title: Model Already Knows the Best Noise: Bayesian Active Noise Selection via
    Attention in Video Diffusion Model
summary: 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/
paper_authors._id: 6833cb9030cd9df52a117557
paper_authors.name: Kwanyoung Kim
paper_authors._id: 6833cb9030cd9df52a117558
paper_authors.name: Sanghyun Kim
paper_title: Model Already Knows the Best Noise: Bayesian Active Noise Selection via
    Attention in Video Diffusion Model
paper_summary: 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/
paper_ai_summary: ANSE enhances video diffusion models by selecting noise seeds based on model confidence, improving video quality and temporal coherence with minimal increase in inference time.
paper_ai_keywords: video diffusion models noise seeds prompt alignment external priors frequency filters inter-frame smoothing ANSE Active Noise Selection for Generation BANSA Bayesian Active Noise Selection via Attention acquisition function entropy disagreement stochastic attention samples score estimation diffusion step temporal coherence