from diffusers import StableDiffusionXLInpaintPipeline from PIL import Image, ImageFilter import gradio as gr import numpy as np import time import math import random import imageio import torch max_64_bit_int = 2**63 - 1 device = "cuda" if torch.cuda.is_available() else "cpu" floatType = torch.float16 if torch.cuda.is_available() else torch.float32 variant = "fp16" if torch.cuda.is_available() else None pipe = StableDiffusionXLInpaintPipeline.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype = floatType, variant = variant) pipe = pipe.to(device) def check( source_img, prompt, uploaded_mask, negative_prompt, denoising_steps, num_inference_steps, guidance_scale, image_guidance_scale, strength, 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.") def inpaint( source_img, prompt, uploaded_mask, negative_prompt, denoising_steps, num_inference_steps, guidance_scale, image_guidance_scale, strength, randomize_seed, seed, debug_mode, progress = gr.Progress() ): check( source_img, prompt, uploaded_mask, negative_prompt, denoising_steps, num_inference_steps, guidance_scale, image_guidance_scale, strength, randomize_seed, seed, debug_mode ) start = time.time() progress(0, desc = "Preparing data...") if negative_prompt is None: negative_prompt = "" if denoising_steps is None: denoising_steps = 1000 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 = 0.99 if randomize_seed: seed = random.randint(0, max_64_bit_int) random.seed(seed) #pipe = pipe.manual_seed(seed) input_image = source_img["image"].convert("RGB") original_height, original_width, original_channel = np.array(input_image).shape output_width = original_width output_height = original_height if uploaded_mask is None: mask_image = source_img["mask"].convert("RGB") else: mask_image = uploaded_mask.convert("RGB") mask_image = mask_image.resize((original_width, 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, 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 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, mask_image ] def toggle_debug(is_debug_mode): if is_debug_mode: return [gr.update(visible = True)] * 2 else: return [gr.update(visible = False)] * 2 with gr.Blocks() as interface: gr.Markdown( """

Inpaint

Modifies one detail of your image, at any resolution, freely, without account, without watermark, without installation, which can be downloaded



🚀 Powered by SDXL 1.0 artificial intellingence.
🐌 Slow process... ~1 hour.
You can duplicate this space on a free account, it works on CPU and should also run on CUDA.

⚖️ You can use, modify and share the generated images but not for commercial uses. """ ) with gr.Column(): source_img = gr.Image(label = "Your image", source = "upload", tool = "sketch", 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 in the entire image") with gr.Accordion("Upload a mask", open = False): uploaded_mask = gr.Image(label = "Already made mask (black pixels will be preserved, white pixels will be redrawn)", source = "upload", type = "pil") 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 = "Ugly, malformed, noise, blur, watermark") 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 = 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") strength = gr.Number(value = 0.99, minimum = 0.01, maximum = 1.0, step = 0.01, label = "Strength", info = "lower=follow the original area, higher=redraw from scratch") 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") submit = gr.Button("Inpaint", variant = "primary") inpainted_image = gr.Image(label = "Inpainted image") information = gr.Label(label = "Information") original_image = gr.Image(label = "Original image", visible = False) mask_image = gr.Image(label = "Mask image", visible = False) submit.click(toggle_debug, debug_mode, [ original_image, mask_image ], queue = False, show_progress = False).then(check, inputs = [ source_img, prompt, uploaded_mask, negative_prompt, denoising_steps, num_inference_steps, guidance_scale, image_guidance_scale, strength, randomize_seed, seed, debug_mode ], outputs = [], queue = False, show_progress = False).success(inpaint, inputs = [ source_img, prompt, uploaded_mask, negative_prompt, denoising_steps, num_inference_steps, guidance_scale, image_guidance_scale, strength, randomize_seed, seed, debug_mode ], outputs = [ inpainted_image, information, original_image, mask_image ], scroll_to_output = True) gr.Examples( inputs = [ source_img, prompt, uploaded_mask, negative_prompt, denoising_steps, num_inference_steps, guidance_scale, image_guidance_scale, strength, randomize_seed, seed, debug_mode ], outputs = [ inpainted_image, information, original_image, mask_image ], examples = [ [ "./Examples/Example1.png", "A deer, in a forest landscape, ultrarealistic, realistic, photorealistic, 8k", "./Examples/Mask1.webp", "Painting, drawing, cartoon, ugly, malformed, noise, blur, watermark", 1000, 25, 7, 1.1, 0.99, True, 42, False ], [ "./Examples/Example3.jpg", "An angry old woman, ultrarealistic, realistic, photorealistic, 8k", "./Examples/Mask3.gif", "Painting, drawing, cartoon, ugly, malformed, noise, blur, watermark", 1000, 25, 7, 1.5, 0.99, True, 42, False ], [ "./Examples/Example4.gif", "A laptop, ultrarealistic, realistic, photorealistic, 8k", "./Examples/Mask4.bmp", "Painting, drawing, cartoon, ugly, malformed, noise, blur, watermark", 1000, 25, 7, 1.1, 0.99, True, 42, False ], [ "./Examples/Example5.bmp", "A sand castle, ultrarealistic, realistic, photorealistic, 8k", "./Examples/Mask5.png", "Painting, drawing, cartoon, ugly, malformed, noise, blur, watermark", 1000, 50, 7, 1.5, 0.5, True, 42, False ], [ "./Examples/Example2.webp", "A cat, ultrarealistic, realistic, photorealistic, 8k", "./Examples/Mask2.png", "Painting, drawing, cartoon, ugly, malformed, noise, blur, watermark", 1000, 25, 7, 1.1, 0.99, True, 42, False ], ], cache_examples = False, ) interface.queue().launch()