

'''simple docstring'''

import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[str]		,lowerCAmelCase__:					List[Any]		,lowerCAmelCase__:					List[Any]		,lowerCAmelCase__:					str		,lowerCAmelCase__:					int   )      -> Tuple:
							# load base model
							SCREAMING_SNAKE_CASE_				=				StableDiffusionPipeline.from_pretrained(lowerCAmelCase__		,torch_dtype=torch.floataa   )

							# load LoRA weight from .safetensors
							SCREAMING_SNAKE_CASE_				=				load_file(lowerCAmelCase__   )

							SCREAMING_SNAKE_CASE_				=				[]

							# 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:
																					SCREAMING_SNAKE_CASE_				=				key.split('.'   )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'   )[-1].split('_'   )
																					SCREAMING_SNAKE_CASE_				=				pipeline.text_encoder
														else:
																					SCREAMING_SNAKE_CASE_				=				key.split('.'   )[0].split(LORA_PREFIX_UNET + '_'   )[-1].split('_'   )
																					SCREAMING_SNAKE_CASE_				=				pipeline.unet

														# find the target layer
														SCREAMING_SNAKE_CASE_				=				layer_infos.pop(0   )
														while len(lowerCAmelCase__   ) > -1:
																					try:
																												SCREAMING_SNAKE_CASE_				=				curr_layer.__getattr__(lowerCAmelCase__   )
																												if len(lowerCAmelCase__   ) > 0:
																																			SCREAMING_SNAKE_CASE_				=				layer_infos.pop(0   )
																												elif len(lowerCAmelCase__   ) == 0:
																																			break
																					except Exception:
																												if len(lowerCAmelCase__   ) > 0:
																																			temp_name += "_" + layer_infos.pop(0   )
																												else:
																																			SCREAMING_SNAKE_CASE_				=				layer_infos.pop(0   )

														SCREAMING_SNAKE_CASE_				=				[]
														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:
																					SCREAMING_SNAKE_CASE_				=				state_dict[pair_keys[0]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
																					SCREAMING_SNAKE_CASE_				=				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:
																					SCREAMING_SNAKE_CASE_				=				state_dict[pair_keys[0]].to(torch.floataa   )
																					SCREAMING_SNAKE_CASE_				=				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__":
							SCREAMING_SNAKE_CASE							:				Optional[int]     	=			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.)")

							SCREAMING_SNAKE_CASE							:				List[Any]     	=			parser.parse_args()

							SCREAMING_SNAKE_CASE							:				Dict     	=			args.base_model_path
							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			args.checkpoint_path
							SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			args.dump_path
							SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			args.lora_prefix_unet
							SCREAMING_SNAKE_CASE							:				Tuple     	=			args.lora_prefix_text_encoder
							SCREAMING_SNAKE_CASE							:				Tuple     	=			args.alpha

							SCREAMING_SNAKE_CASE							:				Any     	=			convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

							SCREAMING_SNAKE_CASE							:				List[str]     	=			pipe.to(args.device)
							pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)








'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											super().__init__()
											self.register_modules(vqvae=_lowercase,     unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = None,     _lowercase = 0.0,     _lowercase = 50,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Union[Tuple, ImagePipelineOutput]:
											SCREAMING_SNAKE_CASE_				=				randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE_				=				latents * self.scheduler.init_noise_sigma

											self.scheduler.set_timesteps(_lowercase       )

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											SCREAMING_SNAKE_CASE_				=				'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step(_lowercase,     _lowercase,     _lowercase,     **_lowercase       ).prev_sample

											# decode the image latents with the VAE
											SCREAMING_SNAKE_CASE_				=				self.vqvae.decode(_lowercase       ).sample

											SCREAMING_SNAKE_CASE_				=				(image / 2 + 0.5).clamp(0,     1       )
											SCREAMING_SNAKE_CASE_				=				image.cpu().permute(0,     2,     3,     1       ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowercase       )





