<Tip warning={true}>  
This script is experimental, and it's easy to overfit and run into issues like catastrophic forgetting. Try exploring different hyperparameters to get the best results on your dataset.  
</Tip>  
Kandinsky 2.2 is a multilingual text-to-image model capable of producing more photorealistic images. The model includes an image prior model for creating image embeddings from text prompts, and a decoder model that generates images based on the prior model's embeddings. That's why you'll find two separate scripts in Diffusers for Kandinsky 2.2, one for training the prior model and one for training the decoder model. You can train both models separately, but to get the best results, you should train both the prior and decoder models.  
Depending on your GPU, you may need to enable `gradient_checkpointing` (⚠️ not supported for the prior model!), `mixed_precision`, and `gradient_accumulation_steps` to help fit the model into memory and to speedup training. You can reduce your memory-usage even more by enabling memory-efficient attention with [xFormers](../optimization/xformers) (version [v0.0.16](https://github.com/huggingface/diffusers/issues/2234#issuecomment-1416931212) fails for training on some GPUs so you may need to install a development version instead).  
This guide explores the [train_text_to_image_prior.py](https://github.com/huggingface/diffusers/blob/main/examples/kandinsky2_2/text_to_image/train_text_to_image_prior.py) and the [train_text_to_image_decoder.py](https://github.com/huggingface/diffusers/blob/main/examples/kandinsky2_2/text_to_image/train_text_to_image_decoder.py) scripts to help you become more familiar with it, and how you can adapt it for your own use-case.  
Before running the scripts, 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 for the script you're using:  
```bash
cd examples/kandinsky2_2/text_to_image
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.  
<Tip>  
The following sections highlight parts of the training scripts that are important for understanding how to modify it, but it doesn't cover every aspect of the scripts in detail. If you're interested in learning more, feel free to read through the scripts and let us know if you have any questions or concerns.  
</Tip>
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/kandinsky.md