Checkpoint conversion script from Diffusers => Stable Diffusion (CompVis) (#701)
* Conversion script * ran black * ran isort * remove unused import * map location so everything gets loaded onto CPU before conversion * ran black again * Update setup.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
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Josh Achiam
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# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
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# *Only* converts the UNet, VAE, and Text Encoder.
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# Does not convert optimizer state or any other thing.
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import argparse
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import os.path as osp
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import torch
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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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# pretty much a no-op
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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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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
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parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
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parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
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args = parser.parse_args()
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assert args.model_path is not None, "Must provide a model path!"
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assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
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unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
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vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
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text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
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# Convert the UNet model
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unet_state_dict = torch.load(unet_path, map_location="cpu")
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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 = torch.load(vae_path, map_location="cpu")
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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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# Convert the text encoder model
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text_enc_dict = torch.load(text_enc_path, map_location="cpu")
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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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state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
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if args.half:
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state_dict = {k: v.half() for k, v in state_dict.items()}
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state_dict = {"state_dict": state_dict}
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torch.save(state_dict, args.checkpoint_path)
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12
setup.py
12
setup.py
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@ -67,12 +67,13 @@ To create the package for pypi.
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you need to go back to main before executing this.
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"""
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import re
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import os
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import re
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from distutils.core import Command
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from setuptools import find_packages, setup
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# IMPORTANT:
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# 1. all dependencies should be listed here with their version requirements if any
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# 2. once modified, run: `make deps_table_update` to update src/diffusers/dependency_versions_table.py
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@ -177,14 +178,7 @@ extras["quality"] = deps_list("black", "isort", "flake8", "hf-doc-builder")
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extras["docs"] = deps_list("hf-doc-builder")
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extras["training"] = deps_list("accelerate", "datasets", "tensorboard", "modelcards")
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extras["test"] = deps_list(
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"datasets",
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"onnxruntime",
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"pytest",
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"pytest-timeout",
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"pytest-xdist",
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"scipy",
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"torchvision",
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"transformers"
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"datasets", "onnxruntime", "pytest", "pytest-timeout", "pytest-xdist", "scipy", "torchvision", "transformers"
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)
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extras["torch"] = deps_list("torch")
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