lora-scripts

Introduction to LoRA-Scripts

LoRA-scripts, also known as SD-Trainer, is a comprehensive toolset and graphical user interface designed for Stable Diffusion and Dreambooth model training. This project serves as a bridge, bringing complex machine learning processes to a more accessible level for users, regardless of their technical expertise.

At the heart of LoRA-scripts is a user-friendly GUI that simplifies the setup and execution of training environments. By integrating the functionalities of various scripts, LoRA-scripts offers a one-stop solution for deploying machine learning models using the kohya-ss/sd-scripts framework.

Features of LoRA-Scripts

Train WebUI

The Train WebUI is a real game-changer, positioning SD-Trainer as a complete Stable Diffusion Training Studio. Users can manage everything from one interface, streamlining the process of model training. Installation is straightforward—users follow the provided guide to set up the GUI, which can be launched on Windows using run_gui.ps1 or on Linux with run_gui.sh.

Integrated Tools

LoRA-scripts includes a suite of integrated tools to support the training process:

• Tensorboard: Provides visualization capabilities, helping users monitor their model training progress with ease.
• WD 1.4 Tagger: An efficient tool for tagging, facilitating better data management and curation.
• Tag Editor: Allows users to modify and manage tags effectively, ensuring data is well-organized.

How to Use LoRA-Scripts

System Requirements

To run LoRA-scripts, users need Python 3.10 and Git installed on their systems.

Installation and Setup

Cloning the Repository

To get started, clone the LoRA-scripts repository along with its submodules using:

git clone --recurse-submodules https://github.com/Akegarasu/lora-scripts

Windows Setup

1. Installation: Running install.ps1 will automatically create a virtual environment (venv) and install the necessary dependencies. Users in China mainland can use install-cn.ps1.
2. Training: Execute run_gui.ps1 to start the GUI, which will automatically open the application at http://127.0.0.1:28000.

Linux Setup

1. Installation: Use install.bash to create a venv and install dependencies.
2. Training: Execute bash run_gui.sh to run the GUI, accessing http://127.0.0.1:28000.

Legacy Training Setup

For those who prefer or require manual control over training scripts, LoRA-scripts also supports a legacy approach:

Windows

• Installation: Similar to GUI setup, install.ps1 will handle the creation of venv and installation of dependencies.
• Training: Users can edit and run train.ps1 for manual training execution.

Linux

• Installation: install.bash prepares the environment as in the GUI setup.
• Training: Activate venv with source venv/bin/activate, modify train.sh, and execute the script for training.

Additional Features

• TensorBoard: Start it with tensorboard.ps1, which will run at http://localhost:6006/.

Program Arguments

LoRA-scripts provides flexible configuration options through command-line arguments, including:

• --host and --port settings for custom server configuration.
• Options to enable features such as TensorBoard and Tag Editor or run in developer mode.

Each parameter is documented to help users tailor the training environment to their needs, promoting both flexibility and user empowerment.

In summary, LoRA-scripts significantly lowers the barrier to entry for Stable Diffusion and Dreambooth model training, wrapping powerful capabilities in an intuitive, easy-to-use package.