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SD-reScripts

✨ Enjoy Stable Diffusion Train！ ✨

v1.6.1

Fork from 秋葉 aaaki/lora-scripts
Modify By Lulynx

Download · Documents · 中文README · AGENTS · FRONTEND · COMPLIANCE · BRANDING

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

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.

✨ 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.

Tensorboard	WD 1.4 Tagger	Tag Editor

✨ 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.

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

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 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 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

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

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

Install backend dependencies:

cd backend
npm install

Install frontend dependencies:

cd ../frontend
npm install

Run the backend locally:

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

Run the frontend in dev mode:

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:

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

Acknowledgements

Special thanks to

DrRelax599

for testing the project and helping improve stability during development.