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from diffusers import StableDiffusionXLInpaintPipeline |
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from PIL import Image, ImageFilter |
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import gradio as gr |
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import numpy as np |
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import time |
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import math |
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import random |
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import imageio |
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import torch |
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max_64_bit_int = 2**63 - 1 |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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floatType = torch.float16 if torch.cuda.is_available() else torch.float32 |
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variant = "fp16" if torch.cuda.is_available() else None |
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pipe = StableDiffusionXLInpaintPipeline.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype = floatType, variant = variant) |
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pipe = pipe.to(device) |
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def check( |
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source_img, |
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prompt, |
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uploaded_mask, |
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negative_prompt, |
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denoising_steps, |
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num_inference_steps, |
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guidance_scale, |
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image_guidance_scale, |
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strength, |
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randomize_seed, |
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seed, |
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debug_mode, |
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progress = gr.Progress() |
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): |
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if source_img is None: |
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raise gr.Error("Please provide an image.") |
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if prompt is None or prompt == "": |
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raise gr.Error("Please provide a prompt input.") |
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def inpaint( |
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source_img, |
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prompt, |
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uploaded_mask, |
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negative_prompt, |
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denoising_steps, |
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num_inference_steps, |
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guidance_scale, |
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image_guidance_scale, |
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strength, |
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randomize_seed, |
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seed, |
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debug_mode, |
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progress = gr.Progress() |
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): |
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check( |
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source_img, |
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prompt, |
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uploaded_mask, |
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negative_prompt, |
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denoising_steps, |
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num_inference_steps, |
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guidance_scale, |
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image_guidance_scale, |
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strength, |
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randomize_seed, |
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seed, |
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debug_mode |
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) |
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start = time.time() |
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progress(0, desc = "Preparing data...") |
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if negative_prompt is None: |
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negative_prompt = "" |
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if denoising_steps is None: |
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denoising_steps = 1000 |
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if num_inference_steps is None: |
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num_inference_steps = 25 |
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if guidance_scale is None: |
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guidance_scale = 7 |
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if image_guidance_scale is None: |
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image_guidance_scale = 1.1 |
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if strength is None: |
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strength = 0.99 |
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if randomize_seed: |
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seed = random.randint(0, max_64_bit_int) |
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random.seed(seed) |
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input_image = source_img["image"].convert("RGB") |
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original_height, original_width, original_channel = np.array(input_image).shape |
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output_width = original_width |
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output_height = original_height |
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if uploaded_mask is None: |
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mask_image = source_img["mask"].convert("RGB") |
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else: |
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mask_image = uploaded_mask.convert("RGB") |
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mask_image = mask_image.resize((original_width, original_height)) |
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if 1024 * 1024 < output_width * output_height: |
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factor = ((1024 * 1024) / (output_width * output_height))**0.5 |
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process_width = math.floor(output_width * factor) |
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process_height = math.floor(output_height * factor) |
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limitation = " Due to technical limitation, the image have been downscaled and then upscaled."; |
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else: |
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process_width = output_width |
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process_height = output_height |
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limitation = ""; |
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if (process_width % 8) != 0 or (process_height % 8) != 0: |
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if ((process_width - (process_width % 8) + 8) * (process_height - (process_height % 8) + 8)) <= (1024 * 1024): |
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process_width = process_width - (process_width % 8) + 8 |
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process_height = process_height - (process_height % 8) + 8 |
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elif (process_height % 8) <= (process_width % 8) and ((process_width - (process_width % 8) + 8) * process_height) <= (1024 * 1024): |
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process_width = process_width - (process_width % 8) + 8 |
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process_height = process_height - (process_height % 8) |
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elif (process_width % 8) <= (process_height % 8) and (process_width * (process_height - (process_height % 8) + 8)) <= (1024 * 1024): |
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process_width = process_width - (process_width % 8) |
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process_height = process_height - (process_height % 8) + 8 |
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else: |
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process_width = process_width - (process_width % 8) |
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process_height = process_height - (process_height % 8) |
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progress(None, desc = "Processing...") |
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output_image = pipe( |
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seeds = [seed], |
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width = process_width, |
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height = process_height, |
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prompt = prompt, |
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negative_prompt = negative_prompt, |
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image = input_image, |
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mask_image = mask_image, |
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num_inference_steps = num_inference_steps, |
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guidance_scale = guidance_scale, |
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image_guidance_scale = image_guidance_scale, |
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strength = strength, |
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denoising_steps = denoising_steps, |
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show_progress_bar = True |
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).images[0] |
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if limitation != "": |
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output_image = output_image.resize((output_width, output_height)) |
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if debug_mode == False: |
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input_image = None |
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mask_image = None |
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end = time.time() |
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secondes = int(end - start) |
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minutes = secondes // 60 |
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secondes = secondes - (minutes * 60) |
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hours = minutes // 60 |
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minutes = minutes - (hours * 60) |
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return [ |
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output_image, |
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"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, |
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input_image, |
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mask_image |
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] |
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def toggle_debug(is_debug_mode): |
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if is_debug_mode: |
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return [gr.update(visible = True)] * 2 |
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else: |
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return [gr.update(visible = False)] * 2 |
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with gr.Blocks() as interface: |
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gr.Markdown( |
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""" |
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<p style="text-align: center;"><b><big><big><big>Inpaint</big></big></big></b></p> |
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<p style="text-align: center;">Modifies one detail of your image, at any resolution, freely, without account, without watermark, without installation, which can be downloaded</p> |
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<br/> |
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<br/> |
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🚀 Powered by <i>SDXL 1.0</i> artificial intellingence. |
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<br/> |
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🐌 Slow process... ~1 hour.<br>You can duplicate this space on a free account, it works on CPU and should also run on CUDA.<br/> |
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<a href='https://huggingface.co/spaces/multimodalart/stable-diffusion-inpainting?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> |
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<br/> |
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⚖️ You can use, modify and share the generated images but not for commercial uses. |
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""" |
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) |
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with gr.Column(): |
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source_img = gr.Image(label = "Your image", source = "upload", tool = "sketch", type = "pil") |
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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") |
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with gr.Accordion("Upload a mask", open = False): |
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uploaded_mask = gr.Image(label = "Already made mask (black pixels will be preserved, white pixels will be redrawn)", source = "upload", type = "pil") |
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with gr.Accordion("Advanced options", open = False): |
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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") |
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denoising_steps = gr.Slider(minimum = 0, maximum = 1000, value = 1000, step = 1, label = "Denoising", info = "lower=irrelevant result, higher=relevant result") |
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num_inference_steps = gr.Slider(minimum = 10, maximum = 100, value = 25, step = 1, label = "Number of inference steps", info = "lower=faster, higher=image quality") |
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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") |
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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") |
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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") |
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randomize_seed = gr.Checkbox(label = "\U0001F3B2 Randomize seed (not working, always checked)", value = True, info = "If checked, result is always different") |
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seed = gr.Slider(minimum = 0, maximum = max_64_bit_int, step = 1, randomize = True, label = "Seed (if not randomized)") |
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debug_mode = gr.Checkbox(label = "Debug mode", value = False, info = "Show intermediate results") |
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submit = gr.Button("Inpaint", variant = "primary") |
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inpainted_image = gr.Image(label = "Inpainted image") |
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information = gr.Label(label = "Information") |
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original_image = gr.Image(label = "Original image", visible = False) |
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mask_image = gr.Image(label = "Mask image", visible = False) |
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submit.click(toggle_debug, debug_mode, [ |
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original_image, |
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mask_image |
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], queue = False, show_progress = False).then(check, inputs = [ |
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source_img, |
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prompt, |
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uploaded_mask, |
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negative_prompt, |
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denoising_steps, |
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num_inference_steps, |
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guidance_scale, |
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image_guidance_scale, |
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strength, |
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randomize_seed, |
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seed, |
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debug_mode |
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], outputs = [], queue = False, show_progress = False).success(inpaint, inputs = [ |
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source_img, |
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prompt, |
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uploaded_mask, |
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negative_prompt, |
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denoising_steps, |
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num_inference_steps, |
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guidance_scale, |
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image_guidance_scale, |
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strength, |
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randomize_seed, |
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seed, |
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debug_mode |
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], outputs = [ |
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inpainted_image, |
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information, |
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original_image, |
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mask_image |
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], scroll_to_output = True) |
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gr.Examples( |
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inputs = [ |
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source_img, |
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prompt, |
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uploaded_mask, |
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negative_prompt, |
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denoising_steps, |
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num_inference_steps, |
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guidance_scale, |
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image_guidance_scale, |
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strength, |
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randomize_seed, |
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seed, |
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debug_mode |
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], |
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outputs = [ |
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inpainted_image, |
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information, |
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original_image, |
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mask_image |
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], |
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examples = [ |
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[ |
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"./Examples/Example1.png", |
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"A deer, in a forest landscape, ultrarealistic, realistic, photorealistic, 8k", |
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"./Examples/Mask1.webp", |
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"Painting, drawing, cartoon, ugly, malformed, noise, blur, watermark", |
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1000, |
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25, |
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7, |
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1.1, |
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0.99, |
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True, |
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42, |
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False |
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], |
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[ |
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"./Examples/Example3.jpg", |
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"An angry old woman, ultrarealistic, realistic, photorealistic, 8k", |
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"./Examples/Mask3.gif", |
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"Painting, drawing, cartoon, ugly, malformed, noise, blur, watermark", |
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1000, |
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25, |
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7, |
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1.5, |
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0.99, |
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True, |
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42, |
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False |
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], |
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[ |
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"./Examples/Example4.gif", |
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"A laptop, ultrarealistic, realistic, photorealistic, 8k", |
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"./Examples/Mask4.bmp", |
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"Painting, drawing, cartoon, ugly, malformed, noise, blur, watermark", |
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1000, |
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25, |
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7, |
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1.1, |
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0.99, |
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True, |
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42, |
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False |
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], |
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[ |
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"./Examples/Example5.bmp", |
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"A sand castle, ultrarealistic, realistic, photorealistic, 8k", |
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"./Examples/Mask5.png", |
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"Painting, drawing, cartoon, ugly, malformed, noise, blur, watermark", |
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1000, |
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50, |
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7, |
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1.5, |
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0.5, |
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True, |
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42, |
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False |
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], |
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[ |
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"./Examples/Example2.webp", |
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"A cat, ultrarealistic, realistic, photorealistic, 8k", |
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"./Examples/Mask2.png", |
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"Painting, drawing, cartoon, ugly, malformed, noise, blur, watermark", |
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1000, |
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25, |
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7, |
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1.1, |
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0.99, |
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True, |
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42, |
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False |
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], |
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], |
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cache_examples = False, |
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) |
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interface.queue().launch() |