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| # 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 re | |
| import torch | |
| from safetensors.torch import load_file, save_file | |
| import gradio as gr | |
| # =================# | |
| # 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."), | |
| ] | |
| # This is probably not the most ideal solution, but it does work. | |
| vae_extra_conversion_map = [ | |
| ("to_q", "q"), | |
| ("to_k", "k"), | |
| ("to_v", "v"), | |
| ("to_out.0", "proj_out"), | |
| ] | |
| def reshape_weight_for_sd(w): | |
| # convert HF linear weights to SD conv2d weights | |
| if not w.ndim == 1: | |
| return w.reshape(*w.shape, 1, 1) | |
| else: | |
| return w | |
| 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"] | |
| keys_to_rename = {} | |
| 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) | |
| for weight_name, real_weight_name in vae_extra_conversion_map: | |
| if f"mid.attn_1.{weight_name}.weight" in k or f"mid.attn_1.{weight_name}.bias" in k: | |
| keys_to_rename[k] = k.replace(weight_name, real_weight_name) | |
| for k, v in keys_to_rename.items(): | |
| if k in new_state_dict: | |
| print(f"Renaming {k} to {v}") | |
| new_state_dict[v] = reshape_weight_for_sd(new_state_dict[k]) | |
| del new_state_dict[k] | |
| return new_state_dict | |
| # =========================# | |
| # Text Encoder Conversion # | |
| # =========================# | |
| textenc_conversion_lst = [ | |
| # (stable-diffusion, HF Diffusers) | |
| ("resblocks.", "text_model.encoder.layers."), | |
| ("ln_1", "layer_norm1"), | |
| ("ln_2", "layer_norm2"), | |
| (".c_fc.", ".fc1."), | |
| (".c_proj.", ".fc2."), | |
| (".attn", ".self_attn"), | |
| ("ln_final.", "transformer.text_model.final_layer_norm."), | |
| ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), | |
| ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), | |
| ] | |
| protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} | |
| textenc_pattern = re.compile("|".join(protected.keys())) | |
| # Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp | |
| code2idx = {"q": 0, "k": 1, "v": 2} | |
| def convert_text_enc_state_dict_v20(text_enc_dict): | |
| new_state_dict = {} | |
| capture_qkv_weight = {} | |
| capture_qkv_bias = {} | |
| for k, v in text_enc_dict.items(): | |
| if ( | |
| k.endswith(".self_attn.q_proj.weight") | |
| or k.endswith(".self_attn.k_proj.weight") | |
| or k.endswith(".self_attn.v_proj.weight") | |
| ): | |
| k_pre = k[: -len(".q_proj.weight")] | |
| k_code = k[-len("q_proj.weight")] | |
| if k_pre not in capture_qkv_weight: | |
| capture_qkv_weight[k_pre] = [None, None, None] | |
| capture_qkv_weight[k_pre][code2idx[k_code]] = v | |
| continue | |
| if ( | |
| k.endswith(".self_attn.q_proj.bias") | |
| or k.endswith(".self_attn.k_proj.bias") | |
| or k.endswith(".self_attn.v_proj.bias") | |
| ): | |
| k_pre = k[: -len(".q_proj.bias")] | |
| k_code = k[-len("q_proj.bias")] | |
| if k_pre not in capture_qkv_bias: | |
| capture_qkv_bias[k_pre] = [None, None, None] | |
| capture_qkv_bias[k_pre][code2idx[k_code]] = v | |
| continue | |
| relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) | |
| new_state_dict[relabelled_key] = v | |
| for k_pre, tensors in capture_qkv_weight.items(): | |
| if None in tensors: | |
| raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") | |
| relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) | |
| new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) | |
| for k_pre, tensors in capture_qkv_bias.items(): | |
| if None in tensors: | |
| raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") | |
| relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) | |
| new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) | |
| return new_state_dict | |
| def convert_text_enc_state_dict(text_enc_dict): | |
| return text_enc_dict | |
| def convert_diffusers_to_safetensors(model_path, checkpoint_path, half = True, progress=gr.Progress(track_tqdm=True)): | |
| progress(0, desc="Start converting...") | |
| # Path for safetensors | |
| unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.safetensors") | |
| vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.safetensors") | |
| text_enc_path = osp.join(model_path, "text_encoder", "model.safetensors") | |
| # Load models from safetensors if it exists, if it doesn't pytorch | |
| if osp.exists(unet_path): | |
| unet_state_dict = load_file(unet_path, device="cpu") | |
| else: | |
| unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin") | |
| unet_state_dict = torch.load(unet_path, map_location="cpu") | |
| if osp.exists(vae_path): | |
| vae_state_dict = load_file(vae_path, device="cpu") | |
| else: | |
| vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin") | |
| vae_state_dict = torch.load(vae_path, map_location="cpu") | |
| if osp.exists(text_enc_path): | |
| text_enc_dict = load_file(text_enc_path, device="cpu") | |
| else: | |
| text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin") | |
| text_enc_dict = torch.load(text_enc_path, map_location="cpu") | |
| # Convert the UNet model | |
| 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 = convert_vae_state_dict(vae_state_dict) | |
| vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} | |
| # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper | |
| is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict | |
| if is_v20_model: | |
| # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm | |
| text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()} | |
| text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict) | |
| text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()} | |
| else: | |
| 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 half: | |
| state_dict = {k: v.half() for k, v in state_dict.items()} | |
| save_file(state_dict, checkpoint_path) | |
| progress(1, desc="Converted.") | |
| def download_repo(repo_id, dir_path, progress=gr.Progress(track_tqdm=True)): | |
| from huggingface_hub import snapshot_download | |
| try: | |
| snapshot_download(repo_id=repo_id, local_dir=dir_path) | |
| except Exception as e: | |
| print(f"Error: Failed to download {repo_id}. ") | |
| return | |
| def upload_safetensors_to_repo(filename, progress=gr.Progress(track_tqdm=True)): | |
| from huggingface_hub import HfApi, hf_hub_url | |
| import os | |
| from pathlib import Path | |
| output_filename = Path(filename).name | |
| hf_token = os.environ.get("HF_TOKEN") | |
| repo_id = os.environ.get("HF_OUTPUT_REPO") | |
| api = HfApi() | |
| try: | |
| progress(0, desc="Start uploading...") | |
| api.upload_file(path_or_fileobj=filename, path_in_repo=output_filename, repo_id=repo_id, token=hf_token) | |
| progress(1, desc="Uploaded.") | |
| url = hf_hub_url(repo_id=repo_id, filename=output_filename) | |
| except Exception as e: | |
| print(f"Error: Failed to upload to {repo_id}. ") | |
| return None | |
| return url | |
| def convert_repo_to_safetensors(repo_id, half = True, progress=gr.Progress(track_tqdm=True)): | |
| download_dir = f"{repo_id.split('/')[0]}_{repo_id.split('/')[-1]}" | |
| output_filename = f"{repo_id.split('/')[0]}_{repo_id.split('/')[-1]}.safetensors" | |
| download_repo(repo_id, download_dir) | |
| convert_diffusers_to_safetensors(download_dir, output_filename, half) | |
| return output_filename | |
| def convert_repo_to_safetensors_multi_sd(repo_id, files, is_upload, urls, half=True, progress=gr.Progress(track_tqdm=True)): | |
| file = convert_repo_to_safetensors(repo_id, half) | |
| if not urls: urls = [] | |
| url = "" | |
| if is_upload: | |
| url = upload_safetensors_to_repo(file) | |
| if url: urls.append(url) | |
| md = "" | |
| for u in urls: | |
| md += f"[Download {str(u).split('/')[-1]}]({str(u)})<br>" | |
| if not files: files = [] | |
| files.append(file) | |
| return gr.update(value=files), gr.update(value=urls, choices=urls), gr.update(value=md) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--repo_id", default=None, type=str, required=True, help="HF Repo ID of the model to convert.") | |
| parser.add_argument("--half", default=True, help="Save weights in half precision.") | |
| args = parser.parse_args() | |
| assert args.repo_id is not None, "Must provide a Repo ID!" | |
| convert_repo_to_safetensors(args.repo_id, args.half) |