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.
d0f025f2d4
Users can now supply the --input argument to load their image by default to the application. |
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|---|---|---|
| lama_cleaner | ||
| .gitignore | ||
| Dockerfile | ||
| LICENSE | ||
| main.py | ||
| README.md | ||
| requirements.txt | ||
| setup.py | ||
Lama-cleaner: Image inpainting tool powered by SOTA AI model
https://user-images.githubusercontent.com/3998421/153323093-b664bb68-2928-480b-b59b-7c1ee24a4507.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
Quick Start
- Install requirements:
pip3 install -r requirements.txt - Start server:
python3 main.py, open http://localhost:8080
Available commands for main.py
| Name | Description | Default |
|---|---|---|
| --model | lama or ldm. See details in Model Comparison | lama |
| --device | cuda or cpu | cuda |
| --ldm-steps | The larger the value, the better the result, but it will be more time-consuming | 50 |
| --crop-trigger-size | If image size large then crop-trigger-size, crop each area from original image to do inference. Mainly for performance and memory reasons on very large image. | 2042,2042 |
| --crop-margin | Margin around bounding box of painted stroke when crop mode triggered. | 256 |
| --port | Port for web server | 8080 |
| --gui | Launch lama-cleaner as a desktop application | |
| --gui_size | Set the window size for the application | 1200 900 |
Model Comparison
Diffusion model(ldm) is MUCH MORE slower than GANs(lama)(1080x720 image takes 8s on 3090), but it's possible to get better result, see below example:
| Original Image | LaMa | LDM |
|---|---|---|
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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
Like My Work?
