sALTaccount
2 years ago
3 changed files with 382 additions and 1 deletions
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import json |
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import os |
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import yaml |
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# because of local import nonsense |
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import sys |
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sys.path.append(os.path.dirname(os.path.realpath(__file__))) |
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import folder_paths |
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from comfy.ldm.util import instantiate_from_config |
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from comfy.sd import ModelPatcher, load_model_weights, CLIP, VAE |
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import os.path as osp |
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import re |
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import torch |
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from safetensors.torch import load_file, save_file |
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# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py |
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# =================# |
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# UNet Conversion # |
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# =================# |
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unet_conversion_map = [ |
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# (stable-diffusion, HF Diffusers) |
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("time_embed.0.weight", "time_embedding.linear_1.weight"), |
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("time_embed.0.bias", "time_embedding.linear_1.bias"), |
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("time_embed.2.weight", "time_embedding.linear_2.weight"), |
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("time_embed.2.bias", "time_embedding.linear_2.bias"), |
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("input_blocks.0.0.weight", "conv_in.weight"), |
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("input_blocks.0.0.bias", "conv_in.bias"), |
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("out.0.weight", "conv_norm_out.weight"), |
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("out.0.bias", "conv_norm_out.bias"), |
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("out.2.weight", "conv_out.weight"), |
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("out.2.bias", "conv_out.bias"), |
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] |
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unet_conversion_map_resnet = [ |
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# (stable-diffusion, HF Diffusers) |
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("in_layers.0", "norm1"), |
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("in_layers.2", "conv1"), |
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("out_layers.0", "norm2"), |
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("out_layers.3", "conv2"), |
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("emb_layers.1", "time_emb_proj"), |
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("skip_connection", "conv_shortcut"), |
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] |
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unet_conversion_map_layer = [] |
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# hardcoded number of downblocks and resnets/attentions... |
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# would need smarter logic for other networks. |
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for i in range(4): |
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# loop over downblocks/upblocks |
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for j in range(2): |
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# loop over resnets/attentions for downblocks |
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hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." |
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sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0." |
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unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) |
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if i < 3: |
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# no attention layers in down_blocks.3 |
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hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." |
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sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." |
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unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) |
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for j in range(3): |
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# loop over resnets/attentions for upblocks |
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hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." |
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sd_up_res_prefix = f"output_blocks.{3 * i + j}.0." |
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unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) |
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if i > 0: |
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# no attention layers in up_blocks.0 |
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hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." |
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sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1." |
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unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) |
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if i < 3: |
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# no downsample in down_blocks.3 |
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hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." |
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sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." |
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unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) |
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# no upsample in up_blocks.3 |
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hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." |
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sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}." |
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unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) |
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hf_mid_atn_prefix = "mid_block.attentions.0." |
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sd_mid_atn_prefix = "middle_block.1." |
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unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) |
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for j in range(2): |
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hf_mid_res_prefix = f"mid_block.resnets.{j}." |
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sd_mid_res_prefix = f"middle_block.{2 * j}." |
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unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) |
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def convert_unet_state_dict(unet_state_dict): |
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# buyer beware: this is a *brittle* function, |
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# and correct output requires that all of these pieces interact in |
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# the exact order in which I have arranged them. |
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mapping = {k: k for k in unet_state_dict.keys()} |
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for sd_name, hf_name in unet_conversion_map: |
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mapping[hf_name] = sd_name |
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for k, v in mapping.items(): |
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if "resnets" in k: |
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for sd_part, hf_part in unet_conversion_map_resnet: |
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v = v.replace(hf_part, sd_part) |
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mapping[k] = v |
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for k, v in mapping.items(): |
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for sd_part, hf_part in unet_conversion_map_layer: |
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v = v.replace(hf_part, sd_part) |
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mapping[k] = v |
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new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} |
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return new_state_dict |
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# ================# |
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# VAE Conversion # |
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# ================# |
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vae_conversion_map = [ |
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# (stable-diffusion, HF Diffusers) |
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("nin_shortcut", "conv_shortcut"), |
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("norm_out", "conv_norm_out"), |
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("mid.attn_1.", "mid_block.attentions.0."), |
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] |
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for i in range(4): |
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# down_blocks have two resnets |
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for j in range(2): |
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hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." |
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sd_down_prefix = f"encoder.down.{i}.block.{j}." |
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vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) |
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if i < 3: |
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hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." |
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sd_downsample_prefix = f"down.{i}.downsample." |
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vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) |
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hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." |
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sd_upsample_prefix = f"up.{3 - i}.upsample." |
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vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) |
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# up_blocks have three resnets |
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# also, up blocks in hf are numbered in reverse from sd |
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for j in range(3): |
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hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." |
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sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." |
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vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) |
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# this part accounts for mid blocks in both the encoder and the decoder |
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for i in range(2): |
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hf_mid_res_prefix = f"mid_block.resnets.{i}." |
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sd_mid_res_prefix = f"mid.block_{i + 1}." |
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vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) |
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vae_conversion_map_attn = [ |
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# (stable-diffusion, HF Diffusers) |
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("norm.", "group_norm."), |
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("q.", "query."), |
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("k.", "key."), |
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("v.", "value."), |
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("proj_out.", "proj_attn."), |
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] |
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def reshape_weight_for_sd(w): |
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# convert HF linear weights to SD conv2d weights |
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return w.reshape(*w.shape, 1, 1) |
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def convert_vae_state_dict(vae_state_dict): |
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mapping = {k: k for k in vae_state_dict.keys()} |
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for k, v in mapping.items(): |
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for sd_part, hf_part in vae_conversion_map: |
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v = v.replace(hf_part, sd_part) |
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mapping[k] = v |
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for k, v in mapping.items(): |
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if "attentions" in k: |
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for sd_part, hf_part in vae_conversion_map_attn: |
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v = v.replace(hf_part, sd_part) |
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mapping[k] = v |
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new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} |
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weights_to_convert = ["q", "k", "v", "proj_out"] |
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for k, v in new_state_dict.items(): |
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for weight_name in weights_to_convert: |
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if f"mid.attn_1.{weight_name}.weight" in k: |
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print(f"Reshaping {k} for SD format") |
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new_state_dict[k] = reshape_weight_for_sd(v) |
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return new_state_dict |
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# =========================# |
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# Text Encoder Conversion # |
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# =========================# |
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textenc_conversion_lst = [ |
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# (stable-diffusion, HF Diffusers) |
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("resblocks.", "text_model.encoder.layers."), |
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("ln_1", "layer_norm1"), |
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("ln_2", "layer_norm2"), |
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(".c_fc.", ".fc1."), |
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(".c_proj.", ".fc2."), |
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(".attn", ".self_attn"), |
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("ln_final.", "transformer.text_model.final_layer_norm."), |
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("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), |
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("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), |
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] |
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protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} |
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textenc_pattern = re.compile("|".join(protected.keys())) |
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# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp |
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code2idx = {"q": 0, "k": 1, "v": 2} |
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def convert_text_enc_state_dict_v20(text_enc_dict): |
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new_state_dict = {} |
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capture_qkv_weight = {} |
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capture_qkv_bias = {} |
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for k, v in text_enc_dict.items(): |
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if ( |
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k.endswith(".self_attn.q_proj.weight") |
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or k.endswith(".self_attn.k_proj.weight") |
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or k.endswith(".self_attn.v_proj.weight") |
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): |
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k_pre = k[: -len(".q_proj.weight")] |
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k_code = k[-len("q_proj.weight")] |
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if k_pre not in capture_qkv_weight: |
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capture_qkv_weight[k_pre] = [None, None, None] |
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capture_qkv_weight[k_pre][code2idx[k_code]] = v |
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continue |
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if ( |
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k.endswith(".self_attn.q_proj.bias") |
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or k.endswith(".self_attn.k_proj.bias") |
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or k.endswith(".self_attn.v_proj.bias") |
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): |
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k_pre = k[: -len(".q_proj.bias")] |
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k_code = k[-len("q_proj.bias")] |
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if k_pre not in capture_qkv_bias: |
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capture_qkv_bias[k_pre] = [None, None, None] |
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capture_qkv_bias[k_pre][code2idx[k_code]] = v |
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continue |
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relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) |
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new_state_dict[relabelled_key] = v |
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for k_pre, tensors in capture_qkv_weight.items(): |
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if None in tensors: |
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raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") |
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relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) |
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new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) |
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for k_pre, tensors in capture_qkv_bias.items(): |
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if None in tensors: |
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raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") |
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relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) |
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new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) |
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return new_state_dict |
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def convert_text_enc_state_dict(text_enc_dict): |
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return text_enc_dict |
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def load_diffusers(model_path, fp16=True, output_vae=True, output_clip=True, embedding_directory=None): |
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diffusers_unet_conf = json.load(open(osp.join(model_path, "unet/config.json"))) |
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diffusers_scheduler_conf = json.load(open(osp.join(model_path, "scheduler/scheduler_config.json"))) |
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# magic |
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v2 = diffusers_unet_conf["sample_size"] == 96 |
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v_pred = diffusers_scheduler_conf['prediction_type'] == 'v_prediction' |
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if v2: |
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if v_pred: |
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config_path = folder_paths.get_full_path("configs", 'v2-inference-v.yaml') |
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else: |
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config_path = folder_paths.get_full_path("configs", 'v2-inference.yaml') |
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else: |
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config_path = folder_paths.get_full_path("configs", 'v1-inference.yaml') |
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with open(config_path, 'r') as stream: |
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config = yaml.safe_load(stream) |
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model_config_params = config['model']['params'] |
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clip_config = model_config_params['cond_stage_config'] |
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scale_factor = model_config_params['scale_factor'] |
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vae_config = model_config_params['first_stage_config'] |
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vae_config['scale_factor'] = scale_factor |
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unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.safetensors") |
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vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.safetensors") |
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text_enc_path = osp.join(model_path, "text_encoder", "model.safetensors") |
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# Load models from safetensors if it exists, if it doesn't pytorch |
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if osp.exists(unet_path): |
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unet_state_dict = load_file(unet_path, device="cpu") |
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else: |
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unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin") |
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unet_state_dict = torch.load(unet_path, map_location="cpu") |
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if osp.exists(vae_path): |
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vae_state_dict = load_file(vae_path, device="cpu") |
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else: |
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vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin") |
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vae_state_dict = torch.load(vae_path, map_location="cpu") |
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if osp.exists(text_enc_path): |
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text_enc_dict = load_file(text_enc_path, device="cpu") |
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else: |
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text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin") |
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text_enc_dict = torch.load(text_enc_path, map_location="cpu") |
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# Convert the UNet model |
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unet_state_dict = convert_unet_state_dict(unet_state_dict) |
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unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} |
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# Convert the VAE model |
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vae_state_dict = convert_vae_state_dict(vae_state_dict) |
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vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} |
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# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper |
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is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict |
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if is_v20_model: |
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# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm |
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text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()} |
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text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict) |
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text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()} |
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else: |
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text_enc_dict = convert_text_enc_state_dict(text_enc_dict) |
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text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()} |
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# Put together new checkpoint |
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sd = {**unet_state_dict, **vae_state_dict, **text_enc_dict} |
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clip = None |
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vae = None |
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class WeightsLoader(torch.nn.Module): |
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pass |
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w = WeightsLoader() |
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load_state_dict_to = [] |
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if output_vae: |
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vae = VAE(scale_factor=scale_factor, config=vae_config) |
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w.first_stage_model = vae.first_stage_model |
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load_state_dict_to = [w] |
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if output_clip: |
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clip = CLIP(config=clip_config, embedding_directory=embedding_directory) |
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w.cond_stage_model = clip.cond_stage_model |
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load_state_dict_to = [w] |
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model = instantiate_from_config(config["model"]) |
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model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to) |
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if fp16: |
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model = model.half() |
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return ModelPatcher(model), clip, vae |
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