Prompt Relay: Inference-Time Temporal Control for Multi-Event Video Generation
Video diffusion models have achieved remarkable progress in generating high-quality videos. However, these models struggle to represent the temporal succession of multiple events in real-world videos and lack explicit mechanisms to control when semantic concepts appear, how long they persist, and the order in which multiple events occur. Such control is especially important for movie-grade video synthesis, where coherent storytelling depends on precise timing, duration, and transitions between events. When using a single paragraph-style prompt to describe a sequence of complex events, models often exhibit semantic entanglement, where concepts intended for different moments in the video bleed into one another, resulting in poor text-video alignment. To address these limitations, we propose Prompt Relay, an inference-time, plug-and-play method to enable fine-grained temporal control in multi-event video generation, requiring no architectural modifications and no additional computational overhead. Prompt Relay introduces a penalty into the cross-attention mechanism, so that each temporal segment attends only to its assigned prompt, allowing the model to represent one semantic concept at a time and thereby improving temporal prompt alignment, reducing semantic interference, and enhancing visual quality.
69ddb5d3aea31e227c22c56c
Gordon Chen
claimed_verified
2026-04-14T08:15:19.545Z
65cf1549c1912289ca0b24cf
https://cdn-avatars.huggingface.co/v1/production/uploads/65cf1549c1912289ca0b24cf/kLCDspsG4HUjRTV6634nC.jpeg
Gordon Chen
gchen019
user
gchen019
69ddb5d3aea31e227c22c56d
Ziqi Huang
claimed_verified
2026-04-14T08:15:21.650Z
60efe7fa0d920bc7805cada5
https://cdn-avatars.huggingface.co/v1/production/uploads/60efe7fa0d920bc7805cada5/2LBrJBjSCOP5ilZIpWLHl.png
Ziqi Huang
Ziqi
user
Ziqi
69ddb5d3aea31e227c22c56e
Ziwei Liu
Prompt Relay: Inference-Time Temporal Control for Multi-Event Video Generation
Video diffusion models have achieved remarkable progress in generating high-quality videos. However, these models struggle to represent the temporal succession of multiple events in real-world videos and lack explicit mechanisms to control when semantic concepts appear, how long they persist, and the order in which multiple events occur. Such control is especially important for movie-grade video synthesis, where coherent storytelling depends on precise timing, duration, and transitions between events. When using a single paragraph-style prompt to describe a sequence of complex events, models often exhibit semantic entanglement, where concepts intended for different moments in the video bleed into one another, resulting in poor text-video alignment. To address these limitations, we propose Prompt Relay, an inference-time, plug-and-play method to enable fine-grained temporal control in multi-event video generation, requiring no architectural modifications and no additional computational overhead. Prompt Relay introduces a penalty into the cross-attention mechanism, so that each temporal segment attends only to its assigned prompt, allowing the model to represent one semantic concept at a time and thereby improving temporal prompt alignment, reducing semantic interference, and enhancing visual quality.
Video diffusion models struggle with temporal control and semantic coherence in multi-event sequences, but a new inference-time method enables fine-grained temporal control through cross-attention penalties that improve alignment and reduce semantic interference.
video diffusion models temporal succession cross-attention mechanism semantic concepts temporal control multi-event video generation text-video alignment semantic entanglement prompt relay temporal segments visual quality