[2505.17561] Model Already Knows the Best Noise: Bayesian Active Noise Selection via Attention in Video Diffusion Model

Computer Science > Computer Vision and Pattern Recognition arXiv:2505.17561 (cs) [Submitted on 23 May 2025 (v1), last revised 1 Mar 2026 (this version, v2)] Title:Model Already Knows the Best Noise: Bayesian Active Noise Selection via Attention in Video Diffusion Model Authors:Kwanyoung Kim, Sanghyun Kim View a PDF of the paper titled Model Already Knows the Best Noise: Bayesian Active Noise Selection via Attention in Video Diffusion Model, by Kwanyoung Kim and 1 other authors View PDF HTML (experimental) Abstract:The choice of initial noise strongly affects quality and prompt alignment in video diffusion; different seeds for the same prompt can yield drastically different results. While recent methods use externally designed priors (e.g., frequency filtering or inter-frame smoothing), they often overlook internal model signals that indicate inherently preferable seeds. To address this, we propose ANSE (Active Noise Selection for Generation), a model-aware framework that selects high-quality seeds by quantifying attention-based uncertainty. At its core is BANSA (Bayesian Active Noise Selection via Attention), an acquisition function that measures entropy disagreement across multiple stochastic attention samples to estimate model confidence and consistency. For efficient inference-time deployment, we introduce a Bernoulli-masked approximation of BANSA that estimates scores from a single diffusion step and a subset of informative attention layers. Experiments across diverse ...