| 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). | |
| The implementation supports GPU inference and TPU training. To generate images with pre-trained models: | |
| ```bash | |
| python cli.py t2i --prompt "Can you generate a photo of a cat on a windowsill?" | |
| ``` | |
| ## 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} | |
| } | |
| ``` |