## Features
- Completely free and open-source, fully self-hosted, support CPU & GPU
- [Windows 1-Click Installer](https://lama-cleaner-docs.vercel.app/install/windows_1click_installer)
- Classical image inpainting algorithm powered by [cv2](https://docs.opencv.org/3.4/df/d3d/tutorial_py_inpainting.html)
- Multiple SOTA AI [models](https://lama-cleaner-docs.vercel.app/models)
- Various inpainting [strategy](https://lama-cleaner-docs.vercel.app/features/inpainting_strategy)
- Run as a [desktop application](https://lama-cleaner-docs.vercel.app/features/desktop_app)
- [Interactive Segmentation](https://lama-cleaner-docs.vercel.app/features/Interactive_segmentation) on any object.
- More features at [lama-cleaner-docs](https://lama-cleaner-docs.vercel.app/)
## Quick Start
Lama Cleaner make it easy to use SOTA AI model in just two commands:
```bash
pip install lama-cleaner
lama-cleaner --model=lama --device=cpu --port=8080
```
That's it, Lama Cleaner is now running at http://localhost:8080
See all command line arguments at [lama-cleaner-docs](https://lama-cleaner-docs.vercel.app/install/pip)
## Development
Only needed if you plan to modify the frontend and recompile yourself.
### Frontend
Frontend code are modified from [cleanup.pictures](https://github.com/initml/cleanup.pictures), You can experience their
great online services [here](https://cleanup.pictures/).
- Install dependencies:`cd lama_cleaner/app/ && pnpm install`
- Start development server: `pnpm start`
- Build: `pnpm build`
