Delete img2img.py

img2img.py

@@ -1,327 +0,0 @@
-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:
-    gr.HTML(
-        """
-        <h1 style="text-align: center;">Image-to-Image</h1>
-        <p style="text-align: center;">Modifies the global render of your image, at any resolution, freely, without account, without watermark, without installation, which can be downloaded</p>
-        <br/>
-        <br/>
-        ✨ Powered by <i>SDXL Turbo</i> artificial intellingence. For illustration purpose, not information purpose. The new content is not based on real information but imagination.
-        <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><a href="https://huggingface.co/spaces/Fabrice-TIERCELIN/SUPIR">SUPIR</a></i>,</li>
-        <li>To change one <b>detail</b> on your image, I recommend to use <i>Inpaint SDXL</i>,</li>
-        <li>If you need to enlarge the <b>viewpoint</b> of your image, I recommend you to use <i>Uncrop</i>,</li>
-        <li>To remove the <b>background</b> of your image, I recommend to use <i>BRIA</i>,</li>
-        <li>To make a <b>tile</b> of your image, I recommend to use <i>Make My Image Tile</i>,</li>
-        <li>To modify <b>anything else</b> on your image, I recommend to use <i>Instruct Pix2Pix</i>.</li>
-        </ul>
-        <br/>
-        🐌 Slow process... ~2 hours. 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 should also run on CUDA.<br/>
-        <a href='https://huggingface.co/spaces/Fabrice-TIERCELIN/Image-to-Image?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", "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()