app.py

import gradio as gr
import numpy as np
import time
import math
import random
import torch

from diffusers import StableDiffusionXLInpaintPipeline
from PIL import Image, ImageFilter

max_64_bit_int = 2**63 - 1

if torch.cuda.is_available():
    device = "cuda"
    floatType = torch.float16
    variant = "fp16"
else:
    device = "cpu"
    floatType = torch.float32
    variant = None

pipe = StableDiffusionXLInpaintPipeline.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype = floatType, variant = variant)
pipe = pipe.to(device)

def update_seed(is_randomize_seed, seed):
    if is_randomize_seed:
        return random.randint(0, max_64_bit_int)
    return seed

def toggle_debug(is_debug_mode):
    return [gr.update(visible = is_debug_mode)] * 3

def noise_color(color, noise):
    return color + random.randint(- noise, noise)

def check(
    input_image,
    enlarge_top,
    enlarge_right,
    enlarge_bottom,
    enlarge_left,
    prompt,
    negative_prompt,
    smooth_border,
    num_inference_steps,
    guidance_scale,
    image_guidance_scale,
    strength,
    denoising_steps,
    is_randomize_seed,
    seed,
    debug_mode,
    progress = gr.Progress()):
    if input_image is None:
        raise gr.Error("Please provide an image.")

    if prompt is None or prompt == "":
        raise gr.Error("Please provide a prompt input.")

    if (not (enlarge_top is None)) and enlarge_top < 0:
        raise gr.Error("Please provide positive top margin.")

    if (not (enlarge_right is None)) and enlarge_right < 0:
        raise gr.Error("Please provide positive right margin.")

    if (not (enlarge_bottom is None)) and enlarge_bottom < 0:
        raise gr.Error("Please provide positive bottom margin.")

    if (not (enlarge_left is None)) and enlarge_left < 0:
        raise gr.Error("Please provide positive left margin.")

    if (
        (enlarge_top is None or enlarge_top == 0)
        and (enlarge_right is None or enlarge_right == 0)
        and (enlarge_bottom is None or enlarge_bottom == 0)
        and (enlarge_left is None or enlarge_left == 0)
    ):
        raise gr.Error("At least one border must be enlarged.")

def uncrop(
    input_image,
    enlarge_top,
    enlarge_right,
    enlarge_bottom,
    enlarge_left,
    prompt,
    negative_prompt,
    smooth_border,
    num_inference_steps,
    guidance_scale,
    image_guidance_scale,
    strength,
    denoising_steps,
    is_randomize_seed,
    seed,
    debug_mode,
    progress = gr.Progress()):
    check(
        input_image,
        enlarge_top,
        enlarge_right,
        enlarge_bottom,
        enlarge_left,
        prompt,
        negative_prompt,
        smooth_border,
        num_inference_steps,
        guidance_scale,
        image_guidance_scale,
        strength,
        denoising_steps,
        is_randomize_seed,
        seed,
        debug_mode
    )
    start = time.time()
    progress(0, desc = "Preparing data...")

    if enlarge_top is None or enlarge_top == "":
        enlarge_top = 0

    if enlarge_right is None or enlarge_right == "":
        enlarge_right = 0

    if enlarge_bottom is None or enlarge_bottom == "":
        enlarge_bottom = 0

    if enlarge_left is None or enlarge_left == "":
        enlarge_left = 0

    if negative_prompt is None:
        negative_prompt = ""

    if smooth_border is None:
        smooth_border = 0

    if num_inference_steps is None:
        num_inference_steps = 50

    if guidance_scale is None:
        guidance_scale = 7

    if image_guidance_scale is None:
        image_guidance_scale = 1.5

    if strength is None:
        strength = 0.99

    if denoising_steps is None:
        denoising_steps = 1000

    if seed is None:
        seed = random.randint(0, max_64_bit_int)

    random.seed(seed)
    torch.manual_seed(seed)

    original_height, original_width, original_channel = np.array(input_image).shape
    output_width = enlarge_left + original_width + enlarge_right
    output_height = enlarge_top + original_height + enlarge_bottom

    # Enlarged image
    enlarged_image = Image.new(mode = input_image.mode, size = (original_width, original_height), color = "black")
    enlarged_image.paste(input_image, (0, 0))
    enlarged_image = enlarged_image.resize((output_width, output_height))
    enlarged_image = enlarged_image.filter(ImageFilter.BoxBlur(20))

    enlarged_image.paste(input_image, (enlarge_left, enlarge_top))

    horizontally_mirrored_input_image = input_image.transpose(Image.FLIP_LEFT_RIGHT).resize((original_width * 2, original_height))
    enlarged_image.paste(horizontally_mirrored_input_image, (enlarge_left - (original_width * 2), enlarge_top))
    enlarged_image.paste(horizontally_mirrored_input_image, (enlarge_left + original_width, enlarge_top))

    vertically_mirrored_input_image = input_image.transpose(Image.FLIP_TOP_BOTTOM).resize((original_width, original_height * 2))
    enlarged_image.paste(vertically_mirrored_input_image, (enlarge_left, enlarge_top - (original_height * 2)))
    enlarged_image.paste(vertically_mirrored_input_image, (enlarge_left, enlarge_top + original_height))

    returned_input_image = input_image.transpose(Image.ROTATE_180).resize((original_width * 2, original_height * 2))
    enlarged_image.paste(returned_input_image, (enlarge_left - (original_width * 2), enlarge_top - (original_height * 2)))
    enlarged_image.paste(returned_input_image, (enlarge_left - (original_width * 2), enlarge_top + original_height))
    enlarged_image.paste(returned_input_image, (enlarge_left + original_width, enlarge_top - (original_height * 2)))
    enlarged_image.paste(returned_input_image, (enlarge_left + original_width, enlarge_top + original_height))

    enlarged_image = enlarged_image.filter(ImageFilter.BoxBlur(20))

    # Noise image
    noise_image = Image.new(mode = input_image.mode, size = (output_width, output_height), color = "black")
    enlarged_pixels = enlarged_image.load()

    for i in range(output_width):
        for j in range(output_height):
            enlarged_pixel = enlarged_pixels[i, j]
            noise = min(max(enlarge_left - i, i - (enlarge_left + original_width), enlarge_top - j, j - (enlarge_top + original_height), 0), 255)
            noise_image.putpixel((i, j), (noise_color(enlarged_pixel[0], noise), noise_color(enlarged_pixel[1], noise), noise_color(enlarged_pixel[2], noise), 255))

    enlarged_image.paste(noise_image, (0, 0))
    enlarged_image.paste(input_image, (enlarge_left, enlarge_top))

    # Mask
    mask_image = Image.new(mode = input_image.mode, size = (output_width, output_height), color = (255, 255, 255, 0))
    black_mask = Image.new(mode = input_image.mode, size = (original_width - smooth_border, original_height - smooth_border), color = (0, 0, 0, 0))
    mask_image.paste(black_mask, (enlarge_left + (smooth_border // 2), enlarge_top + (smooth_border // 2)))
    mask_image = mask_image.filter(ImageFilter.BoxBlur((smooth_border // 2)))

    # Limited to 1 million pixels
    if 1024 * 1024 < output_width * output_height:
        factor = ((1024 * 1024) / (output_width * output_height))**0.5
        process_width = math.floor(output_width * factor)
        process_height = math.floor(output_height * factor)

        limitation = " Due to technical limitation, the image have been downscaled and then upscaled.";
    else:
        process_width = output_width
        process_height = output_height

        limitation = "";

    # Width and height must be multiple of 8
    if (process_width % 8) != 0 or (process_height % 8) != 0:
        if ((process_width - (process_width % 8) + 8) * (process_height - (process_height % 8) + 8)) <= (1024 * 1024):
            process_width = process_width - (process_width % 8) + 8
            process_height = process_height - (process_height % 8) + 8
        elif (process_height % 8) <= (process_width % 8) and ((process_width - (process_width % 8) + 8) * process_height) <= (1024 * 1024):
            process_width = process_width - (process_width % 8) + 8
            process_height = process_height - (process_height % 8)
        elif (process_width % 8) <= (process_height % 8) and (process_width * (process_height - (process_height % 8) + 8)) <= (1024 * 1024):
            process_width = process_width - (process_width % 8)
            process_height = process_height - (process_height % 8) + 8
        else:
            process_width = process_width - (process_width % 8)
            process_height = process_height - (process_height % 8)

    progress(None, desc = "Processing...")

    output_image = pipe(
        seeds = [seed],
        width = process_width,
        height = process_height,
        prompt = prompt,
        negative_prompt = negative_prompt,
        image = enlarged_image,
        mask_image = mask_image,
        num_inference_steps = num_inference_steps,
        guidance_scale = guidance_scale,
        image_guidance_scale = image_guidance_scale,
        strength = strength,
        denoising_steps = denoising_steps,
        show_progress_bar = True
    ).images[0]

    if limitation != "":
        output_image = output_image.resize((output_width, output_height))

    if debug_mode == False:
        input_image = None
        enlarged_image = None
        mask_image = None

    end = time.time()
    secondes = int(end - start)
    minutes = math.floor(secondes / 60)
    secondes = secondes - (minutes * 60)
    hours = math.floor(minutes / 60)
    minutes = minutes - (hours * 60)
    return [
        output_image,
        ("Start again to get a different result. " if is_randomize_seed else "") + "The new image is " + str(output_width) + " pixels large and " + str(output_height) + " pixels high, so an image of " + f'{output_width * output_height:,}' + " pixels. The image has been generated in " + ((str(hours) + " h, ") if hours != 0 else "") + ((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + str(secondes) + " sec." + limitation,
        input_image,
        enlarged_image,
        mask_image
    ]

with gr.Blocks() as interface:
    gr.HTML(
        """
        <h1 style="text-align: center;">Outpainting demo</h1>
        <p style="text-align: center;">Enlarges the point of view of your image, freely, without account, without watermark, without installation, which can be downloaded</p>
        <br/>
        <br/>
        ✨ Powered by <i>SDXL 1.0</i> artificial intellingence.
        <br/>
        💻 Your computer must <u>not</u> enter into standby mode.<br/>You can duplicate this space on a free account, it works on CPU and CUDA.<br/>
        <a href='https://huggingface.co/spaces/clinteroni/outpainting-with-differential-diffusion-demo?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14'></a>
        <br/>
        ⚖️ You can use, modify and share the generated images but not for commercial uses.

        """
    )
    with gr.Row():
        with gr.Column():
            dummy_1 = gr.Label(visible = False)
        with gr.Column():
            enlarge_top = gr.Number(minimum = 0, value = 64, precision = 0, label = "Uncrop on top ⬆️", info = "in pixels")
        with gr.Column():
            dummy_2 = gr.Label(visible = False)
    with gr.Row():
        with gr.Column():
            enlarge_left = gr.Number(minimum = 0, value = 64, precision = 0, label = "Uncrop on left ⬅️", info = "in pixels")
        with gr.Column():
            input_image = gr.Image(label = "Your image", sources = ["upload", "webcam", "clipboard"], type = "pil")
        with gr.Column():
            enlarge_right = gr.Number(minimum = 0, value = 64, precision = 0, label = "Uncrop on right ➡️", info = "in pixels")
    with gr.Row():
        with gr.Column():
            dummy_3 = gr.Label(visible = False)
        with gr.Column():
            enlarge_bottom = gr.Number(minimum = 0, value = 64, precision = 0, label = "Uncrop on bottom ⬇️", info = "in pixels")
        with gr.Column():
            dummy_4 = gr.Label(visible = False)
    with gr.Row():
        prompt = gr.Textbox(label = "Prompt", info = "Describe the subject, the background and the style of image; 77 token limit", placeholder = "Describe what you want to see in the entire image", lines = 2)
    with gr.Row():
        with gr.Accordion("Advanced options", open = False):
            negative_prompt = gr.Textbox(label = "Negative prompt", placeholder = "Describe what you do NOT want to see in the entire image", value = 'Border, frame, painting, scribbling, smear, noise, blur, watermark')
            smooth_border = gr.Slider(minimum = 0, maximum = 1024, value = 0, step = 2, label = "Smooth border", info = "lower=preserve original, higher=seamless")
            num_inference_steps = gr.Slider(minimum = 10, maximum = 100, value = 50, step = 1, label = "Number of inference steps", info = "lower=faster, higher=image quality")
            guidance_scale = gr.Slider(minimum = 1, maximum = 13, value = 7, step = 0.1, label = "Classifier-Free Guidance Scale", info = "lower=image quality, higher=follow the prompt")
            image_guidance_scale = gr.Slider(minimum = 1, value = 1.5, step = 0.1, label = "Image Guidance Scale", info = "lower=image quality, higher=follow the image")
            strength = gr.Slider(value = 0.99, minimum = 0.01, maximum = 1.0, step = 0.01, label = "Strength", info = "lower=follow the original area (discouraged), higher=redraw from scratch")
            denoising_steps = gr.Number(minimum = 0, value = 1000, step = 1, label = "Denoising", info = "lower=irrelevant result, higher=relevant result")
            randomize_seed = gr.Checkbox(label = "\U0001F3B2 Randomize seed", value = True, info = "If checked, result is always different")
            seed = gr.Slider(minimum = 0, maximum = max_64_bit_int, step = 1, randomize = True, label = "Seed")
            debug_mode = gr.Checkbox(label = "Debug mode", value = False, info = "Show intermediate results")

    with gr.Row():
        submit = gr.Button("🚀 Outpaint", variant = "primary")

    with gr.Row():
        uncropped_image = gr.Image(label = "Outpainted image")
    with gr.Row():
        information = gr.HTML()
    with gr.Row():
        original_image = gr.Image(label = "Original image", visible = False)
    with gr.Row():
        enlarged_image = gr.Image(label = "Enlarged image", visible = False)
    with gr.Row():
        mask_image = gr.Image(label = "Mask image", visible = False)

    submit.click(fn = update_seed, inputs = [
        randomize_seed,
        seed
    ], outputs = [
        seed
    ], queue = False, show_progress = False).then(toggle_debug, debug_mode, [
        original_image,
        enlarged_image,
        mask_image
    ], queue = False, show_progress = False).then(check, inputs = [
        input_image,
        enlarge_top,
        enlarge_right,
        enlarge_bottom,
        enlarge_left,
        prompt,
        negative_prompt,
        smooth_border,
        num_inference_steps,
        guidance_scale,
        image_guidance_scale,
        strength,
        denoising_steps,
        randomize_seed,
        seed,
        debug_mode
    ], outputs = [], queue = False,
                     show_progress = False).success(uncrop, inputs = [
        input_image,
        enlarge_top,
        enlarge_right,
        enlarge_bottom,
        enlarge_left,
        prompt,
        negative_prompt,
        smooth_border,
        num_inference_steps,
        guidance_scale,
        image_guidance_scale,
        strength,
        denoising_steps,
        randomize_seed,
        seed,
        debug_mode
    ], outputs = [
        uncropped_image,
        information,
        original_image,
        enlarged_image,
        mask_image
    ], scroll_to_output = True)

    gr.Examples(
        run_on_click = True,
        fn = uncrop,
	    inputs = [
            input_image,
            enlarge_top,
            enlarge_right,
            enlarge_bottom,
            enlarge_left,
            prompt,
            negative_prompt,
            smooth_border,
            num_inference_steps,
            guidance_scale,
            image_guidance_scale,
            strength,
            denoising_steps,
            randomize_seed,
            seed,
            debug_mode
        ],
	    outputs = [
            uncropped_image,
            information,
            original_image,
            enlarged_image,
            mask_image
        ],
        examples = [
                [
                    "./examples/Coucang.jpg",
                    1024,
                    1024,
                    1024,
                    1024,
                    "A white Coucang, in a tree, ultrarealistic, realistic, photorealistic, 8k, bokeh",
                    "Border, frame, painting, drawing, cartoon, anime, 3d, scribbling, smear, noise, blur, watermark",
                    0,
                    50,
                    7,
                    1.5,
                    0.99,
                    1000,
                    False,
                    123,
                    False
                ],
            ],
        cache_examples = False,
    )
    
    gr.Markdown(
        """
        ## How to prompt your image

        To easily read your prompt, start with the subject, then describ the pose or action, then secondary elements, then the background, then the graphical style, then the image quality:
        ```
        A Vietnamese woman, red clothes, walking, smilling, in the street, a car on the left, in a modern city, photorealistic, 8k
        ```

        You can use round brackets to increase the importance of a part:
        ```
        A Vietnamese woman, (red clothes), walking, smilling, in the street, a car on the left, in a modern city, photorealistic, 8k
        ```

        You can use several levels of round brackets to even more increase the importance of a part:
        ```
        A Vietnamese woman, ((red clothes)), (walking), smilling, in the street, a car on the left, in a modern city, photorealistic, 8k
        ```

        You can use number instead of several round brackets:
        ```
        A Vietnamese woman, (red clothes:1.5), (walking), smilling, in the street, a car on the left, in a modern city, photorealistic, 8k
        ```

        You can do the same thing with square brackets to decrease the importance of a part:
        ```
        A [Vietnamese] woman, (red clothes:1.5), (walking), smilling, in the street, a car on the left, in a modern city, photorealistic, 8k
        ```

        To easily read your negative prompt, organize it the same way as your prompt (not important for the AI):
        ```
        man, boy, hat, running, tree, bicycle, forest, drawing, painting, cartoon, 3d, monochrome, blurry, noisy, bokeh
        ```

        ## Credit
        The [example image](https://commons.wikimedia.org/wiki/File:Coucang.jpg) is by Aprisonsan
        and licensed under CC-BY-SA 4.0 International.
        """
    )

    interface.queue().launch()