RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion
  Transformers
Authors: name: Min Zhao, name: Guande He, name: Yixiao Chen, name: Hongzhou Zhu, name: Chongxuan Li, name: Jun Zhu

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/.}