|
|
--- |
|
|
license: mit |
|
|
library_name: transformers |
|
|
pipeline_tag: image-to-image |
|
|
--- |
|
|
|
|
|
# Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders (Scale-RAE) |
|
|
|
|
|
This repository contains artifacts related to the paper [Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders](https://huggingface.co/papers/2601.16208). |
|
|
|
|
|
- **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) |
|
|
|
|
|
## Introduction |
|
|
Representation Autoencoders (RAEs) provide a simplified and powerful alternative to VAEs for large-scale text-to-image generation. Scale-RAE demonstrates that training diffusion models in high-dimensional semantic latent spaces (using encoders like SigLIP-2) leads to faster convergence, better generation quality, and improved stability compared to state-of-the-art VAE-based foundations. |
|
|
|
|
|
## Usage |
|
|
For detailed instructions on installation, training, and inference, please visit the [official GitHub repository](https://github.com/ZitengWangNYU/Scale-RAE). |
|
|
|
|
|
**This decoder is also directly compatitable with original RAE [codebase](https://github.com/bytetriper/RAE).** Try it out by simply swapping the encoder with google/siglip2-so400m-patch14-224! |
|
|
|
|
|
## 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} |
|
|
} |
|
|
``` |
