lama-cleaner-0.4.0.mp4
- Support multiple model architectures
- High resolution support
- Multi stroke support. Press and hold the
cmd/ctrlkey to enable multi stroke mode. - Zoom & Pan
- Keep image EXIF data
Install requirements: pip3 install -r requirements.txt
python3 main.py --device=cuda --port=8080 --model=lamapython3 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/
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 dev - 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
