




'''simple docstring'''





import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


UpperCamelCase_		:     Union[str, 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''')
UpperCamelCase_		:     List[str]          =		parser.parse_args()


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

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

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

# optimize with ipex
UpperCamelCase_		:     Union[str, Any]          =		torch.randn(2, 4, 64, 64)
UpperCamelCase_		:     Union[str, Any]          =		torch.rand(1) * 999
UpperCamelCase_		:     Dict          =		torch.randn(2, 77, 768)
UpperCamelCase_		:     Optional[int]          =		(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_		:     List[str]          =		ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
UpperCamelCase_		:     List[Any]          =		ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   UpperCamelCase_		:     Tuple          =		ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

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

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

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








'''simple docstring'''





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version





def       __a      (  _UpperCamelCase:       str   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       Optional[str] = None       )		->					str:

     """simple docstring"""




     if version.parse(hfh.__version__       ).release < version.parse("0.11.0"       ).release:
          # old versions of hfh don't url-encode the file path
          _snake_case				= quote(_UpperCamelCase       )
     return hfh.hf_hub_url(_UpperCamelCase   ,		_UpperCamelCase   ,		repo_type="dataset"   ,		revision=_UpperCamelCase       )


