





import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowerCAmelCase				:   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''')
_lowerCAmelCase				:   Tuple						       =						parser.parse_args()


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

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

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

# optimize with ipex
_lowerCAmelCase				:   Tuple						       =						torch.randn(2, 4, 64, 64)
_lowerCAmelCase				:   List[str]						       =						torch.rand(1) * 999
_lowerCAmelCase				:   List[Any]						       =						torch.randn(2, 77, 768)
_lowerCAmelCase				:   int						       =						(sample, timestep, encoder_hidden_status)
try:
 _lowerCAmelCase				:   Tuple						       =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
 _lowerCAmelCase				:   Optional[Any]						       =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_lowerCAmelCase				:   Dict						       =						ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_lowerCAmelCase				:   Dict						       =						ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
 _lowerCAmelCase				:   Tuple						       =						ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_lowerCAmelCase				:   Dict						       =						666
_lowerCAmelCase				:   Tuple						       =						torch.Generator(device).manual_seed(seed)
_lowerCAmelCase				:   Tuple						       =						{'''generator''': generator}
if args.steps is not None:
 _lowerCAmelCase				:   Tuple						       =						args.steps

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

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







def   __snake_case    (       _lowerCAmelCase			:			int						)			->    bool:
   if num < 0:
      return False

   A_       :					int        =      num
   A_       :					int        =      0
   while num > 0:
      A_       :					str        =      rev_num * 10 + (num % 10)
      num //= 10

   return num_copy == rev_num


if __name__ == "__main__":
 import doctest

 doctest.testmod()
