| |
|
|
| """ |
| This file is part of ComfyUI. |
| Copyright (C) 2024 Comfy |
| |
| This program is free software: you can redistribute it and/or modify |
| it under the terms of the GNU General Public License as published by |
| the Free Software Foundation, either version 3 of the License, or |
| (at your option) any later version. |
| |
| This program is distributed in the hope that it will be useful, |
| but WITHOUT ANY WARRANTY; without even the implied warranty of |
| MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
| GNU General Public License for more details. |
| |
| You should have received a copy of the GNU General Public License |
| along with this program. If not, see <https://www.gnu.org/licenses/>. |
| """ |
|
|
| import torch |
|
|
| UNET_MAP_ATTENTIONS = { |
| "proj_in.weight", |
| "proj_in.bias", |
| "proj_out.weight", |
| "proj_out.bias", |
| "norm.weight", |
| "norm.bias", |
| } |
|
|
| TRANSFORMER_BLOCKS = { |
| "norm1.weight", |
| "norm1.bias", |
| "norm2.weight", |
| "norm2.bias", |
| "norm3.weight", |
| "norm3.bias", |
| "attn1.to_q.weight", |
| "attn1.to_k.weight", |
| "attn1.to_v.weight", |
| "attn1.to_out.0.weight", |
| "attn1.to_out.0.bias", |
| "attn2.to_q.weight", |
| "attn2.to_k.weight", |
| "attn2.to_v.weight", |
| "attn2.to_out.0.weight", |
| "attn2.to_out.0.bias", |
| "ff.net.0.proj.weight", |
| "ff.net.0.proj.bias", |
| "ff.net.2.weight", |
| "ff.net.2.bias", |
| } |
|
|
| UNET_MAP_RESNET = { |
| "in_layers.2.weight": "conv1.weight", |
| "in_layers.2.bias": "conv1.bias", |
| "emb_layers.1.weight": "time_emb_proj.weight", |
| "emb_layers.1.bias": "time_emb_proj.bias", |
| "out_layers.3.weight": "conv2.weight", |
| "out_layers.3.bias": "conv2.bias", |
| "skip_connection.weight": "conv_shortcut.weight", |
| "skip_connection.bias": "conv_shortcut.bias", |
| "in_layers.0.weight": "norm1.weight", |
| "in_layers.0.bias": "norm1.bias", |
| "out_layers.0.weight": "norm2.weight", |
| "out_layers.0.bias": "norm2.bias", |
| } |
|
|
| UNET_MAP_BASIC = { |
| ("label_emb.0.0.weight", "class_embedding.linear_1.weight"), |
| ("label_emb.0.0.bias", "class_embedding.linear_1.bias"), |
| ("label_emb.0.2.weight", "class_embedding.linear_2.weight"), |
| ("label_emb.0.2.bias", "class_embedding.linear_2.bias"), |
| ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), |
| ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), |
| ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), |
| ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), |
| ("input_blocks.0.0.weight", "conv_in.weight"), |
| ("input_blocks.0.0.bias", "conv_in.bias"), |
| ("out.0.weight", "conv_norm_out.weight"), |
| ("out.0.bias", "conv_norm_out.bias"), |
| ("out.2.weight", "conv_out.weight"), |
| ("out.2.bias", "conv_out.bias"), |
| ("time_embed.0.weight", "time_embedding.linear_1.weight"), |
| ("time_embed.0.bias", "time_embedding.linear_1.bias"), |
| ("time_embed.2.weight", "time_embedding.linear_2.weight"), |
| ("time_embed.2.bias", "time_embedding.linear_2.bias"), |
| } |
|
|
|
|
| def unet_to_diffusers(unet_config): |
| if "num_res_blocks" not in unet_config: |
| return {} |
| num_res_blocks = unet_config["num_res_blocks"] |
| channel_mult = unet_config["channel_mult"] |
| transformer_depth = unet_config["transformer_depth"][:] |
| transformer_depth_output = unet_config["transformer_depth_output"][:] |
| num_blocks = len(channel_mult) |
|
|
| transformers_mid = unet_config.get("transformer_depth_middle", None) |
|
|
| diffusers_unet_map = {} |
| for x in range(num_blocks): |
| n = 1 + (num_res_blocks[x] + 1) * x |
| for i in range(num_res_blocks[x]): |
| for b in UNET_MAP_RESNET: |
| diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b) |
| num_transformers = transformer_depth.pop(0) |
| if num_transformers > 0: |
| for b in UNET_MAP_ATTENTIONS: |
| diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b) |
| for t in range(num_transformers): |
| for b in TRANSFORMER_BLOCKS: |
| diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) |
| n += 1 |
| for k in ["weight", "bias"]: |
| diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k) |
|
|
| i = 0 |
| for b in UNET_MAP_ATTENTIONS: |
| diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b) |
| for t in range(transformers_mid): |
| for b in TRANSFORMER_BLOCKS: |
| diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b) |
|
|
| for i, n in enumerate([0, 2]): |
| for b in UNET_MAP_RESNET: |
| diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b) |
|
|
| num_res_blocks = list(reversed(num_res_blocks)) |
| for x in range(num_blocks): |
| n = (num_res_blocks[x] + 1) * x |
| l = num_res_blocks[x] + 1 |
| for i in range(l): |
| c = 0 |
| for b in UNET_MAP_RESNET: |
| diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b) |
| c += 1 |
| num_transformers = transformer_depth_output.pop() |
| if num_transformers > 0: |
| c += 1 |
| for b in UNET_MAP_ATTENTIONS: |
| diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b) |
| for t in range(num_transformers): |
| for b in TRANSFORMER_BLOCKS: |
| diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) |
| if i == l - 1: |
| for k in ["weight", "bias"]: |
| diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k) |
| n += 1 |
|
|
| for k in UNET_MAP_BASIC: |
| diffusers_unet_map[k[1]] = k[0] |
|
|
| return diffusers_unet_map |
|
|
|
|
| def swap_scale_shift(weight): |
| shift, scale = weight.chunk(2, dim=0) |
| new_weight = torch.cat([scale, shift], dim=0) |
| return new_weight |
|
|
|
|
| def flux_to_diffusers(mmdit_config, output_prefix=""): |
| n_double_layers = mmdit_config.get("depth", 0) |
| n_single_layers = mmdit_config.get("depth_single_blocks", 0) |
| hidden_size = mmdit_config.get("hidden_size", 0) |
|
|
| key_map = {} |
| for index in range(n_double_layers): |
| prefix_from = "transformer_blocks.{}".format(index) |
| prefix_to = "{}double_blocks.{}".format(output_prefix, index) |
|
|
| for end in ("weight", "bias"): |
| k = "{}.attn.".format(prefix_from) |
| qkv = "{}.img_attn.qkv.{}".format(prefix_to, end) |
| key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) |
| key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) |
| key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) |
|
|
| k = "{}.attn.".format(prefix_from) |
| qkv = "{}.txt_attn.qkv.{}".format(prefix_to, end) |
| key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) |
| key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) |
| key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) |
|
|
| block_map = { |
| "attn.to_out.0.weight": "img_attn.proj.weight", |
| "attn.to_out.0.bias": "img_attn.proj.bias", |
| "norm1.linear.weight": "img_mod.lin.weight", |
| "norm1.linear.bias": "img_mod.lin.bias", |
| "norm1_context.linear.weight": "txt_mod.lin.weight", |
| "norm1_context.linear.bias": "txt_mod.lin.bias", |
| "attn.to_add_out.weight": "txt_attn.proj.weight", |
| "attn.to_add_out.bias": "txt_attn.proj.bias", |
| "ff.net.0.proj.weight": "img_mlp.0.weight", |
| "ff.net.0.proj.bias": "img_mlp.0.bias", |
| "ff.net.2.weight": "img_mlp.2.weight", |
| "ff.net.2.bias": "img_mlp.2.bias", |
| "ff_context.net.0.proj.weight": "txt_mlp.0.weight", |
| "ff_context.net.0.proj.bias": "txt_mlp.0.bias", |
| "ff_context.net.2.weight": "txt_mlp.2.weight", |
| "ff_context.net.2.bias": "txt_mlp.2.bias", |
| "attn.norm_q.weight": "img_attn.norm.query_norm.scale", |
| "attn.norm_k.weight": "img_attn.norm.key_norm.scale", |
| "attn.norm_added_q.weight": "txt_attn.norm.query_norm.scale", |
| "attn.norm_added_k.weight": "txt_attn.norm.key_norm.scale", |
| } |
|
|
| for k in block_map: |
| key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k]) |
|
|
| for index in range(n_single_layers): |
| prefix_from = "single_transformer_blocks.{}".format(index) |
| prefix_to = "{}single_blocks.{}".format(output_prefix, index) |
|
|
| for end in ("weight", "bias"): |
| k = "{}.attn.".format(prefix_from) |
| qkv = "{}.linear1.{}".format(prefix_to, end) |
| key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) |
| key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) |
| key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) |
| key_map["{}.proj_mlp.{}".format(prefix_from, end)] = (qkv, (0, hidden_size * 3, hidden_size * 4)) |
|
|
| block_map = { |
| "norm.linear.weight": "modulation.lin.weight", |
| "norm.linear.bias": "modulation.lin.bias", |
| "proj_out.weight": "linear2.weight", |
| "proj_out.bias": "linear2.bias", |
| "attn.norm_q.weight": "norm.query_norm.scale", |
| "attn.norm_k.weight": "norm.key_norm.scale", |
| } |
|
|
| for k in block_map: |
| key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k]) |
|
|
| MAP_BASIC = { |
| ("final_layer.linear.bias", "proj_out.bias"), |
| ("final_layer.linear.weight", "proj_out.weight"), |
| ("img_in.bias", "x_embedder.bias"), |
| ("img_in.weight", "x_embedder.weight"), |
| ("time_in.in_layer.bias", "time_text_embed.timestep_embedder.linear_1.bias"), |
| ("time_in.in_layer.weight", "time_text_embed.timestep_embedder.linear_1.weight"), |
| ("time_in.out_layer.bias", "time_text_embed.timestep_embedder.linear_2.bias"), |
| ("time_in.out_layer.weight", "time_text_embed.timestep_embedder.linear_2.weight"), |
| ("txt_in.bias", "context_embedder.bias"), |
| ("txt_in.weight", "context_embedder.weight"), |
| ("vector_in.in_layer.bias", "time_text_embed.text_embedder.linear_1.bias"), |
| ("vector_in.in_layer.weight", "time_text_embed.text_embedder.linear_1.weight"), |
| ("vector_in.out_layer.bias", "time_text_embed.text_embedder.linear_2.bias"), |
| ("vector_in.out_layer.weight", "time_text_embed.text_embedder.linear_2.weight"), |
| ("guidance_in.in_layer.bias", "time_text_embed.guidance_embedder.linear_1.bias"), |
| ("guidance_in.in_layer.weight", "time_text_embed.guidance_embedder.linear_1.weight"), |
| ("guidance_in.out_layer.bias", "time_text_embed.guidance_embedder.linear_2.bias"), |
| ("guidance_in.out_layer.weight", "time_text_embed.guidance_embedder.linear_2.weight"), |
| ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift), |
| ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift), |
| ("pos_embed_input.bias", "controlnet_x_embedder.bias"), |
| ("pos_embed_input.weight", "controlnet_x_embedder.weight"), |
| } |
|
|
| for k in MAP_BASIC: |
| if len(k) > 2: |
| key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) |
| else: |
| key_map[k[1]] = "{}{}".format(output_prefix, k[0]) |
|
|
| return key_map |
|
|
|
|
| def z_image_to_diffusers(mmdit_config, output_prefix=""): |
| n_layers = mmdit_config.get("n_layers", 0) |
| hidden_size = mmdit_config.get("dim", 0) |
| n_context_refiner = mmdit_config.get("n_refiner_layers", 2) |
| n_noise_refiner = mmdit_config.get("n_refiner_layers", 2) |
| key_map = {} |
|
|
| def add_block_keys(prefix_from, prefix_to, has_adaln=True): |
| for end in ("weight", "bias"): |
| k = "{}.attention.".format(prefix_from) |
| qkv = "{}.attention.qkv.{}".format(prefix_to, end) |
| key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) |
| key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) |
| key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) |
|
|
| block_map = { |
| "attention.norm_q.weight": "attention.q_norm.weight", |
| "attention.norm_k.weight": "attention.k_norm.weight", |
| "attention.to_out.0.weight": "attention.out.weight", |
| "attention.to_out.0.bias": "attention.out.bias", |
| "attention_norm1.weight": "attention_norm1.weight", |
| "attention_norm2.weight": "attention_norm2.weight", |
| "feed_forward.w1.weight": "feed_forward.w1.weight", |
| "feed_forward.w2.weight": "feed_forward.w2.weight", |
| "feed_forward.w3.weight": "feed_forward.w3.weight", |
| "ffn_norm1.weight": "ffn_norm1.weight", |
| "ffn_norm2.weight": "ffn_norm2.weight", |
| } |
| if has_adaln: |
| block_map["adaLN_modulation.0.weight"] = "adaLN_modulation.0.weight" |
| block_map["adaLN_modulation.0.bias"] = "adaLN_modulation.0.bias" |
| for k, v in block_map.items(): |
| key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, v) |
|
|
| for i in range(n_layers): |
| add_block_keys("layers.{}".format(i), "{}layers.{}".format(output_prefix, i)) |
|
|
| for i in range(n_context_refiner): |
| add_block_keys("context_refiner.{}".format(i), "{}context_refiner.{}".format(output_prefix, i)) |
|
|
| for i in range(n_noise_refiner): |
| add_block_keys("noise_refiner.{}".format(i), "{}noise_refiner.{}".format(output_prefix, i)) |
|
|
| MAP_BASIC = [ |
| ("final_layer.linear.weight", "all_final_layer.2-1.linear.weight"), |
| ("final_layer.linear.bias", "all_final_layer.2-1.linear.bias"), |
| ("final_layer.adaLN_modulation.1.weight", "all_final_layer.2-1.adaLN_modulation.1.weight"), |
| ("final_layer.adaLN_modulation.1.bias", "all_final_layer.2-1.adaLN_modulation.1.bias"), |
| ("x_embedder.weight", "all_x_embedder.2-1.weight"), |
| ("x_embedder.bias", "all_x_embedder.2-1.bias"), |
| ("x_pad_token", "x_pad_token"), |
| ("cap_embedder.0.weight", "cap_embedder.0.weight"), |
| ("cap_embedder.1.weight", "cap_embedder.1.weight"), |
| ("cap_embedder.1.bias", "cap_embedder.1.bias"), |
| ("cap_pad_token", "cap_pad_token"), |
| ("t_embedder.mlp.0.weight", "t_embedder.mlp.0.weight"), |
| ("t_embedder.mlp.0.bias", "t_embedder.mlp.0.bias"), |
| ("t_embedder.mlp.2.weight", "t_embedder.mlp.2.weight"), |
| ("t_embedder.mlp.2.bias", "t_embedder.mlp.2.bias"), |
| ] |
|
|
| for c, diffusers in MAP_BASIC: |
| key_map[diffusers] = "{}{}".format(output_prefix, c) |
|
|
| return key_map |
|
|
