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--- |
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pipeline_tag: text-to-image |
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library_name: pytorch |
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--- |
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# SphereAR: Hyperspherical Latents Improve Continuous-Token Autoregressive Generation |
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This repository contains the official PyTorch implementation of the paper [Hyperspherical Latents Improve Continuous-Token Autoregressive Generation](https://huggingface.co/papers/2509.24335). |
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The official code and further details can be found on the GitHub repository: [https://github.com/guolinke/SphereAR](https://github.com/guolinke/SphereAR) |
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## Abstract |
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Autoregressive (AR) models are promising for image generation, yet continuous-token AR variants often trail latent diffusion and masked-generation models. The core issue is heterogeneous variance in VAE latents, which is amplified during AR decoding, especially under classifier-free guidance (CFG), and can cause variance collapse. We propose SphereAR to address this issue. Its core design is to constrain all AR inputs and outputs -- including after CFG -- to lie on a fixed-radius hypersphere (constant $\ell_2$ norm), leveraging hyperspherical VAEs. Our theoretical analysis shows that hyperspherical constraint removes the scale component (the primary cause of variance collapse), thereby stabilizing AR decoding. Empirically, on ImageNet generation, SphereAR-H (943M) sets a new state of the art for AR models, achieving FID 1.34. Even at smaller scales, SphereAR-L (479M) reaches FID 1.54 and SphereAR-B (208M) reaches 1.92, matching or surpassing much larger baselines such as MAR-H (943M, 1.55) and VAR-d30 (2B, 1.92). To our knowledge, this is the first time a pure next-token AR image generator with raster order surpasses diffusion and masked-generation models at comparable parameter scales. |
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<p align="center"> |
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<img src="https://github.com/guolinke/SphereAR/raw/main/figures/grid.jpg" width=780> |
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<p> |
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## Introduction |
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SphereAR is a simple yet effective approach to continuous-token autoregressive (AR) image generation: it makes AR scale-invariant by constraining all AR inputs and outputs---**including after CFG**---to lie on a fixed-radius hypersphere (constant L2 norm) via hyperspherical VAEs. |
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The model is a **pure next-token** AR generator with **raster** order, matching standard language AR modeling (i.e., it is *not* next-scale AR like VAR and *not* next-set AR like MAR/MaskGIT). |
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On ImageNet 256×256, SphereAR achieves a state-of-the-art FID of **1.34** among AR image generators. |
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## Model Checkpoints |
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The following pre-trained models are available for class-conditional image generation on ImageNet: |
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| Name | params | FID (256x256) | weight | |
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|---|:---:|:---:|:---:| |
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| S-VAE | 75M | - | [vae.pt](https://huggingface.co/guolinke/SphereAR/blob/main/vae.pt) | |
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| SphereAR-B | 208M | 1.92 | [SphereAR_B.pt](https://huggingface.co/guolinke/SphereAR/blob/main/SphereAR_B.pt) | |
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| SphereAR-L | 479M | 1.54 | [SphereAR_L.pt](https://huggingface.co/guolinke/SphereAR/blob/main/SphereAR_L.pt) | |
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| SphereAR-H | 943M | 1.34 | [SphereAR_H.pt](https://huggingface.co/guolinke/SphereAR/blob/main/SphereAR_H.pt) | |
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For detailed instructions on evaluation and training using these checkpoints, please refer to the [official GitHub repository](https://github.com/guolinke/SphereAR). |
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## Citation |
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If you find this work useful, please consider citing the paper: |
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```bibtex |
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@article{ke2025hyperspherical, |
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title={Hyperspherical Latents Improve Continuous-Token Autoregressive Generation}, |
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author={Guolin Ke and Hui Xue}, |
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journal={arXiv preprint arXiv:2509.24335}, |
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year={2025} |
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} |
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``` |
