Model Already Knows the Best Noise: Bayesian Active Noise Selection via
  Attention in Video Diffusion Model
Authors: name: Kwanyoung Kim, name: Sanghyun Kim

The choice of initial noise significantly affects the quality and prompt
alignment of video diffusion models, where different noise seeds for the same
prompt can lead to drastically different generations. While recent methods rely
on externally designed priors such as frequency filters or inter-frame
smoothing, they often overlook internal model signals that indicate which noise
seeds are inherently preferable. To address this, we propose ANSE (Active Noise
Selection for Generation), a model-aware framework that selects high-quality
noise 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 enables
score estimation using a single diffusion step and a subset of attention
layers. Experiments on CogVideoX-2B and 5B demonstrate that ANSE improves video
quality and temporal coherence with only an 8% and 13% increase in inference
time, respectively, providing a principled and generalizable approach to noise
selection in video diffusion. See our project page:
https://anse-project.github.io/anse-project/