






'''simple docstring'''



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


SCREAMING_SNAKE_CASE							=						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')
SCREAMING_SNAKE_CASE							=						parser.parse_args()


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

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

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

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

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

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

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





import enum
import shutil
import sys


__A,		__A							=shutil.get_terminal_size()

__A							={'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}




class        _SCREAMING_SNAKE_CASE	( enum.Enum		):
	lowerCAmelCase__   =					0
	lowerCAmelCase__   =					1





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__=""       ):
				sys.stdout.write(str(lowerCamelCase__       ) + end       )
				sys.stdout.flush()





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__=""       ):
				forceWrite(F'\u001b[{color}m{content}\u001b[0m'      ,					lowerCamelCase__       )





def      lowerCamelCase_					(							):
				forceWrite("\r"       )





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				forceWrite(F'\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'       )





def      lowerCamelCase_					(							):
				forceWrite(" " * TERMINAL_WIDTH       )
				reset_cursor()





def      lowerCamelCase_					(							):
				reset_cursor()
				forceWrite("-" * TERMINAL_WIDTH       )


