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- "visionOS" : "1.0",
48
- "watchOS" : "10.0",
49
- "iOS" : "17.0",
50
- "macCatalyst" : "17.0"
51
- },
52
- "modelType" : {
53
- "name" : "MLModelType_mlProgram"
54
- },
55
- "userDefinedMetadata" : {
56
- "com.github.apple.coremltools.conversion_date" : "2026-04-25",
57
- "com.github.apple.coremltools.source" : "torch==2.9.1",
58
- "com.github.apple.coremltools.version" : "9.0",
59
- "com.github.apple.coremltools.source_dialect" : "TorchScript"
60
- },
61
- "inputSchema" : [
62
- {
63
- "hasShapeFlexibility" : "0",
64
- "isOptional" : "0",
65
- "dataType" : "Float32",
66
- "formattedType" : "MultiArray (Float32 1 × 1024)",
67
- "shortDescription" : "",
68
- "shape" : "[1, 1024]",
69
- "name" : "transformer_out",
70
- "type" : "MultiArray"
71
- },
72
- {
73
- "hasShapeFlexibility" : "0",
74
- "isOptional" : "0",
75
- "dataType" : "Float32",
76
- "formattedType" : "MultiArray (Float32 1 × 32)",
77
- "shortDescription" : "",
78
- "shape" : "[1, 32]",
79
- "name" : "latent",
80
- "type" : "MultiArray"
81
- },
82
- {
83
- "hasShapeFlexibility" : "0",
84
- "isOptional" : "0",
85
- "dataType" : "Float32",
86
- "formattedType" : "MultiArray (Float32 1 × 1)",
87
- "shortDescription" : "",
88
- "shape" : "[1, 1]",
89
- "name" : "s",
90
- "type" : "MultiArray"
91
- },
92
- {
93
- "hasShapeFlexibility" : "0",
94
- "isOptional" : "0",
95
- "dataType" : "Float32",
96
- "formattedType" : "MultiArray (Float32 1 × 1)",
97
- "shortDescription" : "",
98
- "shape" : "[1, 1]",
99
- "name" : "t",
100
- "type" : "MultiArray"
101
- }
102
- ],
103
- "generatedClassName" : "flow_decoder_int8",
104
- "method" : "predict"
105
- }
106
- ]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
v2/german/int8/flow_decoder.mlmodelc/model.mil DELETED
@@ -1,322 +0,0 @@
1
- program(1.0)
2
- [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})]
3
- {
4
- func main<ios17>(tensor<fp32, [1, 32]> latent, tensor<fp32, [1, 1]> s, tensor<fp32, [1, 1]> t, tensor<fp32, [1, 1024]> transformer_out) {
5
- tensor<int32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<int32, []>(-1)];
6
- tensor<string, []> latent_to_fp16_dtype_0 = const()[name = tensor<string, []>("latent_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
7
- tensor<fp16, [512, 32]> flow_net_input_proj_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_input_proj_weight_to_fp16"), val = tensor<fp16, [512, 32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
8
- tensor<fp16, [512]> flow_net_input_proj_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_input_proj_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32896)))];
9
- tensor<fp16, [1, 32]> latent_to_fp16 = cast(dtype = latent_to_fp16_dtype_0, x = latent)[name = tensor<string, []>("cast_4")];
10
- tensor<fp16, [1, 512]> linear_0_cast_fp16 = linear(bias = flow_net_input_proj_bias_to_fp16, weight = flow_net_input_proj_weight_to_fp16, x = latent_to_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
11
- tensor<string, []> s_to_fp16_dtype_0 = const()[name = tensor<string, []>("s_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
12
- tensor<fp16, [128]> const_0_to_fp16 = const()[name = tensor<string, []>("const_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33984)))];
13
- tensor<fp16, [1, 1]> s_to_fp16 = cast(dtype = s_to_fp16_dtype_0, x = s)[name = tensor<string, []>("cast_3")];
14
- tensor<fp16, [1, 128]> args_1_cast_fp16 = mul(x = s_to_fp16, y = const_0_to_fp16)[name = tensor<string, []>("args_1_cast_fp16")];
15
- tensor<fp16, [1, 128]> var_39_cast_fp16 = cos(x = args_1_cast_fp16)[name = tensor<string, []>("op_39_cast_fp16")];
16
- tensor<fp16, [1, 128]> var_40_cast_fp16 = sin(x = args_1_cast_fp16)[name = tensor<string, []>("op_40_cast_fp16")];
17
- tensor<bool, []> input_1_interleave_0 = const()[name = tensor<string, []>("input_1_interleave_0"), val = tensor<bool, []>(false)];
18
- tensor<fp16, [1, 256]> input_1_cast_fp16 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_39_cast_fp16, var_40_cast_fp16))[name = tensor<string, []>("input_1_cast_fp16")];
19
- tensor<fp16, [512, 256]> flow_net_time_embed_0_mlp_0_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_time_embed_0_mlp_0_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34304))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166016))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
20
- tensor<fp16, [512]> flow_net_time_embed_0_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_time_embed_0_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(167104)))];
21
- tensor<fp16, [1, 512]> linear_1_cast_fp16 = linear(bias = flow_net_time_embed_0_mlp_0_bias_to_fp16, weight = flow_net_time_embed_0_mlp_0_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")];
22
- tensor<fp16, [1, 512]> input_5_cast_fp16 = silu(x = linear_1_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
23
- tensor<fp16, [512, 512]> flow_net_time_embed_0_mlp_2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_time_embed_0_mlp_2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(168192))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430400))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
24
- tensor<fp16, [512]> flow_net_time_embed_0_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_time_embed_0_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(431488)))];
25
- tensor<fp16, [1, 512]> linear_2_cast_fp16 = linear(bias = flow_net_time_embed_0_mlp_2_bias_to_fp16, weight = flow_net_time_embed_0_mlp_2_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
26
- tensor<int32, [1]> reduce_mean_0_axes_0 = const()[name = tensor<string, []>("reduce_mean_0_axes_0"), val = tensor<int32, [1]>([-1])];
27
- tensor<bool, []> reduce_mean_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_0_keep_dims_0"), val = tensor<bool, []>(true)];
28
- tensor<fp16, [1, 1]> reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = linear_2_cast_fp16)[name = tensor<string, []>("reduce_mean_0_cast_fp16")];
29
- tensor<fp16, [1, 512]> sub_0_cast_fp16 = sub(x = linear_2_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor<string, []>("sub_0_cast_fp16")];
30
- tensor<fp16, [1, 512]> square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor<string, []>("square_0_cast_fp16")];
31
- tensor<int32, [1]> reduce_mean_1_axes_0 = const()[name = tensor<string, []>("reduce_mean_1_axes_0"), val = tensor<int32, [1]>([-1])];
32
- tensor<bool, []> reduce_mean_1_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_1_keep_dims_0"), val = tensor<bool, []>(true)];
33
- tensor<fp16, [1, 1]> reduce_mean_1_cast_fp16 = reduce_mean(axes = reduce_mean_1_axes_0, keep_dims = reduce_mean_1_keep_dims_0, x = square_0_cast_fp16)[name = tensor<string, []>("reduce_mean_1_cast_fp16")];
34
- tensor<fp16, []> real_div_0_to_fp16 = const()[name = tensor<string, []>("real_div_0_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
35
- tensor<fp16, [1, 1]> mul_0_cast_fp16 = mul(x = reduce_mean_1_cast_fp16, y = real_div_0_to_fp16)[name = tensor<string, []>("mul_0_cast_fp16")];
36
- tensor<fp16, []> var_56_to_fp16 = const()[name = tensor<string, []>("op_56_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
37
- tensor<fp16, [1, 1]> var_1_cast_fp16 = add(x = mul_0_cast_fp16, y = var_56_to_fp16)[name = tensor<string, []>("var_1_cast_fp16")];
38
- tensor<fp32, []> var_59_epsilon_0 = const()[name = tensor<string, []>("op_59_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
39
- tensor<fp16, [1, 1]> var_59_cast_fp16 = rsqrt(epsilon = var_59_epsilon_0, x = var_1_cast_fp16)[name = tensor<string, []>("op_59_cast_fp16")];
40
- tensor<fp16, [512]> const_1_to_fp16 = const()[name = tensor<string, []>("const_1_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432576)))];
41
- tensor<fp16, [1, 512]> var_60_cast_fp16 = mul(x = const_1_to_fp16, y = var_59_cast_fp16)[name = tensor<string, []>("op_60_cast_fp16")];
42
- tensor<fp16, [1, 512]> var_61_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_60_cast_fp16)[name = tensor<string, []>("op_61_cast_fp16")];
43
- tensor<string, []> t_to_fp16_dtype_0 = const()[name = tensor<string, []>("t_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
44
- tensor<fp16, [1, 1]> t_to_fp16 = cast(dtype = t_to_fp16_dtype_0, x = t)[name = tensor<string, []>("cast_2")];
45
- tensor<fp16, [1, 128]> args_cast_fp16 = mul(x = t_to_fp16, y = const_0_to_fp16)[name = tensor<string, []>("args_cast_fp16")];
46
- tensor<fp16, [1, 128]> var_69_cast_fp16 = cos(x = args_cast_fp16)[name = tensor<string, []>("op_69_cast_fp16")];
47
- tensor<fp16, [1, 128]> var_70_cast_fp16 = sin(x = args_cast_fp16)[name = tensor<string, []>("op_70_cast_fp16")];
48
- tensor<bool, []> input_7_interleave_0 = const()[name = tensor<string, []>("input_7_interleave_0"), val = tensor<bool, []>(false)];
49
- tensor<fp16, [1, 256]> input_7_cast_fp16 = concat(axis = var_9, interleave = input_7_interleave_0, values = (var_69_cast_fp16, var_70_cast_fp16))[name = tensor<string, []>("input_7_cast_fp16")];
50
- tensor<fp16, [512, 256]> flow_net_time_embed_1_mlp_0_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_time_embed_1_mlp_0_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(433664))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(564800))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
51
- tensor<fp16, [512]> flow_net_time_embed_1_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_time_embed_1_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(565888)))];
52
- tensor<fp16, [1, 512]> linear_3_cast_fp16 = linear(bias = flow_net_time_embed_1_mlp_0_bias_to_fp16, weight = flow_net_time_embed_1_mlp_0_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = tensor<string, []>("linear_3_cast_fp16")];
53
- tensor<fp16, [1, 512]> input_11_cast_fp16 = silu(x = linear_3_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
54
- tensor<fp16, [512, 512]> flow_net_time_embed_1_mlp_2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_time_embed_1_mlp_2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(566976))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(829184))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
55
- tensor<fp16, [512]> flow_net_time_embed_1_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_time_embed_1_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(830272)))];
56
- tensor<fp16, [1, 512]> linear_4_cast_fp16 = linear(bias = flow_net_time_embed_1_mlp_2_bias_to_fp16, weight = flow_net_time_embed_1_mlp_2_weight_to_fp16_quantized, x = input_11_cast_fp16)[name = tensor<string, []>("linear_4_cast_fp16")];
57
- tensor<int32, [1]> reduce_mean_2_axes_0 = const()[name = tensor<string, []>("reduce_mean_2_axes_0"), val = tensor<int32, [1]>([-1])];
58
- tensor<bool, []> reduce_mean_2_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_2_keep_dims_0"), val = tensor<bool, []>(true)];
59
- tensor<fp16, [1, 1]> reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = linear_4_cast_fp16)[name = tensor<string, []>("reduce_mean_2_cast_fp16")];
60
- tensor<fp16, [1, 512]> sub_2_cast_fp16 = sub(x = linear_4_cast_fp16, y = reduce_mean_2_cast_fp16)[name = tensor<string, []>("sub_2_cast_fp16")];
61
- tensor<fp16, [1, 512]> square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor<string, []>("square_1_cast_fp16")];
62
- tensor<int32, [1]> reduce_mean_3_axes_0 = const()[name = tensor<string, []>("reduce_mean_3_axes_0"), val = tensor<int32, [1]>([-1])];
63
- tensor<bool, []> reduce_mean_3_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_3_keep_dims_0"), val = tensor<bool, []>(true)];
64
- tensor<fp16, [1, 1]> reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = square_1_cast_fp16)[name = tensor<string, []>("reduce_mean_3_cast_fp16")];
65
- tensor<fp16, []> real_div_1_to_fp16 = const()[name = tensor<string, []>("real_div_1_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
66
- tensor<fp16, [1, 1]> mul_1_cast_fp16 = mul(x = reduce_mean_3_cast_fp16, y = real_div_1_to_fp16)[name = tensor<string, []>("mul_1_cast_fp16")];
67
- tensor<fp16, []> var_86_to_fp16 = const()[name = tensor<string, []>("op_86_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
68
- tensor<fp16, [1, 1]> var_3_cast_fp16 = add(x = mul_1_cast_fp16, y = var_86_to_fp16)[name = tensor<string, []>("var_3_cast_fp16")];
69
- tensor<fp32, []> var_89_epsilon_0 = const()[name = tensor<string, []>("op_89_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
70
- tensor<fp16, [1, 1]> var_89_cast_fp16 = rsqrt(epsilon = var_89_epsilon_0, x = var_3_cast_fp16)[name = tensor<string, []>("op_89_cast_fp16")];
71
- tensor<fp16, [512]> const_3_to_fp16 = const()[name = tensor<string, []>("const_3_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(831360)))];
72
- tensor<fp16, [1, 512]> var_90_cast_fp16 = mul(x = const_3_to_fp16, y = var_89_cast_fp16)[name = tensor<string, []>("op_90_cast_fp16")];
73
- tensor<fp16, [1, 512]> var_91_cast_fp16 = mul(x = linear_4_cast_fp16, y = var_90_cast_fp16)[name = tensor<string, []>("op_91_cast_fp16")];
74
- tensor<fp16, [1, 512]> var_93_cast_fp16 = add(x = var_61_cast_fp16, y = var_91_cast_fp16)[name = tensor<string, []>("op_93_cast_fp16")];
75
- tensor<fp16, []> _inversed_t_combined_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_t_combined_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
76
- tensor<fp16, [1, 512]> _inversed_t_combined_cast_fp16 = mul(x = var_93_cast_fp16, y = _inversed_t_combined_y_0_to_fp16)[name = tensor<string, []>("_inversed_t_combined_cast_fp16")];
77
- tensor<string, []> transformer_out_to_fp16_dtype_0 = const()[name = tensor<string, []>("transformer_out_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
78
- tensor<fp16, [512, 1024]> flow_net_cond_embed_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_cond_embed_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(832448))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1356800))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
79
- tensor<fp16, [512]> flow_net_cond_embed_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_cond_embed_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1357888)))];
80
- tensor<fp16, [1, 1024]> transformer_out_to_fp16 = cast(dtype = transformer_out_to_fp16_dtype_0, x = transformer_out)[name = tensor<string, []>("cast_1")];
81
- tensor<fp16, [1, 512]> linear_5_cast_fp16 = linear(bias = flow_net_cond_embed_bias_to_fp16, weight = flow_net_cond_embed_weight_to_fp16_quantized, x = transformer_out_to_fp16)[name = tensor<string, []>("linear_5_cast_fp16")];
82
- tensor<fp16, [1, 512]> input_13_cast_fp16 = add(x = _inversed_t_combined_cast_fp16, y = linear_5_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
83
- tensor<fp16, [1, 512]> input_15_cast_fp16 = silu(x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
84
- tensor<fp16, [1536, 512]> flow_net_res_blocks_0_adaLN_modulation_1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_0_adaLN_modulation_1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1358976))), scale = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2147072))), zero_point = tensor<int8, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2145472)))];
85
- tensor<fp16, [1536]> flow_net_res_blocks_0_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_0_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2150208)))];
86
- tensor<fp16, [1, 1536]> linear_6_cast_fp16 = linear(bias = flow_net_res_blocks_0_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_0_adaLN_modulation_1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = tensor<string, []>("linear_6_cast_fp16")];
87
- tensor<int32, [3]> var_108_split_sizes_0 = const()[name = tensor<string, []>("op_108_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
88
- tensor<int32, []> var_108_axis_0 = const()[name = tensor<string, []>("op_108_axis_0"), val = tensor<int32, []>(-1)];
89
- tensor<fp16, [1, 512]> var_108_cast_fp16_0, tensor<fp16, [1, 512]> var_108_cast_fp16_1, tensor<fp16, [1, 512]> var_108_cast_fp16_2 = split(axis = var_108_axis_0, split_sizes = var_108_split_sizes_0, x = linear_6_cast_fp16)[name = tensor<string, []>("op_108_cast_fp16")];
90
- tensor<int32, [1]> mean_1_axes_0 = const()[name = tensor<string, []>("mean_1_axes_0"), val = tensor<int32, [1]>([-1])];
91
- tensor<bool, []> mean_1_keep_dims_0 = const()[name = tensor<string, []>("mean_1_keep_dims_0"), val = tensor<bool, []>(true)];
92
- tensor<fp16, [1, 1]> mean_1_cast_fp16 = reduce_mean(axes = mean_1_axes_0, keep_dims = mean_1_keep_dims_0, x = linear_0_cast_fp16)[name = tensor<string, []>("mean_1_cast_fp16")];
93
- tensor<fp16, [1, 512]> sub_4_cast_fp16 = sub(x = linear_0_cast_fp16, y = mean_1_cast_fp16)[name = tensor<string, []>("sub_4_cast_fp16")];
94
- tensor<fp16, [1, 512]> square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor<string, []>("square_2_cast_fp16")];
95
- tensor<int32, [1]> reduce_mean_5_axes_0 = const()[name = tensor<string, []>("reduce_mean_5_axes_0"), val = tensor<int32, [1]>([-1])];
96
- tensor<bool, []> reduce_mean_5_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_5_keep_dims_0"), val = tensor<bool, []>(true)];
97
- tensor<fp16, [1, 1]> reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_2_cast_fp16)[name = tensor<string, []>("reduce_mean_5_cast_fp16")];
98
- tensor<fp16, []> var_118_to_fp16 = const()[name = tensor<string, []>("op_118_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
99
- tensor<fp16, [1, 1]> var_119_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = var_118_to_fp16)[name = tensor<string, []>("op_119_cast_fp16")];
100
- tensor<fp16, [1, 1]> var_120_cast_fp16 = sqrt(x = var_119_cast_fp16)[name = tensor<string, []>("op_120_cast_fp16")];
101
- tensor<fp16, [1, 512]> x_7_cast_fp16 = real_div(x = sub_4_cast_fp16, y = var_120_cast_fp16)[name = tensor<string, []>("x_7_cast_fp16")];
102
- tensor<fp16, [512]> flow_net_res_blocks_0_in_ln_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_0_in_ln_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2153344)))];
103
- tensor<fp16, [1, 512]> var_122_cast_fp16 = mul(x = x_7_cast_fp16, y = flow_net_res_blocks_0_in_ln_weight_to_fp16)[name = tensor<string, []>("op_122_cast_fp16")];
104
- tensor<fp16, [512]> flow_net_res_blocks_0_in_ln_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_0_in_ln_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2154432)))];
105
- tensor<fp16, [1, 512]> x_9_cast_fp16 = add(x = var_122_cast_fp16, y = flow_net_res_blocks_0_in_ln_bias_to_fp16)[name = tensor<string, []>("x_9_cast_fp16")];
106
- tensor<fp16, []> var_124_promoted_to_fp16 = const()[name = tensor<string, []>("op_124_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
107
- tensor<fp16, [1, 512]> var_125_cast_fp16 = add(x = var_108_cast_fp16_1, y = var_124_promoted_to_fp16)[name = tensor<string, []>("op_125_cast_fp16")];
108
- tensor<fp16, [1, 512]> var_126_cast_fp16 = mul(x = x_9_cast_fp16, y = var_125_cast_fp16)[name = tensor<string, []>("op_126_cast_fp16")];
109
- tensor<fp16, [1, 512]> input_17_cast_fp16 = add(x = var_126_cast_fp16, y = var_108_cast_fp16_0)[name = tensor<string, []>("input_17_cast_fp16")];
110
- tensor<fp16, [512, 512]> flow_net_res_blocks_0_mlp_0_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_0_mlp_0_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2155520))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2417728))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
111
- tensor<fp16, [512]> flow_net_res_blocks_0_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_0_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2418816)))];
112
- tensor<fp16, [1, 512]> linear_7_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_0_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = tensor<string, []>("linear_7_cast_fp16")];
113
- tensor<fp16, [1, 512]> input_21_cast_fp16 = silu(x = linear_7_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
114
- tensor<fp16, [512, 512]> flow_net_res_blocks_0_mlp_2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_0_mlp_2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2419904))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2682112))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
115
- tensor<fp16, [512]> flow_net_res_blocks_0_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_0_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2683200)))];
116
- tensor<fp16, [1, 512]> linear_8_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_2_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = tensor<string, []>("linear_8_cast_fp16")];
117
- tensor<fp16, [1, 512]> var_137_cast_fp16 = mul(x = var_108_cast_fp16_2, y = linear_8_cast_fp16)[name = tensor<string, []>("op_137_cast_fp16")];
118
- tensor<fp16, [1, 512]> x_11_cast_fp16 = add(x = linear_0_cast_fp16, y = var_137_cast_fp16)[name = tensor<string, []>("x_11_cast_fp16")];
119
- tensor<fp16, [1536, 512]> flow_net_res_blocks_1_adaLN_modulation_1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_1_adaLN_modulation_1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2684288))), scale = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3470784))), zero_point = tensor<int8, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2145472)))];
120
- tensor<fp16, [1536]> flow_net_res_blocks_1_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_1_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3473920)))];
121
- tensor<fp16, [1, 1536]> linear_9_cast_fp16 = linear(bias = flow_net_res_blocks_1_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_1_adaLN_modulation_1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = tensor<string, []>("linear_9_cast_fp16")];
122
- tensor<int32, [3]> var_147_split_sizes_0 = const()[name = tensor<string, []>("op_147_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
123
- tensor<int32, []> var_147_axis_0 = const()[name = tensor<string, []>("op_147_axis_0"), val = tensor<int32, []>(-1)];
124
- tensor<fp16, [1, 512]> var_147_cast_fp16_0, tensor<fp16, [1, 512]> var_147_cast_fp16_1, tensor<fp16, [1, 512]> var_147_cast_fp16_2 = split(axis = var_147_axis_0, split_sizes = var_147_split_sizes_0, x = linear_9_cast_fp16)[name = tensor<string, []>("op_147_cast_fp16")];
125
- tensor<int32, [1]> mean_3_axes_0 = const()[name = tensor<string, []>("mean_3_axes_0"), val = tensor<int32, [1]>([-1])];
126
- tensor<bool, []> mean_3_keep_dims_0 = const()[name = tensor<string, []>("mean_3_keep_dims_0"), val = tensor<bool, []>(true)];
127
- tensor<fp16, [1, 1]> mean_3_cast_fp16 = reduce_mean(axes = mean_3_axes_0, keep_dims = mean_3_keep_dims_0, x = x_11_cast_fp16)[name = tensor<string, []>("mean_3_cast_fp16")];
128
- tensor<fp16, [1, 512]> sub_5_cast_fp16 = sub(x = x_11_cast_fp16, y = mean_3_cast_fp16)[name = tensor<string, []>("sub_5_cast_fp16")];
129
- tensor<fp16, [1, 512]> square_3_cast_fp16 = square(x = sub_5_cast_fp16)[name = tensor<string, []>("square_3_cast_fp16")];
130
- tensor<int32, [1]> reduce_mean_7_axes_0 = const()[name = tensor<string, []>("reduce_mean_7_axes_0"), val = tensor<int32, [1]>([-1])];
131
- tensor<bool, []> reduce_mean_7_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_7_keep_dims_0"), val = tensor<bool, []>(true)];
132
- tensor<fp16, [1, 1]> reduce_mean_7_cast_fp16 = reduce_mean(axes = reduce_mean_7_axes_0, keep_dims = reduce_mean_7_keep_dims_0, x = square_3_cast_fp16)[name = tensor<string, []>("reduce_mean_7_cast_fp16")];
133
- tensor<fp16, []> var_157_to_fp16 = const()[name = tensor<string, []>("op_157_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
134
- tensor<fp16, [1, 1]> var_158_cast_fp16 = add(x = reduce_mean_7_cast_fp16, y = var_157_to_fp16)[name = tensor<string, []>("op_158_cast_fp16")];
135
- tensor<fp16, [1, 1]> var_159_cast_fp16 = sqrt(x = var_158_cast_fp16)[name = tensor<string, []>("op_159_cast_fp16")];
136
- tensor<fp16, [1, 512]> x_13_cast_fp16 = real_div(x = sub_5_cast_fp16, y = var_159_cast_fp16)[name = tensor<string, []>("x_13_cast_fp16")];
137
- tensor<fp16, [512]> flow_net_res_blocks_1_in_ln_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_1_in_ln_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3477056)))];
138
- tensor<fp16, [1, 512]> var_161_cast_fp16 = mul(x = x_13_cast_fp16, y = flow_net_res_blocks_1_in_ln_weight_to_fp16)[name = tensor<string, []>("op_161_cast_fp16")];
139
- tensor<fp16, [512]> flow_net_res_blocks_1_in_ln_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_1_in_ln_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3478144)))];
140
- tensor<fp16, [1, 512]> x_15_cast_fp16 = add(x = var_161_cast_fp16, y = flow_net_res_blocks_1_in_ln_bias_to_fp16)[name = tensor<string, []>("x_15_cast_fp16")];
141
- tensor<fp16, []> var_163_promoted_to_fp16 = const()[name = tensor<string, []>("op_163_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
142
- tensor<fp16, [1, 512]> var_164_cast_fp16 = add(x = var_147_cast_fp16_1, y = var_163_promoted_to_fp16)[name = tensor<string, []>("op_164_cast_fp16")];
143
- tensor<fp16, [1, 512]> var_165_cast_fp16 = mul(x = x_15_cast_fp16, y = var_164_cast_fp16)[name = tensor<string, []>("op_165_cast_fp16")];
144
- tensor<fp16, [1, 512]> input_25_cast_fp16 = add(x = var_165_cast_fp16, y = var_147_cast_fp16_0)[name = tensor<string, []>("input_25_cast_fp16")];
145
- tensor<fp16, [512, 512]> flow_net_res_blocks_1_mlp_0_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_1_mlp_0_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3479232))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3741440))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
146
- tensor<fp16, [512]> flow_net_res_blocks_1_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_1_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3742528)))];
147
- tensor<fp16, [1, 512]> linear_10_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_0_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = tensor<string, []>("linear_10_cast_fp16")];
148
- tensor<fp16, [1, 512]> input_29_cast_fp16 = silu(x = linear_10_cast_fp16)[name = tensor<string, []>("input_29_cast_fp16")];
149
- tensor<fp16, [512, 512]> flow_net_res_blocks_1_mlp_2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_1_mlp_2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3743616))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4005824))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
150
- tensor<fp16, [512]> flow_net_res_blocks_1_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_1_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4006912)))];
151
- tensor<fp16, [1, 512]> linear_11_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_2_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = tensor<string, []>("linear_11_cast_fp16")];
152
- tensor<fp16, [1, 512]> var_176_cast_fp16 = mul(x = var_147_cast_fp16_2, y = linear_11_cast_fp16)[name = tensor<string, []>("op_176_cast_fp16")];
153
- tensor<fp16, [1, 512]> x_17_cast_fp16 = add(x = x_11_cast_fp16, y = var_176_cast_fp16)[name = tensor<string, []>("x_17_cast_fp16")];
154
- tensor<fp16, [1536, 512]> flow_net_res_blocks_2_adaLN_modulation_1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_2_adaLN_modulation_1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4008000))), scale = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4794496))), zero_point = tensor<int8, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2145472)))];
155
- tensor<fp16, [1536]> flow_net_res_blocks_2_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_2_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4797632)))];
156
- tensor<fp16, [1, 1536]> linear_12_cast_fp16 = linear(bias = flow_net_res_blocks_2_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_2_adaLN_modulation_1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = tensor<string, []>("linear_12_cast_fp16")];
157
- tensor<int32, [3]> var_186_split_sizes_0 = const()[name = tensor<string, []>("op_186_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
158
- tensor<int32, []> var_186_axis_0 = const()[name = tensor<string, []>("op_186_axis_0"), val = tensor<int32, []>(-1)];
159
- tensor<fp16, [1, 512]> var_186_cast_fp16_0, tensor<fp16, [1, 512]> var_186_cast_fp16_1, tensor<fp16, [1, 512]> var_186_cast_fp16_2 = split(axis = var_186_axis_0, split_sizes = var_186_split_sizes_0, x = linear_12_cast_fp16)[name = tensor<string, []>("op_186_cast_fp16")];
160
- tensor<int32, [1]> mean_5_axes_0 = const()[name = tensor<string, []>("mean_5_axes_0"), val = tensor<int32, [1]>([-1])];
161
- tensor<bool, []> mean_5_keep_dims_0 = const()[name = tensor<string, []>("mean_5_keep_dims_0"), val = tensor<bool, []>(true)];
162
- tensor<fp16, [1, 1]> mean_5_cast_fp16 = reduce_mean(axes = mean_5_axes_0, keep_dims = mean_5_keep_dims_0, x = x_17_cast_fp16)[name = tensor<string, []>("mean_5_cast_fp16")];
163
- tensor<fp16, [1, 512]> sub_6_cast_fp16 = sub(x = x_17_cast_fp16, y = mean_5_cast_fp16)[name = tensor<string, []>("sub_6_cast_fp16")];
164
- tensor<fp16, [1, 512]> square_4_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor<string, []>("square_4_cast_fp16")];
165
- tensor<int32, [1]> reduce_mean_9_axes_0 = const()[name = tensor<string, []>("reduce_mean_9_axes_0"), val = tensor<int32, [1]>([-1])];
166
- tensor<bool, []> reduce_mean_9_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_9_keep_dims_0"), val = tensor<bool, []>(true)];
167
- tensor<fp16, [1, 1]> reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = square_4_cast_fp16)[name = tensor<string, []>("reduce_mean_9_cast_fp16")];
168
- tensor<fp16, []> var_196_to_fp16 = const()[name = tensor<string, []>("op_196_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
169
- tensor<fp16, [1, 1]> var_197_cast_fp16 = add(x = reduce_mean_9_cast_fp16, y = var_196_to_fp16)[name = tensor<string, []>("op_197_cast_fp16")];
170
- tensor<fp16, [1, 1]> var_198_cast_fp16 = sqrt(x = var_197_cast_fp16)[name = tensor<string, []>("op_198_cast_fp16")];
171
- tensor<fp16, [1, 512]> x_19_cast_fp16 = real_div(x = sub_6_cast_fp16, y = var_198_cast_fp16)[name = tensor<string, []>("x_19_cast_fp16")];
172
- tensor<fp16, [512]> flow_net_res_blocks_2_in_ln_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_2_in_ln_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4800768)))];
173
- tensor<fp16, [1, 512]> var_200_cast_fp16 = mul(x = x_19_cast_fp16, y = flow_net_res_blocks_2_in_ln_weight_to_fp16)[name = tensor<string, []>("op_200_cast_fp16")];
174
- tensor<fp16, [512]> flow_net_res_blocks_2_in_ln_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_2_in_ln_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4801856)))];
175
- tensor<fp16, [1, 512]> x_21_cast_fp16 = add(x = var_200_cast_fp16, y = flow_net_res_blocks_2_in_ln_bias_to_fp16)[name = tensor<string, []>("x_21_cast_fp16")];
176
- tensor<fp16, []> var_202_promoted_to_fp16 = const()[name = tensor<string, []>("op_202_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
177
- tensor<fp16, [1, 512]> var_203_cast_fp16 = add(x = var_186_cast_fp16_1, y = var_202_promoted_to_fp16)[name = tensor<string, []>("op_203_cast_fp16")];
178
- tensor<fp16, [1, 512]> var_204_cast_fp16 = mul(x = x_21_cast_fp16, y = var_203_cast_fp16)[name = tensor<string, []>("op_204_cast_fp16")];
179
- tensor<fp16, [1, 512]> input_33_cast_fp16 = add(x = var_204_cast_fp16, y = var_186_cast_fp16_0)[name = tensor<string, []>("input_33_cast_fp16")];
180
- tensor<fp16, [512, 512]> flow_net_res_blocks_2_mlp_0_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_2_mlp_0_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4802944))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5065152))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
181
- tensor<fp16, [512]> flow_net_res_blocks_2_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_2_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5066240)))];
182
- tensor<fp16, [1, 512]> linear_13_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_0_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = tensor<string, []>("linear_13_cast_fp16")];
183
- tensor<fp16, [1, 512]> input_37_cast_fp16 = silu(x = linear_13_cast_fp16)[name = tensor<string, []>("input_37_cast_fp16")];
184
- tensor<fp16, [512, 512]> flow_net_res_blocks_2_mlp_2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_2_mlp_2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5067328))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5329536))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
185
- tensor<fp16, [512]> flow_net_res_blocks_2_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_2_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5330624)))];
186
- tensor<fp16, [1, 512]> linear_14_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_2_weight_to_fp16_quantized, x = input_37_cast_fp16)[name = tensor<string, []>("linear_14_cast_fp16")];
187
- tensor<fp16, [1, 512]> var_215_cast_fp16 = mul(x = var_186_cast_fp16_2, y = linear_14_cast_fp16)[name = tensor<string, []>("op_215_cast_fp16")];
188
- tensor<fp16, [1, 512]> x_23_cast_fp16 = add(x = x_17_cast_fp16, y = var_215_cast_fp16)[name = tensor<string, []>("x_23_cast_fp16")];
189
- tensor<fp16, [1536, 512]> flow_net_res_blocks_3_adaLN_modulation_1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_3_adaLN_modulation_1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5331712))), scale = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6118208))), zero_point = tensor<int8, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2145472)))];
190
- tensor<fp16, [1536]> flow_net_res_blocks_3_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_3_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6121344)))];
191
- tensor<fp16, [1, 1536]> linear_15_cast_fp16 = linear(bias = flow_net_res_blocks_3_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_3_adaLN_modulation_1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = tensor<string, []>("linear_15_cast_fp16")];
192
- tensor<int32, [3]> var_225_split_sizes_0 = const()[name = tensor<string, []>("op_225_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
193
- tensor<int32, []> var_225_axis_0 = const()[name = tensor<string, []>("op_225_axis_0"), val = tensor<int32, []>(-1)];
194
- tensor<fp16, [1, 512]> var_225_cast_fp16_0, tensor<fp16, [1, 512]> var_225_cast_fp16_1, tensor<fp16, [1, 512]> var_225_cast_fp16_2 = split(axis = var_225_axis_0, split_sizes = var_225_split_sizes_0, x = linear_15_cast_fp16)[name = tensor<string, []>("op_225_cast_fp16")];
195
- tensor<int32, [1]> mean_7_axes_0 = const()[name = tensor<string, []>("mean_7_axes_0"), val = tensor<int32, [1]>([-1])];
196
- tensor<bool, []> mean_7_keep_dims_0 = const()[name = tensor<string, []>("mean_7_keep_dims_0"), val = tensor<bool, []>(true)];
197
- tensor<fp16, [1, 1]> mean_7_cast_fp16 = reduce_mean(axes = mean_7_axes_0, keep_dims = mean_7_keep_dims_0, x = x_23_cast_fp16)[name = tensor<string, []>("mean_7_cast_fp16")];
198
- tensor<fp16, [1, 512]> sub_7_cast_fp16 = sub(x = x_23_cast_fp16, y = mean_7_cast_fp16)[name = tensor<string, []>("sub_7_cast_fp16")];
199
- tensor<fp16, [1, 512]> square_5_cast_fp16 = square(x = sub_7_cast_fp16)[name = tensor<string, []>("square_5_cast_fp16")];
200
- tensor<int32, [1]> reduce_mean_11_axes_0 = const()[name = tensor<string, []>("reduce_mean_11_axes_0"), val = tensor<int32, [1]>([-1])];
201
- tensor<bool, []> reduce_mean_11_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_11_keep_dims_0"), val = tensor<bool, []>(true)];
202
- tensor<fp16, [1, 1]> reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_5_cast_fp16)[name = tensor<string, []>("reduce_mean_11_cast_fp16")];
203
- tensor<fp16, []> var_235_to_fp16 = const()[name = tensor<string, []>("op_235_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
204
- tensor<fp16, [1, 1]> var_236_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = var_235_to_fp16)[name = tensor<string, []>("op_236_cast_fp16")];
205
- tensor<fp16, [1, 1]> var_237_cast_fp16 = sqrt(x = var_236_cast_fp16)[name = tensor<string, []>("op_237_cast_fp16")];
206
- tensor<fp16, [1, 512]> x_25_cast_fp16 = real_div(x = sub_7_cast_fp16, y = var_237_cast_fp16)[name = tensor<string, []>("x_25_cast_fp16")];
207
- tensor<fp16, [512]> flow_net_res_blocks_3_in_ln_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_3_in_ln_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6124480)))];
208
- tensor<fp16, [1, 512]> var_239_cast_fp16 = mul(x = x_25_cast_fp16, y = flow_net_res_blocks_3_in_ln_weight_to_fp16)[name = tensor<string, []>("op_239_cast_fp16")];
209
- tensor<fp16, [512]> flow_net_res_blocks_3_in_ln_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_3_in_ln_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6125568)))];
210
- tensor<fp16, [1, 512]> x_27_cast_fp16 = add(x = var_239_cast_fp16, y = flow_net_res_blocks_3_in_ln_bias_to_fp16)[name = tensor<string, []>("x_27_cast_fp16")];
211
- tensor<fp16, []> var_241_promoted_to_fp16 = const()[name = tensor<string, []>("op_241_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
212
- tensor<fp16, [1, 512]> var_242_cast_fp16 = add(x = var_225_cast_fp16_1, y = var_241_promoted_to_fp16)[name = tensor<string, []>("op_242_cast_fp16")];
213
- tensor<fp16, [1, 512]> var_243_cast_fp16 = mul(x = x_27_cast_fp16, y = var_242_cast_fp16)[name = tensor<string, []>("op_243_cast_fp16")];
214
- tensor<fp16, [1, 512]> input_41_cast_fp16 = add(x = var_243_cast_fp16, y = var_225_cast_fp16_0)[name = tensor<string, []>("input_41_cast_fp16")];
215
- tensor<fp16, [512, 512]> flow_net_res_blocks_3_mlp_0_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_3_mlp_0_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6126656))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6388864))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
216
- tensor<fp16, [512]> flow_net_res_blocks_3_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_3_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6389952)))];
217
- tensor<fp16, [1, 512]> linear_16_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_0_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = tensor<string, []>("linear_16_cast_fp16")];
218
- tensor<fp16, [1, 512]> input_45_cast_fp16 = silu(x = linear_16_cast_fp16)[name = tensor<string, []>("input_45_cast_fp16")];
219
- tensor<fp16, [512, 512]> flow_net_res_blocks_3_mlp_2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_3_mlp_2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6391040))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6653248))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
220
- tensor<fp16, [512]> flow_net_res_blocks_3_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_3_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6654336)))];
221
- tensor<fp16, [1, 512]> linear_17_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_2_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = tensor<string, []>("linear_17_cast_fp16")];
222
- tensor<fp16, [1, 512]> var_254_cast_fp16 = mul(x = var_225_cast_fp16_2, y = linear_17_cast_fp16)[name = tensor<string, []>("op_254_cast_fp16")];
223
- tensor<fp16, [1, 512]> x_29_cast_fp16 = add(x = x_23_cast_fp16, y = var_254_cast_fp16)[name = tensor<string, []>("x_29_cast_fp16")];
224
- tensor<fp16, [1536, 512]> flow_net_res_blocks_4_adaLN_modulation_1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_4_adaLN_modulation_1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6655424))), scale = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7441920))), zero_point = tensor<int8, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2145472)))];
225
- tensor<fp16, [1536]> flow_net_res_blocks_4_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_4_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7445056)))];
226
- tensor<fp16, [1, 1536]> linear_18_cast_fp16 = linear(bias = flow_net_res_blocks_4_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_4_adaLN_modulation_1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = tensor<string, []>("linear_18_cast_fp16")];
227
- tensor<int32, [3]> var_264_split_sizes_0 = const()[name = tensor<string, []>("op_264_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
228
- tensor<int32, []> var_264_axis_0 = const()[name = tensor<string, []>("op_264_axis_0"), val = tensor<int32, []>(-1)];
229
- tensor<fp16, [1, 512]> var_264_cast_fp16_0, tensor<fp16, [1, 512]> var_264_cast_fp16_1, tensor<fp16, [1, 512]> var_264_cast_fp16_2 = split(axis = var_264_axis_0, split_sizes = var_264_split_sizes_0, x = linear_18_cast_fp16)[name = tensor<string, []>("op_264_cast_fp16")];
230
- tensor<int32, [1]> mean_9_axes_0 = const()[name = tensor<string, []>("mean_9_axes_0"), val = tensor<int32, [1]>([-1])];
231
- tensor<bool, []> mean_9_keep_dims_0 = const()[name = tensor<string, []>("mean_9_keep_dims_0"), val = tensor<bool, []>(true)];
232
- tensor<fp16, [1, 1]> mean_9_cast_fp16 = reduce_mean(axes = mean_9_axes_0, keep_dims = mean_9_keep_dims_0, x = x_29_cast_fp16)[name = tensor<string, []>("mean_9_cast_fp16")];
233
- tensor<fp16, [1, 512]> sub_8_cast_fp16 = sub(x = x_29_cast_fp16, y = mean_9_cast_fp16)[name = tensor<string, []>("sub_8_cast_fp16")];
234
- tensor<fp16, [1, 512]> square_6_cast_fp16 = square(x = sub_8_cast_fp16)[name = tensor<string, []>("square_6_cast_fp16")];
235
- tensor<int32, [1]> reduce_mean_13_axes_0 = const()[name = tensor<string, []>("reduce_mean_13_axes_0"), val = tensor<int32, [1]>([-1])];
236
- tensor<bool, []> reduce_mean_13_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_13_keep_dims_0"), val = tensor<bool, []>(true)];
237
- tensor<fp16, [1, 1]> reduce_mean_13_cast_fp16 = reduce_mean(axes = reduce_mean_13_axes_0, keep_dims = reduce_mean_13_keep_dims_0, x = square_6_cast_fp16)[name = tensor<string, []>("reduce_mean_13_cast_fp16")];
238
- tensor<fp16, []> var_274_to_fp16 = const()[name = tensor<string, []>("op_274_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
239
- tensor<fp16, [1, 1]> var_275_cast_fp16 = add(x = reduce_mean_13_cast_fp16, y = var_274_to_fp16)[name = tensor<string, []>("op_275_cast_fp16")];
240
- tensor<fp16, [1, 1]> var_276_cast_fp16 = sqrt(x = var_275_cast_fp16)[name = tensor<string, []>("op_276_cast_fp16")];
241
- tensor<fp16, [1, 512]> x_31_cast_fp16 = real_div(x = sub_8_cast_fp16, y = var_276_cast_fp16)[name = tensor<string, []>("x_31_cast_fp16")];
242
- tensor<fp16, [512]> flow_net_res_blocks_4_in_ln_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_4_in_ln_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7448192)))];
243
- tensor<fp16, [1, 512]> var_278_cast_fp16 = mul(x = x_31_cast_fp16, y = flow_net_res_blocks_4_in_ln_weight_to_fp16)[name = tensor<string, []>("op_278_cast_fp16")];
244
- tensor<fp16, [512]> flow_net_res_blocks_4_in_ln_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_4_in_ln_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7449280)))];
245
- tensor<fp16, [1, 512]> x_33_cast_fp16 = add(x = var_278_cast_fp16, y = flow_net_res_blocks_4_in_ln_bias_to_fp16)[name = tensor<string, []>("x_33_cast_fp16")];
246
- tensor<fp16, []> var_280_promoted_to_fp16 = const()[name = tensor<string, []>("op_280_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
247
- tensor<fp16, [1, 512]> var_281_cast_fp16 = add(x = var_264_cast_fp16_1, y = var_280_promoted_to_fp16)[name = tensor<string, []>("op_281_cast_fp16")];
248
- tensor<fp16, [1, 512]> var_282_cast_fp16 = mul(x = x_33_cast_fp16, y = var_281_cast_fp16)[name = tensor<string, []>("op_282_cast_fp16")];
249
- tensor<fp16, [1, 512]> input_49_cast_fp16 = add(x = var_282_cast_fp16, y = var_264_cast_fp16_0)[name = tensor<string, []>("input_49_cast_fp16")];
250
- tensor<fp16, [512, 512]> flow_net_res_blocks_4_mlp_0_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_4_mlp_0_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7450368))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7712576))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
251
- tensor<fp16, [512]> flow_net_res_blocks_4_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_4_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7713664)))];
252
- tensor<fp16, [1, 512]> linear_19_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_0_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = tensor<string, []>("linear_19_cast_fp16")];
253
- tensor<fp16, [1, 512]> input_53_cast_fp16 = silu(x = linear_19_cast_fp16)[name = tensor<string, []>("input_53_cast_fp16")];
254
- tensor<fp16, [512, 512]> flow_net_res_blocks_4_mlp_2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_4_mlp_2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7714752))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7976960))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
255
- tensor<fp16, [512]> flow_net_res_blocks_4_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_4_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7978048)))];
256
- tensor<fp16, [1, 512]> linear_20_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_2_weight_to_fp16_quantized, x = input_53_cast_fp16)[name = tensor<string, []>("linear_20_cast_fp16")];
257
- tensor<fp16, [1, 512]> var_293_cast_fp16 = mul(x = var_264_cast_fp16_2, y = linear_20_cast_fp16)[name = tensor<string, []>("op_293_cast_fp16")];
258
- tensor<fp16, [1, 512]> x_35_cast_fp16 = add(x = x_29_cast_fp16, y = var_293_cast_fp16)[name = tensor<string, []>("x_35_cast_fp16")];
259
- tensor<fp16, [1536, 512]> flow_net_res_blocks_5_adaLN_modulation_1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_5_adaLN_modulation_1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7979136))), scale = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8765632))), zero_point = tensor<int8, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2145472)))];
260
- tensor<fp16, [1536]> flow_net_res_blocks_5_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_5_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8768768)))];
261
- tensor<fp16, [1, 1536]> linear_21_cast_fp16 = linear(bias = flow_net_res_blocks_5_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_5_adaLN_modulation_1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = tensor<string, []>("linear_21_cast_fp16")];
262
- tensor<int32, [3]> var_303_split_sizes_0 = const()[name = tensor<string, []>("op_303_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
263
- tensor<int32, []> var_303_axis_0 = const()[name = tensor<string, []>("op_303_axis_0"), val = tensor<int32, []>(-1)];
264
- tensor<fp16, [1, 512]> var_303_cast_fp16_0, tensor<fp16, [1, 512]> var_303_cast_fp16_1, tensor<fp16, [1, 512]> var_303_cast_fp16_2 = split(axis = var_303_axis_0, split_sizes = var_303_split_sizes_0, x = linear_21_cast_fp16)[name = tensor<string, []>("op_303_cast_fp16")];
265
- tensor<int32, [1]> mean_11_axes_0 = const()[name = tensor<string, []>("mean_11_axes_0"), val = tensor<int32, [1]>([-1])];
266
- tensor<bool, []> mean_11_keep_dims_0 = const()[name = tensor<string, []>("mean_11_keep_dims_0"), val = tensor<bool, []>(true)];
267
- tensor<fp16, [1, 1]> mean_11_cast_fp16 = reduce_mean(axes = mean_11_axes_0, keep_dims = mean_11_keep_dims_0, x = x_35_cast_fp16)[name = tensor<string, []>("mean_11_cast_fp16")];
268
- tensor<fp16, [1, 512]> sub_9_cast_fp16 = sub(x = x_35_cast_fp16, y = mean_11_cast_fp16)[name = tensor<string, []>("sub_9_cast_fp16")];
269
- tensor<fp16, [1, 512]> square_7_cast_fp16 = square(x = sub_9_cast_fp16)[name = tensor<string, []>("square_7_cast_fp16")];
270
- tensor<int32, [1]> reduce_mean_15_axes_0 = const()[name = tensor<string, []>("reduce_mean_15_axes_0"), val = tensor<int32, [1]>([-1])];
271
- tensor<bool, []> reduce_mean_15_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_15_keep_dims_0"), val = tensor<bool, []>(true)];
272
- tensor<fp16, [1, 1]> reduce_mean_15_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = square_7_cast_fp16)[name = tensor<string, []>("reduce_mean_15_cast_fp16")];
273
- tensor<fp16, []> var_313_to_fp16 = const()[name = tensor<string, []>("op_313_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
274
- tensor<fp16, [1, 1]> var_314_cast_fp16 = add(x = reduce_mean_15_cast_fp16, y = var_313_to_fp16)[name = tensor<string, []>("op_314_cast_fp16")];
275
- tensor<fp16, [1, 1]> var_315_cast_fp16 = sqrt(x = var_314_cast_fp16)[name = tensor<string, []>("op_315_cast_fp16")];
276
- tensor<fp16, [1, 512]> x_37_cast_fp16 = real_div(x = sub_9_cast_fp16, y = var_315_cast_fp16)[name = tensor<string, []>("x_37_cast_fp16")];
277
- tensor<fp16, [512]> flow_net_res_blocks_5_in_ln_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_5_in_ln_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8771904)))];
278
- tensor<fp16, [1, 512]> var_317_cast_fp16 = mul(x = x_37_cast_fp16, y = flow_net_res_blocks_5_in_ln_weight_to_fp16)[name = tensor<string, []>("op_317_cast_fp16")];
279
- tensor<fp16, [512]> flow_net_res_blocks_5_in_ln_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_5_in_ln_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8772992)))];
280
- tensor<fp16, [1, 512]> x_39_cast_fp16 = add(x = var_317_cast_fp16, y = flow_net_res_blocks_5_in_ln_bias_to_fp16)[name = tensor<string, []>("x_39_cast_fp16")];
281
- tensor<fp16, []> var_319_promoted_to_fp16 = const()[name = tensor<string, []>("op_319_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
282
- tensor<fp16, [1, 512]> var_320_cast_fp16 = add(x = var_303_cast_fp16_1, y = var_319_promoted_to_fp16)[name = tensor<string, []>("op_320_cast_fp16")];
283
- tensor<fp16, [1, 512]> var_321_cast_fp16 = mul(x = x_39_cast_fp16, y = var_320_cast_fp16)[name = tensor<string, []>("op_321_cast_fp16")];
284
- tensor<fp16, [1, 512]> input_57_cast_fp16 = add(x = var_321_cast_fp16, y = var_303_cast_fp16_0)[name = tensor<string, []>("input_57_cast_fp16")];
285
- tensor<fp16, [512, 512]> flow_net_res_blocks_5_mlp_0_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_5_mlp_0_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8774080))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9036288))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
286
- tensor<fp16, [512]> flow_net_res_blocks_5_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_5_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9037376)))];
287
- tensor<fp16, [1, 512]> linear_22_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_0_weight_to_fp16_quantized, x = input_57_cast_fp16)[name = tensor<string, []>("linear_22_cast_fp16")];
288
- tensor<fp16, [1, 512]> input_61_cast_fp16 = silu(x = linear_22_cast_fp16)[name = tensor<string, []>("input_61_cast_fp16")];
289
- tensor<fp16, [512, 512]> flow_net_res_blocks_5_mlp_2_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_res_blocks_5_mlp_2_weight_to_fp16_quantized"), quantized_data = tensor<int8, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9038464))), scale = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9300672))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165440)))];
290
- tensor<fp16, [512]> flow_net_res_blocks_5_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_5_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9301760)))];
291
- tensor<fp16, [1, 512]> linear_23_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_2_weight_to_fp16_quantized, x = input_61_cast_fp16)[name = tensor<string, []>("linear_23_cast_fp16")];
292
- tensor<fp16, [1, 512]> var_332_cast_fp16 = mul(x = var_303_cast_fp16_2, y = linear_23_cast_fp16)[name = tensor<string, []>("op_332_cast_fp16")];
293
- tensor<fp16, [1, 512]> x_41_cast_fp16 = add(x = x_35_cast_fp16, y = var_332_cast_fp16)[name = tensor<string, []>("x_41_cast_fp16")];
294
- tensor<fp16, [1024, 512]> flow_net_final_layer_adaLN_modulation_1_weight_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("flow_net_final_layer_adaLN_modulation_1_weight_to_fp16_quantized"), quantized_data = tensor<int8, [1024, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9302848))), scale = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9828288))), zero_point = tensor<int8, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9827200)))];
295
- tensor<fp16, [1024]> flow_net_final_layer_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_final_layer_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9830400)))];
296
- tensor<fp16, [1, 1024]> linear_24_cast_fp16 = linear(bias = flow_net_final_layer_adaLN_modulation_1_bias_to_fp16, weight = flow_net_final_layer_adaLN_modulation_1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = tensor<string, []>("linear_24_cast_fp16")];
297
- tensor<int32, [2]> var_341_split_sizes_0 = const()[name = tensor<string, []>("op_341_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
298
- tensor<int32, []> var_341_axis_0 = const()[name = tensor<string, []>("op_341_axis_0"), val = tensor<int32, []>(-1)];
299
- tensor<fp16, [1, 512]> var_341_cast_fp16_0, tensor<fp16, [1, 512]> var_341_cast_fp16_1 = split(axis = var_341_axis_0, split_sizes = var_341_split_sizes_0, x = linear_24_cast_fp16)[name = tensor<string, []>("op_341_cast_fp16")];
300
- tensor<int32, [1]> mean_axes_0 = const()[name = tensor<string, []>("mean_axes_0"), val = tensor<int32, [1]>([-1])];
301
- tensor<bool, []> mean_keep_dims_0 = const()[name = tensor<string, []>("mean_keep_dims_0"), val = tensor<bool, []>(true)];
302
- tensor<fp16, [1, 1]> mean_cast_fp16 = reduce_mean(axes = mean_axes_0, keep_dims = mean_keep_dims_0, x = x_41_cast_fp16)[name = tensor<string, []>("mean_cast_fp16")];
303
- tensor<fp16, [1, 512]> sub_10_cast_fp16 = sub(x = x_41_cast_fp16, y = mean_cast_fp16)[name = tensor<string, []>("sub_10_cast_fp16")];
304
- tensor<fp16, [1, 512]> square_8_cast_fp16 = square(x = sub_10_cast_fp16)[name = tensor<string, []>("square_8_cast_fp16")];
305
- tensor<int32, [1]> reduce_mean_17_axes_0 = const()[name = tensor<string, []>("reduce_mean_17_axes_0"), val = tensor<int32, [1]>([-1])];
306
- tensor<bool, []> reduce_mean_17_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_17_keep_dims_0"), val = tensor<bool, []>(true)];
307
- tensor<fp16, [1, 1]> reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_8_cast_fp16)[name = tensor<string, []>("reduce_mean_17_cast_fp16")];
308
- tensor<fp16, []> var_348_to_fp16 = const()[name = tensor<string, []>("op_348_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
309
- tensor<fp16, [1, 1]> var_349_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = var_348_to_fp16)[name = tensor<string, []>("op_349_cast_fp16")];
310
- tensor<fp16, [1, 1]> var_350_cast_fp16 = sqrt(x = var_349_cast_fp16)[name = tensor<string, []>("op_350_cast_fp16")];
311
- tensor<fp16, [1, 512]> x_cast_fp16 = real_div(x = sub_10_cast_fp16, y = var_350_cast_fp16)[name = tensor<string, []>("x_cast_fp16")];
312
- tensor<fp16, []> var_352_promoted_to_fp16 = const()[name = tensor<string, []>("op_352_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
313
- tensor<fp16, [1, 512]> var_353_cast_fp16 = add(x = var_341_cast_fp16_1, y = var_352_promoted_to_fp16)[name = tensor<string, []>("op_353_cast_fp16")];
314
- tensor<fp16, [1, 512]> var_354_cast_fp16 = mul(x = x_cast_fp16, y = var_353_cast_fp16)[name = tensor<string, []>("op_354_cast_fp16")];
315
- tensor<fp16, [1, 512]> input_cast_fp16 = add(x = var_354_cast_fp16, y = var_341_cast_fp16_0)[name = tensor<string, []>("input_cast_fp16")];
316
- tensor<fp16, [32, 512]> flow_net_final_layer_linear_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_final_layer_linear_weight_to_fp16"), val = tensor<fp16, [32, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9832512)))];
317
- tensor<fp16, [32]> flow_net_final_layer_linear_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_final_layer_linear_bias_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9865344)))];
318
- tensor<fp16, [1, 32]> linear_25_cast_fp16 = linear(bias = flow_net_final_layer_linear_bias_to_fp16, weight = flow_net_final_layer_linear_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("linear_25_cast_fp16")];
319
- tensor<string, []> linear_25_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("linear_25_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
320
- tensor<fp32, [1, 32]> var_358 = cast(dtype = linear_25_cast_fp16_to_fp32_dtype_0, x = linear_25_cast_fp16)[name = tensor<string, []>("cast_0")];
321
- } -> (var_358);
322
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- PocketTTS german (6L) — int8 weight-only quantized CoreML
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- ============================================================
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-
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- Path: languages/german/int8/
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- Total size: 313M
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-
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- Files (mlpackage source + compiled mlmodelc):
8
- cond_step mlpackage=64M mlmodelc=64M # speaker similarity 0.984, Pearson 0.94 — safe
9
- flowlm_step mlpackage=73M mlmodelc=73M # speaker similarity 0.989, Pearson 0.94 — safe
10
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24
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- compounds per-frame quantization noise audibly).
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-
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- Generated: 2026-04-26 00:30:05 EDT
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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