---
license: mit
task_categories:
- text-to-image
---

# Scale RAE Data

[Project Page](https://rae-dit.github.io/scale-rae/) | [Paper](https://huggingface.co/papers/2601.16208) | [Code](https://github.com/ZitengWangNYU/Scale-RAE)

This repository contains data associated with the paper "Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders". 

The dataset is used for training and evaluating Scale-RAE, a framework that investigates scaling Representation Autoencoders (RAEs) for large-scale, freeform text-to-image (T2I) generation. It includes data used for scaling RAE decoders beyond ImageNet, featuring web, synthetic, and text-rendering data, as well as high-quality instruction datasets for fine-tuning.

## Citation

If you find this work useful, please cite:

```bibtex
@article{scale-rae-2026,
  title={Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders},
  author={Shengbang Tong and Boyang Zheng and Ziteng Wang and Bingda Tang and Nanye Ma and Ellis Brown and Jihan Yang and Rob Fergus and Yann LeCun and Saining Xie},
  journal={arXiv preprint arXiv:2601.16208},
  year={2026}
}
```