kilimro/IOPaint

Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.

IOPaint

A free and open-source inpainting & outpainting tool powered by SOTA AI model.

Erase(LaMa)	Replace Object(PowerPaint)
IOPaint-erase-markdown.mp4	iopaint-inpaint-markdown.mp4

Draw Text(AnyText)	Out-painting(PowerPaint)
AnyText-markdown.mp4	outpainting.mp4

Features

• Completely free and open-source, fully self-hosted, support CPU & GPU & Apple Silicon
• Windows 1-Click Installer
• Supports various AI models to perform erase, inpainting or outpainting task.
  • Erase models: These models can be used to remove unwanted object, defect, watermarks, people from image. I have also developed a macOS native app called OptiClean that provides this feature.
  • Diffusion models: These models can be used to replace objects or perform outpainting. Some popular used models include:
    • runwayml/stable-diffusion-inpainting
    • diffusers/stable-diffusion-xl-1.0-inpainting-0.1
    • andregn/Realistic_Vision_V3.0-inpainting
    • Lykon/dreamshaper-8-inpainting
    • Sanster/anything-4.0-inpainting
    • Sanster/PowerPaint-V1-stable-diffusion-inpainting
    • Sanster/AnyText
    • Fantasy-Studio/Paint-by-Example
• Plugins:
  • Segment Anything: Accurate and fast Interactive Object Segmentation
  • RemoveBG: Remove image background or generate masks for foreground objects
  • Anime Segmentation: Similar to RemoveBG, the model is specifically trained for anime images.
  • RealESRGAN: Super Resolution
  • GFPGAN: Face Restoration
  • RestoreFormer: Face Restoration
• FileManager: Browse your pictures conveniently and save them directly to the output directory.

Quick Start

Start webui

IOPaint provides a convenient webui for using the latest AI models to edit your images. You can install and start IOPaint easily by running following command:

# In order to use GPU, install cuda version of pytorch first.
# pip3 install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu118
# AMD GPU users, please utilize the following command, only works on linux, as pytorch is not yet supported on Windows with ROCm.
# pip3 install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/rocm5.6

pip3 install iopaint
iopaint start --model=lama --device=cpu --port=8080

That's it, you can start using IOPaint by visiting http://localhost:8080 in your web browser.

All models will be downloaded automatically at startup. If you want to change the download directory, you can add --model-dir. More documentation can be found here

You can see other supported models at here and how to use local sd ckpt/safetensors file at here.

Plugins

You can specify which plugins to use when starting the service, and you can view the commands to enable plugins by using iopaint start --help.

More demonstrations of the Plugin can be seen here

iopaint start --enable-interactive-seg --interactive-seg-device=cuda

Batch processing

You can also use IOPaint in the command line to batch process images:

iopaint run --model=lama --device=cpu \
--image=/path/to/image_folder \
--mask=/path/to/mask_folder \
--output=output_dir

--image is the folder containing input images, --mask is the folder containing corresponding mask images. When --mask is a path to a mask file, all images will be processed using this mask.

You can see more information about the available models and plugins supported by IOPaint below.

Development

Install nodejs, then install the front-end dependencies.

git clone https://github.com/Sanster/IOPaint.git
cd IOPaint/web_app
npm install

Create a .env.local file in web_app and fill in the backend IP and port.

VITE_BACKEND=http://127.0.0.1:8080

Start front-end development environment

npm run dev

Install back-end requirements and start back-end service

pip install -r requirements.txt
python3 main.py --model lama --port 8080

Then you can visit http://localhost:5173/ for development. The front-end code will automatically update after being modified, but the back-end needs to restart the service after modifying the python code.