





'''simple docstring'''

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


UpperCAmelCase					:		Any		=							argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
UpperCAmelCase					:		Any		=							parser.parse_args()


UpperCAmelCase					:		Tuple		=							'cpu'
UpperCAmelCase					:		List[Any]		=							'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

UpperCAmelCase					:		Optional[Any]		=							'path-to-your-trained-model'
UpperCAmelCase					:		str		=							StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
   UpperCAmelCase					:		Optional[int]		=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
UpperCAmelCase					:		int		=							pipe.to(device)

# to channels last
UpperCAmelCase					:		Optional[int]		=							pipe.unet.to(memory_format=torch.channels_last)
UpperCAmelCase					:		int		=							pipe.vae.to(memory_format=torch.channels_last)
UpperCAmelCase					:		Union[str, Any]		=							pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
   UpperCAmelCase					:		Optional[int]		=							pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
UpperCAmelCase					:		Dict		=							torch.randn(2, 4, 6_4, 6_4)
UpperCAmelCase					:		Tuple		=							torch.rand(1) * 9_9_9
UpperCAmelCase					:		Dict		=							torch.randn(2, 7_7, 7_6_8)
UpperCAmelCase					:		Union[str, Any]		=							(sample, timestep, encoder_hidden_status)
try:
   UpperCAmelCase					:		Optional[Any]		=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   UpperCAmelCase					:		int		=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
UpperCAmelCase					:		Optional[Any]		=							ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
UpperCAmelCase					:		Optional[Any]		=							ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   UpperCAmelCase					:		Tuple		=							ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
UpperCAmelCase					:		List[Any]		=							6_6_6
UpperCAmelCase					:		Union[str, Any]		=							torch.Generator(device).manual_seed(seed)
UpperCAmelCase					:		Optional[int]		=							{'generator': generator}
if args.steps is not None:
   UpperCAmelCase					:		Optional[int]		=							args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
   UpperCAmelCase					:		Tuple		=							pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')









'''simple docstring'''

from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands




def    _a    (							):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     ArgumentParser('''Transformers CLI tool'''			,      usage='''transformers-cli <command> [<args>]'''	)
      _snake_case		:	List[str]					     =     parser.add_subparsers(help='''transformers-cli command helpers'''	)

      # Register commands
      ConvertCommand.register_subcommand(lowerCAmelCase_	)
      DownloadCommand.register_subcommand(lowerCAmelCase_	)
      EnvironmentCommand.register_subcommand(lowerCAmelCase_	)
      RunCommand.register_subcommand(lowerCAmelCase_	)
      ServeCommand.register_subcommand(lowerCAmelCase_	)
      UserCommands.register_subcommand(lowerCAmelCase_	)
      AddNewModelCommand.register_subcommand(lowerCAmelCase_	)
      AddNewModelLikeCommand.register_subcommand(lowerCAmelCase_	)
      LfsCommands.register_subcommand(lowerCAmelCase_	)
      PTtoTFCommand.register_subcommand(lowerCAmelCase_	)

      # Let's go
      _snake_case		:	str					     =     parser.parse_args()

      if not hasattr(lowerCAmelCase_			,      '''func'''	):
            parser.print_help()
            exit(1	)

      # Run
      _snake_case		:	Union[str, Any]					     =     args.func(lowerCAmelCase_	)
      service.run()


if __name__ == "__main__":
   main()


