


'''simple docstring'''

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


a_						:       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""")
a_						:       Union[str, Any]      			=				parser.parse_args()


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

a_						:       Any      			=				"""path-to-your-trained-model"""
a_						:       List[str]      			=				StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
     a_						:       List[str]      			=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
a_						:       Any      			=				pipe.to(device)

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

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

# compute
a_						:       Union[str, Any]      			=				666
a_						:       int      			=				torch.Generator(device).manual_seed(seed)
a_						:       int      			=				{"""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[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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_						:       List[Any]      			=				{
    """configuration_owlvit""": [
        """OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """OwlViTConfig""",
        """OwlViTOnnxConfig""",
        """OwlViTTextConfig""",
        """OwlViTVisionConfig""",
    ],
    """processing_owlvit""": ["""OwlViTProcessor"""],
}


try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:       int      			=				["""OwlViTFeatureExtractor"""]
     a_						:       Any      			=				["""OwlViTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:       Tuple      			=				[
         """OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """OwlViTModel""",
         """OwlViTPreTrainedModel""",
         """OwlViTTextModel""",
         """OwlViTVisionModel""",
         """OwlViTForObjectDetection""",
     ]

if TYPE_CHECKING:
     from .configuration_owlvit import (
         OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         OwlViTConfig,
         OwlViTOnnxConfig,
         OwlViTTextConfig,
         OwlViTVisionConfig,
     )
     from .processing_owlvit import OwlViTProcessor

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_owlvit import OwlViTFeatureExtractor
          from .image_processing_owlvit import OwlViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_owlvit import (
              OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OwlViTForObjectDetection,
              OwlViTModel,
              OwlViTPreTrainedModel,
              OwlViTTextModel,
              OwlViTVisionModel,
          )

else:
     import sys

     a_						:       Tuple      			=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



