


'''simple docstring'''

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


a_						:       List[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""")
a_						:       Union[str, Any]      			=				parser.parse_args()


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

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

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

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

# compute
a_						:       Union[str, Any]      			=				666
a_						:       int      			=				torch.Generator(device).manual_seed(seed)
a_						:       int      			=				{"""generator""": generator}
if args.steps is not None:
   a_						:       List[Any]      			=				args.steps

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

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







'''simple docstring'''

def __snake_case      (   UpperCAmelCase_		:		int = 100	):
 lowerCamelCase_  				=						n * (n + 1) * (2 * n + 1) / 6
 lowerCamelCase_  				=						(n * (n + 1) / 2) ** 2
 return int(square_of_sum - sum_of_squares	)


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



