app.py

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

from diffusers import AutoPipelineForImage2Image
from PIL import Image, ImageFilter

max_64_bit_int = 2**63 - 1

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

pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo", 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)]

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

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

def redraw(
    source_img,
    prompt,
    negative_prompt,
    num_inference_steps,
    guidance_scale,
    image_guidance_scale,
    strength,
    denoising_steps,
    is_randomize_seed,
    seed,
    debug_mode,
    progress = gr.Progress()
):
    check(
        source_img,
        prompt,
        negative_prompt,
        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 negative_prompt is None:
        negative_prompt = ""

    if num_inference_steps is None:
        num_inference_steps = 25

    if guidance_scale is None:
        guidance_scale = 7

    if image_guidance_scale is None:
        image_guidance_scale = 1.1

    if strength is None:
        strength = 0.5

    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)

    input_image = source_img.convert("RGB")

    original_height, original_width, original_channel = np.array(input_image).shape
    output_width = original_width
    output_height = original_height

    # 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 = input_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

    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 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
    ]

with gr.Blocks() as interface:
    with gr.Column():
        source_img = gr.Image(label = "Your image", sources = ["upload", "webcam", "clipboard"], type = "pil")
        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", lines = 2)
        strength = gr.Slider(value = 0.5, minimum = 0.01, maximum = 1.0, step = 0.01, label = "Strength", info = "lower=follow the original image, higher=follow the prompt")
        with gr.Accordion("Advanced options", open = False):
             negative_prompt = gr.Textbox(label = "Negative prompt", placeholder = "Describe what you do NOT want to see", value = "Ugly, malformed, noise, blur, watermark")
             num_inference_steps = gr.Slider(minimum = 10, maximum = 100, value = 25, 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 = 1.1, step = 0.1, label = "Image Guidance Scale", info = "lower=image quality, higher=follow the image")
             denoising_steps = gr.Slider(minimum = 0, maximum = 1000, 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")

        submit = gr.Button("🚀 Redraw", variant = "primary")

        redrawn_image = gr.Image(label = "Redrawn image")
        information = gr.HTML()
        original_image = gr.Image(label = "Original image", visible = False)

    submit.click(update_seed, inputs = [
        randomize_seed,
        seed
    ], outputs = [
        seed
    ], queue = False, show_progress = False).then(toggle_debug, debug_mode, [
        original_image
    ], queue = False, show_progress = False).then(check, inputs = [
        source_img,
        prompt,
        negative_prompt,
        num_inference_steps,
        guidance_scale,
        image_guidance_scale,
        strength,
        denoising_steps,
        randomize_seed,
        seed,
        debug_mode
    ], outputs = [], queue = False, show_progress = False).success(redraw, inputs = [
        source_img,
        prompt,
        negative_prompt,
        num_inference_steps,
        guidance_scale,
        image_guidance_scale,
        strength,
        denoising_steps,
        randomize_seed,
        seed,
        debug_mode
    ], outputs = [
        redrawn_image,
        information,
        original_image
    ], scroll_to_output = True)

    gr.Examples(
        run_on_click = True,
        fn = redraw,
	    inputs = [
            source_img,
            prompt,
            negative_prompt,
            num_inference_steps,
            guidance_scale,
            image_guidance_scale,
            strength,
            denoising_steps,
            randomize_seed,
            seed,
            debug_mode
        ],
	    outputs = [
            redrawn_image,
            information,
            original_image
        ],
        examples = [
                [
                    "./Examples/Example1.png",
                    "Drawn image, line art, illustration, picture",
                    "3d, photo, realistic, noise, blur, watermark",
                    25,
                    7,
                    1.1,
                    0.6,
                    1000,
                    False,
                    42,
                    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
        ```
        """
    )

    interface.queue().launch()