Upgrade to 4.41.0

README.md

@@ -1,23 +1,23 @@
----
-title: Inpaint SDXL (any size)
-emoji: ↕️
-colorFrom: blue
-colorTo: purple
-tags:
-- Image-to-Image
-- Image-2-Image
-- Img-to-Img
-- Img-2-Img
-- SDXL
-- Stable Diffusion
-- language models
-- LLMs
-sdk: gradio
-sdk_version: 3.48.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: Modifies one detail of your image, at any resolution, freely
----
-
+---
+title: Inpaint SDXL (any size)
+emoji: ↕️
+colorFrom: blue
+colorTo: purple
+tags:
+- Image-to-Image
+- Image-2-Image
+- Img-to-Img
+- Img-2-Img
+- SDXL
+- Stable Diffusion
+- language models
+- LLMs
+sdk: gradio
+sdk_version: 4.41.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: Modifies one detail of your image, at any resolution, freely
+---
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,413 +1,459 @@
-import gradio as gr
-import numpy as np
-import time
-import math
-import random
-import imageio
-import torch
-import spaces
-
-from diffusers import StableDiffusionXLInpaintPipeline
-from PIL import Image, ImageFilter
-
-max_64_bit_int = 2**63 - 1
-
-if torch.cuda.is_available():
- device = "cuda"
- floatType = torch.float16
- variant = "fp16"
-else:
- device = "cpu"
- floatType = torch.float32
- variant = None
-
-pipe = StableDiffusionXLInpaintPipeline.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", 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)] * 2
-
-def check(
- source_img,
- prompt,
- uploaded_mask,
- negative_prompt,
- num_inference_steps,
- guidance_scale,
- image_guidance_scale,
- strength,
- denoising_steps,
- is_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.")
-
-@spaces.GPU(duration=420)
-def inpaint(
- source_img,
- prompt,
- uploaded_mask,
- 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,
- uploaded_mask,
- 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 = 0.99
-
- if denoising_steps is None:
- denoising_steps = 1000
-
- if seed is None:
- 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 = 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,
- mask_image
- ]
-
-with gr.Blocks() as interface:
- gr.HTML(
- """
- <h1 style="text-align: center;">Inpaint</h1>
- <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>
- <br/>
- <br/>
- ✨ Powered by <i>SDXL 1.0</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 <b>slightly change</b> your image, I recommend to use <i>Image-to-Image 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/>
- """ + ("🏃‍♀️ Estimated time: few minutes." if torch.cuda.is_available() else "🐌 Slow process... ~1 hour.") + """
- Your computer must not enter into standby mode.<br/>You can duplicate this space on a free account, it's designed to work on CPU, GPU and ZeroGPU.<br/>
- <a href='https://huggingface.co/spaces/Fabrice-TIERCELIN/Inpaint?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", 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", lines = 2)
- 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")
- 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.Slider(value = 0.99, minimum = 0.01, maximum = 1.0, step = 0.01, label = "Strength", info = "lower=follow the original area, higher=redraw from scratch")
- denoising_steps = gr.Number(minimum = 0, 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("🚀 Inpaint", variant = "primary")
-
- inpainted_image = gr.Image(label = "Inpainted image")
- information = gr.HTML()
- original_image = gr.Image(label = "Original image", visible = False)
- mask_image = gr.Image(label = "Mask 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,
- mask_image
- ], queue = False, show_progress = False).then(check, inputs = [
- source_img,
- prompt,
- uploaded_mask,
- 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(inpaint, inputs = [
- source_img,
- prompt,
- uploaded_mask,
- negative_prompt,
- num_inference_steps,
- guidance_scale,
- image_guidance_scale,
- strength,
- denoising_steps,
- randomize_seed,
- seed,
- debug_mode
- ], outputs = [
- inpainted_image,
- information,
- original_image,
- mask_image
- ], scroll_to_output = True)
-
- gr.Examples(
- fn = inpaint,
- inputs = [
- source_img,
- prompt,
- uploaded_mask,
- negative_prompt,
- num_inference_steps,
- guidance_scale,
- image_guidance_scale,
- strength,
- denoising_steps,
- 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",
- "Ugly, malformed, painting, drawing, cartoon, anime, 3d, noise, blur, watermark",
- 25,
- 7,
- 1.1,
- 0.99,
- 1000,
- False,
- 42,
- False
- ],
- [
- "./Examples/Example3.jpg",
- "An angry old woman, ultrarealistic, realistic, photorealistic, 8k",
- "./Examples/Mask3.gif",
- "Ugly, malformed, painting, drawing, cartoon, anime, 3d, noise, blur, watermark",
- 25,
- 7,
- 1.5,
- 0.99,
- 1000,
- False,
- 42,
- False
- ],
- [
- "./Examples/Example4.gif",
- "A laptop, ultrarealistic, realistic, photorealistic, 8k",
- "./Examples/Mask4.bmp",
- "Ugly, malformed, painting, drawing, cartoon, anime, 3d, noise, blur, watermark",
- 25,
- 7,
- 1.1,
- 0.99,
- 1000,
- False,
- 42,
- False
- ],
- [
- "./Examples/Example5.bmp",
- "A sand castle, ultrarealistic, realistic, photorealistic, 8k",
- "./Examples/Mask5.png",
- "Ugly, malformed, painting, drawing, cartoon, anime, 3d, noise, blur, watermark",
- 50,
- 7,
- 1.5,
- 0.5,
- 1000,
- False,
- 42,
- False
- ],
- [
- "./Examples/Example2.webp",
- "A cat, ultrarealistic, realistic, photorealistic, 8k",
- "./Examples/Mask2.png",
- "Ugly, malformed, painting, drawing, cartoon, anime, 3d, noise, blur, watermark",
- 25,
- 7,
- 1.1,
- 0.99,
- 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
- ```
- """
- )
-
+import gradio as gr
+import numpy as np
+import time
+import math
+import random
+import imageio
+import torch
+import spaces
+
+from diffusers import StableDiffusionXLInpaintPipeline
+from PIL import Image, ImageFilter, ImageEnhance
+import PIL.ImageOps
+
+max_64_bit_int = 2**63 - 1
+
+if torch.cuda.is_available():
+ device = "cuda"
+ floatType = torch.float16
+ variant = "fp16"
+else:
+ device = "cpu"
+ floatType = torch.float32
+ variant = None
+
+pipe = StableDiffusionXLInpaintPipeline.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", 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)] * 2
+
+def check(
+ source_img,
+ prompt,
+ uploaded_mask,
+ negative_prompt,
+ num_inference_steps,
+ guidance_scale,
+ image_guidance_scale,
+ strength,
+ denoising_steps,
+ is_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,
+ num_inference_steps,
+ guidance_scale,
+ image_guidance_scale,
+ strength,
+ denoising_steps,
+ is_randomize_seed,
+ seed,
+ debug_mode,
+ progress = gr.Progress()
+):
+ check(
+ source_img,
+ prompt,
+ uploaded_mask,
+ 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 = 0.99
+
+ if denoising_steps is None:
+ denoising_steps = 1000
+
+ if seed is None:
+ seed = random.randint(0, max_64_bit_int)
+
+ random.seed(seed)
+ #pipe = pipe.manual_seed(seed)
+
+ input_image = source_img["background"].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["layers"][0].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 = inpaint_on_gpu(
+ seed,
+ process_width,
+ process_height,
+ prompt,
+ negative_prompt,
+ input_image,
+ mask_image,
+ num_inference_steps,
+ guidance_scale,
+ image_guidance_scale,
+ strength,
+ denoising_steps
+ )
+
+ 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 = 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,
+ mask_image
+ ]
+
+def inpaint_on_gpu2(
+ seed,
+ process_width,
+ process_height,
+ prompt,
+ negative_prompt,
+ input_image,
+ mask_image,
+ num_inference_steps,
+ guidance_scale,
+ image_guidance_scale,
+ strength,
+ denoising_steps
+):
+ return input_image
+
+@spaces.GPU(duration=420)
+def inpaint_on_gpu(
+ seed,
+ process_width,
+ process_height,
+ prompt,
+ negative_prompt,
+ input_image,
+ mask_image,
+ num_inference_steps,
+ guidance_scale,
+ image_guidance_scale,
+ strength,
+ denoising_steps
+):
+ return 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]
+
+with gr.Blocks() as interface:
+ gr.HTML(
+ """
+ <h1 style="text-align: center;">Inpaint</h1>
+ <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>
+ <br/>
+ <br/>
+ ✨ Powered by <i>SDXL 1.0</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 <b>slightly change</b> your image, I recommend to use <i>Image-to-Image 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/>
+ """ + ("🏃‍♀️ Estimated time: few minutes. Current device: GPU." if torch.cuda.is_available() else "🐌 Slow process... ~1 hour. Current device: CPU.") + """
+ Your computer must not enter into standby mode.<br/>You can duplicate this space on a free account, it's designed to work on CPU, GPU and ZeroGPU.<br/>
+ <a href='https://huggingface.co/spaces/Fabrice-TIERCELIN/Inpaint?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.ImageMask(label = "Your image", type = "pil", brush=gr.Brush(colors=["white"], color_mode="fixed"))
+ 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", lines = 2)
+ 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)", sources = ["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")
+ 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.Slider(value = 0.99, minimum = 0.01, maximum = 1.0, step = 0.01, label = "Strength", info = "lower=follow the original area, higher=redraw from scratch")
+ denoising_steps = gr.Number(minimum = 0, 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("🚀 Inpaint", variant = "primary")
+
+ inpainted_image = gr.Image(label = "Inpainted image")
+ information = gr.HTML()
+ original_image = gr.Image(label = "Original image", visible = False)
+ mask_image = gr.Image(label = "Mask 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,
+ mask_image
+ ], queue = False, show_progress = False).then(check, inputs = [
+ source_img,
+ prompt,
+ uploaded_mask,
+ 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(inpaint, inputs = [
+ source_img,
+ prompt,
+ uploaded_mask,
+ negative_prompt,
+ num_inference_steps,
+ guidance_scale,
+ image_guidance_scale,
+ strength,
+ denoising_steps,
+ randomize_seed,
+ seed,
+ debug_mode
+ ], outputs = [
+ inpainted_image,
+ information,
+ original_image,
+ mask_image
+ ], scroll_to_output = True)
+
+ gr.Examples(
+ fn = inpaint,
+ inputs = [
+ source_img,
+ prompt,
+ uploaded_mask,
+ negative_prompt,
+ num_inference_steps,
+ guidance_scale,
+ image_guidance_scale,
+ strength,
+ denoising_steps,
+ 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",
+ "Ugly, malformed, painting, drawing, cartoon, anime, 3d, noise, blur, watermark",
+ 25,
+ 7,
+ 1.1,
+ 0.99,
+ 1000,
+ False,
+ 42,
+ False
+ ],
+ [
+ "./Examples/Example3.jpg",
+ "An angry old woman, ultrarealistic, realistic, photorealistic, 8k",
+ "./Examples/Mask3.gif",
+ "Ugly, malformed, painting, drawing, cartoon, anime, 3d, noise, blur, watermark",
+ 25,
+ 7,
+ 1.5,
+ 0.99,
+ 1000,
+ False,
+ 42,
+ False
+ ],
+ [
+ "./Examples/Example4.gif",
+ "A laptop, ultrarealistic, realistic, photorealistic, 8k",
+ "./Examples/Mask4.bmp",
+ "Ugly, malformed, painting, drawing, cartoon, anime, 3d, noise, blur, watermark",
+ 25,
+ 7,
+ 1.1,
+ 0.99,
+ 1000,
+ False,
+ 42,
+ False
+ ],
+ [
+ "./Examples/Example5.bmp",
+ "A sand castle, ultrarealistic, realistic, photorealistic, 8k",
+ "./Examples/Mask5.png",
+ "Ugly, malformed, painting, drawing, cartoon, anime, 3d, noise, blur, watermark",
+ 50,
+ 7,
+ 1.5,
+ 0.5,
+ 1000,
+ False,
+ 42,
+ False
+ ],
+ [
+ "./Examples/Example2.webp",
+ "A cat, ultrarealistic, realistic, photorealistic, 8k",
+ "./Examples/Mask2.png",
+ "Ugly, malformed, painting, drawing, cartoon, anime, 3d, noise, blur, watermark",
+ 25,
+ 7,
+ 1.1,
+ 0.99,
+ 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()