




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  # load base model
  lowercase			:int							=  StableDiffusionPipeline.from_pretrained(lowerCamelCase,	torch_dtype=torch.floataa      )

  # load LoRA weight from .safetensors
  lowercase			:Any							=  load_file(lowerCamelCase      )

  lowercase			:Dict							=  []

  # 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:
      lowercase			:Optional[Any]							=  key.split("."      )[0].split(LORA_PREFIX_TEXT_ENCODER + "_"      )[-1].split("_"      )
      lowercase			:Optional[int]							=  pipeline.text_encoder
    else:
      lowercase			:Optional[int]							=  key.split("."      )[0].split(LORA_PREFIX_UNET + "_"      )[-1].split("_"      )
      lowercase			:int							=  pipeline.unet

    # find the target layer
    lowercase			:Union[str, Any]							=  layer_infos.pop(0      )
    while len(lowerCamelCase      ) > -1:
      try:
        lowercase			:Union[str, Any]							=  curr_layer.__getattr__(lowerCamelCase      )
        if len(lowerCamelCase      ) > 0:
          lowercase			:Optional[int]							=  layer_infos.pop(0      )
        elif len(lowerCamelCase      ) == 0:
          break
      except Exception:
        if len(lowerCamelCase      ) > 0:
          temp_name += "_" + layer_infos.pop(0      )
        else:
          lowercase			:str							=  layer_infos.pop(0      )

    lowercase			:Optional[int]							=  []
    if "lora_down" in key:
      pair_keys.append(key.replace("lora_down",	"lora_up"      )      )
      pair_keys.append(lowerCamelCase      )
    else:
      pair_keys.append(lowerCamelCase      )
      pair_keys.append(key.replace("lora_up",	"lora_down"      )      )

    # update weight
    if len(state_dict[pair_keys[0]].shape      ) == 4:
      lowercase			:Optional[Any]							=  state_dict[pair_keys[0]].squeeze(3      ).squeeze(2      ).to(torch.floataa      )
      lowercase			:Any							=  state_dict[pair_keys[1]].squeeze(3      ).squeeze(2      ).to(torch.floataa      )
      curr_layer.weight.data += alpha * torch.mm(lowerCamelCase,	lowerCamelCase      ).unsqueeze(2      ).unsqueeze(3      )
    else:
      lowercase			:Optional[int]							=  state_dict[pair_keys[0]].to(torch.floataa      )
      lowercase			:Optional[Any]							=  state_dict[pair_keys[1]].to(torch.floataa      )
      curr_layer.weight.data += alpha * torch.mm(lowerCamelCase,	lowerCamelCase      )

    # update visited list
    for item in pair_keys:
      visited.append(lowerCamelCase      )

  return pipeline


if __name__ == "__main__":
     _UpperCAmelCase		:  List[Any]	=   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=0.7_5, 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.)")

     _UpperCAmelCase		:  Tuple	=   parser.parse_args()

     _UpperCAmelCase		:  int	=   args.base_model_path
     _UpperCAmelCase		:  Tuple	=   args.checkpoint_path
     _UpperCAmelCase		:  Optional[int]	=   args.dump_path
     _UpperCAmelCase		:  Dict	=   args.lora_prefix_unet
     _UpperCAmelCase		:  Dict	=   args.lora_prefix_text_encoder
     _UpperCAmelCase		:  Optional[Any]	=   args.alpha

     _UpperCAmelCase		:  Optional[Any]	=   convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

     _UpperCAmelCase		:  Any	=   pipe.to(args.device)
     pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:List[str]							=  ""
  for word_or_phrase in separated:
    if not isinstance(lowerCamelCase,	lowerCamelCase      ):
      raise Exception("join() accepts only strings to be joined"      )
    joined += word_or_phrase + separator
  return joined.strip(lowerCamelCase      )


if __name__ == "__main__":
     from doctest import testmod

     testmod()



