Hyperspherical Latents Improve Continuous-Token Autoregressive
  Generation
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.
68db4439d2bf1f4b15ec7374
Guolin Ke
claimed_verified
2025-09-30T06:30:33.722Z
6348de0c62c668c7b48d83c9
/avatars/7296ea9bb301e19c10926022959b2023.svg
Guolin Ke
user
guolinke
68db4439d2bf1f4b15ec7375
Hui Xue
Hyperspherical Latents Improve Continuous-Token Autoregressive
  Generation
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.
SphereAR, an autoregressive model with hyperspherical constraints, achieves state-of-the-art performance in image generation, surpassing diffusion and masked-generation models at similar parameter scales.
autoregressive models continuous-token AR latent diffusion masked-generation models VAE latents classifier-free guidance variance collapse hypersphere hyperspherical VAEs FID SphereAR-H SphereAR-L SphereAR-B MAR-H VAR-d30