app.py

from diffusers import (
    ControlNetModel,
    DiffusionPipeline,
    StableDiffusionControlNetPipeline,
)
import gradio as gr
import numpy as np
import os
import time
import math
import random
import imageio
from PIL import Image, ImageFilter
import torch

max_64_bit_int = 2**63 - 1

device = "cuda" if torch.cuda.is_available() else "cpu"
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11e_sd15_ip2p", torch_dtype = torch.float32)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", safety_checker = None, controlnet = controlnet, torch_dtype = torch.float32
)
pipe = pipe.to(device)

def check(
    source_img,
    prompt,
    negative_prompt,
    denoising_steps,
    num_inference_steps,
    guidance_scale,
    image_guidance_scale,
    randomize_seed,
    seed,
    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 pix2pix(
    source_img,
    prompt,
    negative_prompt,
    denoising_steps,
    num_inference_steps,
    guidance_scale,
    image_guidance_scale,
    randomize_seed,
    seed,
    progress = gr.Progress()):
    check(
        source_img,
        prompt,
        negative_prompt,
        denoising_steps,
        num_inference_steps,
        guidance_scale,
        image_guidance_scale,
        randomize_seed,
        seed
    )
    start = time.time()
    progress(0, desc = "Preparing data...")

    if negative_prompt is None:
        negative_prompt = ""

    if denoising_steps is None:
        denoising_steps = 0

    if num_inference_steps is None:
        num_inference_steps = 20

    if guidance_scale is None:
        guidance_scale = 5

    if image_guidance_scale is None:
        image_guidance_scale = 1.5

    if randomize_seed:
        seed = random.randint(0, max_64_bit_int)

    random.seed(seed)
    #pipe = pipe.manual_seed(seed)

    try:
        imageio.imwrite("data.png", source_img)
    except:
        raise gr.Error("Can't read input image. You can try to first save your image in another format (.webp, .png, .jpeg, .bmp...).")

    # Input image
    try:
        input_image = Image.open("data.png").convert("RGB")
    except:
        raise gr.Error("Can't open input image. You can try to first save your image in another format (.webp, .png, .jpeg, .bmp...).")

    original_height, original_width, dummy_channel = np.array(input_image).shape
    output_width = original_width
    output_height = original_height
    mask_image = Image.new(mode = input_image.mode, size = (output_width, output_height), color = "white")

    limitation = "";

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

        limitation = " Due to technical limitation, the image have been downscaled and then upscaled.";

    # Width and height must be multiple of 8
    output_width = output_width - (output_width % 8)
    output_height = output_height - (output_height % 8)
    progress(None, desc = "Processing...")

    output_image = pipe(
        seeds=[seed],
        width = output_width,
        height = output_height,
        prompt = prompt,
        negative_prompt = negative_prompt,
        image = input_image,
        mask_image = mask_image,
        num_inference_steps = num_inference_steps,
        guidance_scale = guidance_scale,
        image_guidance_scale = image_guidance_scale,
        denoising_steps = denoising_steps,
        show_progress_bar = True
    ).images[0]

    if limitation != "":
        output_image = output_image.resize((original_width, original_height))

    end = time.time()
    secondes = int(end - start)
    minutes = secondes // 60
    secondes = secondes - (minutes * 60)
    hours = minutes // 60
    minutes = minutes - (hours * 60)
    return [
        output_image,
        "Start again to get a different result. 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 have been generated in " + str(hours) + " h, " + str(minutes) + " min, " + str(secondes) + " sec." + limitation
    ]

with gr.Blocks() as interface:
    gr.Markdown(
        """
        <p style="text-align: center;"><b><big><big><big>Instruct Pix2Pix demo</big></big></big></b></p>
        <p style="text-align: center;">Modifies your image using a textual instruction, up to 1 million pixels, freely, without account, without watermark, without installation, which can be downloaded</p>
        <br/>
        <br/>
        🚀 Powered by <i>SD 1.5</i> and <i>ControlNet</i>
        <br/>
        <ul>
        <li>To change the <b>view angle</b> of your image, I recommend to use <i>Zero123</i>,</li>
        <li>To <b>upscale</b> your image, I recommend to use <i>Ilaria Upscaler</i>,</li>
        <li>To <b>slightly change</b> your image, I recommend to use <i>Image-to-Image SDXL</i>,</li>
        <li>To change <b>one detail</b> on your image, I recommend to use <i>Inpaint SDXL</i>,</li>
        <li>To remove the <b>background</b> of your image, I recommend to use <i>BRIA</i>,</li>
        <li>To enlarge the <b>viewpoint</b> of your image, I recommend to use <i>Uncrop</i>,</li>
        <li>To make a <b>tile</b> of your image, I recommend to use <i>Make My Image Tile</i>,</li>
        </ul>
        <br/>
        🐌 Slow process... ~1 hour. If this space does not work or you want a faster run, use <i>Instruct Pix2Pix</i> available on terrapretapermaculture's <i>ControlNet-v1-1</i> space (last tab) or on <i>Dezgo</i> site.<br>You can duplicate this space on a free account, it works on CPU.<br/>
        <a href='https://huggingface.co/spaces/Fabrice-TIERCELIN/Instruct-Pix2Pix?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.Column():
        source_img = gr.Image(label = "Your image", sources = ["upload"], type = "numpy")
        prompt = gr.Textbox(label = 'Prompt', info = "Instruct what to change in the image", placeholder = 'Order the AI what to change in the image')
        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 image', value = 'Watermark')
            denoising_steps = gr.Slider(minimum = 0, maximum = 1000, value = 0, step = 1, label = "Denoising", info = "lower=irrelevant result, higher=relevant result")
            num_inference_steps = gr.Slider(minimum = 10, maximum = 500, value = 20, step = 1, label = "Number of inference steps", info = "lower=faster, higher=image quality")
            guidance_scale = gr.Slider(minimum = 1, maximum = 13, value = 5, 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")
            randomize_seed = gr.Checkbox(label = "\U0001F3B2 Randomize seed (not working, always checked)", 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 (if not randomized)")

        submit = gr.Button("Modify", variant = "primary")

        modified_image = gr.Image(label = "Modified image")
        information = gr.Label(label = "Information")

    submit.click(check, inputs = [
        source_img,
        prompt,
        negative_prompt,
        denoising_steps,
        num_inference_steps,
        guidance_scale,
        image_guidance_scale,
        randomize_seed,
        seed
    ], outputs = [], queue = False, show_progress = False).success(pix2pix, inputs = [
        source_img,
        prompt,
        negative_prompt,
        denoising_steps,
        num_inference_steps,
        guidance_scale,
        image_guidance_scale,
        randomize_seed,
        seed
    ], outputs = [
        modified_image,
        information
    ], scroll_to_output = True)

    gr.Examples(
	    inputs = [
            source_img,
            prompt,
            negative_prompt,
            denoising_steps,
            num_inference_steps,
            guidance_scale,
            image_guidance_scale,
            randomize_seed,
            seed
        ],
	    outputs = [
            modified_image,
            information
        ],
        examples = [
                [
                    "Example1.webp",
                    "What if it's snowing?",
                    "Watermark",
                    1,
                    20,
                    5,
                    1.5,
                    True,
                    42
                ],
                [
                    "Example2.png",
                    "What if this woman had brown hair?",
                    "Watermark",
                    1,
                    20,
                    5,
                    1.5,
                    True,
                    42
                ],
                [
                    "Example3.jpeg",
                    "Replace the house by a windmill",
                    "Watermark",
                    1,
                    20,
                    5,
                    1.5,
                    True,
                    42
                ],
            ],
        cache_examples = False,
    )

    interface.queue().launch()