mmx0626/lama-cleaner

Image inpainting tool powered by SOTA AI Model

Lama-cleaner: Image inpainting tool powered by SOTA AI model

lama-cleaner-0.4.0.mp4

• Support multiple model architectures
  1. LaMa
  2. LDM
• High resolution support
• Multi stroke support. Press and hold the cmd/ctrl key to enable multi stroke mode.
• Zoom & Pan
• Keep image EXIF data

Quick Start

Install requirements: pip3 install -r requirements.txt

Start server with LaMa model

python3 main.py --device=cuda --port=8080 --model=lama

Start server with LDM model

python3 main.py --device=cuda --port=8080 --model=ldm --ldm-steps=50

--ldm-steps: The larger the value, the better the result, but it will be more time-consuming

Diffusion model is MUCH MORE slower than GANs(1080x720 image takes 8s on 3090), but it's possible to get better results than LaMa.

Original Image	LaMa	LDM

Blogs about diffusion models:

• https://lilianweng.github.io/posts/2021-07-11-diffusion-models/
• https://yang-song.github.io/blog/2021/score/

Development

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

Fronted

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 dev
• Build: yarn build

Docker

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.

Build Docker image

docker build -f Dockerfile -t lamacleaner .

Run Docker (cpu)

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

Run Docker (gpu)

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

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