lama-cleaner 0.15.0

Image inpainting tool powered by SOTA AI Model

Lama Cleaner

A free and open-source inpainting tool powered by SOTA AI model.

Features

• Completely free and open-source
• Fully self-hosted
• Multiple SOTA AI models
  1. LaMa
  2. LDM
  3. ZITS
• Support CPU & GPU
• Various high-resolution image processing strategy
• Run as a desktop APP

Usage

Usage	Before	After
Remove unwanted things
Remove unwanted person
Remove watermark
Fix old photo

Quick Start

pip install lama-cleaner

# Model will be downloaded automatically
lama-cleaner --model=lama --device=cpu --port=8080
# Lama Cleaner is now running at http://localhost:8080

Available arguments:

Name	Description	Default
--model	lama/ldm/zits. See details in Inpaint Model	lama
--device	cuda or cpu	cuda
--port	Port for backend flask web server	8080
--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
--debug	Enable debug mode for flask web server

Inpainting Model

Model	Description	Config
LaMa	:+1: Generalizes well on high resolutions(~2k)
LDM	:+1: Possiblablity to get better and more detail result :+1: The balance of time and quality can be achieved by adjusting steps :neutral_face: Slower than GAN model :neutral_face: Need more GPU memory	Steps: You can get better result with large steps, but it will be more time-consuming Sampler: ddim or plms. In general plms can get better results with fewer steps
ZITS	:+1: Better holistic structures compared with previous methods :neutral_face: Wireframe module is very slow on CPU	Wireframe: Enable edge and line detect

LaMa vs LDM

Original Image	LaMa	LDM

LaMa vs ZITS

Original Image	ZITS	LaMa

Image is from ZITS paper. I didn't find a good example to show the advantages of ZITS and let me know if you have a good example. There can also be possible problems with my code, if you find them, please let me know too!

High Resolution Strategy

There are three strategies for handling high-resolution images.

• Original: Use the original resolution of the picture, suitable for picture size below 2K.
• Resize: Resize the image to a smaller size, then do inpainting on the resized image. The inpainting area will be croped, upsampled and pasted back on the original image to make sure other part of image not loss quality.
• Crop: Crop masking area from the original image to do inpainting, and paste the result back. Mainly for performance and memory reasons on high resolution image.

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