Unconditional image generation models are not conditioned on text or images during training. It only generates images that resemble its training data distribution.  
This guide will explore the [train_unconditional.py](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py) training script to help you become familiar with it, and how you can adapt it for your own use-case.  
Before running the script, make sure you install the library from source:  
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```  
Then navigate to the example folder containing the training script and install the required dependencies:  
```bash
cd examples/unconditional_image_generation
pip install -r requirements.txt
```  
<Tip>  
🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It'll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate [Quick tour](https://huggingface.co/docs/accelerate/quicktour) to learn more.  
</Tip>  
Initialize an 🤗 Accelerate environment:  
```bash
accelerate config
```  
To setup a default 🤗 Accelerate environment without choosing any configurations:  
```bash
accelerate config default
```  
Or if your environment doesn't support an interactive shell like a notebook, you can use:  
```py
from accelerate.utils import write_basic_config

write_basic_config()
```  
Lastly, if you want to train a model on your own dataset, take a look at the [Create a dataset for training](create_dataset) guide to learn how to create a dataset that works with the training script.
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/unconditional_training.md