

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
			UpperCAmelCase_							:					str		 =   argparse.ArgumentParser()

			parser.add_argument(
			    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
			)
			# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
			parser.add_argument(
			    '''--original_config_file''',
			    default=None,
			    type=str,
			    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(
			    '''--scheduler_type''',
			    default='''pndm''',
			    type=str,
			    help='''Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']''',
			)
			parser.add_argument(
			    '''--pipeline_type''',
			    default=None,
			    type=str,
			    help=(
			        '''The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''''
			        '''. If `None` pipeline will be automatically inferred.'''
			    ),
			)
			parser.add_argument(
			    '''--image_size''',
			    default=None,
			    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(
			    '''--prediction_type''',
			    default=None,
			    type=str,
			    help=(
			        '''The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'''
			        ''' Diffusion v2 Base. Use \'v_prediction\' 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.)''')
			parser.add_argument(
			    '''--stable_unclip''',
			    type=str,
			    default=None,
			    required=False,
			    help='''Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.''',
			)
			parser.add_argument(
			    '''--stable_unclip_prior''',
			    type=str,
			    default=None,
			    required=False,
			    help='''Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.''',
			)
			parser.add_argument(
			    '''--clip_stats_path''',
			    type=str,
			    help='''Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.''',
			    required=False,
			)
			parser.add_argument(
			    '''--controlnet''', action='''store_true''', default=None, help='''Set flag if this is a controlnet checkpoint.'''
			)
			parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
			parser.add_argument(
			    '''--vae_path''',
			    type=str,
			    default=None,
			    required=False,
			    help='''Set to a path, hub id to an already converted vae to not convert it again.''',
			)
			UpperCAmelCase_							:					Union[str, Any]		 =   parser.parse_args()

			UpperCAmelCase_							:					Any		 =   download_from_original_stable_diffusion_ckpt(
			    checkpoint_path=args.checkpoint_path,
			    original_config_file=args.original_config_file,
			    image_size=args.image_size,
			    prediction_type=args.prediction_type,
			    model_type=args.pipeline_type,
			    extract_ema=args.extract_ema,
			    scheduler_type=args.scheduler_type,
			    num_in_channels=args.num_in_channels,
			    upcast_attention=args.upcast_attention,
			    from_safetensors=args.from_safetensors,
			    device=args.device,
			    stable_unclip=args.stable_unclip,
			    stable_unclip_prior=args.stable_unclip_prior,
			    clip_stats_path=args.clip_stats_path,
			    controlnet=args.controlnet,
			    vae_path=args.vae_path,
			)

			if args.half:
						pipe.to(torch_dtype=torch.floataa)

			if args.controlnet:
						# only save the controlnet model
						pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
			else:
						pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)








def 							__SCREAMING_SNAKE_CASE     (   a__    :   int       )					->				int:
	if not isinstance(a__			,a__       ):
		raise TypeError("""Input value must be an 'int' type"""       )

	__A :			Union[str, Any]		    =			0
	while number:
		position += 1
		number >>= 1

	return position


if __name__ == "__main__":
			import doctest

			doctest.testmod()





