

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase_									=   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')
lowercase_									=   parser.parse_args()


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

lowercase_									=   'path-to-your-trained-model'
lowercase_									=   StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
  lowercase_									=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase_									=   pipe.to(device)

# to channels last
lowercase_									=   pipe.unet.to(memory_format=torch.channels_last)
lowercase_									=   pipe.vae.to(memory_format=torch.channels_last)
lowercase_									=   pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
  lowercase_									=   pipe.safety_checker.to(memory_format=torch.channels_last)

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

# compute
lowercase_									=   6_6_6
lowercase_									=   torch.Generator(device).manual_seed(seed)
lowercase_									=   {'generator': generator}
if args.steps is not None:
  lowercase_									=   args.steps

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

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





from __future__ import annotations
def      a      (			A__	:   list[int]     )    ->      int:





      """simple docstring"""



      if not nums:
            return 0
      _lowercase	       =nums[0]
      _lowercase	       =0
      for num in nums[1:]:
            _lowercase       ,       _lowercase	       =(
                max_excluding + num,
                max(A__      ,   A__     ),
            )
      return max(A__      ,   A__     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


