# 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_motion_lora_ckpt_to_diffusers(pipeline, state_dict, alpha=1.0): # directly update weight in diffusers model for key in state_dict: # only process lora down key if "up." in key: continue up_key = key.replace(".down.", ".up.") model_key = key.replace("processor.", "").replace("_lora", "").replace("down.", "").replace("up.", "") model_key = model_key.replace("to_out.", "to_out.0.") layer_infos = model_key.split(".")[:-1] curr_layer = pipeline.unet while len(layer_infos) > 0: temp_name = layer_infos.pop(0) curr_layer = curr_layer.__getattr__(temp_name) weight_down = state_dict[key] weight_up = state_dict[up_key] curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).to(curr_layer.weight.data.device) return pipeline def convert_motion_lora_ckpt_to_diffusers_test(pipeline, state_dict, alpha=1.0): # directly update weight in diffusers model for key in state_dict: if "weight" in key: # only process lora down key if "up." in key: continue up_key = key.replace("_down.", "_up.") model_key = key.replace('-', '.').replace("_lora", "").replace("lora_down.", "").replace("lora_up.", "") print(up_key) print(key) print(model_key) layer_infos = model_key.split(".")[:-1] curr_layer = pipeline.unet while len(layer_infos) > 0: temp_name = layer_infos.pop(0) curr_layer = curr_layer.__getattr__(temp_name) weight_down = state_dict[key] weight_up = state_dict[up_key] curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).to(curr_layer.weight.data.device) print(weight_down) print(weight_up) print("------") print(curr_layer) return pipeline 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 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)