




'''simple docstring'''





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:   Tuple      		= 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.",
		)
		_SCREAMING_SNAKE_CASE							:   Dict      		= parser.parse_args()

		_SCREAMING_SNAKE_CASE							:   List[str]      		= 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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase							:   str  						=	{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:   Dict  						=	[
     'RagModel',
     'RagPreTrainedModel',
     'RagSequenceForGeneration',
     'RagTokenForGeneration',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:   Optional[int]  						=	[
     'TFRagModel',
     'TFRagPreTrainedModel',
     'TFRagSequenceForGeneration',
     'TFRagTokenForGeneration',
 ]


if TYPE_CHECKING:
 from .configuration_rag import RagConfig
 from .retrieval_rag import RagRetriever
 from .tokenization_rag import RagTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rag import (
      TFRagModel,
      TFRagPreTrainedModel,
      TFRagSequenceForGeneration,
      TFRagTokenForGeneration,
  )

else:
 import sys

 lowerCamelCase							:   Tuple  						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




