





'''simple docstring'''






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A   : List[str]             =			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   : int             =			parser.parse_args()


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

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

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

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

# compute
A   : Optional[Any]             =			666
A   : Tuple             =			torch.Generator(device).manual_seed(seed)
A   : Tuple             =			{'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[int]             =			pipe(prompt, **generate_kwargs).images[0]

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












'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A   : Dict             =			{'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			['SpeechEncoderDecoderModel']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Union[str, Any]             =			['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
		from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
		import sys

		A   : int             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





