title: RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion
    Transformers
summary: Recent advancements in video generation have enabled models to synthesize
  high-quality, minute-long videos. However, generating even longer videos with
  temporal coherence remains a major challenge, and existing length extrapolation
  methods lead to temporal repetition or motion deceleration. In this work, we
  systematically analyze the role of frequency components in positional
  embeddings and identify an intrinsic frequency that primarily governs
  extrapolation behavior. Based on this insight, we propose RIFLEx, a minimal yet
  effective approach that reduces the intrinsic frequency to suppress repetition
  while preserving motion consistency, without requiring any additional
  modifications. RIFLEx offers a true free lunch--achieving high-quality
  2times extrapolation on state-of-the-art video diffusion transformers in a
  completely training-free manner. Moreover, it enhances quality and enables
  3times extrapolation by minimal fine-tuning without long videos. Project
  page and codes:
  https://riflex-video.github.io/{https://riflex-video.github.io/.}
paper_authors._id: 67bd3bd26faf9f04b2170f61
paper_authors.name: Min Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:25:16.045Z
paper_authors._id: 65d0aa91617d1f7450cfcc3b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/lIoRhlq_Bmwt5X-V6LztR.png
paper_authors.fullname: min zhao
paper_authors.type: user
paper_authors.user: caomi
paper_authors._id: 67bd3bd26faf9f04b2170f62
paper_authors.name: Guande He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:25:07.460Z
paper_authors._id: 67492ee82ad3cfc108a41bbb
paper_authors.avatarUrl: /avatars/7ad03e55a8791c62f1271a5c9bf8cc60.svg
paper_authors.fullname: Guande He
paper_authors.type: user
paper_authors.user: gdhe17
paper_authors._id: 67bd3bd26faf9f04b2170f63
paper_authors.name: Yixiao Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:24:59.862Z
paper_authors._id: 67505e0990ba48ec35e748e2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/K9bfDDD9AZJTxasc9yGfC.png
paper_authors.fullname: yixiaochen
paper_authors.type: user
paper_authors.user: yixiaochen
paper_authors._id: 67bd3bd26faf9f04b2170f64
paper_authors.name: Hongzhou Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-25T09:39:23.502Z
paper_authors._id: 64c269a52d73768f07ac266c
paper_authors.avatarUrl: /avatars/d497a960f8aef6a974907b68ed750c1c.svg
paper_authors.fullname: Zhu Hongzhou
paper_authors.type: user
paper_authors.user: zhuhz22
paper_authors._id: 67bd3bd26faf9f04b2170f65
paper_authors.name: Chongxuan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:24:52.448Z
paper_authors._id: 64c07b488e2612254361153b
paper_authors.avatarUrl: /avatars/ade0f783cc4c2d3e73f402637f595471.svg
paper_authors.fullname: chongxuan li
paper_authors.type: user
paper_authors.user: zhenxuan00
paper_authors._id: 67bd3bd26faf9f04b2170f66
paper_authors.name: Jun Zhu
paper_title: RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion
    Transformers
paper_summary: Recent advancements in video generation have enabled models to synthesize
  high-quality, minute-long videos. However, generating even longer videos with
  temporal coherence remains a major challenge, and existing length extrapolation
  methods lead to temporal repetition or motion deceleration. In this work, we
  systematically analyze the role of frequency components in positional
  embeddings and identify an intrinsic frequency that primarily governs
  extrapolation behavior. Based on this insight, we propose RIFLEx, a minimal yet
  effective approach that reduces the intrinsic frequency to suppress repetition
  while preserving motion consistency, without requiring any additional
  modifications. RIFLEx offers a true free lunch--achieving high-quality
  2times extrapolation on state-of-the-art video diffusion transformers in a
  completely training-free manner. Moreover, it enhances quality and enables
  3times extrapolation by minimal fine-tuning without long videos. Project
  page and codes:
  https://riflex-video.github.io/{https://riflex-video.github.io/.}
paper_ai_summary: By analyzing positional embeddings, RIFLEx reduces frequency components to achieve high-quality video extrapolation without additional training or extensive fine-tuning.
paper_ai_keywords: positional embeddings temporal coherence frequency components video diffusion transformers extrapolation repetition suppression motion consistency