title: Tokenize Image as a Set
summary: This paper proposes a fundamentally new paradigm for image generation through
  set-based tokenization and distribution modeling. Unlike conventional methods
  that serialize images into fixed-position latent codes with a uniform
  compression ratio, we introduce an unordered token set representation to
  dynamically allocate coding capacity based on regional semantic complexity.
  This TokenSet enhances global context aggregation and improves robustness
  against local perturbations. To address the critical challenge of modeling
  discrete sets, we devise a dual transformation mechanism that bijectively
  converts sets into fixed-length integer sequences with summation constraints.
  Further, we propose Fixed-Sum Discrete Diffusion--the first framework to
  simultaneously handle discrete values, fixed sequence length, and summation
  invariance--enabling effective set distribution modeling. Experiments
  demonstrate our method's superiority in semantic-aware representation and
  generation quality. Our innovations, spanning novel representation and modeling
  strategies, advance visual generation beyond traditional sequential token
  paradigms. Our code and models are publicly available at
  https://github.com/Gengzigang/TokenSet.
paper_authors._id: 67dd38adbf4c007db3bf4b98
paper_authors.name: Zigang Geng
paper_authors._id: 67dd38adbf4c007db3bf4b99
paper_authors.name: Mengde Xu
paper_authors._id: 67dd38adbf4c007db3bf4b9a
paper_authors.name: Han Hu
paper_authors._id: 67dd38adbf4c007db3bf4b9b
paper_authors.name: Shuyang Gu
paper_title: Tokenize Image as a Set
paper_summary: This paper proposes a fundamentally new paradigm for image generation through
  set-based tokenization and distribution modeling. Unlike conventional methods
  that serialize images into fixed-position latent codes with a uniform
  compression ratio, we introduce an unordered token set representation to
  dynamically allocate coding capacity based on regional semantic complexity.
  This TokenSet enhances global context aggregation and improves robustness
  against local perturbations. To address the critical challenge of modeling
  discrete sets, we devise a dual transformation mechanism that bijectively
  converts sets into fixed-length integer sequences with summation constraints.
  Further, we propose Fixed-Sum Discrete Diffusion--the first framework to
  simultaneously handle discrete values, fixed sequence length, and summation
  invariance--enabling effective set distribution modeling. Experiments
  demonstrate our method's superiority in semantic-aware representation and
  generation quality. Our innovations, spanning novel representation and modeling
  strategies, advance visual generation beyond traditional sequential token
  paradigms. Our code and models are publicly available at
  https://github.com/Gengzigang/TokenSet.
paper_ai_summary: TokenSet uses set-based tokenization and Fixed-Sum Discrete Diffusion to model image distributions, enhancing representation and generation quality by dynamically allocating complexity.
paper_ai_keywords: set-based tokenization token set global context aggregation robustness dual transformation mechanism fixed-length integer sequences summation constraints Fixed-Sum Discrete Diffusion semantic-aware representation generation quality