

"""simple docstring"""





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
							__UpperCamelCase    : 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     __SCREAMING_SNAKE_CASE    ( A_					):
								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)

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

							__UpperCamelCase    : 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 torch

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







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			42
					lowercase__												=			42







					def __init__(			self     :							int  ,lowercase_     :							UNetaDModel  ,lowercase_     :							ScoreSdeVeScheduler						):
						super().__init__()
						self.register_modules(unet=lowercase_  ,scheduler=lowercase_						)







					@torch.no_grad()
					def __call__(			self     :							List[str]  ,lowercase_     :							int = 1  ,lowercase_     :							int = 2_0_0_0  ,lowercase_     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,lowercase_     :							Optional[str] = "pil"  ,lowercase_     :							bool = True  ,**lowercase_     :							Dict  ,):
						lowerCAmelCase__      :		str    							=      self.unet.config.sample_size
						lowerCAmelCase__      :		int    							=      (batch_size, 3, img_size, img_size)

						lowerCAmelCase__      :		List[Any]    							=      self.unet

						lowerCAmelCase__      :		Tuple    							=      randn_tensor(lowercase_  ,generator=lowercase_						) * self.scheduler.init_noise_sigma
						lowerCAmelCase__      :		List[Any]    							=      sample.to(self.device						)

						self.scheduler.set_timesteps(lowercase_						)
						self.scheduler.set_sigmas(lowercase_						)

						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
							lowerCAmelCase__      :		Tuple    							=      self.scheduler.sigmas[i] * torch.ones(shape[0]  ,device=self.device						)

							# correction step
							for _ in range(self.scheduler.config.correct_steps						):
								lowerCAmelCase__      :		str    							=      self.unet(lowercase_  ,lowercase_						).sample
								lowerCAmelCase__      :		List[str]    							=      self.scheduler.step_correct(lowercase_  ,lowercase_  ,generator=lowercase_						).prev_sample

							# prediction step
							lowerCAmelCase__      :		Dict    							=      model(lowercase_  ,lowercase_						).sample
							lowerCAmelCase__      :		Optional[int]    							=      self.scheduler.step_pred(lowercase_  ,lowercase_  ,lowercase_  ,generator=lowercase_						)

							lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      output.prev_sample, output.prev_sample_mean

						lowerCAmelCase__      :		List[str]    							=      sample_mean.clamp(0  ,1						)
						lowerCAmelCase__      :		Union[str, Any]    							=      sample.cpu().permute(0  ,2  ,3  ,1						).numpy()
						if output_type == "pil":
							lowerCAmelCase__      :		Union[str, Any]    							=      self.numpy_to_pil(lowercase_						)

						if not return_dict:
							return (sample,)

						return ImagePipelineOutput(images=lowercase_						)




