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| # coding=utf-8 | |
| # Copyright 2023, Haofan Wang, Qixun Wang, All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ Conversion script for the LoRA's safetensors checkpoints. """ | |
| import argparse | |
| import torch | |
| from safetensors.torch import load_file | |
| from diffusers import StableDiffusionPipeline | |
| import pdb | |
| def convert_lora(pipeline, state_dict, LORA_PREFIX_UNET="lora_unet", LORA_PREFIX_TEXT_ENCODER="lora_te", alpha=0.6): | |
| # load base model | |
| # pipeline = StableDiffusionPipeline.from_pretrained(base_model_path, torch_dtype=torch.float32) | |
| # load LoRA weight from .safetensors | |
| # state_dict = load_file(checkpoint_path) | |
| visited = [] | |
| # directly update weight in diffusers model | |
| for key in state_dict: | |
| # it is suggested to print out the key, it usually will be something like below | |
| # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight" | |
| # as we have set the alpha beforehand, so just skip | |
| if ".alpha" in key or key in visited: | |
| continue | |
| if "text" in key: | |
| layer_infos = key.split(".")[0].split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_") | |
| curr_layer = pipeline.text_encoder | |
| else: | |
| layer_infos = key.split(".")[0].split(LORA_PREFIX_UNET + "_")[-1].split("_") | |
| curr_layer = pipeline.unet | |
| # find the target layer | |
| temp_name = layer_infos.pop(0) | |
| while len(layer_infos) > -1: | |
| try: | |
| curr_layer = curr_layer.__getattr__(temp_name) | |
| if len(layer_infos) > 0: | |
| temp_name = layer_infos.pop(0) | |
| elif len(layer_infos) == 0: | |
| break | |
| except Exception: | |
| if len(temp_name) > 0: | |
| temp_name += "_" + layer_infos.pop(0) | |
| else: | |
| temp_name = layer_infos.pop(0) | |
| pair_keys = [] | |
| if "lora_down" in key: | |
| pair_keys.append(key.replace("lora_down", "lora_up")) | |
| pair_keys.append(key) | |
| else: | |
| pair_keys.append(key) | |
| pair_keys.append(key.replace("lora_up", "lora_down")) | |
| # update weight | |
| if len(state_dict[pair_keys[0]].shape) == 4: | |
| weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32) | |
| weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32) | |
| curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3).to(curr_layer.weight.data.device) | |
| else: | |
| weight_up = state_dict[pair_keys[0]].to(torch.float32) | |
| weight_down = state_dict[pair_keys[1]].to(torch.float32) | |
| curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).to(curr_layer.weight.data.device) | |
| # update visited list | |
| for item in pair_keys: | |
| visited.append(item) | |
| return pipeline | |
| def convert_lora_model_level(state_dict, unet, text_encoder=None, LORA_PREFIX_UNET="lora_unet", LORA_PREFIX_TEXT_ENCODER="lora_te", alpha=0.6): | |
| """convert lora in model level instead of pipeline leval | |
| """ | |
| visited = [] | |
| # directly update weight in diffusers model | |
| for key in state_dict: | |
| # it is suggested to print out the key, it usually will be something like below | |
| # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight" | |
| # as we have set the alpha beforehand, so just skip | |
| if ".alpha" in key or key in visited: | |
| continue | |
| if "text" in key: | |
| layer_infos = key.split(".")[0].split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_") | |
| assert text_encoder is not None, ( | |
| 'text_encoder must be passed since lora contains text encoder layers') | |
| curr_layer = text_encoder | |
| else: | |
| layer_infos = key.split(".")[0].split(LORA_PREFIX_UNET + "_")[-1].split("_") | |
| curr_layer = unet | |
| # find the target layer | |
| temp_name = layer_infos.pop(0) | |
| while len(layer_infos) > -1: | |
| try: | |
| curr_layer = curr_layer.__getattr__(temp_name) | |
| if len(layer_infos) > 0: | |
| temp_name = layer_infos.pop(0) | |
| elif len(layer_infos) == 0: | |
| break | |
| except Exception: | |
| if len(temp_name) > 0: | |
| temp_name += "_" + layer_infos.pop(0) | |
| else: | |
| temp_name = layer_infos.pop(0) | |
| pair_keys = [] | |
| if "lora_down" in key: | |
| pair_keys.append(key.replace("lora_down", "lora_up")) | |
| pair_keys.append(key) | |
| else: | |
| pair_keys.append(key) | |
| pair_keys.append(key.replace("lora_up", "lora_down")) | |
| # update weight | |
| # NOTE: load lycon, meybe have bugs :( | |
| if 'conv_in' in pair_keys[0]: | |
| weight_up = state_dict[pair_keys[0]].to(torch.float32) | |
| weight_down = state_dict[pair_keys[1]].to(torch.float32) | |
| weight_up = weight_up.view(weight_up.size(0), -1) | |
| weight_down = weight_down.view(weight_down.size(0), -1) | |
| shape = [e for e in curr_layer.weight.data.shape] | |
| shape[1] = 4 | |
| curr_layer.weight.data[:, :4, ...] += alpha * (weight_up @ weight_down).view(*shape) | |
| elif 'conv' in pair_keys[0]: | |
| weight_up = state_dict[pair_keys[0]].to(torch.float32) | |
| weight_down = state_dict[pair_keys[1]].to(torch.float32) | |
| weight_up = weight_up.view(weight_up.size(0), -1) | |
| weight_down = weight_down.view(weight_down.size(0), -1) | |
| shape = [e for e in curr_layer.weight.data.shape] | |
| curr_layer.weight.data += alpha * (weight_up @ weight_down).view(*shape) | |
| elif len(state_dict[pair_keys[0]].shape) == 4: | |
| weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32) | |
| weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32) | |
| curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3).to(curr_layer.weight.data.device) | |
| else: | |
| weight_up = state_dict[pair_keys[0]].to(torch.float32) | |
| weight_down = state_dict[pair_keys[1]].to(torch.float32) | |
| curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).to(curr_layer.weight.data.device) | |
| # update visited list | |
| for item in pair_keys: | |
| visited.append(item) | |
| return unet, text_encoder | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format." | |
| ) | |
| parser.add_argument( | |
| "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." | |
| ) | |
| parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") | |
| parser.add_argument( | |
| "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors" | |
| ) | |
| parser.add_argument( | |
| "--lora_prefix_text_encoder", | |
| default="lora_te", | |
| type=str, | |
| help="The prefix of text encoder weight in safetensors", | |
| ) | |
| parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW") | |
| parser.add_argument( | |
| "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not." | |
| ) | |
| parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") | |
| args = parser.parse_args() | |
| base_model_path = args.base_model_path | |
| checkpoint_path = args.checkpoint_path | |
| dump_path = args.dump_path | |
| lora_prefix_unet = args.lora_prefix_unet | |
| lora_prefix_text_encoder = args.lora_prefix_text_encoder | |
| alpha = args.alpha | |
| pipe = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha) | |
| pipe = pipe.to(args.device) | |
| pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) | |