Understanding Attention Mechanism in Video Diffusion Models

• URL: http://arxiv.org/abs/2504.12027v2
• Date: Thu, 17 Apr 2025 01:49:31 GMT
• Title: Understanding Attention Mechanism in Video Diffusion Models
• Authors: Bingyan Liu, Chengyu Wang, Tongtong Su, Huan Ten, Jun Huang, Kailing Guo, Kui Jia,
• Abstract summary: We conduct an in-depth perturbation analysis of the spatial and temporal attention blocks of T2V models.<n>Our results indicate that temporal and spatial attention maps affect not only the timing and layout of the videos but also the aesthetic quality of the synthesized videos.<n>We propose two methods to enhance video quality and enable text-guided video editing.
• Score: 41.08683408629373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-video (T2V) synthesis models, such as OpenAI's Sora, have garnered significant attention due to their ability to generate high-quality videos from a text prompt. In diffusion-based T2V models, the attention mechanism is a critical component. However, it remains unclear what intermediate features are learned and how attention blocks in T2V models affect various aspects of video synthesis, such as image quality and temporal consistency. In this paper, we conduct an in-depth perturbation analysis of the spatial and temporal attention blocks of T2V models using an information-theoretic approach. Our results indicate that temporal and spatial attention maps affect not only the timing and layout of the videos but also the complexity of spatiotemporal elements and the aesthetic quality of the synthesized videos. Notably, high-entropy attention maps are often key elements linked to superior video quality, whereas low-entropy attention maps are associated with the video's intra-frame structure. Based on our findings, we propose two novel methods to enhance video quality and enable text-guided video editing. These methods rely entirely on lightweight manipulation of the attention matrices in T2V models. The efficacy and effectiveness of our methods are further validated through experimental evaluation across multiple datasets.

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