From ecded50ad58b6c297da261397b9fc3930690812f Mon Sep 17 00:00:00 2001 From: realliujiaxu <72550220+realliujiaxu@users.noreply.github.com> Date: Tue, 22 Aug 2023 21:41:06 +0800 Subject: [PATCH] add convert diffuser pipeline of XL to original stable diffusion (#4596) convert diffuser pipeline of XL to original stable diffusion Co-authored-by: Dhruv Nair --- scripts/convert_diffusers_to_original_sdxl.py | 340 ++++++++++++++++++ 1 file changed, 340 insertions(+) create mode 100644 scripts/convert_diffusers_to_original_sdxl.py diff --git a/scripts/convert_diffusers_to_original_sdxl.py b/scripts/convert_diffusers_to_original_sdxl.py new file mode 100644 index 0000000000..1f11ef4570 --- /dev/null +++ b/scripts/convert_diffusers_to_original_sdxl.py @@ -0,0 +1,340 @@ +# 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 + + +# =================# +# 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"), + # the following are for sdxl + ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), + ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), + ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), + ("label_emb.0.2.bias", "add_embedding.linear_2.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(3): + # 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 > 0: + 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(4): + # 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 < 2: + # 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)) +unet_conversion_map_layer.append(("output_blocks.2.2.conv.", "output_blocks.2.1.conv.")) + +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 = {sd_name: unet_state_dict[hf_name] for hf_name, sd_name 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."), + # the following are for SDXL + ("q.", "to_q."), + ("k.", "to_k."), + ("v.", "to_v."), + ("proj_out.", "to_out.0."), +] + + +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 # +# =========================# + + +textenc_conversion_lst = [ + # (stable-diffusion, HF Diffusers) + ("transformer.resblocks.", "text_model.encoder.layers."), + ("ln_1", "layer_norm1"), + ("ln_2", "layer_norm2"), + (".c_fc.", ".fc1."), + (".c_proj.", ".fc2."), + (".attn", ".self_attn"), + ("ln_final.", "text_model.final_layer_norm."), + ("token_embedding.weight", "text_model.embeddings.token_embedding.weight"), + ("positional_embedding", "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_openclip_text_enc_state_dict(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_openai_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.") + parser.add_argument( + "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt." + ) + + 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!" + + # Path for safetensors + unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors") + vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors") + text_enc_path = osp.join(args.model_path, "text_encoder", "model.safetensors") + text_enc_2_path = osp.join(args.model_path, "text_encoder_2", "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(args.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(args.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(args.model_path, "text_encoder", "pytorch_model.bin") + text_enc_dict = torch.load(text_enc_path, map_location="cpu") + + if osp.exists(text_enc_2_path): + text_enc_2_dict = load_file(text_enc_2_path, device="cpu") + else: + text_enc_2_path = osp.join(args.model_path, "text_encoder_2", "pytorch_model.bin") + text_enc_2_dict = torch.load(text_enc_2_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()} + + text_enc_dict = convert_openai_text_enc_state_dict(text_enc_dict) + text_enc_dict = {"conditioner.embedders.0.transformer." + k: v for k, v in text_enc_dict.items()} + + text_enc_2_dict = convert_openclip_text_enc_state_dict(text_enc_2_dict) + text_enc_2_dict = {"conditioner.embedders.1.model." + k: v for k, v in text_enc_2_dict.items()} + + # Put together new checkpoint + state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict, **text_enc_2_dict} + + if args.half: + state_dict = {k: v.half() for k, v in state_dict.items()} + + if args.use_safetensors: + save_file(state_dict, args.checkpoint_path) + else: + state_dict = {"state_dict": state_dict} + torch.save(state_dict, args.checkpoint_path)