


'''simple docstring'''

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
							lowerCAmelCase				:Optional[int]        =      argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
							)
							parser.add_argument(
							    '''--original_config_file''',
							    type=str,
							    required=True,
							    help='''The YAML config file corresponding to the original architecture.''',
							)
							parser.add_argument(
							    '''--num_in_channels''',
							    default=None,
							    type=int,
							    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
							)
							parser.add_argument(
							    '''--image_size''',
							    default=5_1_2,
							    type=int,
							    help=(
							        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
							        ''' Base. Use 768 for Stable Diffusion v2.'''
							    ),
							)
							parser.add_argument(
							    '''--extract_ema''',
							    action='''store_true''',
							    help=(
							        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
							        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
							        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
							    ),
							)
							parser.add_argument(
							    '''--upcast_attention''',
							    action='''store_true''',
							    help=(
							        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
							        ''' diffusion 2.1.'''
							    ),
							)
							parser.add_argument(
							    '''--from_safetensors''',
							    action='''store_true''',
							    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
							)
							parser.add_argument(
							    '''--to_safetensors''',
							    action='''store_true''',
							    help='''Whether to store pipeline in safetensors format or not.''',
							)
							parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
							parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')





							def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		):





								"""simple docstring"""







								if string == "True":
									return True
								elif string == "False":
									return False
								else:
									raise ValueError(f'could not parse string as bool {string}'		)

							parser.add_argument(
							    '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
							)

							parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

							lowerCAmelCase				:Tuple        =      parser.parse_args()

							lowerCAmelCase				:List[str]        =      download_controlnet_from_original_ckpt(
							    checkpoint_path=args.checkpoint_path,
							    original_config_file=args.original_config_file,
							    image_size=args.image_size,
							    extract_ema=args.extract_ema,
							    num_in_channels=args.num_in_channels,
							    upcast_attention=args.upcast_attention,
							    from_safetensors=args.from_safetensors,
							    device=args.device,
							    use_linear_projection=args.use_linear_projection,
							    cross_attention_dim=args.cross_attention_dim,
							)





							controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



'''simple docstring'''

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase				:Optional[Any]        =      transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	if isinstance(lowerCAmelCase		,		torch.Tensor		):
		return image
	elif isinstance(lowerCAmelCase		,		PIL.Image.Image		):
		__magic_name__	:   List[Any]					     =					[image]

	__magic_name__	:   List[Any]					     =					[trans(img.convert('RGB'		)		) for img in image]
	__magic_name__	:   Dict					     =					torch.stack(lowerCAmelCase		)
	return image





class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Optional[Any]  ,					_A		:						str  ,					_A		:						int      )  ->			Dict:
			super().__init__()

			# make sure scheduler can always be converted to DDIM
			__magic_name__	:   Optional[int]					     =					DDIMScheduler.from_config(scheduler.config      )

			self.register_modules(unet=_A  ,					scheduler=_A      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						Union[str, Any]      )  ->			Optional[int]:
			if strength < 0 or strength > 1:
				raise ValueError(F'The value of strength should in [0.0, 1.0] but is {strength}'      )







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						List[str]  ,					_A		:						Optional[Any]  ,					_A		:						int      )  ->			List[Any]:
			# get the original timestep using init_timestep
			__magic_name__	:   Tuple					     =					min(int(num_inference_steps * strength      )  ,					_A      )

			__magic_name__	:   Any					     =					max(num_inference_steps - init_timestep  ,					0      )
			__magic_name__	:   List[str]					     =					self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						str  ,					_A		:						Optional[int]  ,					_A		:						Tuple  ,					_A		:						List[str]  ,					_A		:						str  ,					_A		:						Optional[int]=None      )  ->			Dict:
			if not isinstance(_A  ,					(torch.Tensor, PIL.Image.Image, list)      ):
				raise ValueError(
				    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A      )}'      )

			__magic_name__	:   Union[str, Any]					     =					image.to(device=_A  ,					dtype=_A      )

			if isinstance(_A  ,					_A      ) and len(_A      ) != batch_size:
				raise ValueError(
				    F'You have passed a list of generators of length {len(_A      )}, but requested an effective batch'
				    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'      )

			__magic_name__	:   Tuple					     =					init_latents.shape
			__magic_name__	:   Any					     =					randn_tensor(_A  ,					generator=_A  ,					device=_A  ,					dtype=_A      )

			# get latents
			print('add noise to latents at timestep'  ,					_A      )
			__magic_name__	:   List[str]					     =					self.scheduler.add_noise(_A  ,					_A  ,					_A      )
			__magic_name__	:   List[str]					     =					init_latents

			return latents







		@torch.no_grad()
		def __call__( self		:						Tuple  ,					_A		:						Union[torch.FloatTensor, PIL.Image.Image] = None  ,					_A		:						float = 0.8  ,					_A		:						int = 1  ,					_A		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,					_A		:						float = 0.0  ,					_A		:						int = 50  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[str] = "pil"  ,					_A		:						bool = True  ,					)  ->			Union[ImagePipelineOutput, Tuple]:
			self.check_inputs(_A      )

			# 2. Preprocess image
			__magic_name__	:   int					     =					preprocess(_A      )

			# 3. set timesteps
			self.scheduler.set_timesteps(_A  ,					device=self.device      )
			__magic_name__     ,    __magic_name__	:   Dict					     =					self.get_timesteps(_A  ,					_A  ,					self.device      )
			__magic_name__	:   Dict					     =					timesteps[:1].repeat(_A      )

			# 4. Prepare latent variables
			__magic_name__	:   Optional[Any]					     =					self.prepare_latents(_A  ,					_A  ,					_A  ,					self.unet.dtype  ,					self.device  ,					_A      )
			__magic_name__	:   Optional[Any]					     =					latents

			# 5. Denoising loop
			for t in self.progress_bar(_A      ):
				# 1. predict noise model_output
				__magic_name__	:   Dict					     =					self.unet(_A  ,					_A      ).sample

				# 2. predict previous mean of image x_t-1 and add variance depending on eta
				# eta corresponds to η in paper and should be between [0, 1]
				# do x_t -> x_t-1
				__magic_name__	:   List[Any]					     =					self.scheduler.step(
				    _A  ,					_A  ,					_A  ,					eta=_A  ,					use_clipped_model_output=_A  ,					generator=_A  ,					).prev_sample

			__magic_name__	:   Union[str, Any]					     =					(image / 2 + 0.5).clamp(0  ,					1      )
			__magic_name__	:   List[Any]					     =					image.cpu().permute(0  ,					2  ,					3  ,					1      ).numpy()
			if output_type == "pil":
				__magic_name__	:   Dict					     =					self.numpy_to_pil(_A      )

			if not return_dict:
				return (image, latent_timestep.item())

			return ImagePipelineOutput(images=_A      )