91 lines
4.1 KiB
Markdown
91 lines
4.1 KiB
Markdown
# Lama-cleaner: Image inpainting tool powered by SOTA AI model
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https://user-images.githubusercontent.com/3998421/153323093-b664bb68-2928-480b-b59b-7c1ee24a4507.mp4
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- [x] Support multiple model architectures
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1. [LaMa](https://github.com/saic-mdal/lama)
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1. [LDM](https://github.com/CompVis/latent-diffusion)
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- [x] High resolution support
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- [x] Run as a desktop APP
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- [x] Multi stroke support. Press and hold the `cmd/ctrl` key to enable multi stroke mode.
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- [x] Zoom & Pan
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- [ ] Keep image EXIF data
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## Install
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```bash
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pip install lama-cleaner
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lama-cleaner --device=cpu --port=8080
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```
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Available commands:
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| Name | Description | Default |
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| ---------- | ------------------------------------------------ | -------- |
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| --model | lama or ldm. See details in **Model Comparison** | lama |
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| --device | cuda or cpu | cuda |
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| --gui | Launch lama-cleaner as a desktop application | |
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| --gui_size | Set the window size for the application | 1200 900 |
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| --input | Path to image you want to load by default | None |
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| --port | Port for flask web server | 8080 |
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| --debug | Enable debug mode for flask web server | |
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## Model Comparison
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Diffusion model(ldm) is **MUCH MORE** slower than GANs(lama)(1080x720 image takes 8s on 3090), but it's possible to get better
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result, see below example:
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| Original Image | LaMa | LDM |
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| ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
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Blogs about diffusion models:
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- https://lilianweng.github.io/posts/2021-07-11-diffusion-models/
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- https://yang-song.github.io/blog/2021/score/
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## Development
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Only needed if you plan to modify the frontend and recompile yourself.
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### Fronted
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Frontend code are modified from [cleanup.pictures](https://github.com/initml/cleanup.pictures), You can experience their
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great online services [here](https://cleanup.pictures/).
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- Install dependencies:`cd lama_cleaner/app/ && yarn`
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- Start development server: `yarn start`
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- Build: `yarn build`
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## Docker
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Run within a Docker container. Set the `CACHE_DIR` to models location path. Optionally add a `-d` option to
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the `docker run` command below to run as a daemon.
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### Build Docker image
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```
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docker build -f Dockerfile -t lamacleaner .
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```
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### Run Docker (cpu)
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```
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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
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```
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### Run Docker (gpu)
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```
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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
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```
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Then open [http://localhost:8080](http://localhost:8080)
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## Like My Work?
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<a href="https://www.buymeacoffee.com/Sanster">
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<img height="50em" src="https://cdn.buymeacoffee.com/buttons/v2/default-blue.png" alt="Sanster" />
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</a>
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