Tuning-Free Multi-Event Long Video Generation via Synchronized Coupled
  Sampling
Authors: name: Subin Kim, name: Seoung Wug Oh, name: Jui-Hsien Wang, name: Joon-Young Lee, name: Jinwoo Shin

While recent advancements in text-to-video diffusion models enable
high-quality short video generation from a single prompt, generating real-world
long videos in a single pass remains challenging due to limited data and high
computational costs. To address this, several works propose tuning-free
approaches, i.e., extending existing models for long video generation,
specifically using multiple prompts to allow for dynamic and controlled content
changes. However, these methods primarily focus on ensuring smooth transitions
between adjacent frames, often leading to content drift and a gradual loss of
semantic coherence over longer sequences. To tackle such an issue, we propose
Synchronized Coupled Sampling (SynCoS), a novel inference framework that
synchronizes denoising paths across the entire video, ensuring long-range
consistency across both adjacent and distant frames. Our approach combines two
complementary sampling strategies: reverse and optimization-based sampling,
which ensure seamless local transitions and enforce global coherence,
respectively. However, directly alternating between these samplings misaligns
denoising trajectories, disrupting prompt guidance and introducing unintended
content changes as they operate independently. To resolve this, SynCoS
synchronizes them through a grounded timestep and a fixed baseline noise,
ensuring fully coupled sampling with aligned denoising paths. Extensive
experiments show that SynCoS significantly improves multi-event long video
generation, achieving smoother transitions and superior long-range coherence,
outperforming previous approaches both quantitatively and qualitatively.