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

• Completely free and open-source
• Fully self-hosted
• Multiple SOTA AI models
  1. LaMa
  2. LDM
  3. ZITS
• Support CPU & GPU
• Various high-resolution image processing strategy
• Run as a desktop APP

Usage	Before	After
Remove unwanted things
Remove unwanted person
Remove Text
Remove watermark
Fix old photo

pip install lama-cleaner

# Model will be downloaded automatically
lama-cleaner --model=lama --device=cpu --port=8080
# Lama Cleaner is now running at http://localhost:8080

Available arguments:

Original Image	LaMa	LDM

Original Image	ZITS	LaMa

Image is from ZITS paper. I didn't find a good example to show the advantages of ZITS and let me know if you have a good example. There can also be possible problems with my code, if you find them, please let me know too!

There are three strategies for handling high-resolution images.

• Original: Use the original resolution of the picture, suitable for picture size below 2K.
• Resize: Resize the image to a smaller size, then do inpainting on the resized image. The inpainting area will be croped, upsampled and pasted back on the original image to make sure other part of image not loss quality.
• Crop: Crop masking area from the original image to do inpainting, and paste the result back. Mainly for performance and memory reasons on high resolution image.

If you have problems downloading the model automatically when lama-cleaner start, you can download it manually. By default lama-cleaner will load model from TORCH_HOME=~/.cache/torch/hub/checkpoints/, you can set TORCH_HOME to other folder and put the models there.

• Github:
  • LaMa
  • LDM
  • ZITS
• Baidu:
  • https://pan.baidu.com/s/1vUd3BVqIpK6e8N_EA_ZJfw
  • passward: flsu

Only needed if you plan to modify the frontend and recompile yourself.

Frontend code are modified from cleanup.pictures, You can experience their great online services here.

• Install dependencies:cd lama_cleaner/app/ && yarn
• Start development server: yarn start
• Build: yarn build

Run within a Docker container. Set the CACHE_DIR to models location path. Optionally add a -d option to the docker run command below to run as a daemon.

docker build -f Dockerfile -t lamacleaner .

docker run -p 8080:8080 -e CACHE_DIR=/app/models -v  $(pwd)/models:/app/models -v $(pwd):/app --rm lamacleaner python3 main.py --device=cpu --port=8080

docker run --gpus all -p 8080:8080 -e CACHE_DIR=/app/models -v $(pwd)/models:/app/models -v $(pwd):/app --rm lamacleaner python3 main.py --device=cuda --port=8080

Then open http://localhost:8080