lora-rescripts

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SD-re(Refactor)Scripts is a maintained fork / continuation of LoRA-scripts (a.k.a. SD-Trainer).

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## Download

<a href="https://github.com/WhitecrowAurora/lora-rescripts/releases/">Open Releases Download Page</a>

# SD-reScripts

_✨ Enjoy Stable Diffusion Train！ ✨_

**v1.6.1**

Fork from 秋葉 `aaaki/lora-scripts`  
Modify By `Lulynx`

</div>

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SD-reScripts is a maintained fork / continuation of LoRA-scripts (a.k.a. SD-Trainer).

This is an experimental project currently in beta, and there are tons of bugs.

LoRA & Dreambooth training GUI & scripts preset & one key training environment for [kohya-ss/sd-scripts](https://github.com/kohya-ss/sd-scripts.git)

## Recent Updates

### v1.5.7

- added a dependency cache manager with prefetch, batch caching, progress, ETA, and install-time cache reuse
- added global proxy settings plus optional trainer-side proxy inheritance for downloads and preflight flows
- fixed shared runtime install/update scripts dropping pip or git arguments, which could break runtime setup
- fixed several training and tooling regressions, including SD3 log output cleanup and Dataset Tag Editor torch bootstrap fallback

## ✨ NEW: SD-reScripts Launcher

A dedicated desktop launcher is now included for runtime setup, launch control, runtime diagnostics, managed preset import, and safer day-to-day startup flow.

![SD-reScripts Launcher (English)](https://raw.githubusercontent.com/WhitecrowAurora/lora-rescripts/main/assets/launcher-en.png)

## ✨ NEW: Train WebUI

The **REAL** Stable Diffusion Training Studio. Everything in one WebUI.

Follow the installation guide below to install the GUI, then run `run_gui.ps1` (Windows) or `run_gui.sh` (Linux) to start it.

![Train WebUI](https://github.com/Akegarasu/lora-scripts/assets/36563862/d3fcf5ad-fb8f-4e1d-81f9-c903376c19c6)

| Tensorboard | WD 1.4 Tagger | Tag Editor |
| ------------ | ------------ | ------------ |
| ![Tensorboard](https://github.com/Akegarasu/lora-scripts/assets/36563862/b2ac5c36-3edf-43a6-9719-cb00b757fc76) | ![WD 1.4 Tagger](https://github.com/Akegarasu/lora-scripts/assets/36563862/9504fad1-7d77-46a7-a68f-91fbbdbc7407) | ![Tag Editor](https://github.com/Akegarasu/lora-scripts/assets/36563862/4597917b-caa8-4e90-b950-8b01738996f2) |

## ✨ NEW: UI Design

A redesigned community UI is also supported through the frontend profile system. You can switch to it from the launcher or your configured frontend profile workflow.

![New UI Design](https://raw.githubusercontent.com/WhitecrowAurora/lora-rescripts/main/assets/new-ui-cn.png)

# Usage

### Required Dependencies

If you use the bundled portable Python runtime in the repo root, system Python is optional.

Otherwise you need:

- Python 3.10+
- Git

### Clone repo with submodules

```sh
git clone --recurse-submodules https://github.com/WhitecrowAurora/lora-rescripts.git
```

## ✨ SD-reScripts GUI

### Windows

#### Installation

Run `run_For_≤RTX40series.bat` or `run_For_SageAttention_Experimental.bat`.

- If a ready-to-run `python` folder already exists in the repo root, the installer will use it first
- Otherwise it falls back to creating a virtual environment
- `setup_embeddable_python.bat` is now mainly a repair helper for broken raw embeddable Python installs, not a normal first-run requirement

#### Train

run `run_gui.ps1`, then program will open [http://127.0.0.1:28000](http://127.0.0.1:28000) automanticlly

#### SageAttention Experimental Startup

If you want to try `sageattn`, there are now dedicated experimental startup scripts on Windows:

- `run_For_SageAttention_Experimental.bat`: general SageAttention runtime for NVIDIA GPUs
- `run_For_NVIDIA_SageAttention_Experimental.bat`: compatibility alias for the same general SageAttention runtime
- `run_For_Only_Blackwell_SageAttention_Experimental.bat`: recommended experimental path for RTX 50 / RTX PRO Blackwell users when xformers is unreliable

Notes:

- the first run will automatically prepare a dedicated runtime and keep the main `python` / `python_blackwell` / xformers environments untouched
- SageAttention only affects routes and configs that explicitly enable `sageattn`; launching with a SageAttention script does not force every trainer to stop using `sdpa` or `xformers`
- you can verify the runtime with `check_sageattention_env.bat` or `check_sageattention_env.bat --blackwell`
- if you want to provide a prebuilt local wheel, place it in `sageattention-wheels` or `sageattention_wheels`
- for the Blackwell runtime, wheel names containing `blackwell` or `sm120` are preferred automatically

Current validated experimental base stack:

- Python `3.11.9`
- Torch `2.10.0+cu128`
- TorchVision `0.25.0+cu128`
- Triton Windows `3.5.1.post24`
- SageAttention `1.0.6`

### Linux

#### Installation

Run `install.bash`.

- if `python/bin/python` already exists, the installer will use it first
- otherwise it will use `venv/bin/python` if present
- otherwise it will create `venv` automatically unless you explicitly pass `--disable-venv`
- it now installs the same base PyTorch / dependency stack as the current Windows installer

#### Train

Run `bash run_gui.sh`, then program will open [http://127.0.0.1:28000](http://127.0.0.1:28000) automatically.

- `run_gui.sh` now auto-detects `python/bin/python`, `venv/bin/python`, or system python
- if base dependencies are missing, it will run `install.bash` for you
- if tag editor dependencies are missing and the current Python is compatible, it will run `install_tageditor.sh`
- for mainland China mirror settings, use `bash run_gui_cn.sh`
- on Windows, use `run_gui_cn.bat`, `run_auto_cn.bat`, or `run_manual_cn.bat`
- dedicated experimental routes also provide matching `_cn.bat` launchers
- the first CN startup will let you choose a PyPI mirror; pressing Enter keeps the default Tsinghua preset and saves it to `config/china_mirror.json`

#### TensorBoard

TensorBoard is already integrated into the GUI startup path.

## Hosted Preset Sharing

The launcher `Managed` tab can connect to a hosted preset site for one-click preset import, rollback, and 24-hour local cache sync.

Reference repository:

- [WhitecrowAurora/lulynx-lora-share](https://github.com/WhitecrowAurora/lulynx-lora-share)

Recommended Linux prerequisites for the hosted preset site:

- `git`
- `Node.js 20+`
- `npm 10+`
- if native modules need local compilation: `build-essential`, `python3`, `pkg-config`, `libvips-dev`

### Linux quick start

```sh
git clone https://github.com/WhitecrowAurora/lulynx-lora-share.git
cd lulynx-lora-share
```

Install backend dependencies:

```sh
cd backend
npm install
```

Install frontend dependencies:

```sh
cd ../frontend
npm install
```

Run the backend locally:

```sh
cd ../backend
PORT=3000 CORS_ORIGIN=http://127.0.0.1:5173 npm run start
```

Run the frontend in dev mode:

```sh
cd ../frontend
VITE_API_URL=http://127.0.0.1:3000/api npm run dev -- --host 0.0.0.0 --port 5173
```

Create a production frontend build:

```sh
cd frontend
VITE_API_URL=https://your-domain.example/api npm run build
```

Then configure your reverse proxy to serve `frontend/dist` and forward `/api` to the backend server.

After the site is online, create an API key in LORA Share and paste the server URL + API key into the launcher `Managed` tab.

## Program arguments

| Parameter Name                | Type  | Default Value | Description                                      |
|-------------------------------|-------|---------------|--------------------------------------------------|
| `--host`                      | str   | "127.0.0.1"   | Hostname for the server                          |
| `--port`                      | int   | 28000         | Port to run the server                           |
| `--listen`                    | bool  | false         | Enable listening mode for the server             |
| `--skip-prepare-environment`  | bool  | false         | Skip the environment preparation step            |
| `--disable-tensorboard`       | bool  | false         | Disable TensorBoard                              |
| `--disable-tageditor`         | bool  | false         | Disable tag editor                               |
| `--tensorboard-host`          | str   | "127.0.0.1"   | Host to run TensorBoard                          |
| `--tensorboard-port`          | int   | 6006          | Port to run TensorBoard                          |
| `--localization`              | str   |               | Localization settings for the interface          |
| `--dev`                       | bool  | false         | Developer mode to disale some checks             |

## Open-source Credits

This project stands on the work of multiple open-source communities. Respect and thanks to:

- [aaaki/lora-scripts](https://github.com/aaaki/lora-scripts): direct upstream fork base for this repository.
- [Akegarasu/lora-scripts](https://github.com/Akegarasu/lora-scripts): earlier foundation of the script-first and GUI workflow.
- [kohya-ss/sd-scripts](https://github.com/kohya-ss/sd-scripts): core training backend used by this project.
- [kozistr/pytorch_optimizer](https://github.com/kozistr/pytorch_optimizer): optimizer and scheduler collection used for the extended optimizer/scheduler options in this project.
- [67372a/LoRA_Easy_Training_Scripts](https://github.com/67372a/LoRA_Easy_Training_Scripts): reference project for several advanced training-route ideas explored and adapted in this repository.

## Acknowledgements

Special thanks to <a href="https://github.com/DrRelax599">DrRelax599</a> for testing the project and helping improve stability during development.

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