title: Parallelized Autoregressive Visual Generation
summary: Autoregressive models have emerged as a powerful approach for visual
  generation but suffer from slow inference speed due to their sequential
  token-by-token prediction process. In this paper, we propose a simple yet
  effective approach for parallelized autoregressive visual generation that
  improves generation efficiency while preserving the advantages of
  autoregressive modeling. Our key insight is that parallel generation depends on
  visual token dependencies-tokens with weak dependencies can be generated in
  parallel, while strongly dependent adjacent tokens are difficult to generate
  together, as their independent sampling may lead to inconsistencies. Based on
  this observation, we develop a parallel generation strategy that generates
  distant tokens with weak dependencies in parallel while maintaining sequential
  generation for strongly dependent local tokens. Our approach can be seamlessly
  integrated into standard autoregressive models without modifying the
  architecture or tokenizer. Experiments on ImageNet and UCF-101 demonstrate that
  our method achieves a 3.6x speedup with comparable quality and up to 9.5x
  speedup with minimal quality degradation across both image and video generation
  tasks. We hope this work will inspire future research in efficient visual
  generation and unified autoregressive modeling. Project page:
  https://epiphqny.github.io/PAR-project.
paper_authors._id: 6764fd9b10330426aecdded7
paper_authors.name: Yuqing Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-23T11:11:21.169Z
paper_authors._id: 63ea23b9dedfeebe54d02bdf
paper_authors.avatarUrl: /avatars/4d9f9a546aa8c63e277161ea700075c4.svg
paper_authors.fullname: Yuqing Wang
paper_authors.type: user
paper_authors.user: Epiphqny
paper_authors._id: 6764fd9b10330426aecdded8
paper_authors.name: Shuhuai Ren
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-23T11:11:23.352Z
paper_authors._id: 60d2e681b8448e1785bbda06
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1624434302056-noauth.jpeg
paper_authors.fullname: Shuhuai Ren
paper_authors.type: user
paper_authors.user: ShuhuaiRen
paper_authors._id: 6764fd9b10330426aecdded9
paper_authors.name: Zhijie Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-23T11:30:34.250Z
paper_authors._id: 64415957bd0c9726529802f6
paper_authors.avatarUrl: /avatars/1132d1ee68fb58ec635d57c8175caacd.svg
paper_authors.fullname: Zhijie Lin
paper_authors.type: user
paper_authors.user: Ikuinen
paper_authors._id: 6764fd9b10330426aecddeda
paper_authors.name: Yujin Han
paper_authors._id: 6764fd9b10330426aecddedb
paper_authors.name: Haoyuan Guo
paper_authors._id: 6764fd9b10330426aecddedc
paper_authors.name: Zhenheng Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-23T11:24:14.038Z
paper_authors._id: 6421183b69a2c2933882d652
paper_authors.avatarUrl: /avatars/66813a8fa22915087cccd4dbfb945ca7.svg
paper_authors.fullname: Zhenheng Yang
paper_authors.type: user
paper_authors.user: zhenheny
paper_authors._id: 6764fd9b10330426aecddedd
paper_authors.name: Difan Zou
paper_authors._id: 6764fd9b10330426aecddede
paper_authors.name: Jiashi Feng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-23T11:23:58.910Z
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: 6764fd9b10330426aecddedf
paper_authors.name: Xihui Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-23T11:11:19.211Z
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: Parallelized Autoregressive Visual Generation
paper_summary: Autoregressive models have emerged as a powerful approach for visual
  generation but suffer from slow inference speed due to their sequential
  token-by-token prediction process. In this paper, we propose a simple yet
  effective approach for parallelized autoregressive visual generation that
  improves generation efficiency while preserving the advantages of
  autoregressive modeling. Our key insight is that parallel generation depends on
  visual token dependencies-tokens with weak dependencies can be generated in
  parallel, while strongly dependent adjacent tokens are difficult to generate
  together, as their independent sampling may lead to inconsistencies. Based on
  this observation, we develop a parallel generation strategy that generates
  distant tokens with weak dependencies in parallel while maintaining sequential
  generation for strongly dependent local tokens. Our approach can be seamlessly
  integrated into standard autoregressive models without modifying the
  architecture or tokenizer. Experiments on ImageNet and UCF-101 demonstrate that
  our method achieves a 3.6x speedup with comparable quality and up to 9.5x
  speedup with minimal quality degradation across both image and video generation
  tasks. We hope this work will inspire future research in efficient visual
  generation and unified autoregressive modeling. Project page:
  https://epiphqny.github.io/PAR-project.
paper_ai_keywords: autoregressive models visual generation sequential token-by-token prediction parallelized autoregressive visual generation visual token dependencies parallel generation strategy sequential generation ImageNet UCF-101