

'''simple docstring'''
import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowerCamelCase      					=     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""")
_lowerCamelCase      					=     parser.parse_args()


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

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

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

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

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

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

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










from abc import ABC, abstractmethod
from argparse import ArgumentParser
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						@staticmethod
						@abstractmethod
						def 			snake_case__		(		__lowercase				:	ArgumentParser		):






							"""simple docstring"""






							raise NotImplementedError()






						@abstractmethod
						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							raise NotImplementedError()
