




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
		__lowerCAmelCase	:       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.',
		)
		__lowerCAmelCase	:       Any     =	parser.parse_args()

		__lowerCAmelCase	:       Tuple     =	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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase	:       Dict     =	{
    'configuration_blip': [
        'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlipConfig',
        'BlipTextConfig',
        'BlipVisionConfig',
    ],
    'processing_blip': ['BlipProcessor'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	['BlipImageProcessor']


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       List[Any]     =	[
		    'BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BlipModel',
		    'BlipPreTrainedModel',
		    'BlipForConditionalGeneration',
		    'BlipForQuestionAnswering',
		    'BlipVisionModel',
		    'BlipTextModel',
		    'BlipForImageTextRetrieval',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Any     =	[
		    'TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBlipModel',
		    'TFBlipPreTrainedModel',
		    'TFBlipForConditionalGeneration',
		    'TFBlipForQuestionAnswering',
		    'TFBlipVisionModel',
		    'TFBlipTextModel',
		    'TFBlipForImageTextRetrieval',
		]

if TYPE_CHECKING:
		from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
		from .processing_blip import BlipProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_blip import BlipImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip import (
				    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipForConditionalGeneration,
				    BlipForImageTextRetrieval,
				    BlipForQuestionAnswering,
				    BlipModel,
				    BlipPreTrainedModel,
				    BlipTextModel,
				    BlipVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blip import (
				    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBlipForConditionalGeneration,
				    TFBlipForImageTextRetrieval,
				    TFBlipForQuestionAnswering,
				    TFBlipModel,
				    TFBlipPreTrainedModel,
				    TFBlipTextModel,
				    TFBlipVisionModel,
				)

else:
		import sys

		__lowerCAmelCase	:       List[str]     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






