



"""simple docstring"""
import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_UpperCAmelCase     =			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     =			parser.parse_args()


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

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

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

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

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

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

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








"""simple docstring"""
def      __magic_name__				(							lowercase = 200_0000 ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=[0 for i in range(n + 1 )]
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=1
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=1

   for i in range(2							,			int(n**0.5 ) + 1 ):
      if primality_list[i] == 0:
         for j in range(i * i							,			n + 1							,			lowercase ):
            SCREAMING_SNAKE_CASE_: Optional[int]     			=1
   SCREAMING_SNAKE_CASE_: Dict     			=0
   for i in range(lowercase ):
      if primality_list[i] == 0:
         sum_of_primes += i
   return sum_of_primes


if __name__ == "__main__":
   print(f"""{solution() = }""")



