



import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
      _lowerCamelCase						    =  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.""",
      )
      _lowerCamelCase						    =  parser.parse_args()

      _lowerCamelCase						    =  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)











from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							List[str]     ,  UpperCamelCase :							Optional[NestedDataStructureLike[PathLike]] = None     ,  UpperCamelCase :							Optional[NamedSplit] = None     ,  UpperCamelCase :							Optional[Features] = None     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  **UpperCamelCase :							Dict     ,  )->int:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	path_or_paths
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	split if split or isinstance(UpperCamelCase     ,  UpperCamelCase				) else "train"
      __SCREAMING_SNAKE_CASE       :    Dict       =	features
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	cache_dir
      __SCREAMING_SNAKE_CASE       :    Tuple       =	keep_in_memory
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	streaming
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_proc
      __SCREAMING_SNAKE_CASE       :    int       =	kwargs






     @abstractmethod
     def 	__snake_case    (       self :							Optional[int]				)->Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
      pass






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							Optional[Any]     ,  UpperCamelCase :							Optional[Features] = None     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							Optional[int] = None     ,  **UpperCamelCase :							Dict     ,  )->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	features
      __SCREAMING_SNAKE_CASE       :    str       =	cache_dir
      __SCREAMING_SNAKE_CASE       :    Any       =	keep_in_memory
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	streaming
      __SCREAMING_SNAKE_CASE       :    int       =	num_proc
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	kwargs






     @abstractmethod
     def 	__snake_case    (       self :							str				)->Union[Dataset, IterableDataset]:
      pass






