





import torch

from diffusers import StableDiffusionPipeline


SCREAMING_SNAKE_CASE_   :      Any			=   "path-to-your-trained-model"
SCREAMING_SNAKE_CASE_   :      List[str]			=   StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

SCREAMING_SNAKE_CASE_   :      int			=   "A photo of sks dog in a bucket"
SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')







'''simple docstring'''



import colorsys

from PIL import Image  # type: ignore


def       snake_case   (	snake_case      : float					,		snake_case      : float					,		snake_case      : int			) ->  float:


    """simple docstring"""


    lowerCAmelCase 						=							x
    lowerCAmelCase 						=							y
    for step in range(snake_case			):  # noqa: B007
        lowerCAmelCase 						=							a * a - b * b + x
        lowerCAmelCase 						=							2 * a * b + y
        lowerCAmelCase 						=							a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)


def       snake_case   (	snake_case      : float			) ->  tuple:


    """simple docstring"""


    if distance == 1:
        return (0, 0, 0)
    else:
        return (255, 255, 255)


def       snake_case   (	snake_case      : float			) ->  tuple:


    """simple docstring"""


    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 255			) for i in colorsys.hsv_to_rgb(snake_case					,		1					,		1			)			)


def       snake_case   (	snake_case      : int = 800					,		snake_case      : int = 600					,		snake_case      : float = -0.6					,		snake_case      : float = 0					,		snake_case      : float = 3.2					,		snake_case      : int = 50					,		snake_case      : bool = True					,		) ->  Image.Image:


    """simple docstring"""


    lowerCAmelCase 						=							Image.new('RGB'					,		(image_width, image_height)			)
    lowerCAmelCase 						=							img.load()

    # loop through the image-coordinates
    for image_x in range(snake_case			):
        for image_y in range(snake_case			):
            # determine the figure-coordinates based on the image-coordinates
            lowerCAmelCase 						=							figure_width / image_width * image_height
            lowerCAmelCase 						=							figure_center_x + (image_x / image_width - 0.5) * figure_width
            lowerCAmelCase 						=							figure_center_y + (image_y / image_height - 0.5) * figure_height

            lowerCAmelCase 						=							get_distance(snake_case					,		snake_case					,		snake_case			)

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                lowerCAmelCase 						=							get_color_coded_rgb(snake_case			)
            else:
                lowerCAmelCase 						=							get_black_and_white_rgb(snake_case			)

    return img


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # colored version, full figure
     _UpperCamelCase						:  Dict							       =				get_image()

     # uncomment for colored version, different section, zoomed in
     # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
     # figure_width = 0.8)

     # uncomment for black and white version, full figure
     # img = get_image(use_distance_color_coding = False)

     # uncomment to save the image
     # img.save("mandelbrot.png")

     img.show()





