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README.md

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+---
+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}
+}
+```