diffusers loader
This commit is contained in:
sALTaccount
parent
d5cce8345f
commit
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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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19
nodes.py
19
nodes.py
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@ -4,13 +4,14 @@ import os
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import sys
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import json
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import hashlib
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import copy
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import traceback
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from PIL import Image
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from PIL.PngImagePlugin import PngInfo
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import numpy as np
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from comfy.diffusers_convert import load_diffusers
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sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
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@ -219,6 +220,21 @@ class CheckpointLoaderSimple:
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out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
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return out
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class DiffusersLoader:
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {"model_path": (os.listdir(os.path.join(folder_paths.models_dir, 'diffusers'), ),),
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}}
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RETURN_TYPES = ("MODEL", "CLIP", "VAE")
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FUNCTION = "load_checkpoint"
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CATEGORY = "loaders"
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def load_checkpoint(self, model_path, output_vae=True, output_clip=True):
|
||||
model_path = os.path.join(folder_paths.models_dir, 'diffusers', model_path)
|
||||
return load_diffusers(model_path, fp16=True, output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
|
||||
|
||||
|
||||
class unCLIPCheckpointLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
|
|
@ -1076,6 +1092,7 @@ NODE_CLASS_MAPPINGS = {
|
|||
"TomePatchModel": TomePatchModel,
|
||||
"unCLIPCheckpointLoader": unCLIPCheckpointLoader,
|
||||
"CheckpointLoader": CheckpointLoader,
|
||||
"DiffusersLoader": DiffusersLoader,
|
||||
}
|
||||
|
||||
def load_custom_node(module_path):
|
||||
|
|
|
|||
Loading…
Reference in New Issue