
"""simple docstring"""





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
				_a :    List[Any]														=		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.""",
				)
				_a :    List[str]														=		parser.parse_args()

				_a :    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)








"""simple docstring"""





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
				from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline






