Spaces:
Runtime error
Runtime error
| # 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. | |
| # Written by jachiam | |
| import argparse | |
| import os.path as osp | |
| import torch | |
| import gc | |
| # =================# | |
| # 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"] | |
| print("[1;32mConverting to CKPT ...") | |
| 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: | |
| 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 | |
| def convert(model_path, checkpoint_path): | |
| unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin") | |
| vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin") | |
| text_enc_path = osp.join(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} | |
| state_dict = {k:v.half() for k,v in state_dict.items()} | |
| state_dict = {"state_dict": state_dict} | |
| torch.save(state_dict, checkpoint_path) | |
| del state_dict, text_enc_dict, vae_state_dict, unet_state_dict | |
| torch.cuda.empty_cache() | |
| gc.collect() | |