RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion
  Transformers
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/.}
67bd3bd26faf9f04b2170f61
Min Zhao
admin_assigned
2025-02-25T16:25:16.045Z
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min zhao
user
caomi
67bd3bd26faf9f04b2170f62
Guande He
admin_assigned
2025-02-25T16:25:07.460Z
67492ee82ad3cfc108a41bbb
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Guande He
user
gdhe17
67bd3bd26faf9f04b2170f63
Yixiao Chen
admin_assigned
2025-02-25T16:24:59.862Z
67505e0990ba48ec35e748e2
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/K9bfDDD9AZJTxasc9yGfC.png
yixiaochen
user
yixiaochen
67bd3bd26faf9f04b2170f64
Hongzhou Zhu
claimed_verified
2025-02-25T09:39:23.502Z
64c269a52d73768f07ac266c
/avatars/d497a960f8aef6a974907b68ed750c1c.svg
Zhu Hongzhou
user
zhuhz22
67bd3bd26faf9f04b2170f65
Chongxuan Li
admin_assigned
2025-02-25T16:24:52.448Z
64c07b488e2612254361153b
/avatars/ade0f783cc4c2d3e73f402637f595471.svg
chongxuan li
user
zhenxuan00
67bd3bd26faf9f04b2170f66
Jun Zhu
RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion
  Transformers
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/.}
By analyzing positional embeddings, RIFLEx reduces frequency components to achieve high-quality video extrapolation without additional training or extensive fine-tuning.
positional embeddings temporal coherence frequency components video diffusion transformers extrapolation repetition suppression motion consistency