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.

Lama Cleaner

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

Features

• Completely free and open-source
• Fully self-hosted
• Classical image inpainting algorithm powered by cv2
• Multiple SOTA AI models
  1. LaMa
  2. LDM
  3. ZITS
  4. MAT
  5. FcF
  6. SD1.5
• Support CPU & GPU
• Various inpainting strategy
• Run as a desktop APP

Usage

1. Remove any unwanted things on the image

Usage	Before	After
Remove unwanted things
Remove unwanted person
Remove Text
Remove watermark

2. Fix old photo

Usage	Before	After
Fix old photo

3. Replace something on the image

Usage	Before	After
Text Driven Inpainting		Prompt: a fox sitting on a bench

Quick Start

The easiest way to use Lama Cleaner is to install it using pip:

pip install lama-cleaner

# Models will be downloaded at first time used
lama-cleaner --model=lama --device=cpu --port=8080
# Lama Cleaner is now running at http://localhost:8080

For stable-diffusion model, you need to accepting the terms to access, and get an access token from here huggingface access token

If you prefer to use docker, you can check out docker

If you hava no idea what is docker or pip, please check One Click Installer

Available command line arguments:

Inpainting Model

See model comparison detail

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!

LaMa vs FcF

Original Image	Lama	FcF

Inpainting Strategy

Lama Cleaner provides three ways to run inpainting model on images, you can change it in the settings dialog.

Download Model Manually

If you have problems downloading the model automatically when lama-cleaner start, you can download it manually. By default lama-cleaner will load model from TORCH_HOME=~/.cache/torch/hub/checkpoints/, you can set TORCH_HOME to other folder and put the models there.

• Github:
  • LaMa
  • LDM
  • ZITS
  • MAT
  • FcF
• Baidu:
  • https://pan.baidu.com/s/1vUd3BVqIpK6e8N_EA_ZJfw
  • passward: flsu

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

You can use pre-build docker image to run Lama Cleaner. The model will be downloaded to the cache directory when first time used. You can mount existing cache directory to start the container, so you don't have to download the model every time you start the container.

The cache directories for different models correspond as follows:

• lama/ldm/zits/mat/fcf: /root/.cache/torch
• sd1.5: /root/.cache/huggingface

Run Docker (cpu)

docker run -p 8080:8080 \
-v /path/to/torch_cache:/root/.cache/torch \
-v /path/to/huggingface_cache:/root/.cache/huggingface \
--rm cwq1913/lama-cleaner:cpu-0.24.4 \
lama-cleaner --device=cpu --port=8080 --host=0.0.0.0

Run Docker (gpu)

• cuda11.6
• pytorch1.12.1
• minimum nvidia driver 510.39.01+

docker run --gpus all -p 8080:8080 \
-v /path/to/torch_cache:/root/.cache/torch \
-v /path/to/huggingface_cache:/root/.cache/huggingface \
--rm cwq1913/lama-cleaner:gpu-0.24.4 \
lama-cleaner --device=cuda --port=8080 --host=0.0.0.0

Then open http://localhost:8080

Build Docker image

cpu only

docker build -f --build-arg version=0.x.0 ./docker/CPUDockerfile -t lamacleaner .

gpu & cpu

docker build -f --build-arg version=0.x.0 ./docker/GPUDockerfile -t lamacleaner .