Model Already Knows the Best Noise: Bayesian Active Noise Selection via
  Attention in Video Diffusion Model
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/
6833cb9030cd9df52a117557
Kwanyoung Kim
6833cb9030cd9df52a117558
Sanghyun Kim
Model Already Knows the Best Noise: Bayesian Active Noise Selection via
  Attention in Video Diffusion Model
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/
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.
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