Lama Cleaner

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

example-0.24.0.mp4

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:

Name	Description	Default
--model	lama/ldm/zits/mat/fcf/sd1.5 See details in Inpaint Model	lama
--hf_access_token	stable-diffusion need huggingface access token to download model
--sd-run-local	Once the model as downloaded, you can pass this arg and remove `--hf_access_token`
--sd-disable-nsfw	Disable stable-diffusion NSFW checker.
--sd-cpu-textencoder	Always run stable-diffusion TextEncoder model on CPU.
--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
cv2	👍 No GPU is required, and for simple backgrounds, the results may even be better than AI models.
LaMa	👍 Generalizes well on high resolutions(~2k)
LDM	👍 Possible to get better and more detail result 👍 The balance of time and quality can be achieved by adjusting `steps` 😐 Slower than GAN model 😐 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	👍 Better holistic structures compared with previous methods 😐 Wireframe module is very slow on CPU	`Wireframe`: Enable edge and line detect
MAT	TODO
FcF	👍 Better structure and texture generation 😐 Only support fixed size (512x512) input
SD1.5	👍 SOTA text-to-image diffusion 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.

Strategy	Description	VRAM	Speed
Original	Use the resolution of the original image	High	⚡
Resize	Resize the image to a smaller size before inpainting. The area outside the mask will not loss quality.	Midium	⚡ ⚡
Crop	Crop masking area from the original image to do inpainting	Low	⚡ ⚡ ⚡

Download Model Mannually

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 lama-cleaner:cpu-0.21.0 \
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 lama-cleaner:cpu-0.21.0 \
lama-cleaner --device=cuda --port=8080 --host=0.0.0.0

Then open http://localhost:8080

Build Docker image

cpu only

docker build -f ./docker/CPUDockerfile -t lamacleaner .

gpu & cpu

docker build -f ./docker/GPUDockerfile -t lamacleaner .

yzqfjxm/lama-cleaner