From 6b8a93f46f053668c8023520225a18445d48d8f1 Mon Sep 17 00:00:00 2001 From: Volpeon Date: Sat, 25 Mar 2023 16:34:48 +0100 Subject: Update --- ...nvert_diffusers_to_original_stable_diffusion.py | 234 --------------------- 1 file changed, 234 deletions(-) delete mode 100644 scripts/convert_diffusers_to_original_stable_diffusion.py (limited to 'scripts/convert_diffusers_to_original_stable_diffusion.py') diff --git a/scripts/convert_diffusers_to_original_stable_diffusion.py b/scripts/convert_diffusers_to_original_stable_diffusion.py deleted file mode 100644 index 9888f62..0000000 --- a/scripts/convert_diffusers_to_original_stable_diffusion.py +++ /dev/null @@ -1,234 +0,0 @@ -# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. -# *Only* converts the UNet, VAE, and Text Encoder. -# Does not convert optimizer state or any other thing. - -import argparse -import os.path as osp - -import torch - - -# =================# -# UNet Conversion # -# =================# - -unet_conversion_map = [ - # (stable-diffusion, HF Diffusers) - ("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"), - ("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"), -] - -unet_conversion_map_resnet = [ - # (stable-diffusion, HF Diffusers) - ("in_layers.0", "norm1"), - ("in_layers.2", "conv1"), - ("out_layers.0", "norm2"), - ("out_layers.3", "conv2"), - ("emb_layers.1", "time_emb_proj"), - ("skip_connection", "conv_shortcut"), -] - -unet_conversion_map_layer = [] -# hardcoded number of downblocks and resnets/attentions... -# would need smarter logic for other networks. -for i in range(4): - # loop over downblocks/upblocks - - for j in range(2): - # loop over resnets/attentions for downblocks - hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." - sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0." - unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) - - if i < 3: - # no attention layers in down_blocks.3 - hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." - sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1." - unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) - - for j in range(3): - # loop over resnets/attentions for upblocks - hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." - sd_up_res_prefix = f"output_blocks.{3*i + j}.0." - unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) - - if i > 0: - # no attention layers in up_blocks.0 - hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." - sd_up_atn_prefix = f"output_blocks.{3*i + j}.1." - unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) - - if i < 3: - # no downsample in down_blocks.3 - hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." - sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op." - unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) - - # no upsample in up_blocks.3 - hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." - sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}." - unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) - -hf_mid_atn_prefix = "mid_block.attentions.0." -sd_mid_atn_prefix = "middle_block.1." -unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) - -for j in range(2): - hf_mid_res_prefix = f"mid_block.resnets.{j}." - sd_mid_res_prefix = f"middle_block.{2*j}." - unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) - - -def convert_unet_state_dict(unet_state_dict): - # buyer beware: this is a *brittle* function, - # and correct output requires that all of these pieces interact in - # the exact order in which I have arranged them. - mapping = {k: k for k in unet_state_dict.keys()} - for sd_name, hf_name in unet_conversion_map: - mapping[hf_name] = sd_name - for k, v in mapping.items(): - if "resnets" in k: - for sd_part, hf_part in unet_conversion_map_resnet: - v = v.replace(hf_part, sd_part) - mapping[k] = v - for k, v in mapping.items(): - for sd_part, hf_part in unet_conversion_map_layer: - v = v.replace(hf_part, sd_part) - mapping[k] = v - new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} - return new_state_dict - - -# ================# -# VAE Conversion # -# ================# - -vae_conversion_map = [ - # (stable-diffusion, HF Diffusers) - ("nin_shortcut", "conv_shortcut"), - ("norm_out", "conv_norm_out"), - ("mid.attn_1.", "mid_block.attentions.0."), -] - -for i in range(4): - # down_blocks have two resnets - for j in range(2): - hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." - sd_down_prefix = f"encoder.down.{i}.block.{j}." - vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) - - if i < 3: - hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." - sd_downsample_prefix = f"down.{i}.downsample." - vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) - - hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." - sd_upsample_prefix = f"up.{3-i}.upsample." - vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) - - # up_blocks have three resnets - # also, up blocks in hf are numbered in reverse from sd - for j in range(3): - hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." - sd_up_prefix = f"decoder.up.{3-i}.block.{j}." - vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) - -# this part accounts for mid blocks in both the encoder and the decoder -for i in range(2): - hf_mid_res_prefix = f"mid_block.resnets.{i}." - sd_mid_res_prefix = f"mid.block_{i+1}." - vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) - - -vae_conversion_map_attn = [ - # (stable-diffusion, HF Diffusers) - ("norm.", "group_norm."), - ("q.", "query."), - ("k.", "key."), - ("v.", "value."), - ("proj_out.", "proj_attn."), -] - - -def reshape_weight_for_sd(w): - # convert HF linear weights to SD conv2d weights - return w.reshape(*w.shape, 1, 1) - - -def convert_vae_state_dict(vae_state_dict): - mapping = {k: k for k in vae_state_dict.keys()} - for k, v in mapping.items(): - for sd_part, hf_part in vae_conversion_map: - v = v.replace(hf_part, sd_part) - mapping[k] = v - for k, v in mapping.items(): - if "attentions" in k: - for sd_part, hf_part in vae_conversion_map_attn: - v = v.replace(hf_part, sd_part) - mapping[k] = v - new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} - weights_to_convert = ["q", "k", "v", "proj_out"] - for k, v in new_state_dict.items(): - for weight_name in weights_to_convert: - if f"mid.attn_1.{weight_name}.weight" in k: - print(f"Reshaping {k} for SD format") - new_state_dict[k] = reshape_weight_for_sd(v) - return new_state_dict - - -# =========================# -# Text Encoder Conversion # -# =========================# -# pretty much a no-op - - -def convert_text_enc_state_dict(text_enc_dict): - return text_enc_dict - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.") - parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--half", action="store_true", help="Save weights in half precision.") - - args = parser.parse_args() - - assert args.model_path is not None, "Must provide a model path!" - - assert args.checkpoint_path is not None, "Must provide a checkpoint path!" - - unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin") - vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin") - text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin") - - # Convert the UNet model - unet_state_dict = torch.load(unet_path, map_location="cpu") - unet_state_dict = convert_unet_state_dict(unet_state_dict) - unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} - - # Convert the VAE model - vae_state_dict = torch.load(vae_path, map_location="cpu") - vae_state_dict = convert_vae_state_dict(vae_state_dict) - vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} - - # Convert the text encoder model - text_enc_dict = torch.load(text_enc_path, map_location="cpu") - text_enc_dict = convert_text_enc_state_dict(text_enc_dict) - text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()} - - # Put together new checkpoint - state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict} - if args.half: - state_dict = {k: v.half() for k, v in state_dict.items()} - state_dict = {"state_dict": state_dict} - torch.save(state_dict, args.checkpoint_path) -- cgit v1.2.3-54-g00ecf