from diffusers import StableDiffusionXLInpaintPipeline import gradio as gr import numpy as np 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" pipe = StableDiffusionXLInpaintPipeline.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1") pipe = pipe.to(device) def noise_color(color, noise): return color + random.randint(- noise, noise) def check( source_img, enlarge_top, enlarge_right, enlarge_bottom, enlarge_left, prompt, negative_prompt, smooth_border, denoising_steps, num_inference_steps, guidance_scale, randomize_seed, 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.") 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 queue(): return [] def uncrop( source_img, enlarge_top, enlarge_right, enlarge_bottom, enlarge_left, prompt, negative_prompt, smooth_border, denoising_steps, num_inference_steps, guidance_scale, randomize_seed, seed, debug_mode, progress = gr.Progress()): check( source_img, enlarge_top, enlarge_right, enlarge_bottom, enlarge_left, prompt, negative_prompt, smooth_border, denoising_steps, num_inference_steps, guidance_scale, 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 = 20 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, 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 = (127, 127, 127, 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))) 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."; # 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 = enlarged_image, mask_image = mask_image, num_inference_steps = num_inference_steps, guidance_scale = guidance_scale, denoising_steps = denoising_steps, show_progress_bar = True).images[0] if debug_mode == False: input_image = None enlarged_image = None mask_image = None 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, input_image, enlarged_image, mask_image ] def toggle_debug(is_debug_mode): if is_debug_mode: return [gr.update(visible = True)] * 3 else: return [gr.update(visible = False)] * 3 with gr.Blocks() as interface: gr.Markdown( """

Uncrop

Enlarges the point of view of your image, up to 1 million pixels, freely, without account, without watermark, without installation, which can be downloaded



🚀 Powered by SDXL 1.0 artificial intellingence. For illustration purpose, not information purpose. The new content is not based on real information but imagination.

🐌 Slow process... ~40 min.
You can duplicate this space on a free account, it works on CPU.

⚖️ 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 = "Enlarge 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 = "Enlarge on left ⬅️", info = "in pixels") with gr.Column(): source_img = gr.Image(label = "Your image", sources = ["upload"], type = "numpy") with gr.Column(): enlarge_right = gr.Number(minimum = 0, value = 64, precision = 0, label = "Enlarge 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 = "Enlarge 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') 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 = 20, step = 2, label = "Smooth border", info = "lower=preserve original, higher=seamless") denoising_steps = gr.Slider(minimum = 0, maximum = 1000, value = 1000, step = 1, label = "Denoising", info = "lower=irrelevant result, higher=relevant result") num_inference_steps = gr.Slider(minimum = 10, maximum = 25, value = 20, 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") 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)") debug_mode = gr.Checkbox(label = "Debug mode", value = False, info = "Show intermediate results") with gr.Row(): submit = gr.Button("Uncrop", variant = "primary") with gr.Row(): uncropped_image = gr.Image(label = "Uncropped image") with gr.Row(): information = gr.Label(label = "Information") 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(toggle_debug, debug_mode, [ original_image, enlarged_image, mask_image ], queue = False, show_progress = False).then(check, inputs = [ source_img, enlarge_top, enlarge_right, enlarge_bottom, enlarge_left, prompt, negative_prompt, smooth_border, denoising_steps, num_inference_steps, guidance_scale, randomize_seed, seed, debug_mode ], outputs = [], queue = False, show_progress = False).success(fn = queue, inputs = [], outputs = [], queue = True, show_progress = False).then(uncrop, inputs = [ source_img, enlarge_top, enlarge_right, enlarge_bottom, enlarge_left, prompt, negative_prompt, smooth_border, denoising_steps, num_inference_steps, guidance_scale, randomize_seed, seed, debug_mode ], outputs = [ uncropped_image, information, original_image, enlarged_image, mask_image ], scroll_to_output = True) gr.Examples( inputs = [ source_img, enlarge_top, enlarge_right, enlarge_bottom, enlarge_left, prompt, negative_prompt, smooth_border, denoising_steps, num_inference_steps, guidance_scale, randomize_seed, seed, debug_mode ], outputs = [ uncropped_image, information, original_image, enlarged_image, mask_image ], examples = [ [ "Example1.png", 64, 64, 64, 64, "Stone wall, front view, homogene light", "Border, frame, painting, scribbling, smear, noise, blur, watermark", 20, 1000, 20, 7, True, 42, False ], ], cache_examples = False, ) interface.queue().launch()