




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Any  =     StableDiffusionPipeline.from_pretrained(_lowercase					,		torch_dtype=torch.floataa )

     # load LoRA weight from .safetensors
     UpperCAmelCase_     :				Tuple  =     load_file(_lowercase )

     UpperCAmelCase_     :				Union[str, Any]  =     []

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

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

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

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

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

     return pipeline


if __name__ == "__main__":
     __a							     =					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.)')

     __a							     =					parser.parse_args()

     __a							     =					args.base_model_path
     __a							     =					args.checkpoint_path
     __a							     =					args.dump_path
     __a							     =					args.lora_prefix_unet
     __a							     =					args.lora_prefix_text_encoder
     __a							     =					args.alpha

     __a							     =					convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

     __a							     =					pipe.to(args.device)
     pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)





