



"""simple docstring"""






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


__snake_case				:  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')
__snake_case				:  List[str] =       parser.parse_args()


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

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

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

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

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

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
	__snake_case				:  Union[str, Any] =       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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				:  int =       {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Optional[Any] =       ['PLBartTokenizer']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  List[str] =       [
	    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'PLBartForCausalLM',
	    'PLBartForConditionalGeneration',
	    'PLBartForSequenceClassification',
	    'PLBartModel',
	    'PLBartPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_plbart import PLBartTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_plbart import (
		    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PLBartForCausalLM,
		    PLBartForConditionalGeneration,
		    PLBartForSequenceClassification,
		    PLBartModel,
		    PLBartPreTrainedModel,
		)


else:
	import sys

	__snake_case				:  Any =       _LazyModule(__name__, globals()['__file__'], _import_structure)