




import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


snake_case_ : str    		=       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_ : Tuple    		=       parser.parse_args()


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

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

# to channels last
snake_case_ : List[Any]    		=       pipe.unet.to(memory_format=torch.channels_last)
snake_case_ : List[Any]    		=       pipe.vae.to(memory_format=torch.channels_last)
snake_case_ : Tuple    		=       pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
    snake_case_ : Union[str, 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_ : int    		=       torch.rand(1) * 999
snake_case_ : List[Any]    		=       torch.randn(2, 77, 768)
snake_case_ : Optional[Any]    		=       (sample, timestep, encoder_hidden_status)
try:
    snake_case_ : Tuple    		=       ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
    snake_case_ : Optional[Any]    		=       ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
snake_case_ : Tuple    		=       ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
snake_case_ : Dict    		=       ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
    snake_case_ : Optional[int]    		=       ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
snake_case_ : str    		=       666
snake_case_ : List[Any]    		=       torch.Generator(device).manual_seed(seed)
snake_case_ : List[str]    		=       {"generator": generator}
if args.steps is not None:
    snake_case_ : List[str]    		=       args.steps

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

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









import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def      a      (			A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int      ,   A__	:   int     )    ->      np.ndarray:





      """simple docstring"""



      if (ksize % 2) == 0:
            _lowercase	       =ksize + 1
      _lowercase	       =np.zeros((ksize, ksize)      ,   dtype=np.floataa     )

      # each value
      for y in range(A__     ):
            for x in range(A__     ):
                  # distance from center
                  _lowercase	       =x - ksize // 2
                  _lowercase	       =y - ksize // 2

                  # degree to radiant
                  _lowercase	       =theta / 180 * np.pi
                  _lowercase	       =np.cos(_theta     )
                  _lowercase	       =np.sin(_theta     )

                  # get kernel x
                  _lowercase	       =cos_theta * px + sin_theta * py

                  # get kernel y
                  _lowercase	       =-sin_theta * px + cos_theta * py

                  # fill kernel
                  _lowercase	       =np.exp(
                      -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)     ) * np.cos(2 * np.pi * _x / lambd + psi     )

      return gabor


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # read original image
  lowercase_									=   imread('../image_data/lena.jpg')
  # turn image in gray scale value
  lowercase_									=   cvtColor(img, COLOR_BGR2GRAY)

  # Apply multiple Kernel to detect edges
  lowercase_									=   np.zeros(gray.shape[:2])
  for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
    lowercase_									=   gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
    out += filteraD(gray, CV_8UC3, kernel_aa)
  lowercase_									=   out / out.max() * 2_5_5
  lowercase_									=   out.astype(np.uinta)

  imshow('Original', gray)
  imshow('Gabor filter with 20x20 mask and 6 directions', out)

  waitKey(0)


