title: Accelerate TarFlow Sampling with GS-Jacobi Iteration
summary: Image generation models have achieved widespread applications. As an
  instance, the TarFlow model combines the transformer architecture with
  Normalizing Flow models, achieving state-of-the-art results on multiple
  benchmarks. However, due to the causal form of attention requiring sequential
  computation, TarFlow's sampling process is extremely slow. In this paper, we
  demonstrate that through a series of optimization strategies, TarFlow sampling
  can be greatly accelerated by using the Gauss-Seidel-Jacobi (abbreviated as
  GS-Jacobi) iteration method. Specifically, we find that blocks in the TarFlow
  model have varying importance: a small number of blocks play a major role in
  image generation tasks, while other blocks contribute relatively little; some
  blocks are sensitive to initial values and prone to numerical overflow, while
  others are relatively robust. Based on these two characteristics, we propose
  the Convergence Ranking Metric (CRM) and the Initial Guessing Metric (IGM): CRM
  is used to identify whether a TarFlow block is "simple" (converges in few
  iterations) or "tough" (requires more iterations); IGM is used to evaluate
  whether the initial value of the iteration is good. Experiments on four TarFlow
  models demonstrate that GS-Jacobi sampling can significantly enhance sampling
  efficiency while maintaining the quality of generated images (measured by FID),
  achieving speed-ups of 4.53x in Img128cond, 5.32x in AFHQ, 2.96x in
  Img64uncond, and 2.51x in Img64cond without degrading FID scores or sample
  quality. Code and checkpoints are accessible on
  https://github.com/encoreus/GS-Jacobi_for_TarFlow
paper_authors._id: 682bedba4be8e1707067bdb2
paper_authors.name: Ben Liu
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-20T02:49:33.265Z
paper_authors._id: 682459b20ee49a8c3822a525
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/FCAtfQ40wZU3zai3DoAyq.png
paper_authors.fullname: Ben
paper_authors.type: user
paper_authors.user: encoreus
paper_authors._id: 682bedba4be8e1707067bdb3
paper_authors.name: Zhen Qin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-20T08:40:41.443Z
paper_authors._id: 649014b91d71e55664838d2d
paper_authors.avatarUrl: /avatars/f0e0f2830c5cb7428cbbc9634d95c34b.svg
paper_authors.fullname: Zhen Qin
paper_authors.type: user
paper_authors.user: zhenqincn
paper_title: Accelerate TarFlow Sampling with GS-Jacobi Iteration
paper_summary: Image generation models have achieved widespread applications. As an
  instance, the TarFlow model combines the transformer architecture with
  Normalizing Flow models, achieving state-of-the-art results on multiple
  benchmarks. However, due to the causal form of attention requiring sequential
  computation, TarFlow's sampling process is extremely slow. In this paper, we
  demonstrate that through a series of optimization strategies, TarFlow sampling
  can be greatly accelerated by using the Gauss-Seidel-Jacobi (abbreviated as
  GS-Jacobi) iteration method. Specifically, we find that blocks in the TarFlow
  model have varying importance: a small number of blocks play a major role in
  image generation tasks, while other blocks contribute relatively little; some
  blocks are sensitive to initial values and prone to numerical overflow, while
  others are relatively robust. Based on these two characteristics, we propose
  the Convergence Ranking Metric (CRM) and the Initial Guessing Metric (IGM): CRM
  is used to identify whether a TarFlow block is "simple" (converges in few
  iterations) or "tough" (requires more iterations); IGM is used to evaluate
  whether the initial value of the iteration is good. Experiments on four TarFlow
  models demonstrate that GS-Jacobi sampling can significantly enhance sampling
  efficiency while maintaining the quality of generated images (measured by FID),
  achieving speed-ups of 4.53x in Img128cond, 5.32x in AFHQ, 2.96x in
  Img64uncond, and 2.51x in Img64cond without degrading FID scores or sample
  quality. Code and checkpoints are accessible on
  https://github.com/encoreus/GS-Jacobi_for_TarFlow
paper_ai_summary: GS-Jacobi iteration method optimizes TarFlow's sampling process, significantly improving efficiency without compromising image quality.
paper_ai_keywords: transformer architecture Normalizing Flow models causal form attention Gauss-Seidel-Jacobi iteration Convergence Ranking Metric Initial Guessing Metric FID Img128cond AFHQ Img64uncond Img64cond