



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"""

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)





