


'''simple docstring'''




import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCAmelCase_      :		str          =		version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")


def    UpperCAmelCase_  (       A		,	A		,	A		,	A		,	A		,	A		,	A		,	A=False		,	):
   '''simple docstring'''





   output_path.parent.mkdir(parents=A		,	exist_ok=A      )
   # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
   # so we check the torch version for backwards compatibility
   if is_torch_less_than_1_11:
      export(
          A		,	A		,	f=output_path.as_posix()		,	input_names=A		,	output_names=A		,	dynamic_axes=A		,	do_constant_folding=A		,	use_external_data_format=A		,	enable_onnx_checker=A		,	opset_version=A		,	)
   else:
      export(
          A		,	A		,	f=output_path.as_posix()		,	input_names=A		,	output_names=A		,	dynamic_axes=A		,	do_constant_folding=A		,	opset_version=A		,	)


@torch.no_grad()
def    UpperCAmelCase_  (       A		,	A		,	A		,	A = False      ):
   '''simple docstring'''





   _a					:  Union[str, Any]						=  torch.floataa if fpaa else torch.floataa
   if fpaa and torch.cuda.is_available():
      _a					:  Optional[Any]						=  'cuda'
   elif fpaa and not torch.cuda.is_available():
      raise ValueError('`float16` model export is only supported on GPUs with CUDA'      )
   else:
      _a					:  int						=  'cpu'
   _a					:  Optional[int]						=  Path(A      )

   # VAE DECODER
   _a					:  str						=  AutoencoderKL.from_pretrained(model_path + '/vae'      )
   _a					:  Union[str, Any]						=  vae_decoder.config.latent_channels
   # forward only through the decoder part
   _a					:  Union[str, Any]						=  vae_decoder.decode
   onnx_export(
       A		,	model_args=(
           torch.randn(1		,	A		,	2_5		,	2_5      ).to(device=A		,	dtype=A      ),
           False,
       )		,	output_path=output_path / 'vae_decoder' / 'model.onnx'		,	ordered_input_names=['latent_sample', 'return_dict']		,	output_names=['sample']		,	dynamic_axes={
           'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
       }		,	opset=A		,	)
   del vae_decoder


if __name__ == "__main__":
 UpperCAmelCase_      :		Any          =		argparse.ArgumentParser()

 parser.add_argument(
     "--model_path",
     type=str,
     required=True,
     help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
 )

 parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
 parser.add_argument(
     "--opset",
     default=14,
     type=int,
     help="The version of the ONNX operator set to use.",
 )
 parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

 UpperCAmelCase_      :		Dict          =		parser.parse_args()
 print(args.output_path)
 convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
 print("SD: Done: ONNX")






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				Any       ,       __magic_name__	:				List[str]       ,       __magic_name__	:				Optional[int]       ,       __magic_name__	:				Tuple	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						StableDiffusionPipeline.from_pretrained(__magic_name__       ,       torch_dtype=torch.floataa	)

		# load LoRA weight from .safetensors
		lowercase__	     =						load_file(__magic_name__	)

		lowercase__	     =						[]

		# directly update weight in diffusers model
		for key in state_dict:
				# it is suggested to print out the key, it usually will be something like below
				# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

				# as we have set the alpha beforehand, so just skip
				if ".alpha" in key or key in visited:
						continue

				if "text" in key:
						lowercase__	     =						key.split("""."""	)[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""	)[-1].split("""_"""	)
						lowercase__	     =						pipeline.text_encoder
				else:
						lowercase__	     =						key.split("""."""	)[0].split(LORA_PREFIX_UNET + """_"""	)[-1].split("""_"""	)
						lowercase__	     =						pipeline.unet

				# find the target layer
				lowercase__	     =						layer_infos.pop(0	)
				while len(__magic_name__	) > -1:
						try:
								lowercase__	     =						curr_layer.__getattr__(__magic_name__	)
								if len(__magic_name__	) > 0:
										lowercase__	     =						layer_infos.pop(0	)
								elif len(__magic_name__	) == 0:
										break
						except Exception:
								if len(__magic_name__	) > 0:
										temp_name += "_" + layer_infos.pop(0	)
								else:
										lowercase__	     =						layer_infos.pop(0	)

				lowercase__	     =						[]
				if "lora_down" in key:
						pair_keys.append(key.replace("""lora_down"""       ,       """lora_up"""	)	)
						pair_keys.append(__magic_name__	)
				else:
						pair_keys.append(__magic_name__	)
						pair_keys.append(key.replace("""lora_up"""       ,       """lora_down"""	)	)

				# update weight
				if len(state_dict[pair_keys[0]].shape	) == 4:
						lowercase__	     =						state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						lowercase__	     =						state_dict[pair_keys[1]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						curr_layer.weight.data += alpha * torch.mm(__magic_name__       ,       __magic_name__	).unsqueeze(2	).unsqueeze(3	)
				else:
						lowercase__	     =						state_dict[pair_keys[0]].to(torch.floataa	)
						lowercase__	     =						state_dict[pair_keys[1]].to(torch.floataa	)
						curr_layer.weight.data += alpha * torch.mm(__magic_name__       ,       __magic_name__	)

				# update visited list
				for item in pair_keys:
						visited.append(__magic_name__	)

		return pipeline


if __name__ == "__main__":
	A			:						int        =  argparse.ArgumentParser()

	parser.add_argument(
	    '--base_model_path', default=None, type=str, required=True, help='Path to the base model in diffusers format.'
	)
	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
	)
	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
	parser.add_argument(
	    '--lora_prefix_unet', default='lora_unet', type=str, help='The prefix of UNet weight in safetensors'
	)
	parser.add_argument(
	    '--lora_prefix_text_encoder',
	    default='lora_te',
	    type=str,
	    help='The prefix of text encoder weight in safetensors',
	)
	parser.add_argument('--alpha', default=0.75, type=float, help='The merging ratio in W = W0 + alpha * deltaW')
	parser.add_argument(
	    '--to_safetensors', action='store_true', help='Whether to store pipeline in safetensors format or not.'
	)
	parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')

	A			:						str        =  parser.parse_args()

	A			:						Tuple        =  args.base_model_path
	A			:						List[str]        =  args.checkpoint_path
	A			:						Optional[int]        =  args.dump_path
	A			:						Optional[int]        =  args.lora_prefix_unet
	A			:						Any        =  args.lora_prefix_text_encoder
	A			:						Any        =  args.alpha

	A			:						List[str]        =  convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

	A			:						int        =  pipe.to(args.device)
	pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

