

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase_								=     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""")
lowercase_								=     parser.parse_args()


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

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

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

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

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

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

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_								=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TimesformerModel""",
							    """TimesformerForVideoClassification""",
							    """TimesformerPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_timesformer import (
														    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TimesformerForVideoClassification,
														    TimesformerModel,
														    TimesformerPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




