






"""simple docstring"""


import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


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

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

# to channels last
_UpperCamelCase    :   Tuple   			=						pipe.unet.to(memory_format=torch.channels_last)
_UpperCamelCase    :   int   			=						pipe.vae.to(memory_format=torch.channels_last)
_UpperCamelCase    :   Optional[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, 64, 64)
_UpperCamelCase    :   Any   			=						torch.rand(1) * 999
_UpperCamelCase    :   List[str]   			=						torch.randn(2, 77, 768)
_UpperCamelCase    :   List[Any]   			=						(sample, timestep, encoder_hidden_status)
try:
   _UpperCamelCase    :   List[str]   			=						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   _UpperCamelCase    :   List[str]   			=						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_UpperCamelCase    :   Union[str, Any]   			=						ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_UpperCamelCase    :   int   			=						ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   _UpperCamelCase    :   int   			=						ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_UpperCamelCase    :   List[Any]   			=						666
_UpperCamelCase    :   List[Any]   			=						torch.Generator(device).manual_seed(seed)
_UpperCamelCase    :   List[Any]   			=						{'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"""


def       snake_case  (A_				:int				):







  '''simple docstring'''

  if isinstance(A_     ,      A_				):
    raise TypeError('\'float\' object cannot be interpreted as an integer'				)
  if isinstance(A_     ,      A_				):
    raise TypeError('\'str\' object cannot be interpreted as an integer'				)

  if num == 0:
    return "0b0"

  a					:      List[Any]    =       False

  if num < 0:
    a					:      Optional[int]    =       True
    a					:      Dict    =       -num

  a					:      list[int]    =       []
  while num > 0:
    binary.insert(0     ,      num % 2				)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(A_				) for e in binary				)

  return "0b" + "".join(str(A_				) for e in binary				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()




