



import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
	__A       =						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       =						parser.parse_args()

	__A       =						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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A       =						{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A       =						['''CLIPTokenizerFast''']

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A       =						['''CLIPFeatureExtractor''']
	__A       =						['''CLIPImageProcessor''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A       =						[
	    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''CLIPModel''',
	    '''CLIPPreTrainedModel''',
	    '''CLIPTextModel''',
	    '''CLIPTextModelWithProjection''',
	    '''CLIPVisionModel''',
	    '''CLIPVisionModelWithProjection''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A       =						[
	    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFCLIPModel''',
	    '''TFCLIPPreTrainedModel''',
	    '''TFCLIPTextModel''',
	    '''TFCLIPVisionModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A       =						[
	    '''FlaxCLIPModel''',
	    '''FlaxCLIPPreTrainedModel''',
	    '''FlaxCLIPTextModel''',
	    '''FlaxCLIPTextPreTrainedModel''',
	    '''FlaxCLIPVisionModel''',
	    '''FlaxCLIPVisionPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_clip import (
	    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    CLIPConfig,
	    CLIPOnnxConfig,
	    CLIPTextConfig,
	    CLIPVisionConfig,
	)
	from .processing_clip import CLIPProcessor
	from .tokenization_clip import CLIPTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_clip_fast import CLIPTokenizerFast

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_clip import CLIPFeatureExtractor
		from .image_processing_clip import CLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_clip import (
		    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CLIPModel,
		    CLIPPreTrainedModel,
		    CLIPTextModel,
		    CLIPTextModelWithProjection,
		    CLIPVisionModel,
		    CLIPVisionModelWithProjection,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_clip import (
		    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCLIPModel,
		    TFCLIPPreTrainedModel,
		    TFCLIPTextModel,
		    TFCLIPVisionModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_clip import (
		    FlaxCLIPModel,
		    FlaxCLIPPreTrainedModel,
		    FlaxCLIPTextModel,
		    FlaxCLIPTextPreTrainedModel,
		    FlaxCLIPVisionModel,
		    FlaxCLIPVisionPreTrainedModel,
		)


else:
	import sys

	__A       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




