title: DetailFlow: 1D Coarse-to-Fine Autoregressive Image Generation via
    Next-Detail Prediction
summary: This paper presents DetailFlow, a coarse-to-fine 1D autoregressive (AR) image
  generation method that models images through a novel next-detail prediction
  strategy. By learning a resolution-aware token sequence supervised with
  progressively degraded images, DetailFlow enables the generation process to
  start from the global structure and incrementally refine details. This
  coarse-to-fine 1D token sequence aligns well with the autoregressive inference
  mechanism, providing a more natural and efficient way for the AR model to
  generate complex visual content. Our compact 1D AR model achieves high-quality
  image synthesis with significantly fewer tokens than previous approaches, i.e.
  VAR/VQGAN. We further propose a parallel inference mechanism with
  self-correction that accelerates generation speed by approximately 8x while
  reducing accumulation sampling error inherent in teacher-forcing supervision.
  On the ImageNet 256x256 benchmark, our method achieves 2.96 gFID with 128
  tokens, outperforming VAR (3.3 FID) and FlexVAR (3.05 FID), which both require
  680 tokens in their AR models. Moreover, due to the significantly reduced token
  count and parallel inference mechanism, our method runs nearly 2x faster
  inference speed compared to VAR and FlexVAR. Extensive experimental results
  demonstrate DetailFlow's superior generation quality and efficiency compared to
  existing state-of-the-art methods.
paper_authors._id: 68368b211314d4ac399e462c
paper_authors.name: Yiheng Liu
paper_authors._id: 68368b211314d4ac399e462d
paper_authors.name: Liao Qu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:56:56.031Z
paper_authors._id: 64b796079ebb7e6c7ddcdabf
paper_authors.avatarUrl: /avatars/51af43cab078705b8745b4f942f542e5.svg
paper_authors.fullname: Liao Qu
paper_authors.type: user
paper_authors.user: leo1117
paper_authors._id: 68368b211314d4ac399e462e
paper_authors.name: Huichao Zhang
paper_authors._id: 68368b211314d4ac399e462f
paper_authors.name: Xu Wang
paper_authors._id: 68368b211314d4ac399e4630
paper_authors.name: Yi Jiang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:56:58.428Z
paper_authors._id: 6344dcb1cd37e44d9ed46508
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6344dcb1cd37e44d9ed46508/J92UKSxKR3iziD2WJfih4.jpeg
paper_authors.fullname: Yi Jiang
paper_authors.type: user
paper_authors.user: JiangYi
paper_authors._id: 68368b211314d4ac399e4631
paper_authors.name: Yiming Gao
paper_authors._id: 68368b211314d4ac399e4632
paper_authors.name: Hu Ye
paper_authors._id: 68368b211314d4ac399e4633
paper_authors.name: Xian Li
paper_authors._id: 68368b211314d4ac399e4634
paper_authors.name: Shuai Wang
paper_authors._id: 68368b211314d4ac399e4635
paper_authors.name: Daniel K. Du
paper_authors._id: 68368b211314d4ac399e4636
paper_authors.name: Shu Cheng
paper_authors._id: 68368b211314d4ac399e4637
paper_authors.name: Zehuan Yuan
paper_authors._id: 68368b211314d4ac399e4638
paper_authors.name: Xinglong Wu
paper_title: DetailFlow: 1D Coarse-to-Fine Autoregressive Image Generation via
    Next-Detail Prediction
paper_summary: This paper presents DetailFlow, a coarse-to-fine 1D autoregressive (AR) image
  generation method that models images through a novel next-detail prediction
  strategy. By learning a resolution-aware token sequence supervised with
  progressively degraded images, DetailFlow enables the generation process to
  start from the global structure and incrementally refine details. This
  coarse-to-fine 1D token sequence aligns well with the autoregressive inference
  mechanism, providing a more natural and efficient way for the AR model to
  generate complex visual content. Our compact 1D AR model achieves high-quality
  image synthesis with significantly fewer tokens than previous approaches, i.e.
  VAR/VQGAN. We further propose a parallel inference mechanism with
  self-correction that accelerates generation speed by approximately 8x while
  reducing accumulation sampling error inherent in teacher-forcing supervision.
  On the ImageNet 256x256 benchmark, our method achieves 2.96 gFID with 128
  tokens, outperforming VAR (3.3 FID) and FlexVAR (3.05 FID), which both require
  680 tokens in their AR models. Moreover, due to the significantly reduced token
  count and parallel inference mechanism, our method runs nearly 2x faster
  inference speed compared to VAR and FlexVAR. Extensive experimental results
  demonstrate DetailFlow's superior generation quality and efficiency compared to
  existing state-of-the-art methods.
paper_ai_summary: DetailFlow, a coarse-to-fine 1D autoregressive image generation method, improves quality and efficiency by using a novel next-detail prediction strategy, fewer tokens, and a parallel inference mechanism.
paper_ai_keywords: coarse-to-fine autoregressive token sequence resolution-aware autoregressive inference parallel inference self-correction gFID VAR VQGAN FlexVAR