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. For illustration purpose, not information purpose. The new content is not based on real information but imagination.
- To change the view angle of your image, I recommend to use Zero123,
- To upscale your image, I recommend to use Ilaria Upscaler,
- To slightly change your image, I recommend to use Image-to-Image SDXL,
- If you need to enlarge the viewpoint of your image, I recommend you to use Uncrop,
- To remove the background of your image, I recommend to use BRIA,
- To make a tile of your image, I recommend to use Make My Image Tile,
- To modify anything else on your image, I recommend to use Instruct Pix2Pix.
🐌 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/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
],
[
"./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
],
],
cache_examples = False,
)
interface.queue().launch()