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
1. LaMa
2. LDM
High resolution support
Run as a desktop APP
Multi stroke support. Press and hold the cmd/ctrl key to enable multi stroke mode.
Zoom & Pan
Keep image EXIF data

Install

pip install lama-cleaner

lama-cleaner --device=cpu --port=8080

Available commands:

Name	Description	Default
--model	lama or ldm. See details in Model Comparison	lama
--device	cuda or cpu	cuda
--gui	Launch lama-cleaner as a desktop application
--gui_size	Set the window size for the application	1200 900
--input	Path to image you want to load by default	None
--port	Port for flask web server	8080
--debug	Enable debug mode for flask web server

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

Blogs about diffusion models:

Development

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

Frontend

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 start
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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