---
license: mit
library_name: transformers
pipeline_tag: image-to-image
---

# Scale-RAE: Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders

Official model weights for the paper [Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders](https://huggingface.co/papers/2601.16208).

Representation Autoencoders (RAEs) enable diffusion modeling in high-dimensional semantic latent spaces. Scale-RAE scales this framework to large-scale, freeform text-to-image generation. RAEs consistently outperform traditional VAEs during pretraining across various model scales, offering faster convergence and better generation quality.

- **Project Page:** [https://rae-dit.github.io/scale-rae/](https://rae-dit.github.io/scale-rae/)
- **GitHub Repository:** [https://github.com/ZitengWangNYU/Scale-RAE](https://github.com/ZitengWangNYU/Scale-RAE)
- **Paper:** [https://arxiv.org/abs/2601.16208](https://arxiv.org/abs/2601.16208)

## Usage

For full text-to-image generation using Scale-RAE, please follow the installation and inference instructions in the [official repository](https://github.com/ZitengWangNYU/Scale-RAE).

## Citation

```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}
}
```