232 lines
17 KiB
Markdown
232 lines
17 KiB
Markdown
<h1 align="center">Lama Cleaner</h1>
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<p align="center">A free and open-source inpainting tool powered by SOTA AI model.</p>
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<p align="center">
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<a href="https://github.com/Sanster/lama-cleaner">
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<img alt="total download" src="https://pepy.tech/badge/lama-cleaner" />
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</a>
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<a href="https://pypi.org/project/lama-cleaner/">
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<img alt="version" src="https://img.shields.io/pypi/v/lama-cleaner" />
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</a>
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<a href="https://colab.research.google.com/drive/1e3ZkAJxvkK3uzaTGu91N9TvI_Mahs0Wb?usp=sharing">
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<img alt="Open in Colab" src="https://colab.research.google.com/assets/colab-badge.svg" />
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</a>
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<a href="https://www.python.org/downloads/">
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<img alt="python version" src="https://img.shields.io/badge/python-3.7+-blue.svg" />
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</a>
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<a href="https://hub.docker.com/r/cwq1913/lama-cleaner">
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<img alt="version" src="https://img.shields.io/docker/pulls/cwq1913/lama-cleaner" />
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</a>
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</p>
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https://user-images.githubusercontent.com/3998421/196976498-ba1ad3ab-fa18-4c55-965f-5c6683141375.mp4
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## Features
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- Completely free and open-source
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- Fully self-hosted
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- Classical image inpainting algorithm powered by [cv2](https://docs.opencv.org/3.4/df/d3d/tutorial_py_inpainting.html)
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- Multiple SOTA AI models
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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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1. [ZITS](https://github.com/DQiaole/ZITS_inpainting)
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1. [MAT](https://github.com/fenglinglwb/MAT)
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1. [FcF](https://github.com/SHI-Labs/FcF-Inpainting)
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1. [SD1.5](https://github.com/runwayml/stable-diffusion)
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- Support CPU & GPU
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- Various inpainting [strategy](#inpainting-strategy)
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- Run as a desktop APP
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## Usage
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<details>
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<summary>1. Remove any unwanted things on the image</summary>
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| Usage | Before | After |
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| ---------------------- | --------------------------------------------- | --------------------------------------------------- |
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| Remove unwanted things |  |  |
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| Remove unwanted person |  |  |
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| Remove Text |  |  |
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| Remove watermark |  |  |
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</details>
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<details>
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<summary>2. Fix old photo</summary>
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| Usage | Before | After |
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| ------------- | ----------------------------------- | ----------------------------------------------- |
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| Fix old photo |  |  |
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</details>
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<details>
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<summary>3. Replace something on the image </summary>
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| Usage | Before | After |
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| ---------------------- | ------------------------ | -------------------------------------------------------------- |
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| Text Driven Inpainting |  | Prompt: a fox sitting on a bench<br/>  |
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</details>
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## Quick Start
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The easiest way to use Lama Cleaner is to install it using `pip`:
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```bash
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pip install lama-cleaner
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# Models will be downloaded at first time used
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lama-cleaner --model=lama --device=cpu --port=8080
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# Lama Cleaner is now running at http://localhost:8080
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```
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If you prefer to use docker, you can check out [docker](#docker)
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Available command line arguments:
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| Name | Description | Default |
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| -------------------- | ----------------------------------------------------------------------------------------------------------------------------- | -------- |
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| --model | lama/ldm/zits/mat/fcf/sd1.5 See details in [Inpaint Model](#inpainting-model) | lama |
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| --hf_access_token | stable-diffusion(sd) model need [huggingface access token](https://huggingface.co/docs/hub/security-tokens) to download model | |
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| --sd-run-local | Once the model as downloaded, you can pass this arg and remove `--hf_access_token` | |
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| --sd-disable-nsfw | Disable stable-diffusion NSFW checker. | |
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| --sd-cpu-textencoder | Always run stable-diffusion TextEncoder model on CPU. | |
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| --device | cuda or cpu | cuda |
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| --port | Port for backend flask web server | 8080 |
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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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| --debug | Enable debug mode for flask web server | |
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## Inpainting Model
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| Model | Description | Config |
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| ----- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| cv2 | :+1: No GPU is required, and for simple backgrounds, the results may even be better than AI models. | |
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| LaMa | :+1: Generalizes well on high resolutions(~2k)<br/> | |
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| LDM | :+1: Possible to get better and more detail result <br/> :+1: The balance of time and quality can be achieved by adjusting `steps` <br/> :neutral_face: Slower than GAN model<br/> :neutral_face: Need more GPU memory | `Steps`: You can get better result with large steps, but it will be more time-consuming <br/> `Sampler`: ddim or [plms](https://arxiv.org/abs/2202.09778). In general plms can get [better results](https://github.com/Sanster/lama-cleaner/releases/tag/0.13.0) with fewer steps |
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| ZITS | :+1: Better holistic structures compared with previous methods <br/> :neutral_face: Wireframe module is **very** slow on CPU | `Wireframe`: Enable edge and line detect |
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| MAT | TODO | |
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| FcF | :+1: Better structure and texture generation <br/> :neutral_face: Only support fixed size (512x512) input | |
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| SD1.5 | :+1: SOTA text-to-image diffusion model | |
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<details>
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<summary> See model comparison detail</summary>
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**LaMa vs LDM**
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| Original Image | LaMa | LDM |
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| ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
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|  |  |  |
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**LaMa vs ZITS**
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| Original Image | ZITS | LaMa |
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| ---------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- |
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|  |  |  |
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Image is from [ZITS](https://github.com/DQiaole/ZITS_inpainting) 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!
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**LaMa vs FcF**
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| Original Image | Lama | FcF |
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| ----------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
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|  |  |  |
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</details>
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## Inpainting Strategy
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Lama Cleaner provides three ways to run inpainting model on images, you can change it in the settings dialog.
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| Strategy | Description | VRAM | Speed |
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| ------------ | ------------------------------------------------------------------------------------------------------ | ------ | ----------------- |
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| **Original** | Use the resolution of the original image | High | :zap: |
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| **Resize** | Resize the image to a smaller size before inpainting. The area outside the mask will not loss quality. | Midium | :zap: :zap: |
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| **Crop** | Crop masking area from the original image to do inpainting | Low | :zap: :zap: :zap: |
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## Download Model Mannually
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If you have problems downloading the model automatically when lama-cleaner start,
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you can download it manually. By default lama-cleaner will load model from `TORCH_HOME=~/.cache/torch/hub/checkpoints/`,
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you can set `TORCH_HOME` to other folder and put the models there.
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- Github:
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- [LaMa](https://github.com/Sanster/models/releases/tag/add_big_lama)
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- [LDM](https://github.com/Sanster/models/releases/tag/add_ldm)
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- [ZITS](https://github.com/Sanster/models/releases/tag/add_zits)
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- [MAT](https://github.com/Sanster/models/releases/tag/add_mat)
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- [FcF](https://github.com/Sanster/models/releases/tag/add_fcf)
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- Baidu:
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- https://pan.baidu.com/s/1vUd3BVqIpK6e8N_EA_ZJfw
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- passward: flsu
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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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### Frontend
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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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You can use [pre-build docker image]() to run Lama Cleaner. The model will be downloaded to the cache directory when first time used.
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You can mount existing cache directory to start the container,
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so you don't have to download the model every time you start the container.
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The cache directories for different models correspond as follows:
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- lama/ldm/zits/mat/fcf: /root/.cache/torch
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- sd1.5: /root/.cache/huggingface
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### Run Docker (cpu)
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```
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docker run -p 8080:8080 \
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-v /path/to/torch_cache:/root/.cache/torch \
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-v /path/to/huggingface_cache:/root/.cache/huggingface \
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--rm lama-cleaner:cpu-0.21.0 \
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lama-cleaner --device=cpu --port=8080 --host=0.0.0.0
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```
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### Run Docker (gpu)
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- cuda11.6
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- pytorch1.12.1
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- minimum nvidia driver 510.39.01+
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```
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docker run --gpus all -p 8080:8080 \
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-v /path/to/torch_cache:/root/.cache/torch \
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-v /path/to/huggingface_cache:/root/.cache/huggingface \
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--rm lama-cleaner:cpu-0.21.0 \
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lama-cleaner --device=cuda --port=8080 --host=0.0.0.0
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```
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Then open [http://localhost:8080](http://localhost:8080)
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### Build Docker image
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cpu only
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```
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docker build -f ./docker/CPUDockerfile -t lamacleaner .
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```
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gpu & cpu
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```
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docker build -f ./docker/GPUDockerfile -t lamacleaner .
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```
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## One Click Installer
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TODO
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