


"""simple docstring"""


import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


__snake_case    					=					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    					=					parser.parse_args()


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

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

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

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

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

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
   __snake_case    					=					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_torch_available


__snake_case    					=					{'configuration_mmbt': ['MMBTConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
   from .configuration_mmbt import MMBTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
   import sys

   __snake_case    					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)