title: Bridging Continuous and Discrete Tokens for Autoregressive Visual
    Generation
summary: 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.
paper_authors._id: 67e0bd81b04d9e836829c468
paper_authors.name: Yuqing Wang
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/4d9f9a546aa8c63e277161ea700075c4.svg
paper_authors.fullname: Yuqing Wang
paper_authors.type: user
paper_authors.user: Epiphqny
paper_authors._id: 67e0bd81b04d9e836829c469
paper_authors.name: Zhijie Lin
paper_authors.status: admin_assigned
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paper_authors.fullname: Zhijie Lin
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paper_authors.user: Ikuinen
paper_authors._id: 67e0bd81b04d9e836829c46a
paper_authors.name: Yao Teng
paper_authors.status: admin_assigned
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paper_authors.fullname: yao teng
paper_authors.type: user
paper_authors.user: tytyt
paper_authors._id: 67e0bd81b04d9e836829c46b
paper_authors.name: Yuanzhi Zhu
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paper_authors.fullname: Yuanzhi Zhu
paper_authors.type: user
paper_authors.user: Yuanzhi
paper_authors._id: 67e0bd81b04d9e836829c46c
paper_authors.name: Shuhuai Ren
paper_authors.status: claimed_verified
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paper_authors.fullname: Shuhuai Ren
paper_authors.type: user
paper_authors.user: ShuhuaiRen
paper_authors._id: 67e0bd81b04d9e836829c46d
paper_authors.name: Jiashi Feng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:41:16.160Z
paper_authors._id: 67298e44017b96a1d0101dc4
paper_authors.avatarUrl: /avatars/1f8ed1a3e911e6a3021087b9371d284c.svg
paper_authors.fullname: Jiashi Feng
paper_authors.type: user
paper_authors.user: jshfeng
paper_authors._id: 67e0bd81b04d9e836829c46e
paper_authors.name: Xihui Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:41:22.229Z
paper_authors._id: 65d5ec74cd05bc1eaa125040
paper_authors.avatarUrl: /avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
paper_authors.fullname: Xihui Liu
paper_authors.type: user
paper_authors.user: XihuiLiu
paper_title: Bridging Continuous and Discrete Tokens for Autoregressive Visual
    Generation
paper_summary: 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.
paper_ai_summary: TokenBridge addresses the dilemma between discrete and continuous token representations in autoregressive visual generation by using post-training quantization to preserve visual details while maintaining modeling simplicity.
paper_ai_keywords: autoregressive visual generation models tokenizers discrete tokens continuous tokens cross-entropy loss distribution modeling TokenBridge dimension-wise quantization lightweight autoregressive prediction mechanism