Heptapod: Language Modeling on Visual Signals
Authors: name: Yongxin Zhu, name: Jiawei Chen, name: Yuanzhe Chen, name: Zhuo Chen, name: Dongya Jia, name: Jian Cong, name: Xiaobin Zhuang, name: Yuping Wang, name: Yuxuan Wang
Organization: ByteDance Seed

We introduce Heptapod, an image autoregressive model that adheres to the
foundational principles of language modeling. Heptapod employs causal
attention, eliminates reliance on CFG, and eschews the trend
of semantic tokenizers. Our key innovation is next 2D distribution
prediction: a causal Transformer with reconstruction-focused visual tokenizer,
learns to predict the distribution over the entire 2D spatial grid of images at
each timestep. This learning objective unifies the sequential modeling of
autoregressive framework with the holistic self-supervised learning of masked
autoencoding, enabling the model to capture comprehensive image semantics via
generative training. On the ImageNet generation benchmark, Heptapod achieves an
FID of 2.70, significantly outperforming previous causal autoregressive
approaches. We hope our work inspires a principled rethinking of language
modeling on visual signals and beyond.