var_1958_cast_fp16 = conv(bias = var_1958_bias_0_to_fp16, dilations = h_127_dilations_0, groups = h_127_groups_0, pad = h_127_pad_0, pad_type = h_127_pad_type_0, strides = h_127_strides_0, weight = var_1958_weight_0_to_fp16, x = input_325_cast_fp16)[name = string("op_1958_cast_fp16")]; + tensor out_53_cast_fp16 = add(x = x_167_cast_fp16, y = var_1958_cast_fp16)[name = string("out_53_cast_fp16")]; + tensor x_171_cast_fp16 = mul(x = out_53_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_171_cast_fp16")]; + tensor x_173_cast_fp16 = mul(x = x_171_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_173_cast_fp16")]; + tensor var_1972_cast_fp16 = mul(x = x_173_cast_fp16, y = last_1_cast_fp16)[name = string("op_1972_cast_fp16")]; + tensor x_last_43_axes_0 = const()[name = string("x_last_43_axes_0"), val = tensor([-1])]; + bool x_last_43_keep_dims_0 = const()[name = string("x_last_43_keep_dims_0"), val = bool(true)]; + tensor x_last_43_cast_fp16 = reduce_sum(axes = x_last_43_axes_0, keep_dims = x_last_43_keep_dims_0, x = var_1972_cast_fp16)[name = string("x_last_43_cast_fp16")]; + tensor var_1975_cast_fp16 = mul(x = x_173_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_1975_cast_fp16")]; + tensor var_1977_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_43_cast_fp16)[name = string("op_1977_cast_fp16")]; + tensor input_329_cast_fp16 = add(x = var_1975_cast_fp16, y = var_1977_cast_fp16)[name = string("input_329_cast_fp16")]; + tensor input_331_pad_0 = const()[name = string("input_331_pad_0"), val = tensor([0, 0, 0, 0, 16, 16])]; + string input_331_mode_0 = const()[name = string("input_331_mode_0"), val = string("replicate")]; + fp16 const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = fp16(0x0p+0)]; + tensor input_331_cast_fp16 = pad(constant_val = const_46_to_fp16, mode = input_331_mode_0, pad = input_331_pad_0, x = input_329_cast_fp16)[name = string("input_331_cast_fp16")]; + string h_129_pad_type_0 = const()[name = string("h_129_pad_type_0"), val = string("valid")]; + tensor h_129_dilations_0 = const()[name = string("h_129_dilations_0"), val = tensor([8])]; + int32 h_129_groups_0 = const()[name = string("h_129_groups_0"), val = int32(512)]; + tensor h_129_strides_0 = const()[name = string("h_129_strides_0"), val = tensor([1])]; + tensor h_129_pad_0 = const()[name = string("h_129_pad_0"), val = tensor([0, 0])]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95686272)))]; + tensor main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95691456)))]; + tensor h_129_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_dwconv__conv_bias_to_fp16, dilations = h_129_dilations_0, groups = h_129_groups_0, pad = h_129_pad_0, pad_type = h_129_pad_type_0, strides = h_129_strides_0, weight = main_blocks_3_convnext_0_3_dwconv__conv_weight_to_fp16, x = input_331_cast_fp16)[name = string("h_129_cast_fp16")]; + tensor x_175_cast_fp16 = mul(x = h_129_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_175_cast_fp16")]; + tensor input_333_perm_0 = const()[name = string("input_333_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1995_axes_0 = const()[name = string("op_1995_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95692544)))]; + tensor main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95693632)))]; + tensor input_333_cast_fp16 = transpose(perm = input_333_perm_0, x = x_175_cast_fp16)[name = string("transpose_28")]; + tensor var_1995_cast_fp16 = layer_norm(axes = var_1995_axes_0, beta = main_blocks_3_convnext_0_3_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_0_3_norm_norm_weight_to_fp16, x = input_333_cast_fp16)[name = string("op_1995_cast_fp16")]; + tensor input_335_perm_0 = const()[name = string("input_335_perm_0"), val = tensor([0, 2, 1])]; + string h_131_pad_type_0 = const()[name = string("h_131_pad_type_0"), val = string("valid")]; + tensor h_131_strides_0 = const()[name = string("h_131_strides_0"), val = tensor([1])]; + tensor h_131_pad_0 = const()[name = string("h_131_pad_0"), val = tensor([0, 0])]; + tensor h_131_dilations_0 = const()[name = string("h_131_dilations_0"), val = tensor([1])]; + int32 h_131_groups_0 = const()[name = string("h_131_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95694720)))]; + tensor main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97791936)))]; + tensor input_335_cast_fp16 = transpose(perm = input_335_perm_0, x = var_1995_cast_fp16)[name = string("transpose_27")]; + tensor h_131_cast_fp16 = conv(bias = main_blocks_3_convnext_0_3_pwconv1_bias_to_fp16, dilations = h_131_dilations_0, groups = h_131_groups_0, pad = h_131_pad_0, pad_type = h_131_pad_type_0, strides = h_131_strides_0, weight = main_blocks_3_convnext_0_3_pwconv1_weight_to_fp16, x = input_335_cast_fp16)[name = string("h_131_cast_fp16")]; + string input_337_mode_0 = const()[name = string("input_337_mode_0"), val = string("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = h_131_cast_fp16)[name = string("input_337_cast_fp16")]; + string h_133_pad_type_0 = const()[name = string("h_133_pad_type_0"), val = string("valid")]; + tensor h_133_strides_0 = const()[name = string("h_133_strides_0"), val = tensor([1])]; + tensor h_133_pad_0 = const()[name = string("h_133_pad_0"), val = tensor([0, 0])]; + tensor h_133_dilations_0 = const()[name = string("h_133_dilations_0"), val = tensor([1])]; + int32 h_133_groups_0 = const()[name = string("h_133_groups_0"), val = int32(1)]; + tensor var_2012_weight_0_to_fp16 = const()[name = string("op_2012_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97796096)))]; + tensor var_2012_bias_0_to_fp16 = const()[name = string("op_2012_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99893312)))]; + tensor var_2012_cast_fp16 = conv(bias = var_2012_bias_0_to_fp16, dilations = h_133_dilations_0, groups = h_133_groups_0, pad = h_133_pad_0, pad_type = h_133_pad_type_0, strides = h_133_strides_0, weight = var_2012_weight_0_to_fp16, x = input_337_cast_fp16)[name = string("op_2012_cast_fp16")]; + tensor out_55_cast_fp16 = add(x = x_173_cast_fp16, y = var_2012_cast_fp16)[name = string("out_55_cast_fp16")]; + tensor x_177_cast_fp16 = mul(x = out_55_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_177_cast_fp16")]; + tensor main_blocks_3_time_cond_linear_linear_weight_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99894400)))]; + tensor main_blocks_3_time_cond_linear_linear_bias_to_fp16 = const()[name = string("main_blocks_3_time_cond_linear_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99960000)))]; + tensor linear_29_cast_fp16 = linear(bias = main_blocks_3_time_cond_linear_linear_bias_to_fp16, weight = main_blocks_3_time_cond_linear_linear_weight_to_fp16, x = input_55_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor t_axes_0 = const()[name = string("t_axes_0"), val = tensor([-1])]; + tensor t_cast_fp16 = expand_dims(axes = t_axes_0, x = linear_29_cast_fp16)[name = string("t_cast_fp16")]; + tensor var_2022_cast_fp16 = add(x = x_177_cast_fp16, y = t_cast_fp16)[name = string("op_2022_cast_fp16")]; + tensor x_179_cast_fp16 = mul(x = var_2022_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_179_cast_fp16")]; + tensor x_181_cast_fp16 = mul(x = x_179_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_181_cast_fp16")]; + tensor var_2035_cast_fp16 = mul(x = x_181_cast_fp16, y = last_1_cast_fp16)[name = string("op_2035_cast_fp16")]; + tensor x_last_45_axes_0 = const()[name = string("x_last_45_axes_0"), val = tensor([-1])]; + bool x_last_45_keep_dims_0 = const()[name = string("x_last_45_keep_dims_0"), val = bool(true)]; + tensor x_last_45_cast_fp16 = reduce_sum(axes = x_last_45_axes_0, keep_dims = x_last_45_keep_dims_0, x = var_2035_cast_fp16)[name = string("x_last_45_cast_fp16")]; + tensor var_2038_cast_fp16 = mul(x = x_181_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2038_cast_fp16")]; + tensor var_2040_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_45_cast_fp16)[name = string("op_2040_cast_fp16")]; + tensor input_343_cast_fp16 = add(x = var_2038_cast_fp16, y = var_2040_cast_fp16)[name = string("input_343_cast_fp16")]; + tensor input_345_pad_0 = const()[name = string("input_345_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_345_mode_0 = const()[name = string("input_345_mode_0"), val = string("replicate")]; + fp16 const_48_to_fp16 = const()[name = string("const_48_to_fp16"), val = fp16(0x0p+0)]; + tensor input_345_cast_fp16 = pad(constant_val = const_48_to_fp16, mode = input_345_mode_0, pad = input_345_pad_0, x = input_343_cast_fp16)[name = string("input_345_cast_fp16")]; + string h_135_pad_type_0 = const()[name = string("h_135_pad_type_0"), val = string("valid")]; + int32 h_135_groups_0 = const()[name = string("h_135_groups_0"), val = int32(512)]; + tensor h_135_strides_0 = const()[name = string("h_135_strides_0"), val = tensor([1])]; + tensor h_135_pad_0 = const()[name = string("h_135_pad_0"), val = tensor([0, 0])]; + tensor h_135_dilations_0 = const()[name = string("h_135_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99961088)))]; + tensor main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99966272)))]; + tensor h_135_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_dwconv__conv_bias_to_fp16, dilations = h_135_dilations_0, groups = h_135_groups_0, pad = h_135_pad_0, pad_type = h_135_pad_type_0, strides = h_135_strides_0, weight = main_blocks_3_convnext_1_0_dwconv__conv_weight_to_fp16, x = input_345_cast_fp16)[name = string("h_135_cast_fp16")]; + tensor x_183_cast_fp16 = mul(x = h_135_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_183_cast_fp16")]; + tensor input_347_perm_0 = const()[name = string("input_347_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2058_axes_0 = const()[name = string("op_2058_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99967360)))]; + tensor main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99968448)))]; + tensor input_347_cast_fp16 = transpose(perm = input_347_perm_0, x = x_183_cast_fp16)[name = string("transpose_26")]; + tensor var_2058_cast_fp16 = layer_norm(axes = var_2058_axes_0, beta = main_blocks_3_convnext_1_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_1_0_norm_norm_weight_to_fp16, x = input_347_cast_fp16)[name = string("op_2058_cast_fp16")]; + tensor input_349_perm_0 = const()[name = string("input_349_perm_0"), val = tensor([0, 2, 1])]; + string h_137_pad_type_0 = const()[name = string("h_137_pad_type_0"), val = string("valid")]; + tensor h_137_strides_0 = const()[name = string("h_137_strides_0"), val = tensor([1])]; + tensor h_137_pad_0 = const()[name = string("h_137_pad_0"), val = tensor([0, 0])]; + tensor h_137_dilations_0 = const()[name = string("h_137_dilations_0"), val = tensor([1])]; + int32 h_137_groups_0 = const()[name = string("h_137_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99969536)))]; + tensor main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102066752)))]; + tensor input_349_cast_fp16 = transpose(perm = input_349_perm_0, x = var_2058_cast_fp16)[name = string("transpose_25")]; + tensor h_137_cast_fp16 = conv(bias = main_blocks_3_convnext_1_0_pwconv1_bias_to_fp16, dilations = h_137_dilations_0, groups = h_137_groups_0, pad = h_137_pad_0, pad_type = h_137_pad_type_0, strides = h_137_strides_0, weight = main_blocks_3_convnext_1_0_pwconv1_weight_to_fp16, x = input_349_cast_fp16)[name = string("h_137_cast_fp16")]; + string input_351_mode_0 = const()[name = string("input_351_mode_0"), val = string("EXACT")]; + tensor input_351_cast_fp16 = gelu(mode = input_351_mode_0, x = h_137_cast_fp16)[name = string("input_351_cast_fp16")]; + string h_139_pad_type_0 = const()[name = string("h_139_pad_type_0"), val = string("valid")]; + tensor h_139_strides_0 = const()[name = string("h_139_strides_0"), val = tensor([1])]; + tensor h_139_pad_0 = const()[name = string("h_139_pad_0"), val = tensor([0, 0])]; + tensor h_139_dilations_0 = const()[name = string("h_139_dilations_0"), val = tensor([1])]; + int32 h_139_groups_0 = const()[name = string("h_139_groups_0"), val = int32(1)]; + tensor var_2075_weight_0_to_fp16 = const()[name = string("op_2075_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102070912)))]; + tensor var_2075_bias_0_to_fp16 = const()[name = string("op_2075_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104168128)))]; + tensor var_2075_cast_fp16 = conv(bias = var_2075_bias_0_to_fp16, dilations = h_139_dilations_0, groups = h_139_groups_0, pad = h_139_pad_0, pad_type = h_139_pad_type_0, strides = h_139_strides_0, weight = var_2075_weight_0_to_fp16, x = input_351_cast_fp16)[name = string("op_2075_cast_fp16")]; + tensor out_57_cast_fp16 = add(x = x_181_cast_fp16, y = var_2075_cast_fp16)[name = string("out_57_cast_fp16")]; + tensor x_185_cast_fp16 = mul(x = out_57_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_185_cast_fp16")]; + tensor x_m_13_cast_fp16 = mul(x = x_185_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_13_cast_fp16")]; + tensor var_2085_shape_cast_fp16 = shape(x = x_185_cast_fp16)[name = string("op_2085_shape_cast_fp16")]; + int32 gather_19_axis_0 = const()[name = string("gather_19_axis_0"), val = int32(0)]; + int32 gather_19_batch_dims_0 = const()[name = string("gather_19_batch_dims_0"), val = int32(0)]; + bool gather_19_validate_indices_0 = const()[name = string("gather_19_validate_indices_0"), val = bool(false)]; + string var_2085_shape_cast_fp16_to_uint16_dtype_0 = const()[name = string("op_2085_shape_cast_fp16_to_uint16_dtype_0"), val = string("uint16")]; + uint16 gather_19_indices_0_to_uint16 = const()[name = string("gather_19_indices_0_to_uint16"), val = uint16(2)]; + tensor var_2085_shape_cast_fp16_to_uint16 = cast(dtype = var_2085_shape_cast_fp16_to_uint16_dtype_0, x = var_2085_shape_cast_fp16)[name = string("cast_87")]; + uint16 gather_19_cast_uint16 = gather(axis = gather_19_axis_0, batch_dims = gather_19_batch_dims_0, indices = gather_19_indices_0_to_uint16, validate_indices = gather_19_validate_indices_0, x = var_2085_shape_cast_fp16_to_uint16)[name = string("gather_19_cast_uint16")]; + string gather_19_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_19_cast_uint16_to_int32_dtype_0"), val = string("int32")]; + tensor input_353_perm_0 = const()[name = string("input_353_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_text_attn_W_query_linear_weight_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104169216)))]; + tensor main_blocks_3_text_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104693568)))]; + tensor input_353_cast_fp16 = transpose(perm = input_353_perm_0, x = x_185_cast_fp16)[name = string("transpose_24")]; + tensor linear_30_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_query_linear_weight_to_fp16, x = input_353_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor concat_34x = const()[name = string("concat_34x"), val = tensor([2, -1, 8, 64])]; + tensor var_2094_cast_fp16 = reshape(shape = concat_34x, x = linear_30_cast_fp16)[name = string("op_2094_cast_fp16")]; + tensor x_187_perm_0 = const()[name = string("x_187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_key_linear_weight_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104694656)))]; + tensor main_blocks_3_text_attn_W_key_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_key_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104956864)))]; + tensor linear_31_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_key_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_key_linear_weight_to_fp16, x = input_71_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor concat_35x = const()[name = string("concat_35x"), val = tensor([2, -1, 8, 64])]; + tensor var_2102_cast_fp16 = reshape(shape = concat_35x, x = linear_31_cast_fp16)[name = string("op_2102_cast_fp16")]; + tensor x_189_perm_0 = const()[name = string("x_189_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_text_attn_W_value_linear_weight_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104957952)))]; + tensor main_blocks_3_text_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105220160)))]; + tensor linear_32_cast_fp16 = linear(bias = main_blocks_3_text_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_text_attn_W_value_linear_weight_to_fp16, x = input_71_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor concat_36x = const()[name = string("concat_36x"), val = tensor([2, -1, 8, 64])]; + tensor var_2110_cast_fp16 = reshape(shape = concat_36x, x = linear_32_cast_fp16)[name = string("op_2110_cast_fp16")]; + tensor v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor([0, 2, 1, 3])]; + int32 concat_37_values0_0 = const()[name = string("concat_37_values0_0"), val = int32(1)]; + int32 concat_37_values2_0 = const()[name = string("concat_37_values2_0"), val = int32(1)]; + int32 concat_37_axis_0 = const()[name = string("concat_37_axis_0"), val = int32(0)]; + bool concat_37_interleave_0 = const()[name = string("concat_37_interleave_0"), val = bool(false)]; + int32 gather_19_cast_uint16_to_int32 = cast(dtype = gather_19_cast_uint16_to_int32_dtype_0, x = gather_19_cast_uint16)[name = string("cast_86")]; + tensor concat_37 = concat(axis = concat_37_axis_0, interleave = concat_37_interleave_0, values = (concat_37_values0_0, gather_19_cast_uint16_to_int32, concat_37_values2_0))[name = string("concat_37")]; + tensor var_2113_begin_0 = const()[name = string("op_2113_begin_0"), val = tensor([0, 0, 0])]; + tensor var_2113_end_mask_0 = const()[name = string("op_2113_end_mask_0"), val = tensor([true, false, true])]; + tensor var_2113_cast_fp16 = slice_by_index(begin = var_2113_begin_0, end = concat_37, end_mask = var_2113_end_mask_0, x = main_blocks_0_text_attn_increments_to_fp16)[name = string("op_2113_cast_fp16")]; + tensor concat_38 = const()[name = string("concat_38"), val = tensor([2, -1, -1])]; + tensor shape_7_cast_fp16 = shape(x = var_2113_cast_fp16)[name = string("shape_7_cast_fp16")]; + tensor equal_7 = const()[name = string("equal_7"), val = tensor([false, true, true])]; + tensor select_7 = select(a = shape_7_cast_fp16, b = concat_38, cond = equal_7)[name = string("select_7")]; + tensor real_div_7 = real_div(x = select_7, y = shape_7_cast_fp16)[name = string("real_div_7")]; + tensor var_2116_cast_fp16 = tile(reps = real_div_7, x = var_2113_cast_fp16)[name = string("op_2116_cast_fp16")]; + tensor scaled_13_cast_fp16 = real_div(x = var_2116_cast_fp16, y = var_488_cast_fp16)[name = string("scaled_13_cast_fp16")]; + tensor main_blocks_3_text_attn_theta_to_fp16 = const()[name = string("main_blocks_3_text_attn_theta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105221248)))]; + tensor angles_13_cast_fp16 = mul(x = scaled_13_cast_fp16, y = main_blocks_3_text_attn_theta_to_fp16)[name = string("angles_13_cast_fp16")]; + tensor var_2132_cast_fp16 = cos(x = angles_13_cast_fp16)[name = string("op_2132_cast_fp16")]; + tensor cos_13_axes_0 = const()[name = string("cos_13_axes_0"), val = tensor([1])]; + tensor cos_13_cast_fp16 = expand_dims(axes = cos_13_axes_0, x = var_2132_cast_fp16)[name = string("cos_13_cast_fp16")]; + tensor var_2134_cast_fp16 = sin(x = angles_13_cast_fp16)[name = string("op_2134_cast_fp16")]; + tensor sin_13_axes_0 = const()[name = string("sin_13_axes_0"), val = tensor([1])]; + tensor sin_13_cast_fp16 = expand_dims(axes = sin_13_axes_0, x = var_2134_cast_fp16)[name = string("sin_13_cast_fp16")]; + tensor x_a_13_begin_0 = const()[name = string("x_a_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_13_end_0 = const()[name = string("x_a_13_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_13_end_mask_0 = const()[name = string("x_a_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_187_cast_fp16 = transpose(perm = x_187_perm_0, x = var_2094_cast_fp16)[name = string("transpose_23")]; + tensor x_a_13_cast_fp16 = slice_by_index(begin = x_a_13_begin_0, end = x_a_13_end_0, end_mask = x_a_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_a_13_cast_fp16")]; + tensor x_b_13_begin_0 = const()[name = string("x_b_13_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_13_end_0 = const()[name = string("x_b_13_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_13_end_mask_0 = const()[name = string("x_b_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_13_cast_fp16 = slice_by_index(begin = x_b_13_begin_0, end = x_b_13_end_0, end_mask = x_b_13_end_mask_0, x = x_187_cast_fp16)[name = string("x_b_13_cast_fp16")]; + tensor var_2138_cast_fp16 = mul(x = x_a_13_cast_fp16, y = cos_13_cast_fp16)[name = string("op_2138_cast_fp16")]; + tensor var_2139_cast_fp16 = mul(x = x_b_13_cast_fp16, y = sin_13_cast_fp16)[name = string("op_2139_cast_fp16")]; + tensor rot_a_13_cast_fp16 = sub(x = var_2138_cast_fp16, y = var_2139_cast_fp16)[name = string("rot_a_13_cast_fp16")]; + tensor var_2141_cast_fp16 = mul(x = x_a_13_cast_fp16, y = sin_13_cast_fp16)[name = string("op_2141_cast_fp16")]; + tensor var_2142_cast_fp16 = mul(x = x_b_13_cast_fp16, y = cos_13_cast_fp16)[name = string("op_2142_cast_fp16")]; + tensor rot_b_13_cast_fp16 = add(x = var_2141_cast_fp16, y = var_2142_cast_fp16)[name = string("rot_b_13_cast_fp16")]; + bool q_13_interleave_0 = const()[name = string("q_13_interleave_0"), val = bool(false)]; + tensor q_13_cast_fp16 = concat(axis = var_1775, interleave = q_13_interleave_0, values = (rot_a_13_cast_fp16, rot_b_13_cast_fp16))[name = string("q_13_cast_fp16")]; + tensor x_a_begin_0 = const()[name = string("x_a_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x_a_end_0 = const()[name = string("x_a_end_0"), val = tensor([2, 8, 0, 32])]; + tensor x_a_end_mask_0 = const()[name = string("x_a_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_189_cast_fp16 = transpose(perm = x_189_perm_0, x = var_2102_cast_fp16)[name = string("transpose_22")]; + tensor x_a_cast_fp16 = slice_by_index(begin = x_a_begin_0, end = x_a_end_0, end_mask = x_a_end_mask_0, x = x_189_cast_fp16)[name = string("x_a_cast_fp16")]; + tensor x_b_begin_0 = const()[name = string("x_b_begin_0"), val = tensor([0, 0, 0, 32])]; + tensor x_b_end_0 = const()[name = string("x_b_end_0"), val = tensor([2, 8, 0, 64])]; + tensor x_b_end_mask_0 = const()[name = string("x_b_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x_b_cast_fp16 = slice_by_index(begin = x_b_begin_0, end = x_b_end_0, end_mask = x_b_end_mask_0, x = x_189_cast_fp16)[name = string("x_b_cast_fp16")]; + tensor var_2156_cast_fp16 = mul(x = x_a_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2156_cast_fp16")]; + tensor var_2157_cast_fp16 = mul(x = x_b_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2157_cast_fp16")]; + tensor rot_a_cast_fp16 = sub(x = var_2156_cast_fp16, y = var_2157_cast_fp16)[name = string("rot_a_cast_fp16")]; + tensor var_2159_cast_fp16 = mul(x = x_a_cast_fp16, y = sin_3_cast_fp16)[name = string("op_2159_cast_fp16")]; + tensor var_2160_cast_fp16 = mul(x = x_b_cast_fp16, y = cos_3_cast_fp16)[name = string("op_2160_cast_fp16")]; + tensor rot_b_cast_fp16 = add(x = var_2159_cast_fp16, y = var_2160_cast_fp16)[name = string("rot_b_cast_fp16")]; + bool k_13_interleave_0 = const()[name = string("k_13_interleave_0"), val = bool(false)]; + tensor k_13_cast_fp16 = concat(axis = var_1775, interleave = k_13_interleave_0, values = (rot_a_cast_fp16, rot_b_cast_fp16))[name = string("k_13_cast_fp16")]; + bool var_2165_transpose_x_1 = const()[name = string("op_2165_transpose_x_1"), val = bool(false)]; + bool var_2165_transpose_y_1 = const()[name = string("op_2165_transpose_y_1"), val = bool(true)]; + tensor var_2165_cast_fp16 = matmul(transpose_x = var_2165_transpose_x_1, transpose_y = var_2165_transpose_y_1, x = q_13_cast_fp16, y = k_13_cast_fp16)[name = string("op_2165_cast_fp16")]; + fp16 _inversed_scores_y_0_to_fp16 = const()[name = string("_inversed_scores_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_scores_cast_fp16 = mul(x = var_2165_cast_fp16, y = _inversed_scores_y_0_to_fp16)[name = string("_inversed_scores_cast_fp16")]; + tensor input_359_cast_fp16 = sub(x = _inversed_scores_cast_fp16, y = var_530_cast_fp16)[name = string("input_359_cast_fp16")]; + tensor attn_19_cast_fp16 = softmax(axis = var_1775, x = input_359_cast_fp16)[name = string("attn_19_cast_fp16")]; + bool var_2174_transpose_x_0 = const()[name = string("op_2174_transpose_x_0"), val = bool(false)]; + bool var_2174_transpose_y_0 = const()[name = string("op_2174_transpose_y_0"), val = bool(false)]; + tensor v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2110_cast_fp16)[name = string("transpose_21")]; + tensor var_2174_cast_fp16 = matmul(transpose_x = var_2174_transpose_x_0, transpose_y = var_2174_transpose_y_0, x = attn_19_cast_fp16, y = v_13_cast_fp16)[name = string("op_2174_cast_fp16")]; + tensor var_2175_perm_0 = const()[name = string("op_2175_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_41x = const()[name = string("concat_41x"), val = tensor([2, -1, 512])]; + tensor var_2175_cast_fp16 = transpose(perm = var_2175_perm_0, x = var_2174_cast_fp16)[name = string("transpose_20")]; + tensor input_361_cast_fp16 = reshape(shape = concat_41x, x = var_2175_cast_fp16)[name = string("input_361_cast_fp16")]; + tensor main_blocks_3_text_attn_out_fc_linear_weight_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105221376)))]; + tensor main_blocks_3_text_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_text_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105745728)))]; + tensor linear_33_cast_fp16 = linear(bias = main_blocks_3_text_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_text_attn_out_fc_linear_weight_to_fp16, x = input_361_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor out_59_perm_0 = const()[name = string("out_59_perm_0"), val = tensor([0, 2, 1])]; + tensor out_59_cast_fp16 = transpose(perm = out_59_perm_0, x = linear_33_cast_fp16)[name = string("transpose_19")]; + tensor var_2183_cast_fp16 = mul(x = out_59_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2183_cast_fp16")]; + tensor x_191_cast_fp16 = add(x = x_m_13_cast_fp16, y = var_2183_cast_fp16)[name = string("x_191_cast_fp16")]; + tensor input_363_perm_0 = const()[name = string("input_363_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2190_axes_0 = const()[name = string("op_2190_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_text_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105746816)))]; + tensor main_blocks_3_text_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_text_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105747904)))]; + tensor input_363_cast_fp16 = transpose(perm = input_363_perm_0, x = x_191_cast_fp16)[name = string("transpose_18")]; + tensor var_2190_cast_fp16 = layer_norm(axes = var_2190_axes_0, beta = main_blocks_3_text_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_text_norm_norm_weight_to_fp16, x = input_363_cast_fp16)[name = string("op_2190_cast_fp16")]; + tensor var_2191_perm_0 = const()[name = string("op_2191_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2191_cast_fp16 = transpose(perm = var_2191_perm_0, x = var_2190_cast_fp16)[name = string("transpose_17")]; + tensor x_193_cast_fp16 = mul(x = var_2191_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_193_cast_fp16")]; + tensor x_195_cast_fp16 = mul(x = x_193_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_195_cast_fp16")]; + tensor var_2204_cast_fp16 = mul(x = x_195_cast_fp16, y = last_1_cast_fp16)[name = string("op_2204_cast_fp16")]; + tensor x_last_47_axes_0 = const()[name = string("x_last_47_axes_0"), val = tensor([-1])]; + bool x_last_47_keep_dims_0 = const()[name = string("x_last_47_keep_dims_0"), val = bool(true)]; + tensor x_last_47_cast_fp16 = reduce_sum(axes = x_last_47_axes_0, keep_dims = x_last_47_keep_dims_0, x = var_2204_cast_fp16)[name = string("x_last_47_cast_fp16")]; + tensor var_2207_cast_fp16 = mul(x = x_195_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2207_cast_fp16")]; + tensor var_2209_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_47_cast_fp16)[name = string("op_2209_cast_fp16")]; + tensor input_367_cast_fp16 = add(x = var_2207_cast_fp16, y = var_2209_cast_fp16)[name = string("input_367_cast_fp16")]; + tensor input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_369_mode_0 = const()[name = string("input_369_mode_0"), val = string("replicate")]; + fp16 const_50_to_fp16 = const()[name = string("const_50_to_fp16"), val = fp16(0x0p+0)]; + tensor input_369_cast_fp16 = pad(constant_val = const_50_to_fp16, mode = input_369_mode_0, pad = input_369_pad_0, x = input_367_cast_fp16)[name = string("input_369_cast_fp16")]; + string h_141_pad_type_0 = const()[name = string("h_141_pad_type_0"), val = string("valid")]; + int32 h_141_groups_0 = const()[name = string("h_141_groups_0"), val = int32(512)]; + tensor h_141_strides_0 = const()[name = string("h_141_strides_0"), val = tensor([1])]; + tensor h_141_pad_0 = const()[name = string("h_141_pad_0"), val = tensor([0, 0])]; + tensor h_141_dilations_0 = const()[name = string("h_141_dilations_0"), val = tensor([1])]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105748992)))]; + tensor main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105754176)))]; + tensor h_141_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_dwconv__conv_bias_to_fp16, dilations = h_141_dilations_0, groups = h_141_groups_0, pad = h_141_pad_0, pad_type = h_141_pad_type_0, strides = h_141_strides_0, weight = main_blocks_3_convnext_2_0_dwconv__conv_weight_to_fp16, x = input_369_cast_fp16)[name = string("h_141_cast_fp16")]; + tensor x_197_cast_fp16 = mul(x = h_141_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_197_cast_fp16")]; + tensor input_371_perm_0 = const()[name = string("input_371_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2227_axes_0 = const()[name = string("op_2227_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105755264)))]; + tensor main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105756352)))]; + tensor input_371_cast_fp16 = transpose(perm = input_371_perm_0, x = x_197_cast_fp16)[name = string("transpose_16")]; + tensor var_2227_cast_fp16 = layer_norm(axes = var_2227_axes_0, beta = main_blocks_3_convnext_2_0_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_convnext_2_0_norm_norm_weight_to_fp16, x = input_371_cast_fp16)[name = string("op_2227_cast_fp16")]; + tensor input_373_perm_0 = const()[name = string("input_373_perm_0"), val = tensor([0, 2, 1])]; + string h_143_pad_type_0 = const()[name = string("h_143_pad_type_0"), val = string("valid")]; + tensor h_143_strides_0 = const()[name = string("h_143_strides_0"), val = tensor([1])]; + tensor h_143_pad_0 = const()[name = string("h_143_pad_0"), val = tensor([0, 0])]; + tensor h_143_dilations_0 = const()[name = string("h_143_dilations_0"), val = tensor([1])]; + int32 h_143_groups_0 = const()[name = string("h_143_groups_0"), val = int32(1)]; + tensor main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105757440)))]; + tensor main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16 = const()[name = string("main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107854656)))]; + tensor input_373_cast_fp16 = transpose(perm = input_373_perm_0, x = var_2227_cast_fp16)[name = string("transpose_15")]; + tensor h_143_cast_fp16 = conv(bias = main_blocks_3_convnext_2_0_pwconv1_bias_to_fp16, dilations = h_143_dilations_0, groups = h_143_groups_0, pad = h_143_pad_0, pad_type = h_143_pad_type_0, strides = h_143_strides_0, weight = main_blocks_3_convnext_2_0_pwconv1_weight_to_fp16, x = input_373_cast_fp16)[name = string("h_143_cast_fp16")]; + string input_375_mode_0 = const()[name = string("input_375_mode_0"), val = string("EXACT")]; + tensor input_375_cast_fp16 = gelu(mode = input_375_mode_0, x = h_143_cast_fp16)[name = string("input_375_cast_fp16")]; + string h_145_pad_type_0 = const()[name = string("h_145_pad_type_0"), val = string("valid")]; + tensor h_145_strides_0 = const()[name = string("h_145_strides_0"), val = tensor([1])]; + tensor h_145_pad_0 = const()[name = string("h_145_pad_0"), val = tensor([0, 0])]; + tensor h_145_dilations_0 = const()[name = string("h_145_dilations_0"), val = tensor([1])]; + int32 h_145_groups_0 = const()[name = string("h_145_groups_0"), val = int32(1)]; + tensor var_2244_weight_0_to_fp16 = const()[name = string("op_2244_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107858816)))]; + tensor var_2244_bias_0_to_fp16 = const()[name = string("op_2244_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109956032)))]; + tensor var_2244_cast_fp16 = conv(bias = var_2244_bias_0_to_fp16, dilations = h_145_dilations_0, groups = h_145_groups_0, pad = h_145_pad_0, pad_type = h_145_pad_type_0, strides = h_145_strides_0, weight = var_2244_weight_0_to_fp16, x = input_375_cast_fp16)[name = string("op_2244_cast_fp16")]; + tensor out_61_cast_fp16 = add(x = x_195_cast_fp16, y = var_2244_cast_fp16)[name = string("out_61_cast_fp16")]; + tensor x_199_cast_fp16 = mul(x = out_61_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_199_cast_fp16")]; + tensor x_m_cast_fp16 = mul(x = x_199_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_m_cast_fp16")]; + tensor input_377_perm_0 = const()[name = string("input_377_perm_0"), val = tensor([0, 2, 1])]; + tensor main_blocks_3_style_attn_W_query_linear_weight_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109957120)))]; + tensor main_blocks_3_style_attn_W_query_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_query_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110219328)))]; + tensor input_377_cast_fp16 = transpose(perm = input_377_perm_0, x = x_199_cast_fp16)[name = string("transpose_14")]; + tensor linear_34_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_query_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_query_linear_weight_to_fp16, x = input_377_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor concat_42x = const()[name = string("concat_42x"), val = tensor([2, -1, 2, 128])]; + tensor var_2261_cast_fp16 = reshape(shape = concat_42x, x = linear_34_cast_fp16)[name = string("op_2261_cast_fp16")]; + tensor q_perm_0 = const()[name = string("q_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor main_blocks_3_style_attn_W_value_linear_weight_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110219904)))]; + tensor main_blocks_3_style_attn_W_value_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_W_value_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110351040)))]; + tensor linear_36_cast_fp16 = linear(bias = main_blocks_3_style_attn_W_value_linear_bias_to_fp16, weight = main_blocks_3_style_attn_W_value_linear_weight_to_fp16, x = input_95_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_2274 = const()[name = string("op_2274"), val = tensor([2, 50, 2, 128])]; + tensor var_2275_cast_fp16 = reshape(shape = var_2274, x = linear_36_cast_fp16)[name = string("op_2275_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, 1, 3])]; + bool var_2279_transpose_x_0 = const()[name = string("op_2279_transpose_x_0"), val = bool(false)]; + bool var_2279_transpose_y_0 = const()[name = string("op_2279_transpose_y_0"), val = bool(false)]; + tensor var_2278_to_fp16 = const()[name = string("op_2278_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110351616)))]; + tensor q_cast_fp16 = transpose(perm = q_perm_0, x = var_2261_cast_fp16)[name = string("transpose_13")]; + tensor var_2279_cast_fp16 = matmul(transpose_x = var_2279_transpose_x_0, transpose_y = var_2279_transpose_y_0, x = q_cast_fp16, y = var_2278_to_fp16)[name = string("op_2279_cast_fp16")]; + fp16 _inversed_input_379_y_0_to_fp16 = const()[name = string("_inversed_input_379_y_0_to_fp16"), val = fp16(0x1p-4)]; + tensor _inversed_input_379_cast_fp16 = mul(x = var_2279_cast_fp16, y = _inversed_input_379_y_0_to_fp16)[name = string("_inversed_input_379_cast_fp16")]; + tensor attn_21_cast_fp16 = softmax(axis = var_1775, x = _inversed_input_379_cast_fp16)[name = string("attn_21_cast_fp16")]; + tensor attn_cast_fp16 = mul(x = attn_21_cast_fp16, y = mask_1_cast_fp16)[name = string("attn_cast_fp16")]; + bool var_2286_transpose_x_0 = const()[name = string("op_2286_transpose_x_0"), val = bool(false)]; + bool var_2286_transpose_y_0 = const()[name = string("op_2286_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_2275_cast_fp16)[name = string("transpose_12")]; + tensor var_2286_cast_fp16 = matmul(transpose_x = var_2286_transpose_x_0, transpose_y = var_2286_transpose_y_0, x = attn_cast_fp16, y = v_15_cast_fp16)[name = string("op_2286_cast_fp16")]; + tensor var_2287_perm_0 = const()[name = string("op_2287_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor concat_43x = const()[name = string("concat_43x"), val = tensor([2, -1, 256])]; + tensor var_2287_cast_fp16 = transpose(perm = var_2287_perm_0, x = var_2286_cast_fp16)[name = string("transpose_11")]; + tensor input_381_cast_fp16 = reshape(shape = concat_43x, x = var_2287_cast_fp16)[name = string("input_381_cast_fp16")]; + tensor main_blocks_3_style_attn_out_fc_linear_weight_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110402880)))]; + tensor main_blocks_3_style_attn_out_fc_linear_bias_to_fp16 = const()[name = string("main_blocks_3_style_attn_out_fc_linear_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110665088)))]; + tensor linear_37_cast_fp16 = linear(bias = main_blocks_3_style_attn_out_fc_linear_bias_to_fp16, weight = main_blocks_3_style_attn_out_fc_linear_weight_to_fp16, x = input_381_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor out_63_perm_0 = const()[name = string("out_63_perm_0"), val = tensor([0, 2, 1])]; + tensor out_63_cast_fp16 = transpose(perm = out_63_perm_0, x = linear_37_cast_fp16)[name = string("transpose_10")]; + tensor var_2295_cast_fp16 = mul(x = out_63_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2295_cast_fp16")]; + tensor x_201_cast_fp16 = add(x = x_m_cast_fp16, y = var_2295_cast_fp16)[name = string("x_201_cast_fp16")]; + tensor input_383_perm_0 = const()[name = string("input_383_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2302_axes_0 = const()[name = string("op_2302_axes_0"), val = tensor([-1])]; + tensor main_blocks_3_style_norm_norm_weight_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110666176)))]; + tensor main_blocks_3_style_norm_norm_bias_to_fp16 = const()[name = string("main_blocks_3_style_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110667264)))]; + tensor input_383_cast_fp16 = transpose(perm = input_383_perm_0, x = x_201_cast_fp16)[name = string("transpose_9")]; + tensor var_2302_cast_fp16 = layer_norm(axes = var_2302_axes_0, beta = main_blocks_3_style_norm_norm_bias_to_fp16, epsilon = var_1780_to_fp16, gamma = main_blocks_3_style_norm_norm_weight_to_fp16, x = input_383_cast_fp16)[name = string("op_2302_cast_fp16")]; + tensor var_2303_perm_0 = const()[name = string("op_2303_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2303_cast_fp16 = transpose(perm = var_2303_perm_0, x = var_2302_cast_fp16)[name = string("transpose_8")]; + tensor x_203_cast_fp16 = mul(x = var_2303_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_203_cast_fp16")]; + tensor x_205_cast_fp16 = mul(x = x_203_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_205_cast_fp16")]; + tensor var_2330_cast_fp16 = mul(x = x_205_cast_fp16, y = last_1_cast_fp16)[name = string("op_2330_cast_fp16")]; + tensor x_last_49_axes_0 = const()[name = string("x_last_49_axes_0"), val = tensor([-1])]; + bool x_last_49_keep_dims_0 = const()[name = string("x_last_49_keep_dims_0"), val = bool(true)]; + tensor x_last_49_cast_fp16 = reduce_sum(axes = x_last_49_axes_0, keep_dims = x_last_49_keep_dims_0, x = var_2330_cast_fp16)[name = string("x_last_49_cast_fp16")]; + tensor var_2333_cast_fp16 = mul(x = x_205_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2333_cast_fp16")]; + tensor var_2335_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_49_cast_fp16)[name = string("op_2335_cast_fp16")]; + tensor input_387_cast_fp16 = add(x = var_2333_cast_fp16, y = var_2335_cast_fp16)[name = string("input_387_cast_fp16")]; + tensor input_389_pad_0 = const()[name = string("input_389_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_389_mode_0 = const()[name = string("input_389_mode_0"), val = string("replicate")]; + fp16 const_53_to_fp16 = const()[name = string("const_53_to_fp16"), val = fp16(0x0p+0)]; + tensor input_389_cast_fp16 = pad(constant_val = const_53_to_fp16, mode = input_389_mode_0, pad = input_389_pad_0, x = input_387_cast_fp16)[name = string("input_389_cast_fp16")]; + string h_147_pad_type_0 = const()[name = string("h_147_pad_type_0"), val = string("valid")]; + int32 h_147_groups_0 = const()[name = string("h_147_groups_0"), val = int32(512)]; + tensor h_147_strides_0 = const()[name = string("h_147_strides_0"), val = tensor([1])]; + tensor h_147_pad_0 = const()[name = string("h_147_pad_0"), val = tensor([0, 0])]; + tensor h_147_dilations_0 = const()[name = string("h_147_dilations_0"), val = tensor([1])]; + tensor last_convnext_0_dwconv__conv_weight_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110668352)))]; + tensor last_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110673536)))]; + tensor h_147_cast_fp16 = conv(bias = last_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_147_dilations_0, groups = h_147_groups_0, pad = h_147_pad_0, pad_type = h_147_pad_type_0, strides = h_147_strides_0, weight = last_convnext_0_dwconv__conv_weight_to_fp16, x = input_389_cast_fp16)[name = string("h_147_cast_fp16")]; + tensor x_207_cast_fp16 = mul(x = h_147_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_207_cast_fp16")]; + tensor input_391_perm_0 = const()[name = string("input_391_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2353_axes_0 = const()[name = string("op_2353_axes_0"), val = tensor([-1])]; + tensor last_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_0_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110674624)))]; + tensor last_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_0_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110675712)))]; + fp16 var_2307_to_fp16 = const()[name = string("op_2307_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_391_cast_fp16 = transpose(perm = input_391_perm_0, x = x_207_cast_fp16)[name = string("transpose_7")]; + tensor var_2353_cast_fp16 = layer_norm(axes = var_2353_axes_0, beta = last_convnext_0_norm_norm_bias_to_fp16, epsilon = var_2307_to_fp16, gamma = last_convnext_0_norm_norm_weight_to_fp16, x = input_391_cast_fp16)[name = string("op_2353_cast_fp16")]; + tensor input_393_perm_0 = const()[name = string("input_393_perm_0"), val = tensor([0, 2, 1])]; + string h_149_pad_type_0 = const()[name = string("h_149_pad_type_0"), val = string("valid")]; + tensor h_149_strides_0 = const()[name = string("h_149_strides_0"), val = tensor([1])]; + tensor h_149_pad_0 = const()[name = string("h_149_pad_0"), val = tensor([0, 0])]; + tensor h_149_dilations_0 = const()[name = string("h_149_dilations_0"), val = tensor([1])]; + int32 h_149_groups_0 = const()[name = string("h_149_groups_0"), val = int32(1)]; + tensor last_convnext_0_pwconv1_weight_to_fp16 = const()[name = string("last_convnext_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110676800)))]; + tensor last_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112774016)))]; + tensor input_393_cast_fp16 = transpose(perm = input_393_perm_0, x = var_2353_cast_fp16)[name = string("transpose_6")]; + tensor h_149_cast_fp16 = conv(bias = last_convnext_0_pwconv1_bias_to_fp16, dilations = h_149_dilations_0, groups = h_149_groups_0, pad = h_149_pad_0, pad_type = h_149_pad_type_0, strides = h_149_strides_0, weight = last_convnext_0_pwconv1_weight_to_fp16, x = input_393_cast_fp16)[name = string("h_149_cast_fp16")]; + string input_395_mode_0 = const()[name = string("input_395_mode_0"), val = string("EXACT")]; + tensor input_395_cast_fp16 = gelu(mode = input_395_mode_0, x = h_149_cast_fp16)[name = string("input_395_cast_fp16")]; + string h_151_pad_type_0 = const()[name = string("h_151_pad_type_0"), val = string("valid")]; + tensor h_151_strides_0 = const()[name = string("h_151_strides_0"), val = tensor([1])]; + tensor h_151_pad_0 = const()[name = string("h_151_pad_0"), val = tensor([0, 0])]; + tensor h_151_dilations_0 = const()[name = string("h_151_dilations_0"), val = tensor([1])]; + int32 h_151_groups_0 = const()[name = string("h_151_groups_0"), val = int32(1)]; + tensor var_2370_weight_0_to_fp16 = const()[name = string("op_2370_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112778176)))]; + tensor var_2370_bias_0_to_fp16 = const()[name = string("op_2370_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114875392)))]; + tensor var_2370_cast_fp16 = conv(bias = var_2370_bias_0_to_fp16, dilations = h_151_dilations_0, groups = h_151_groups_0, pad = h_151_pad_0, pad_type = h_151_pad_type_0, strides = h_151_strides_0, weight = var_2370_weight_0_to_fp16, x = input_395_cast_fp16)[name = string("op_2370_cast_fp16")]; + tensor out_65_cast_fp16 = add(x = x_205_cast_fp16, y = var_2370_cast_fp16)[name = string("out_65_cast_fp16")]; + tensor x_209_cast_fp16 = mul(x = out_65_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_209_cast_fp16")]; + tensor x_211_cast_fp16 = mul(x = x_209_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_211_cast_fp16")]; + tensor var_2398_cast_fp16 = mul(x = x_211_cast_fp16, y = last_1_cast_fp16)[name = string("op_2398_cast_fp16")]; + tensor x_last_51_axes_0 = const()[name = string("x_last_51_axes_0"), val = tensor([-1])]; + bool x_last_51_keep_dims_0 = const()[name = string("x_last_51_keep_dims_0"), val = bool(true)]; + tensor x_last_51_cast_fp16 = reduce_sum(axes = x_last_51_axes_0, keep_dims = x_last_51_keep_dims_0, x = var_2398_cast_fp16)[name = string("x_last_51_cast_fp16")]; + tensor var_2401_cast_fp16 = mul(x = x_211_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2401_cast_fp16")]; + tensor var_2403_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_51_cast_fp16)[name = string("op_2403_cast_fp16")]; + tensor input_399_cast_fp16 = add(x = var_2401_cast_fp16, y = var_2403_cast_fp16)[name = string("input_399_cast_fp16")]; + tensor input_401_pad_0 = const()[name = string("input_401_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_401_mode_0 = const()[name = string("input_401_mode_0"), val = string("replicate")]; + fp16 const_55_to_fp16 = const()[name = string("const_55_to_fp16"), val = fp16(0x0p+0)]; + tensor input_401_cast_fp16 = pad(constant_val = const_55_to_fp16, mode = input_401_mode_0, pad = input_401_pad_0, x = input_399_cast_fp16)[name = string("input_401_cast_fp16")]; + string h_153_pad_type_0 = const()[name = string("h_153_pad_type_0"), val = string("valid")]; + int32 h_153_groups_0 = const()[name = string("h_153_groups_0"), val = int32(512)]; + tensor h_153_strides_0 = const()[name = string("h_153_strides_0"), val = tensor([1])]; + tensor h_153_pad_0 = const()[name = string("h_153_pad_0"), val = tensor([0, 0])]; + tensor h_153_dilations_0 = const()[name = string("h_153_dilations_0"), val = tensor([1])]; + tensor last_convnext_1_dwconv__conv_weight_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114876480)))]; + tensor last_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114881664)))]; + tensor h_153_cast_fp16 = conv(bias = last_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_153_dilations_0, groups = h_153_groups_0, pad = h_153_pad_0, pad_type = h_153_pad_type_0, strides = h_153_strides_0, weight = last_convnext_1_dwconv__conv_weight_to_fp16, x = input_401_cast_fp16)[name = string("h_153_cast_fp16")]; + tensor x_213_cast_fp16 = mul(x = h_153_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_213_cast_fp16")]; + tensor input_403_perm_0 = const()[name = string("input_403_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2421_axes_0 = const()[name = string("op_2421_axes_0"), val = tensor([-1])]; + tensor last_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_1_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114882752)))]; + tensor last_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_1_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114883840)))]; + fp16 var_2375_to_fp16 = const()[name = string("op_2375_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_403_cast_fp16 = transpose(perm = input_403_perm_0, x = x_213_cast_fp16)[name = string("transpose_5")]; + tensor var_2421_cast_fp16 = layer_norm(axes = var_2421_axes_0, beta = last_convnext_1_norm_norm_bias_to_fp16, epsilon = var_2375_to_fp16, gamma = last_convnext_1_norm_norm_weight_to_fp16, x = input_403_cast_fp16)[name = string("op_2421_cast_fp16")]; + tensor input_405_perm_0 = const()[name = string("input_405_perm_0"), val = tensor([0, 2, 1])]; + string h_155_pad_type_0 = const()[name = string("h_155_pad_type_0"), val = string("valid")]; + tensor h_155_strides_0 = const()[name = string("h_155_strides_0"), val = tensor([1])]; + tensor h_155_pad_0 = const()[name = string("h_155_pad_0"), val = tensor([0, 0])]; + tensor h_155_dilations_0 = const()[name = string("h_155_dilations_0"), val = tensor([1])]; + int32 h_155_groups_0 = const()[name = string("h_155_groups_0"), val = int32(1)]; + tensor last_convnext_1_pwconv1_weight_to_fp16 = const()[name = string("last_convnext_1_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114884928)))]; + tensor last_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116982144)))]; + tensor input_405_cast_fp16 = transpose(perm = input_405_perm_0, x = var_2421_cast_fp16)[name = string("transpose_4")]; + tensor h_155_cast_fp16 = conv(bias = last_convnext_1_pwconv1_bias_to_fp16, dilations = h_155_dilations_0, groups = h_155_groups_0, pad = h_155_pad_0, pad_type = h_155_pad_type_0, strides = h_155_strides_0, weight = last_convnext_1_pwconv1_weight_to_fp16, x = input_405_cast_fp16)[name = string("h_155_cast_fp16")]; + string input_407_mode_0 = const()[name = string("input_407_mode_0"), val = string("EXACT")]; + tensor input_407_cast_fp16 = gelu(mode = input_407_mode_0, x = h_155_cast_fp16)[name = string("input_407_cast_fp16")]; + string h_157_pad_type_0 = const()[name = string("h_157_pad_type_0"), val = string("valid")]; + tensor h_157_strides_0 = const()[name = string("h_157_strides_0"), val = tensor([1])]; + tensor h_157_pad_0 = const()[name = string("h_157_pad_0"), val = tensor([0, 0])]; + tensor h_157_dilations_0 = const()[name = string("h_157_dilations_0"), val = tensor([1])]; + int32 h_157_groups_0 = const()[name = string("h_157_groups_0"), val = int32(1)]; + tensor var_2438_weight_0_to_fp16 = const()[name = string("op_2438_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116986304)))]; + tensor var_2438_bias_0_to_fp16 = const()[name = string("op_2438_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119083520)))]; + tensor var_2438_cast_fp16 = conv(bias = var_2438_bias_0_to_fp16, dilations = h_157_dilations_0, groups = h_157_groups_0, pad = h_157_pad_0, pad_type = h_157_pad_type_0, strides = h_157_strides_0, weight = var_2438_weight_0_to_fp16, x = input_407_cast_fp16)[name = string("op_2438_cast_fp16")]; + tensor out_67_cast_fp16 = add(x = x_211_cast_fp16, y = var_2438_cast_fp16)[name = string("out_67_cast_fp16")]; + tensor x_215_cast_fp16 = mul(x = out_67_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_215_cast_fp16")]; + tensor x_217_cast_fp16 = mul(x = x_215_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_217_cast_fp16")]; + tensor var_2466_cast_fp16 = mul(x = x_217_cast_fp16, y = last_1_cast_fp16)[name = string("op_2466_cast_fp16")]; + tensor x_last_53_axes_0 = const()[name = string("x_last_53_axes_0"), val = tensor([-1])]; + bool x_last_53_keep_dims_0 = const()[name = string("x_last_53_keep_dims_0"), val = bool(true)]; + tensor x_last_53_cast_fp16 = reduce_sum(axes = x_last_53_axes_0, keep_dims = x_last_53_keep_dims_0, x = var_2466_cast_fp16)[name = string("x_last_53_cast_fp16")]; + tensor var_2469_cast_fp16 = mul(x = x_217_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2469_cast_fp16")]; + tensor var_2471_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_53_cast_fp16)[name = string("op_2471_cast_fp16")]; + tensor input_411_cast_fp16 = add(x = var_2469_cast_fp16, y = var_2471_cast_fp16)[name = string("input_411_cast_fp16")]; + tensor input_413_pad_0 = const()[name = string("input_413_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_413_mode_0 = const()[name = string("input_413_mode_0"), val = string("replicate")]; + fp16 const_57_to_fp16 = const()[name = string("const_57_to_fp16"), val = fp16(0x0p+0)]; + tensor input_413_cast_fp16 = pad(constant_val = const_57_to_fp16, mode = input_413_mode_0, pad = input_413_pad_0, x = input_411_cast_fp16)[name = string("input_413_cast_fp16")]; + string h_159_pad_type_0 = const()[name = string("h_159_pad_type_0"), val = string("valid")]; + int32 h_159_groups_0 = const()[name = string("h_159_groups_0"), val = int32(512)]; + tensor h_159_strides_0 = const()[name = string("h_159_strides_0"), val = tensor([1])]; + tensor h_159_pad_0 = const()[name = string("h_159_pad_0"), val = tensor([0, 0])]; + tensor h_159_dilations_0 = const()[name = string("h_159_dilations_0"), val = tensor([1])]; + tensor last_convnext_2_dwconv__conv_weight_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119084608)))]; + tensor last_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119089792)))]; + tensor h_159_cast_fp16 = conv(bias = last_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_159_dilations_0, groups = h_159_groups_0, pad = h_159_pad_0, pad_type = h_159_pad_type_0, strides = h_159_strides_0, weight = last_convnext_2_dwconv__conv_weight_to_fp16, x = input_413_cast_fp16)[name = string("h_159_cast_fp16")]; + tensor x_219_cast_fp16 = mul(x = h_159_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_219_cast_fp16")]; + tensor input_415_perm_0 = const()[name = string("input_415_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2489_axes_0 = const()[name = string("op_2489_axes_0"), val = tensor([-1])]; + tensor last_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_2_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119090880)))]; + tensor last_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_2_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119091968)))]; + fp16 var_2443_to_fp16 = const()[name = string("op_2443_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_415_cast_fp16 = transpose(perm = input_415_perm_0, x = x_219_cast_fp16)[name = string("transpose_3")]; + tensor var_2489_cast_fp16 = layer_norm(axes = var_2489_axes_0, beta = last_convnext_2_norm_norm_bias_to_fp16, epsilon = var_2443_to_fp16, gamma = last_convnext_2_norm_norm_weight_to_fp16, x = input_415_cast_fp16)[name = string("op_2489_cast_fp16")]; + tensor input_417_perm_0 = const()[name = string("input_417_perm_0"), val = tensor([0, 2, 1])]; + string h_161_pad_type_0 = const()[name = string("h_161_pad_type_0"), val = string("valid")]; + tensor h_161_strides_0 = const()[name = string("h_161_strides_0"), val = tensor([1])]; + tensor h_161_pad_0 = const()[name = string("h_161_pad_0"), val = tensor([0, 0])]; + tensor h_161_dilations_0 = const()[name = string("h_161_dilations_0"), val = tensor([1])]; + int32 h_161_groups_0 = const()[name = string("h_161_groups_0"), val = int32(1)]; + tensor last_convnext_2_pwconv1_weight_to_fp16 = const()[name = string("last_convnext_2_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119093056)))]; + tensor last_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121190272)))]; + tensor input_417_cast_fp16 = transpose(perm = input_417_perm_0, x = var_2489_cast_fp16)[name = string("transpose_2")]; + tensor h_161_cast_fp16 = conv(bias = last_convnext_2_pwconv1_bias_to_fp16, dilations = h_161_dilations_0, groups = h_161_groups_0, pad = h_161_pad_0, pad_type = h_161_pad_type_0, strides = h_161_strides_0, weight = last_convnext_2_pwconv1_weight_to_fp16, x = input_417_cast_fp16)[name = string("h_161_cast_fp16")]; + string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("EXACT")]; + tensor input_419_cast_fp16 = gelu(mode = input_419_mode_0, x = h_161_cast_fp16)[name = string("input_419_cast_fp16")]; + string h_163_pad_type_0 = const()[name = string("h_163_pad_type_0"), val = string("valid")]; + tensor h_163_strides_0 = const()[name = string("h_163_strides_0"), val = tensor([1])]; + tensor h_163_pad_0 = const()[name = string("h_163_pad_0"), val = tensor([0, 0])]; + tensor h_163_dilations_0 = const()[name = string("h_163_dilations_0"), val = tensor([1])]; + int32 h_163_groups_0 = const()[name = string("h_163_groups_0"), val = int32(1)]; + tensor var_2506_weight_0_to_fp16 = const()[name = string("op_2506_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121194432)))]; + tensor var_2506_bias_0_to_fp16 = const()[name = string("op_2506_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123291648)))]; + tensor var_2506_cast_fp16 = conv(bias = var_2506_bias_0_to_fp16, dilations = h_163_dilations_0, groups = h_163_groups_0, pad = h_163_pad_0, pad_type = h_163_pad_type_0, strides = h_163_strides_0, weight = var_2506_weight_0_to_fp16, x = input_419_cast_fp16)[name = string("op_2506_cast_fp16")]; + tensor out_69_cast_fp16 = add(x = x_217_cast_fp16, y = var_2506_cast_fp16)[name = string("out_69_cast_fp16")]; + tensor x_221_cast_fp16 = mul(x = out_69_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_221_cast_fp16")]; + tensor x_223_cast_fp16 = mul(x = x_221_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_223_cast_fp16")]; + tensor var_2534_cast_fp16 = mul(x = x_223_cast_fp16, y = last_1_cast_fp16)[name = string("op_2534_cast_fp16")]; + tensor x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor([-1])]; + bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)]; + tensor x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_2534_cast_fp16)[name = string("x_last_cast_fp16")]; + tensor var_2537_cast_fp16 = mul(x = x_223_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("op_2537_cast_fp16")]; + tensor var_2539_cast_fp16 = mul(x = var_173_cast_fp16, y = x_last_cast_fp16)[name = string("op_2539_cast_fp16")]; + tensor input_423_cast_fp16 = add(x = var_2537_cast_fp16, y = var_2539_cast_fp16)[name = string("input_423_cast_fp16")]; + tensor input_425_pad_0 = const()[name = string("input_425_pad_0"), val = tensor([0, 0, 0, 0, 2, 2])]; + string input_425_mode_0 = const()[name = string("input_425_mode_0"), val = string("replicate")]; + fp16 const_59_to_fp16 = const()[name = string("const_59_to_fp16"), val = fp16(0x0p+0)]; + tensor input_425_cast_fp16 = pad(constant_val = const_59_to_fp16, mode = input_425_mode_0, pad = input_425_pad_0, x = input_423_cast_fp16)[name = string("input_425_cast_fp16")]; + string h_165_pad_type_0 = const()[name = string("h_165_pad_type_0"), val = string("valid")]; + int32 h_165_groups_0 = const()[name = string("h_165_groups_0"), val = int32(512)]; + tensor h_165_strides_0 = const()[name = string("h_165_strides_0"), val = tensor([1])]; + tensor h_165_pad_0 = const()[name = string("h_165_pad_0"), val = tensor([0, 0])]; + tensor h_165_dilations_0 = const()[name = string("h_165_dilations_0"), val = tensor([1])]; + tensor last_convnext_3_dwconv__conv_weight_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123292736)))]; + tensor last_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("last_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123297920)))]; + tensor h_165_cast_fp16 = conv(bias = last_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_165_dilations_0, groups = h_165_groups_0, pad = h_165_pad_0, pad_type = h_165_pad_type_0, strides = h_165_strides_0, weight = last_convnext_3_dwconv__conv_weight_to_fp16, x = input_425_cast_fp16)[name = string("h_165_cast_fp16")]; + tensor x_cast_fp16 = mul(x = h_165_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor([0, 2, 1])]; + tensor var_2557_axes_0 = const()[name = string("op_2557_axes_0"), val = tensor([-1])]; + tensor last_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("last_convnext_3_norm_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123299008)))]; + tensor last_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("last_convnext_3_norm_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123300096)))]; + fp16 var_2511_to_fp16 = const()[name = string("op_2511_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_cast_fp16)[name = string("transpose_1")]; + tensor var_2557_cast_fp16 = layer_norm(axes = var_2557_axes_0, beta = last_convnext_3_norm_norm_bias_to_fp16, epsilon = var_2511_to_fp16, gamma = last_convnext_3_norm_norm_weight_to_fp16, x = input_427_cast_fp16)[name = string("op_2557_cast_fp16")]; + tensor input_429_perm_0 = const()[name = string("input_429_perm_0"), val = tensor([0, 2, 1])]; + string h_167_pad_type_0 = const()[name = string("h_167_pad_type_0"), val = string("valid")]; + tensor h_167_strides_0 = const()[name = string("h_167_strides_0"), val = tensor([1])]; + tensor h_167_pad_0 = const()[name = string("h_167_pad_0"), val = tensor([0, 0])]; + tensor h_167_dilations_0 = const()[name = string("h_167_dilations_0"), val = tensor([1])]; + int32 h_167_groups_0 = const()[name = string("h_167_groups_0"), val = int32(1)]; + tensor last_convnext_3_pwconv1_weight_to_fp16 = const()[name = string("last_convnext_3_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123301184)))]; + tensor last_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("last_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125398400)))]; + tensor input_429_cast_fp16 = transpose(perm = input_429_perm_0, x = var_2557_cast_fp16)[name = string("transpose_0")]; + tensor h_167_cast_fp16 = conv(bias = last_convnext_3_pwconv1_bias_to_fp16, dilations = h_167_dilations_0, groups = h_167_groups_0, pad = h_167_pad_0, pad_type = h_167_pad_type_0, strides = h_167_strides_0, weight = last_convnext_3_pwconv1_weight_to_fp16, x = input_429_cast_fp16)[name = string("h_167_cast_fp16")]; + string input_431_mode_0 = const()[name = string("input_431_mode_0"), val = string("EXACT")]; + tensor input_431_cast_fp16 = gelu(mode = input_431_mode_0, x = h_167_cast_fp16)[name = string("input_431_cast_fp16")]; + string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")]; + tensor h_strides_0 = const()[name = string("h_strides_0"), val = tensor([1])]; + tensor h_pad_0 = const()[name = string("h_pad_0"), val = tensor([0, 0])]; + tensor h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor([1])]; + int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)]; + tensor var_2574_weight_0_to_fp16 = const()[name = string("op_2574_weight_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125402560)))]; + tensor var_2574_bias_0_to_fp16 = const()[name = string("op_2574_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127499776)))]; + tensor var_2574_cast_fp16 = conv(bias = var_2574_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = var_2574_weight_0_to_fp16, x = input_431_cast_fp16)[name = string("op_2574_cast_fp16")]; + tensor out_cast_fp16 = add(x = x_223_cast_fp16, y = var_2574_cast_fp16)[name = string("out_cast_fp16")]; + tensor input_cast_fp16 = mul(x = out_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("input_cast_fp16")]; + string var_2587_pad_type_0 = const()[name = string("op_2587_pad_type_0"), val = string("valid")]; + tensor var_2587_strides_0 = const()[name = string("op_2587_strides_0"), val = tensor([1])]; + tensor var_2587_pad_0 = const()[name = string("op_2587_pad_0"), val = tensor([0, 0])]; + tensor var_2587_dilations_0 = const()[name = string("op_2587_dilations_0"), val = tensor([1])]; + int32 var_2587_groups_0 = const()[name = string("op_2587_groups_0"), val = int32(1)]; + tensor proj_out_weight_to_fp16 = const()[name = string("proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127500864)))]; + tensor var_2587_cast_fp16 = conv(dilations = var_2587_dilations_0, groups = var_2587_groups_0, pad = var_2587_pad_0, pad_type = var_2587_pad_type_0, strides = var_2587_strides_0, weight = proj_out_weight_to_fp16, x = input_cast_fp16)[name = string("op_2587_cast_fp16")]; + tensor v_cast_fp16 = mul(x = var_2587_cast_fp16, y = latent_mask_b_cast_fp16)[name = string("v_cast_fp16")]; + tensor cond_begin_0 = const()[name = string("cond_begin_0"), val = tensor([0, 0, 0])]; + tensor cond_end_0 = const()[name = string("cond_end_0"), val = tensor([1, 144, 0])]; + tensor cond_end_mask_0 = const()[name = string("cond_end_mask_0"), val = tensor([false, true, true])]; + tensor cond_cast_fp16 = slice_by_index(begin = cond_begin_0, end = cond_end_0, end_mask = cond_end_mask_0, x = v_cast_fp16)[name = string("cond_cast_fp16")]; + tensor uncond_begin_0 = const()[name = string("uncond_begin_0"), val = tensor([1, 0, 0])]; + tensor uncond_end_0 = const()[name = string("uncond_end_0"), val = tensor([2, 144, 0])]; + tensor uncond_end_mask_0 = const()[name = string("uncond_end_mask_0"), val = tensor([true, true, true])]; + tensor uncond_cast_fp16 = slice_by_index(begin = uncond_begin_0, end = uncond_end_0, end_mask = uncond_end_mask_0, x = v_cast_fp16)[name = string("uncond_cast_fp16")]; + fp32 var_2597_epsilon_0 = const()[name = string("op_2597_epsilon_0"), val = fp32(0x1.a36e2ep-14)]; + tensor var_2597_cast_fp16 = inverse(epsilon = var_2597_epsilon_0, x = total_step_to_fp16)[name = string("op_2597_cast_fp16")]; + tensor var_2603 = const()[name = string("op_2603"), val = tensor([-1, 1, 1])]; + tensor step_cast_fp16 = reshape(shape = var_2603, x = var_2597_cast_fp16)[name = string("step_cast_fp16")]; + fp16 var_2605_to_fp16 = const()[name = string("op_2605_to_fp16"), val = fp16(0x1p+2)]; + tensor var_2606_cast_fp16 = mul(x = cond_cast_fp16, y = var_2605_to_fp16)[name = string("op_2606_cast_fp16")]; + fp16 var_2607_to_fp16 = const()[name = string("op_2607_to_fp16"), val = fp16(0x1.8p+1)]; + tensor var_2608_cast_fp16 = mul(x = uncond_cast_fp16, y = var_2607_to_fp16)[name = string("op_2608_cast_fp16")]; + tensor var_2610_cast_fp16 = sub(x = var_2606_cast_fp16, y = var_2608_cast_fp16)[name = string("op_2610_cast_fp16")]; + tensor var_2611_cast_fp16 = mul(x = step_cast_fp16, y = var_2610_cast_fp16)[name = string("op_2611_cast_fp16")]; + tensor var_2613_cast_fp16 = add(x = noisy_latent_to_fp16, y = var_2611_cast_fp16)[name = string("op_2613_cast_fp16")]; + tensor var_2614_cast_fp16 = mul(x = var_2613_cast_fp16, y = latent_mask_to_fp16)[name = string("op_2614_cast_fp16")]; + string var_2614_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_2614_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor denoised_latent = cast(dtype = var_2614_cast_fp16_to_fp32_dtype_0, x = var_2614_cast_fp16)[name = string("cast_85")]; + } -> (denoised_latent); +} \ No newline at end of file diff --git a/VectorEstimator_v2.mlmodelc/weights/weight.bin b/VectorEstimator_v2.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..958986e9894aa6516b08f61f380270fe71117cec --- /dev/null +++ b/VectorEstimator_v2.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:101cebe7759b5f5e8f188ed3c2bf5f624bbf048aa657fe5bed9c3110c5ce0c26 +size 127648384 diff --git a/VectorEstimator_v2.mlpackage/Data/com.apple.CoreML/model.mlmodel 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