# OneTrainer

**Repository Path**: analyzesystem/OneTrainer

## Basic Information

- **Project Name**: OneTrainer
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: AGPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-09-14
- **Last Updated**: 2024-10-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# OneTrainer

OneTrainer is a one-stop solution for all your stable diffusion training needs.

<a href="https://discord.gg/KwgcQd5scF"><img src="https://discord.com/api/guilds/1102003518203756564/widget.png" alt="OneTrainer Discord"/></a><br>
Hardware for development sponsored by https://shakker.ai


## Features

- **Supported models**: Stable Diffusion 1.5, 2.0, 2.1, 3.0, SDXL, Würstchen-v2, Stable Cascade, PixArt-Alpha, 
  PixArt-Sigma and inpainting models
- **Model formats**: diffusers and ckpt models
- **Training methods**: Full fine-tuning, LoRA, embeddings
- **Masked Training**: Let the training focus on just certain parts of the samples.
- **Automatic backups**: Fully back up your training progress regularly during training. This includes all information
  to seamlessly continue training.
- **Image augmentation**: Apply random transforms such as rotation, brightness, contrast or saturation to each image
  sample to quickly create a more diverse dataset.
- **Tensorboard**: A simple tensorboard integration to track the training progress.
- **Multiple prompts per image**: Train the model on multiple different prompts per image sample.
- **Noise Scheduler Rescaling**: From the paper
  [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/abs/2305.08891)
- **EMA**: Train you own EMA model. Optionally keep EMA weights in CPU memory to reduce VRAM usage.
- **Aspect Ratio Bucketing**: Automatically train on multiple aspect ratios at a time. Just select the target
  resolutions, buckets are created automatically.
- **Multi Resolution Training**: Train multiple resolutions at the same time.
- **Dataset Tooling**: Automatically caption your dataset using BLIP, BLIP2 and WD-1.4, or create masks for masked
  training using ClipSeg or Rembg.
- **Model Tooling**: Convert between different model formats from a simple UI.
- **Sampling UI**: Sample the model during training without switching to a different application.
- **AlignProp**: A Reinforcement Learning method for diffusion networks from the paper
  [Aligning Text-to-Image Diffusion Models With Reward Backpropagation](https://arxiv.org/abs/2310.03739)

![OneTrainerGUI.gif](resources/images/OneTrainerGUI.gif)

## Installation

Installing OneTrainer requires Python 3.10. You can download Python here https://www.python.org/downloads/windows/.
Then follow these steps:

Automatic installation

- Clone the repository `git clone https://github.com/Nerogar/OneTrainer.git`
- Run:
    - Windows: `install.bat`
    - Unix based systems: `install.sh`

Manual installation

- Clone the repository `git clone https://github.com/Nerogar/OneTrainer.git`
- Navigate into the cloned directory `cd OneTrainer`
- Set up a virtual environment `python -m venv venv`
- Activate the new venv:
    - Windows: `venv\scripts\activate`
    - Unix based systems: `source venv/bin/activate`
- Install the requirements `pip install -r requirements.txt`

In some linux distribution, you might need to install libGL, for instance on ubuntu you will need to run:
```
sudo apt-get update
sudo apt-get install libgl1
```

## Updating

Automatic update

- Run `update.bat` or `update.sh`

Manual update

- Pull changes `git pull`
- Activate the venv `venv\scripts\activate`
- Re-Install all requirements `pip install -r requirements.txt --force-reinstall`

## Usage

To start the UI, run `start-ui.bat`. [You can find a quick start guide here.](docs/QuickStartGuide.md), and a more
detailed overview of different topics [here](docs/Overview.md).

If you need more control, OneTrainer supports two modes of operation. Command line only, and a UI.
All commands need to be run inside the active venv created during installation.

All functionality is split into different scrips located in the `scripts` directory. This currently includes:

- `train.py` The central training script
- `train_ui.py` A UI for training
- `caption_ui.py` A UI for manual or automatic captioning and mask creation for masked training
- `convert_model_ui.py` A UI for model conversions
- `convert_model.py` A utility to convert between different model formats
- `sample.py` A utility to sample any model
- `create_train_files.py` A utility to create files needed when training only from the CLI
- `generate_captions.py` A utility to automatically create captions for your dataset
- `generate_masks.py` A utility to automatically create masks for your dataset
- `calculate_loss.py` A utility to calculate the training loss of every image in your dataset

To learn more about the different parameters, execute `<script-name> -h`. For example `python scripts\train.py -h`

If you are on Mac or Linux, read [the launch script documentation](LAUNCH-SCRIPTS.md) for detailed information about how to run OneTrainer and its various scripts on your system.

## Contributing

Contributions are always welcome in any form. You can open issues, participate in discussions, or even open pull
requests for new or improved functionality. You can find more information [here](docs/Contributing.md)

Before you start looking at the code, I recommend reading about the project structure [here](docs/ProjectStructure.md).
For in depth discussions, you should consider joining the [Discord](https://discord.gg/KwgcQd5scF) server.

## Related Projects

- **[MGDS](https://github.com/Nerogar/mgds)**: A custom dataset implementation for Pytorch that is built around the idea
  of a node based graph.
- **[StableTuner](https://github.com/devilismyfriend/StableTuner)**: Another training application for Stable Diffusion.
  OneTrainer takes a lot of inspiration from StableTuner and wouldn't exist without it.
- **[Visions of Chaos](https://softology.pro/voc.htm)**: A collection of machine learning tools that also includes
  OneTrainer.