Bridging Continuous and Discrete Tokens for Autoregressive Visual
  Generation
Authors: name: Yuqing Wang, name: Zhijie Lin, name: Yao Teng, name: Yuanzhi Zhu, name: Shuhuai Ren, name: Jiashi Feng, name: Xihui Liu

Autoregressive visual generation models typically rely on tokenizers to
compress images into tokens that can be predicted sequentially. A fundamental
dilemma exists in token representation: discrete tokens enable straightforward
modeling with standard cross-entropy loss, but suffer from information loss and
tokenizer training instability; continuous tokens better preserve visual
details, but require complex distribution modeling, complicating the generation
pipeline. In this paper, we propose TokenBridge, which bridges this gap by
maintaining the strong representation capacity of continuous tokens while
preserving the modeling simplicity of discrete tokens. To achieve this, we
decouple discretization from the tokenizer training process through
post-training quantization that directly obtains discrete tokens from
continuous representations. Specifically, we introduce a dimension-wise
quantization strategy that independently discretizes each feature dimension,
paired with a lightweight autoregressive prediction mechanism that efficiently
model the resulting large token space. Extensive experiments show that our
approach achieves reconstruction and generation quality on par with continuous
methods while using standard categorical prediction. This work demonstrates
that bridging discrete and continuous paradigms can effectively harness the
strengths of both approaches, providing a promising direction for high-quality
visual generation with simple autoregressive modeling. Project page:
https://yuqingwang1029.github.io/TokenBridge.