



from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


__UpperCAmelCase	       =	'''CompVis/stable-diffusion-v1-1'''
__UpperCAmelCase	       =	'''CompVis/stable-diffusion-v1-2'''
__UpperCAmelCase	       =	'''CompVis/stable-diffusion-v1-3'''
__UpperCAmelCase	       =	'''CompVis/stable-diffusion-v1-4'''







class     lowerCAmelCase_		(  a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = True,      )					->		Dict:
          super()._init_()

          UpperCamelCase   :  List[Any]						     =     StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[str]						     =     StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  str						     =     StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     StableDiffusionPipeline(
              vae=SCREAMING_SNAKE_CASE_,      text_encoder=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_,      unet=SCREAMING_SNAKE_CASE_,      scheduler=SCREAMING_SNAKE_CASE_,      safety_checker=SCREAMING_SNAKE_CASE_,      feature_extractor=SCREAMING_SNAKE_CASE_,      requires_safety_checker=SCREAMING_SNAKE_CASE_,      )

          self.register_modules(pipelinea=self.pipea,      pipelinea=self.pipea,      pipelinea=self.pipea,      pipelinea=self.pipea	)
       @property
       def  snake_case_	(      self	)					->		Dict[str, Any]:
          return {k: getattr(self,      SCREAMING_SNAKE_CASE_	) for k in self.config.keys() if not k.startswith('_'	)}
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_ = "auto"	)					->		Optional[int]:
          if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             UpperCamelCase   :  List[str]						     =     self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		Tuple:
          self.enable_attention_slicing(SCREAMING_SNAKE_CASE_	)
       @torch.no_grad()
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 512,      SCREAMING_SNAKE_CASE_ = 512,      SCREAMING_SNAKE_CASE_ = 50,      SCREAMING_SNAKE_CASE_ = 7.5,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = 1,      SCREAMING_SNAKE_CASE_ = 0.0,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = "pil",      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = 1,      **SCREAMING_SNAKE_CASE_,      )					->		Optional[Any]:
          return self.pipea(
              prompt=SCREAMING_SNAKE_CASE_,      height=SCREAMING_SNAKE_CASE_,      width=SCREAMING_SNAKE_CASE_,      num_inference_steps=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      negative_prompt=SCREAMING_SNAKE_CASE_,      num_images_per_prompt=SCREAMING_SNAKE_CASE_,      eta=SCREAMING_SNAKE_CASE_,      generator=SCREAMING_SNAKE_CASE_,      latents=SCREAMING_SNAKE_CASE_,      output_type=SCREAMING_SNAKE_CASE_,      return_dict=SCREAMING_SNAKE_CASE_,      callback=SCREAMING_SNAKE_CASE_,      callback_steps=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )
       @torch.no_grad()
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 512,      SCREAMING_SNAKE_CASE_ = 512,      SCREAMING_SNAKE_CASE_ = 50,      SCREAMING_SNAKE_CASE_ = 7.5,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = 1,      SCREAMING_SNAKE_CASE_ = 0.0,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = "pil",      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = 1,      **SCREAMING_SNAKE_CASE_,      )					->		Optional[int]:
          return self.pipea(
              prompt=SCREAMING_SNAKE_CASE_,      height=SCREAMING_SNAKE_CASE_,      width=SCREAMING_SNAKE_CASE_,      num_inference_steps=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      negative_prompt=SCREAMING_SNAKE_CASE_,      num_images_per_prompt=SCREAMING_SNAKE_CASE_,      eta=SCREAMING_SNAKE_CASE_,      generator=SCREAMING_SNAKE_CASE_,      latents=SCREAMING_SNAKE_CASE_,      output_type=SCREAMING_SNAKE_CASE_,      return_dict=SCREAMING_SNAKE_CASE_,      callback=SCREAMING_SNAKE_CASE_,      callback_steps=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )
       @torch.no_grad()
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 512,      SCREAMING_SNAKE_CASE_ = 512,      SCREAMING_SNAKE_CASE_ = 50,      SCREAMING_SNAKE_CASE_ = 7.5,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = 1,      SCREAMING_SNAKE_CASE_ = 0.0,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = "pil",      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = 1,      **SCREAMING_SNAKE_CASE_,      )					->		Optional[Any]:
          return self.pipea(
              prompt=SCREAMING_SNAKE_CASE_,      height=SCREAMING_SNAKE_CASE_,      width=SCREAMING_SNAKE_CASE_,      num_inference_steps=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      negative_prompt=SCREAMING_SNAKE_CASE_,      num_images_per_prompt=SCREAMING_SNAKE_CASE_,      eta=SCREAMING_SNAKE_CASE_,      generator=SCREAMING_SNAKE_CASE_,      latents=SCREAMING_SNAKE_CASE_,      output_type=SCREAMING_SNAKE_CASE_,      return_dict=SCREAMING_SNAKE_CASE_,      callback=SCREAMING_SNAKE_CASE_,      callback_steps=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )
       @torch.no_grad()
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 512,      SCREAMING_SNAKE_CASE_ = 512,      SCREAMING_SNAKE_CASE_ = 50,      SCREAMING_SNAKE_CASE_ = 7.5,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = 1,      SCREAMING_SNAKE_CASE_ = 0.0,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = "pil",      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = 1,      **SCREAMING_SNAKE_CASE_,      )					->		Optional[int]:
          return self.pipea(
              prompt=SCREAMING_SNAKE_CASE_,      height=SCREAMING_SNAKE_CASE_,      width=SCREAMING_SNAKE_CASE_,      num_inference_steps=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      negative_prompt=SCREAMING_SNAKE_CASE_,      num_images_per_prompt=SCREAMING_SNAKE_CASE_,      eta=SCREAMING_SNAKE_CASE_,      generator=SCREAMING_SNAKE_CASE_,      latents=SCREAMING_SNAKE_CASE_,      output_type=SCREAMING_SNAKE_CASE_,      return_dict=SCREAMING_SNAKE_CASE_,      callback=SCREAMING_SNAKE_CASE_,      callback_steps=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )






       @torch.no_grad()
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 512,      SCREAMING_SNAKE_CASE_ = 512,      SCREAMING_SNAKE_CASE_ = 50,      SCREAMING_SNAKE_CASE_ = 7.5,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = 1,      SCREAMING_SNAKE_CASE_ = 0.0,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = "pil",      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = 1,      **SCREAMING_SNAKE_CASE_,      )					->		int:
          UpperCamelCase   :  Optional[Any]						     =     'cuda' if torch.cuda.is_available() else 'cpu'
          self.to(SCREAMING_SNAKE_CASE_	)

          # Checks if the height and width are divisible by 8 or not
          if height % 8 != 0 or width % 8 != 0:
             raise ValueError(F"""`height` and `width` must be divisible by 8 but are {height} and {width}."""	)

          # Get first result from Stable Diffusion Checkpoint v1.1
          UpperCamelCase   :  Any						     =     self.textaimg_sda_a(
              prompt=SCREAMING_SNAKE_CASE_,      height=SCREAMING_SNAKE_CASE_,      width=SCREAMING_SNAKE_CASE_,      num_inference_steps=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      negative_prompt=SCREAMING_SNAKE_CASE_,      num_images_per_prompt=SCREAMING_SNAKE_CASE_,      eta=SCREAMING_SNAKE_CASE_,      generator=SCREAMING_SNAKE_CASE_,      latents=SCREAMING_SNAKE_CASE_,      output_type=SCREAMING_SNAKE_CASE_,      return_dict=SCREAMING_SNAKE_CASE_,      callback=SCREAMING_SNAKE_CASE_,      callback_steps=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

          # Get first result from Stable Diffusion Checkpoint v1.2
          UpperCamelCase   :  str						     =     self.textaimg_sda_a(
              prompt=SCREAMING_SNAKE_CASE_,      height=SCREAMING_SNAKE_CASE_,      width=SCREAMING_SNAKE_CASE_,      num_inference_steps=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      negative_prompt=SCREAMING_SNAKE_CASE_,      num_images_per_prompt=SCREAMING_SNAKE_CASE_,      eta=SCREAMING_SNAKE_CASE_,      generator=SCREAMING_SNAKE_CASE_,      latents=SCREAMING_SNAKE_CASE_,      output_type=SCREAMING_SNAKE_CASE_,      return_dict=SCREAMING_SNAKE_CASE_,      callback=SCREAMING_SNAKE_CASE_,      callback_steps=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

          # Get first result from Stable Diffusion Checkpoint v1.3
          UpperCamelCase   :  Union[str, Any]						     =     self.textaimg_sda_a(
              prompt=SCREAMING_SNAKE_CASE_,      height=SCREAMING_SNAKE_CASE_,      width=SCREAMING_SNAKE_CASE_,      num_inference_steps=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      negative_prompt=SCREAMING_SNAKE_CASE_,      num_images_per_prompt=SCREAMING_SNAKE_CASE_,      eta=SCREAMING_SNAKE_CASE_,      generator=SCREAMING_SNAKE_CASE_,      latents=SCREAMING_SNAKE_CASE_,      output_type=SCREAMING_SNAKE_CASE_,      return_dict=SCREAMING_SNAKE_CASE_,      callback=SCREAMING_SNAKE_CASE_,      callback_steps=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

          # Get first result from Stable Diffusion Checkpoint v1.4
          UpperCamelCase   :  Optional[Any]						     =     self.textaimg_sda_a(
              prompt=SCREAMING_SNAKE_CASE_,      height=SCREAMING_SNAKE_CASE_,      width=SCREAMING_SNAKE_CASE_,      num_inference_steps=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      negative_prompt=SCREAMING_SNAKE_CASE_,      num_images_per_prompt=SCREAMING_SNAKE_CASE_,      eta=SCREAMING_SNAKE_CASE_,      generator=SCREAMING_SNAKE_CASE_,      latents=SCREAMING_SNAKE_CASE_,      output_type=SCREAMING_SNAKE_CASE_,      return_dict=SCREAMING_SNAKE_CASE_,      callback=SCREAMING_SNAKE_CASE_,      callback_steps=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

          # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
          return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]	)








import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 		UpperCamelCase     (       snake_case__	:   List[Any]	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Tuple	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   List[Any]     )     ->					Tuple:
   # load base model
   UpperCamelCase   :  Tuple						     =     StableDiffusionPipeline.from_pretrained(snake_case__	,					torch_dtype=torch.floataa     )

   # load LoRA weight from .safetensors
   UpperCamelCase   :  Union[str, Any]						     =     load_file(snake_case__     )

   UpperCamelCase   :  int						     =     []

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

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

      UpperCamelCase   :  List[Any]						     =     []
      if "lora_down" in key:
         pair_keys.append(key.replace('lora_down'	,					'lora_up'     )     )
         pair_keys.append(snake_case__     )
      else:
         pair_keys.append(snake_case__     )
         pair_keys.append(key.replace('lora_up'	,					'lora_down'     )     )

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

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

   return pipeline


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

    __UpperCAmelCase	       =	parser.parse_args()

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

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

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



