> ## Documentation Index
> Fetch the complete documentation index at: https://docs.rubbrband.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart Guide

> Start training in minutes.

## **Installation**

Rubbrband can be installed through pip.

```bash
pip install rubbrband
```

## Example Usage

To get a feel for finetuning Dreambooth, you can copy and paste these lines in your terminal to start finetuning. This script will finetune a Dreambooth model to learn a specific man's face.

```bash
# install rubbrband
pip install rubbrband

# download dummy dataset and set folder structure
git clone https://github.com/rubbrband/sample_dataset.git
git clone https://github.com/JoePenna/Stable-Diffusion-Regularization-Images.git --depth 1
mkdir regDir
mv ./Stable-Diffusion-Regularization-Images/man_unsplash ./regDir/man

# start training
rubbrband train dreambooth --class-word man --dataset-dir ./sample_dataset --reg-dir ./regDir --log-dir ./logs
```

## Install Docker

Rubbrband requires Docker to be installed. Installation instructions found [here](https://docs.docker.com/get-docker/).

***

## Supported models

* **DreamBooth -** Personalize Stable Diffusion with your own images

* **WebUI Stable Diffusion -** Use a web interface to train and generate outputs with Stable Diffusion models.

* **ControlNet(beta) -** An image model to control diffusion models by adding extra conditions

* **LoRA(beta) -** Fine-tune Stable Diffusion models twice as fast as DreamBooth, by Low-rank Adaptation.

***

## CLI Commands

```bash
# View running MODELS
rubbrband ls
```

```bash
# View all supported MODELS
rubbrband models
```

```bash
# Train a MODEL with a given folder containing data
rubbrband train MODEL --data-dir=""
```

```bash
# Start a webUI model
rubbrband web MODEL
```