diff --git a/README.md b/README.md
index 5e86ebe..09fdf0c 100644
--- a/README.md
+++ b/README.md
@@ -28,178 +28,27 @@ https://user-images.githubusercontent.com/3998421/196976498-ba1ad3ab-fa18-4c55-9
## Features
-- Completely free and open-source
-- Fully self-hosted
-- [Windows 1-Click Installer](./scripts/README.md)
+- Completely free and open-source, fully self-hosted, support CPU & GPU
+- [Windows 1-Click Installer](https://lama-cleaner-docs.vercel.app/install/windows_1click_installer)
- Classical image inpainting algorithm powered by [cv2](https://docs.opencv.org/3.4/df/d3d/tutorial_py_inpainting.html)
-- Multiple SOTA AI models
- 1. [LaMa](https://github.com/saic-mdal/lama)
- 2. [LDM](https://github.com/CompVis/latent-diffusion)
- 3. [ZITS](https://github.com/DQiaole/ZITS_inpainting)
- 4. [MAT](https://github.com/fenglinglwb/MAT)
- 5. [FcF](https://github.com/SHI-Labs/FcF-Inpainting)
- 6. [SD1.5/SD2](https://github.com/runwayml/stable-diffusion)
- 7. [Manga](https://github.com/msxie92/MangaInpainting)
- 8. [Paint by Example](https://github.com/Fantasy-Studio/Paint-by-Example) [YouTube Demo](https://www.youtube.com/watch?v=NSAN3TzfhaI&ab_channel=PanicByte)
-- Support CPU & GPU
-- Various inpainting [strategy](#inpainting-strategy)
-- Run as a desktop APP
-- [Interactive Segmentation](https://github.com/Sanster/lama-cleaner/releases/tag/0.28.0) on any object. [YouTube Demo](https://www.youtube.com/watch?v=xHdo8a4Mn2g&ab_channel=PanicByte)
-
-## Usage
-
-A great introductory [youtube video](https://www.youtube.com/watch?v=aYia7Jvbjno&ab_channel=Aitrepreneur) made by **
-Aitrepreneur**
-
-
-1. Remove any unwanted things on the image
-
-| Usage | Before | After |
-| ----------------------------- | --------------------------------------------- | --------------------------------------------------- |
-| Remove unwanted things | ![unwant_object2](./assets/unwant_object.jpg) | ![unwant_object2](./assets/unwant_object_clean.jpg) |
-| Remove unwanted person | ![unwant_person](./assets/unwant_person.jpg) | ![unwant_person](./assets/unwant_person_clean.jpg) |
-| Remove text | ![text](./assets/unwant_text.jpg) | ![text](./assets/unwant_text_clean.jpg) |
-| Remove watermark | ![watermark](./assets/watermark.jpg) | ![watermark_clean](./assets/watermark_cleanup.jpg) |
-| Remove text balloons on manga | ![manga](./assets/manga.png) | ![manga_clean](./assets/manga_clean.png) |
-
-
-
-
-2. Fix old photo
-
-| Usage | Before | After |
-| ------------- | ----------------------------------- | ----------------------------------------------- |
-| Fix old photo | ![oldphoto](./assets/old_photo.jpg) | ![oldphoto_clean](./assets/old_photo_clean.jpg) |
-
-
-
-
-3. Replace something on the image
-
-SD1.5/SD2
-
-| Usage | Before | After |
-| ---------------------- | ------------------------ | -------------------------------------------------------------- |
-| Text Driven Inpainting | ![dog](./assets/dog.jpg) | Prompt: a fox sitting on a bench
![fox](./assets/fox.jpg) |
-
-Paint by Example
-
-| Original Image | Example Image | Result Image |
-| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
-| | [](https://youtu.be/NSAN3TzfhaI) | [](https://youtu.be/NSAN3TzfhaI) |
-
-
+- Multiple SOTA AI [models](https://lama-cleaner-docs.vercel.app/models/lama)
+- Various inpainting [strategy](https://lama-cleaner-docs.vercel.app/features/inpainting_strategy)
+- Run as a [desktop application](https://lama-cleaner-docs.vercel.app/features/desktop_app)
+- [Interactive Segmentation](https://lama-cleaner-docs.vercel.app/features/Interactive_segmentation) on any object.
+- More features at [lama-cleaner-docs](https://lama-cleaner-docs.vercel.app/)
## Quick Start
-The easiest way to use Lama Cleaner is to install it using `pip`:
+Lama Cleaner make it easy to use SOTA AI model in just two commands:
```bash
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
```
-If you prefer to use docker, you can check out [docker](#docker)
+That's it, Lama Cleaner is now running at http://localhost:8080
-If you hava no idea what is docker or pip, please check [One Click Installer](./scripts/README.md)
-
-Available command line arguments:
-
-| Name | Description | Default |
-| -------------------- | ------------------------------------------------------------------------------------------------------------- | -------- |
-| --model | lama/ldm/zits/mat/fcf/sd1.5/manga/sd2/paint_by_example See details in [Inpaint Model](#inpainting-model) | lama |
-| --sd-disable-nsfw | Disable stable-diffusion NSFW checker. | |
-| --sd-cpu-textencoder | Always run stable-diffusion TextEncoder model on CPU. | |
-| --sd-enable-xformers | Enable xFormers optimizations. See: [facebookresearch/xformers](https://github.com/facebookresearch/xformers) | |
-| --local-files-only | Once the model as downloaded, you can pass this arg to avoid diffusers connect to Hugging Face server | |
-| --cpu-offload | sd/paint_by_example model, offloads all models to CPU, sacrifice speed for reducing vRAM usage. |
-| --no-half | Using full precision for sd/paint_by_exmaple model | |
-| --device | cuda / cpu / mps | 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 | :+1: No GPU is required, and for simple backgrounds, the results may even be better than AI models. | |
-| LaMa | :+1: Generalizes well on high resolutions(~2k)
| |
-| LDM | :+1: Possible 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](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 |
-| ZITS | :+1: Better holistic structures compared with previous methods
:neutral_face: Wireframe module is **very** slow on CPU | `Wireframe`: Enable edge and line detect |
-| MAT | TODO | |
-| FcF | :+1: Better structure and texture generation
:neutral_face: Only support fixed size (512x512) input | |
-| SD1.5 | :+1: SOTA text-to-image diffusion model | |
-
-
- See model comparison detail
-
-**LaMa vs LDM**
-
-| Original Image | LaMa | LDM |
-| ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
-| ![photo-1583445095369-9c651e7e5d34](https://user-images.githubusercontent.com/3998421/156923525-d6afdec3-7b98-403f-ad20-88ebc6eb8d6d.jpg) | ![photo-1583445095369-9c651e7e5d34_cleanup_lama](https://user-images.githubusercontent.com/3998421/156923620-a40cc066-fd4a-4d85-a29f-6458711d1247.png) | ![photo-1583445095369-9c651e7e5d34_cleanup_ldm](https://user-images.githubusercontent.com/3998421/156923652-0d06c8c8-33ad-4a42-a717-9c99f3268933.png) |
-
-**LaMa vs ZITS**
-
-| Original Image | ZITS | LaMa |
-| ---------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- |
-| ![zits_original](https://user-images.githubusercontent.com/3998421/180464918-eb13ebfb-8718-461c-9e8b-7f6d8bb7a84f.png) | ![zits_compare_zits](https://user-images.githubusercontent.com/3998421/180464914-4db722c9-047f-48fe-9bb4-916ba09eb5c6.png) | ![zits_compare_lama](https://user-images.githubusercontent.com/3998421/180464903-ffb5f770-4372-4488-ba76-4b4a8c3323f5.png) |
-
-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!
-
-**LaMa vs FcF**
-
-| Original Image | LaMa | FcF |
-| ----------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
-| ![texture](https://user-images.githubusercontent.com/3998421/188305027-a4260545-c24e-4df7-9739-ac5dc3cae879.jpeg) | ![texture_lama](https://user-images.githubusercontent.com/3998421/188305024-2064ed3e-5af4-4843-ac10-7f9da71e15f8.jpeg) | ![texture_fcf](https://user-images.githubusercontent.com/3998421/188305006-a08d2896-a65f-43d5-b9a5-ef62c3129f0c.jpeg) |
-
-**LaMa vs Manga**
-
-Manga model works better on high-quality manga image then LaMa model.
-
-Original Image
-![manga](./assets/manga.png)
-
-| Model | 1080x740 | 1470x1010 |
-| ----------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------ |
-| Manga | ![manga_1080x740](https://user-images.githubusercontent.com/3998421/202676629-54f40f20-c55b-4e6d-bcc7-0a4e81fbb27d.png) | ![manga_1470x1010](https://user-images.githubusercontent.com/3998421/202675839-4f8012d5-1c10-47ea-9628-20512e86f192.png) |
-| [LaMa](https://github.com/saic-mdal/lama) | ![lama_1080x740](https://user-images.githubusercontent.com/3998421/202675704-53fa7a3d-ec74-4044-a19c-c673d74bdd28.png) | ![lama_1470x1010](https://user-images.githubusercontent.com/3998421/202675746-1e642367-f5d0-4b48-aa8b-5d82f2e29082.png) |
-
-
-
-## 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 | :zap: |
-| **Resize** | Resize the image to a smaller size before inpainting. The area outside the mask will not loss quality. | Midium | :zap: :zap: |
-| **Crop** | Crop masking area from the original image to do inpainting | Low | :zap: :zap: :zap: |
-
-## 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](https://github.com/Sanster/models/releases/tag/add_big_lama)
- - [LDM](https://github.com/Sanster/models/releases/tag/add_ldm)
- - [ZITS](https://github.com/Sanster/models/releases/tag/add_zits)
- - [MAT](https://github.com/Sanster/models/releases/tag/add_mat)
- - [FcF](https://github.com/Sanster/models/releases/tag/add_fcf)
-- Baidu:
- - https://pan.baidu.com/s/1vUd3BVqIpK6e8N_EA_ZJfw
- - passward: flsu
+See all command line arguments at [lama-cleaner-docs](https://lama-cleaner-docs.vercel.app/)
## Development
@@ -213,55 +62,3 @@ great online services [here](https://cleanup.pictures/).
- Install dependencies:`cd lama_cleaner/app/ && yarn`
- Start development server: `yarn start`
- Build: `yarn build`
-
-## Docker
-
-You can use [pre-build docker image](https://github.com/Sanster/lama-cleaner#run-docker-cpu) 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-0.26.1 \
-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-0.26.1 \
-lama-cleaner --device=cuda --port=8080 --host=0.0.0.0
-```
-
-Then open [http://localhost:8080](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 .
-```