


"""simple docstring"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
 __lowercase    =				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=512,
     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 					lowercase      (    A_	)->				List[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)

 __lowercase    =				parser.parse_args()

 __lowercase    =				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"""







__lowercase    =				frozenset(
    [
        """prompt""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """negative_prompt"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(["""image"""])

__lowercase    =				frozenset(
    [
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""image"""])

__lowercase    =				frozenset(
    [
        """prompt""",
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """image""", """negative_prompt"""])

__lowercase    =				frozenset(
    [
        # Text guided image variation with an image mask
        """prompt""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])

__lowercase    =				frozenset(
    [
        # image variation with an image mask
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""image""", """mask_image"""])

__lowercase    =				frozenset(
    [
        """example_image""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

__lowercase    =				frozenset(["""example_image""", """image""", """mask_image"""])

__lowercase    =				frozenset(["""class_labels"""])

__lowercase    =				frozenset(["""class_labels"""])

__lowercase    =				frozenset(["""batch_size"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(["""batch_size"""])

__lowercase    =				frozenset([])

__lowercase    =				frozenset(
    [
        """prompt""",
        """audio_length_in_s""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

__lowercase    =				frozenset(["""prompt""", """negative_prompt"""])
__lowercase    =				frozenset(["""input_tokens"""])

__lowercase    =				frozenset(["""input_tokens"""])






