title: Randomized Autoregressive Visual Generation
summary: This paper presents Randomized AutoRegressive modeling (RAR) for visual
  generation, which sets a new state-of-the-art performance on the image
  generation task while maintaining full compatibility with language modeling
  frameworks. The proposed RAR is simple: during a standard autoregressive
  training process with a next-token prediction objective, the input
  sequence-typically ordered in raster form-is randomly permuted into different
  factorization orders with a probability r, where r starts at 1 and linearly
  decays to 0 over the course of training. This annealing training strategy
  enables the model to learn to maximize the expected likelihood over all
  factorization orders and thus effectively improve the model's capability of
  modeling bidirectional contexts. Importantly, RAR preserves the integrity of
  the autoregressive modeling framework, ensuring full compatibility with
  language modeling while significantly improving performance in image
  generation. On the ImageNet-256 benchmark, RAR achieves an FID score of 1.48,
  not only surpassing prior state-of-the-art autoregressive image generators but
  also outperforming leading diffusion-based and masked transformer-based
  methods. Code and models will be made available at
  https://github.com/bytedance/1d-tokenizer
paper_authors._id: 672837cb4c94449f57de011f
paper_authors.name: Qihang Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-04T09:35:59.609Z
paper_authors._id: 648010b00b9d0f49849adb19
paper_authors.avatarUrl: /avatars/54419bb6e6ee788aba10cd64cd921204.svg
paper_authors.fullname: Qihang Yu
paper_authors.type: user
paper_authors.user: yucornetto
paper_authors._id: 672837cb4c94449f57de0120
paper_authors.name: Ju He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-04T09:36:06.564Z
paper_authors._id: 661c9059bcd78151e5c06ea1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/661c9059bcd78151e5c06ea1/27bfNo1LZeZQ77vWuAa10.png
paper_authors.fullname: Ju He
paper_authors.type: user
paper_authors.user: turkeyju
paper_authors._id: 672837cb4c94449f57de0121
paper_authors.name: Xueqing Deng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-04T14:24:03.091Z
paper_authors._id: 65ca9b1743207e438a95e90c
paper_authors.avatarUrl: /avatars/8f7bde1c44d8e665a29ee08ce7fedfa4.svg
paper_authors.fullname: Xueqing Deng
paper_authors.type: user
paper_authors.user: xdeng77
paper_authors._id: 672837cb4c94449f57de0122
paper_authors.name: Xiaohui Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-04T14:23:56.985Z
paper_authors._id: 6430aa1b32a732121cd81f98
paper_authors.avatarUrl: /avatars/5419f8d6d4d36fa5ac83e30667b9fd99.svg
paper_authors.fullname: Xiaohui Shen
paper_authors.type: user
paper_authors.user: XiaohuiShen
paper_authors._id: 672837cb4c94449f57de0123
paper_authors.name: Liang-Chieh Chen
paper_title: Randomized Autoregressive Visual Generation
paper_summary: This paper presents Randomized AutoRegressive modeling (RAR) for visual
  generation, which sets a new state-of-the-art performance on the image
  generation task while maintaining full compatibility with language modeling
  frameworks. The proposed RAR is simple: during a standard autoregressive
  training process with a next-token prediction objective, the input
  sequence-typically ordered in raster form-is randomly permuted into different
  factorization orders with a probability r, where r starts at 1 and linearly
  decays to 0 over the course of training. This annealing training strategy
  enables the model to learn to maximize the expected likelihood over all
  factorization orders and thus effectively improve the model's capability of
  modeling bidirectional contexts. Importantly, RAR preserves the integrity of
  the autoregressive modeling framework, ensuring full compatibility with
  language modeling while significantly improving performance in image
  generation. On the ImageNet-256 benchmark, RAR achieves an FID score of 1.48,
  not only surpassing prior state-of-the-art autoregressive image generators but
  also outperforming leading diffusion-based and masked transformer-based
  methods. Code and models will be made available at
  https://github.com/bytedance/1d-tokenizer
paper_ai_summary: Randomized AutoRegressive modeling (RAR) improves autoregressive image generation by randomly permuting input sequences during training, outperforming existing methods on the ImageNet-256 benchmark.
paper_ai_keywords: Randomized AutoRegressive modeling RAR autoregressive training next-token prediction factorization orders annealing training strategy bidirectional contexts FID score autoregressive image generators diffusion-based methods masked transformer-based methods