Spaces:
Runtime error
Runtime error
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( | |
""" | |
<p style="text-align: center;"><b><big><big><big>Inpaint</big></big></big></b></p> | |
<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. | |
<br/> | |
🐌 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/> | |
<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> | |
<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") | |
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() |