Model Already Knows the Best Noise: Bayesian Active Noise Selection via Attention  in Video Diffusion Model
Authors: Kwanyoung Kim
Sanghyun Kim
Venue: ICLR 2026 Poster (2026)
Keywords: Video Diffusion Model
Active Noise Selection
Primary area: generative models
TL;DR: Training-free noise selection with ANSE improves video quality across diverse video diffusion backbones with minimal overhead.

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 text-to-video backbones demonstrate improved video quality and temporal coherence with marginal inference overhead, providing a principled and generalizable approach to noise selection in video diffusion.

Published as a conference paper at ICLR 2026
MODEL ALREADY KNOWS THE BEST NOISE: BAYESIAN ACTIVE NOISE SELECTION VIA ATTENTION IN VIDEO DIFFUSION MODEL
Kwanyoung Kim--1, Sanghyun Kim", Department of Al Convergence, Gwangju Institute of Science and Technology (GIST) 1 Samsung Research2
k0. kim@gi st. ac.kr, kr, sanghn. www@samsunggncom
Riada Score 0d5
Setected Seed (Oun
Jogtul Corgi flufly perky ears park, golden sunset costing warm gl scane. The camara zooms in on the Corgis expressive face, capturing its bright eyes and wide, happy grin.."
Reneom
BANCA Score D64
mojestic brown beor, with thick glistening in the doppled sunligtt begins its oscent up towering pine tree in dense forest. The bear's powerful claws grip the rough bark es climas higher, its muscles rippling with each movement.."
Figure 1:
Random Seed Ours. We propose ANSE, noise selection framework, and the BANSA Score, an uncertainty-baat based metric. By selecting initial noise seeds with lower BANSA scores, which indicate more certain noise samples, ANSE improves video generation performance.
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 (c.g., frequency filtering 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-baat based uncertainty. At its core BANSA (Bayesian Active Noise Selection via Attention), acquisition function that measures en- tropy disagreement across multiple stochastic attention samples to estimate model confidence and consistency. For efficient inference-time deployment, intro- duce a Bernoulli maskedd approximation of BANSA that estimates scores from single diffusion step and a subset of informative attention layers. Experiments across diverse text-to-video backbones demonstrate improved video quality and temporal coherence with marginal inference overhead, providing principled and generalizable approach to noise selection in video diffusion. See our project page: https:/ //anse projeer-reee..... .wt.ggit.......... io/ anse io/anse-project...
I First and corresponding author, this work done while the author Samsung Research.

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Published as a conference paper at ICLR 2026
Refinement
Active Noise Selection for Generation (ANSE)
XM
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Video modell sampling
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BANSA Siore
Goutsion notso
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Press Filler
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(a) Conventional Noise Inttialization approaches
(b) Our proposed framework, ANSE
Figure 2: Conceptual comparison of noise initialization. (a) Prior methods (Wu et 2024 Yuan et al. 2025) refine noise via frequency priors and full diffusion sampling, leading to high cost. (b) Our method instead selects noise seeds by estimating attention- based uncertainty at the first denoising step, enabling efficient, model-aware selection.
INTRODUCTION
Diffusion models have rapidly established themselves as a powerful class of generative models. demonstrating state-of-the performance across images and videos (Rombach al. 2022 Esser
et al. 2024 Xie et al. [224 Chen al. 2024b a Wang al. 2025 Yang et al. 2224 Zheng et al. 2024 Team 2025). In particular, Text-to- Video (T2V) diffusion models have received increasing attention for their ability generate temporally coherent and visually rich video sequences. To achieve this, most T2V model architectures extend Text-to-Iman (T2I) diffusion backbones by incorporating temporal modules motion-awan attention layers (Blattmann et al., [023 He et al.)
2022 Wang et al. 2023 Chen et al. 2023 2024a] Guo et al.) 2024b Wang et al.) (025). Furthermore, other works explore video generative structures, such causal autoencoders or video autoencoder-
based models. which aim to generate full video volumes rather than sequence of independent
frames (Hong al. 2022 Yang et al. 2024 HaCohen al., 2024 Kong al. 2024 Zheng et al.)
2024 Team 2025)
Beyond architectural design, another promising direction lies in improving noise initialization at inference time for T2I and T2V generation (Guo et al. [0244] Eyring et al. 2024; Chen et al.) [2024c]
Xu et al. 2025). This aligns with the growing trend of inference-time time scaling, observed not only in Large Language Models (Brown et al., 2024 Snell et al. 2024) but also in diffusion-based generation systems (Ma et al.) 2025). ). Due to the iterative nature of the diffusion process, the choice of initial noise profoundly influences video quality, temporal consistency, and prompt alignment (Ge et al.) 2023; Wu et al. 2024) Qiu et al.] Yuan et al. 2025). As illustrated in Figure [ the same prompt lead drastically different videos depending solely the noise seed, motivating the need for intelligent noise selection.
Recent approaches tackle this by introducing external noise priors. PYoCo (Ge et al. 2023) enforces inter-frame dependent patterns for coherence but requires heavy fine-tuning. Free (Quu et al.) reschedules noise across time with fusion strategy, Freelnit (Wu et al. (024) preserves low- frequency components via frequency filtering, and FreqPrior (Yuan et al. (2025) extends this with Gaussian-shaped priors and partial sampling. While effective, these methods rely on external priors and repeated full diffusion passes, while ignoring internal model signals that identify inherently preferable seeds.
To address this limitation, we propose awdel- noise selection framework, ANSE (Active Noise Selection for Generation), grounded in Bayesian uncertainty. At the core of ANSE is BANSA (Bayesian Active Noise Selection via Attention), an acquisition function that identifies noise seeds inducing confident and consistent attention behaviors under stochastic perturbations. conceptual comparison between our method and prior Trequency-based approaches is illustrated in Figure 2 highlighting the difference between external priors and model-informed uncertainty estimates.
Unlike BALD (Gal et al.) 2017), which estimates uncertainty from classification logits, BANSA measures entropy in attention maps, arguably the most informative signals in diffusion. It compares the mean of per-pass entropies with the entropy of the mean map, capturing both uncertainty and cross-par poossppass disagreement. A lower BANSA score indicates more confident, consistent attention and empirically correlates with more coherent video generation (Fig. []. To make BANSA inference-
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Published as a conference paper at ICLR 2026
friendly, approximate it with Bernoulli-maskee attention, yielding multiple stochastic attention samples from a single forward pass. We further cut cost by evaluating early denoising steps and only subset of informative layers selected via correlation analysis. Our contributions threefold:
We present ANSE, the first active noise selection framework for video diffusion, grounded in Bayesian formulation of attention-based uncertainty.
We introduce BANSA, an acquisition function that measures attention consistency under stochastic perturbations, enabling model-aware noise selection without retraining.
Our method improves video quality and temporal consistency across diverse text-to-vides backbones, with only marginal inference overhead.
PRELIMINARY
Video Diffusion Models Diffusion models (Ho et al., 2020; Song al. 2021b) have achieved strong results across generative tasks. In T2V, pixel-space diffusion is costly, so most video diffusion models adopt latent diffusion and operate compressed latent space. video autoencoder with encoder & and decoder ! reconstructs as x D(E(x)). Let Zo = Exx). The forward diffusion then progressively adds noise over time:
23 Vā,zo + VI- E N(0,D, t= i=.....,
where 0g is pre-defined variance schedule. To learn the reverse process, a denoising network eg is trained using the denoising score matching loss (Vincent 2011):
La = Ez, [|Ee(ze,
c, =El?
where is the conditioning text. Sampling starts from Gaussian noise zr ~ NO I) and uses deterministic DDIM solver (Song et al., 2021ap. Each step updates as:
Zt-1 Vā. 20( + vI a: Ea C,
where the denoised latent estimate (t( a c.tt is obtained using Tweedie's for-
vār
mula (Efron 2011: Kim (0,2022). This iterative process continues until = 1, yielding the final denoised latent Zo. which is decoded into a video via D.
Bayesian Active Learning by Disagreement (BALD) Active learning selects the most informative samples from unlabeled pool improve training. Acquisition functions commonly uncertainty- based (Houlsby et al. 2011 Gal et al. 2017 Kirsch et al. 2019 Yoo & Kweon 2019) or distribution-
based (Mac Aodha et al.) 2014 Yang al. 2015; Sener & Savarese 2017 7 Sinha al. 2019), and
some use external modules such as auxiliary predictors (Yoo & Kweon 2019 Tran et al. 2019
Kim al. 202I). While most prior work targets image classification, adapt uncertainty-biae based acquisition to text-to-video generation without additional models.
Predictive entropy widely used, but reflects data noise and does not isolate parameter uncertainty. BALD instead measures epistemic uncertainty via the mutual information between predictions y and parameters
BALD(x) H[p(y[x)] Ep(oyDo) [H|p(y | 0)]]
(1)
= where H(y) = E,, P(y) log p(y) is Shannon entropy (Shannon 1948). The first term is the entropy of the mean prediction, and the second is the average entropy across stochastic forward passes. A high BALD score means predictions are confident yet disagree, indicating high epistemic uncertainty. Since the posterior over 0 is intractable, BALD is approximated using K stochastic forward passes (e.g., Monte Carlo dropout):
K
BALD(x) H
1
(ylx)
H (1) (x/xx)
K
K
We reinterpret BALD for inference-time generative modeling. Rather than selecting samples for labeling, we apply BALD to rank noise seeds by their epistemic uncertainty. Selecting seeds with lower BALD scores results in more stable model behavior and leads to higher-quality generations.
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promptt c.
Video Diffusion Model
BANSA Scoreti
Partal NFE
Naise pool
Loyor Atlentionn
X
X
BANSA Scorel
Score min Seleci!
DANSA Score/
Noise candidate
Average
BANSA-E
BANSA-E
K Σ (2,,)) Am
BANSA-E
Entropy (z,
Entropy
(2,
Sof
Bernoull
Figure 3: Overview of our BANSA-based noise selection process. Given a text prompt c, we compute BANSA scores for multiple noise seeds {Zı,..., 2MM using Bernoulli-masked attention maps from selected layers at an early diffusion step. The seed with the lowest score, indicating confident and consistent attention, is selected for generation.
METHODS
We propose ANSE, framework for selecting high-quality noise seeds in T2V diffusion based on model uncertainty (Fig. [ ANSE centers on BANSA, an acquisition function that transfers uncertainty criteria from classification to the attention space of generative diffusion models (Sec. 3.1)
For efficient inference, we approximate BANSA via Bernoulli- masked attention sampling (Sec. 3.2)
To avoid redundancy, we select representative attention layer using correlation-base linear probing The full pipeline appears Fig.
(Sec. 3.3
3.1 BANSA: BAYESIAN ACTIVE NOISE SELECTION VIA ATTENTION
We introduce BANSA, acquisition function for selecting optimal noise seeds in T2V diffusion. Unlike classifiers with explicit predictive distributions, diffusion models do not expose such outputs, so we estimate uncertainty in the attention space where text and visual tokens align during generation. We treat attention maps stochastic predictions conditioned on the seed z, prompt c, and timestep t. BANSA measures both disagreement and confidence across multiple attention samples, providing BALD-style uncertainty criterion tailored to the generative setting.
Definition (BANSA Score). Let noise seed, e a text prompt, and t a diffusion timestep. Let Q c. t), K(z, c, t) RN xd denote the query and key matrices from denoising network eg. The attention map is computed as:
A(z, c, Stftmax (Q(z, K(z, NRN
Let A(z, c,,, (A(1) A()) denote set of K stochastic attention maps obtained via forward passes with random perturbations (e.g., Bernoulli masking). The BANSA score is defined as:
BANSA c, H
(())
M H(A
where H(A) = ΣN Σn - Aij fidence) and the consistency (agreement) of attention behavior. BANSA can be applied to various attention types (e.g., cross-, self-, temporal) and allows layer-wise interpretability.
Aij log Aij- This formulation captures both the sharpness (con-
Given a noise pool = {zi...., Zud }. we select the optimal noise seed that minimizes score:
z = arg min BANSA(Z, Z
(5)
desirable property of BANSA is that its score becomes zero when all attention samples are identical, reflecting complete agreement and certainty. We formalize this follows:
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Published as a conference paper at ICLR 2026
Algorithm 1: Active Noise Selection with BANSA Score for Video Generation
Input:
Text prompt e, noise pool Z = [Zi....,22. 2M}. timestep t, cutoff layer d' foreach z; E Z do
Compute BANSA score: BANSA-Eçaf (Zi, e, 1) via Eq. 7
Select optimal noise: z" arg ming; BANSA-Eça (Zi, C,t);
return Generate video: i = SampleVideo ct,);;
A(K)) be a set of row- Proposition (BANSA Zero Condition). Let c(z,c,t) (A.))..,,A(
stochastic attention maps. Then:
BANSA(Z, = A =...=
The proof is deferred to the Appendix B
This result implies that minimizing the BANSA score encourages attention that is both confident and consistent under stochastic perturbations. Empirically, low BANSA correlates with better prompt alignment, temporal coherence, and visual fidelity. Thus, BANSA provides principled, model-aware criterion for noise selection T2V.
3.2 STOCHASTIC APPROXIMATION OF BANSA VIA BERNOULLI-MASKEE ATTENTION
While BANSA is principled for noise selection, computing it with K independent passes per seed costly. We instead draw stochastic attention samples from a single pass via Bernoullimmasee attention. Rather than running K passes, inject stochasticity directly into the attention scores. For each k= 1......
......,,, K, sample a binary mask my E {0,1) NxN i.i.d. from Bernoullipp) and compute masked attention map, A (z, (z,c, c, == A(z, e, @ my, O denotes element- multiplication and rows re-normalized. Using these K such samples, we define the approximate BANSA:
K
B(2,A-E(Z,c,t)- =
A (2,c,)) Σ k=1
Σ H(A (2,
(6)
K
K
By concavity of entropy, BANSA-E 2 O. Although approximate, it efficiently captures attention-level
epistemic uncertainty and serves as a practical surrogate for model-aware noise selection. As shown Table 4 experimental validation confirms that this method is sufficient for selecting optimal noisy samples from the model's perspective.
3.3 LAYER SELECTION VIA CUMULATIVE BANSA CORRELATION
BANSA can be computed at any layer, but behavior differs across depth. Using all layers is costly, so we truncate at the smallest depth d' where the average BANSA up to d remains highly correlated with the full-layer score. Given a noise seed z, E 2 {zı,.... ......ZM } and attention layers, we compute par-layer scores BANSA-E() (z,, C, t) and define the cumulative average up to layer d as:
Z
Σ
1
BANSA-E,d(Z,c. c,t) d
BANSA-E (z,, c,t.
1=1
To find dx, we compute the Pearson correlation Pearsso 1895) between the partial average BANSA-Eed and the full-layer average BANSA-EŞL, and choose the smallest such that Com BANSA-Eçd, BANSA-EgL) 2 We validate this procedure using 100 prompts and 10 noise seeds across all evaluated models as detailed Appendix D As shown in Figure 7 the correla- tion quickly stabilizes a moderate depth, allowing us select d without using all layers. We then define the BANSA score as BANSÁ-E<ar to guide noise selection, as summarized in Algorithm With d' fixed per model, it adds runtime cost, and BANSA-Eed (truncated layer) consistently matches the full-layer score and robustly preserves generation quality across models, confirming its reliability as a practical surrogate shown Appendix D and B (see detail).
We use "Bayesian" in the sense of epistemic uncertainty estimation, following BALD Houlshy al. rather than full posterior derivation.
201
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4 ANALYSIS OF BANSA-SELECTER NOISE
In this section, we aim to investigate the property of noise seeds selected by BANSA, and consist of three experiments which declare framework and interpret the noise seeds. Our framework does not seek universal "golden" seed, but selects the most suitable one for each prompt by minimizing uncertainty. The effectiveness of noise seed therefore prompt-dependent: seed that improves one prompt may degrade another.
Experiment 1:
Cross-Prompt Behavior. To
Table I:
Cross-prompt evaluation of a high- BANSA seed from A.
demonstrate this prompt-dependent behavior, We select a high-BANSA (worst-pefforming seed from prompt A and applied to prompts B and C (Table D and measures Subject and Background Consistency. The seed degraded quality for prompt C but improved performance for prompt B, con- firming that noise effectiveness depends on prompt context rather than intrinsic seed quality.
From
To
Subject.
BackGround
A
B
118491-0.887
0,9157 +0,9538 0.9368 +0.9169
A
C
0.7683 0.77213
While Experiment shows how noise effectiveness varies across prompts, the following two experi- ments examine whether similar patterns also emerge within a fixed prompt.
Experiment 2: Intra-Prompe Attention Consis- tency. To examine whether BANSA uncertainty re- lates to attention stability, we generate five samples per prompt (total 100 prompts), selected the highest- and lowest-scoring seeds, and computed pairwise Eu- clidean distances between their attention maps (Ta- ble 2) Low-BANSA seeds showed smaller intra-group distances, indicating more stable and consis- tent patterns, while high-BANSA seeds exhibited higher variability.
Table 2: Pairwise distances within attention maps.
Type
High (intra)
Low (intra)
Cross
Euclid. 1.635
1.567
1.735
Experiment 3: Latent Trajectory and Expressiveness. Table 3: Latent stability and visual dy- Beyond attention maps, we further investigate latent-space dynamics. Across 100 prompts with five repetitions each, we compare high- and low- -AN-AANA seeds over all denois- ing steps (Table 3). We measured two metrics: (1) Latent Trajectory Variation, obtained by applying Butterworth low-pass filter to isolate low-frequency structural compo-
namics.
Group
Traj. Var. 4
Var. 1
High BANSA
52.34
0.041
Low BANSA
51.07
0.053
nents (Wu et al. 2024 Yuan et al. 2025) and computing their temporal variation, where lower values indicate smoother and more stable trajectories; and (2) fntra-frame Variance, the average spatial variance within frames, where higher values indicate richer dynamic expressiveness. Low-BANSA seeds showed both lower trajectory variation and higher intra-frame variance, combining temporal stability with more expressive video generation.
Takeaway and Relation to Prior Work. The three findings indicate that BANSA identifies seeds with lower uncertainty, leading to (1) prompt-specific improvements, (2) more stable attention, and (3) smoother yet more expressive latent dynamics. This is consistent with FreeInit Wu et al. (2024) and FreeQPrior (Yuan et al.) (025), which emphasize stable latent initialization and low- wwwfrreuency structure. BANSA complements these insights by offering an mncertainty-driver selector that explains why such seeds generalize within a prompt while remaining prompt-dependent across prompts.
5 EXPERIMENTS
Experimental Setting. We evaluate ANSE on diverse T2V diffusion backbones-AAnieen AnimateDiff (Guo al.] 2024b), Cog - (Yang et al.) 2024). Wan2.1 (Team) 2025), and Hunyuan- Video (Kong et al., 2024). To rigorously evaluate our method, we follow each model's official sampling protocol; for resolution, Wan2.1 evaluated at 480p and Hunyuan Video at 360p due to resource limits. Results for noise-prior refinement baselines (FreqPrior (Yuan et al. 2025 ) are reported only on AnimateDiff, as they lack official support on the others and incur ~ 3 inference time. ANSE is orthogonal and can be combined with these priors. Unless noted, use noise pool of M-10 with K-10 stochastic passes per seed, and cemoouli-masker attention with p=0.2. All experiments run NVIDIA H100 GPUs. Further details are in the Appendix C

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Published as a conference paper at ICLR 2026
Table 4: Quantitative results on VBench using Animate Cog -2 and -5B.
Backbone Model
Method
Quality Score
Semantic Score
Total Score
Inference Time
Vanilla
80.22
69.03
77.98
28.23s
Diff
Guo et al. 2024b
81.66
71.09
79.33
31.335(-10.969))
Freqprior
81.22
70.45
79.07
(+
Ours
82.23
73,23
80,43
61. (1.12%
CogVideoX-28
Vanilla
82.08
76.83
81.03
247.8s
Yang 2024
Ours
82.56
78.06
81.66
269. 35; 8.07%)
Cog VideoX-SB Yang al. 2024
Vanilla
82.53
77.50
81.52
667.3s
82.70
78.10
81.71
13..1)
Table 5: Quantitative results on quality score metries for Hunyuan Video and Wan2.1
Rackbone Midell
Method
Subject Considennyy
Rackground Consistrnry
Medien Smoothoes
Ansteetic Quality
Imaging Quality
Temporal Flickoring
Inderence Time
Vanilla
0,9830
0.6137
0UME1
5024
Ours
0,9612
0,9661
0,9858
0,6151
0,9938
59.36
Ware I
Vanilla
0,9562
0.9056
0.9850
0.6276
0.6137
0,9943
43.8
Team 2015
Oars
0.9612
0.9661
0.9658
0.6268
0.6151
0,5956
50.Ta, mil
Evaluation Metric. We evaluate with VBench (Huang al. 2024). which reports a quality score and semantic score, combined into a total score on a (0-10) scale. For AnimateDiff and Cog Videox- 2B/5B, we report both quality and semantic scores. For Hunyuan Video and Wan2.1, evaluation to the six quality dimensions due to computational constraints, while noting that semantic evaluation can also be applied in principle. All reported results are obtained by repeating each evaluation dimension five times and averaging the scores.
restrict
Quantitative Comparison. As shown in Table 4 ANSE consistently improves performance across diverse T2V backbones. On AnimateDiff, ANSE outperforms the vanilla baseline and further demonstrates clear advantages over voise-initialization methods such as FreqPrior (Yuan et al. 2025).
Notably, ANSE is also fully compatible with FreqPrior, achieving even higher scores when combined. Table also also reports results on CogVideoX-2B and -5B, which adopt the more advanced MMDIT ar- chitecture. ANSE improves quality, semantic, and total VBench scores on both scales, demonstrating robust generalization across architectures and model sizes.
Table 5 presents additional results on recent state-of-the-art models, Hunyuan Video and 1an2.11 ANSE achieves con- sistent improvements across quality metrics, underscoring its plug-and-play applicability to a wide spectrum video diffusion models from U-Net MMDiT, and from mid- scale to the latest state-of oftte-----h-aa-- -tthh-rrt systems. The statistical signicanse analysis of these are provied in Appendix [
Table 6: Comparison with FVMD met- rics on MSRVTT dataset.
Hackhone
Methods
FVMD 1
Model
HonyuanViloo
Vanilla
17641.19
Kong et 2024 2024
+ Ours
16491.68
Vanilla
16495.59
Team
2025
Ours
14306.19
We further evaluate motion quality using the Fréchet Video Motion Distance (FVMD) (Liu et al. 2024) on MSR-VTT (Xu al. 2016), shown in Table 6 We sample 200 text prompts from the test split and generate videos with ANSE, using the corresponding real videos as the reference distribution. ANSE consistently obtains lower FVMD scores, indicating improved motion fidelity and reinforcing the motion-related gains observed in VBench.
Qualitative Comparison. Figure 4 presents representative qualitative comparisons on Cog VideoX- 2B, Cog VideeoX-S5B and Wan2.1 with and without ANSE. Our approach consistently enhances semantic fidelity, motion realism, and visual clarity across diverse prompts. For example, in separate prompts such as "exploding" and "descends gracefully" ANSE captures critical semantic transi- tions-generating visible explosions in the former and preserving smooth temporal continuity the latter. In other prompts like "koala playing the piano" and "tasting beer", it produces anatomically coherent bodies with natural, expressive motion. These examples highlight ANSE's s ability to im- prove ppatial-temport fidelity while generalizing effectively to large-scale video diffusion models. Additional qualitative results, including other backbones, are provided in Appendix L
Computational Cost. As shown in Table and Table 5] ANSE introduces only minimal increase in inference time while delivering consistent quality gains. On AnimateDiff, inference time increases by just + 10.98% compared to more than + +105% when combined with FreqPrior and over +200%
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Coghndeck Oura
MAkSA Score 638
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Figure 4: Qualitative comparison with and without ANSE. (CogvideoX-2DB 5B and Wan2. I). With ANSE, videos exhibit improved visual quality, better text alignment, and smoother motion transitions compared to the baseline. White numbers show BANSA scores for the same prompt.
Freelnit. On Cog VideoX models, the overhead is similarly small: +8% on the 2B variant and +13% the 5B variant. For more recent architectures, ANSE adds only + +14% on Hunyuan Video and +15% on Wan2.1. The overhead comes only from seed evaluation and leaves the sampling process and memory usage unchanged. In contrast, Freelnit and FreqPrior require multiple full passes, causing large slowdowns. ANSE improves quality across diverse backbones while keeping inference overhead below +15%, making practical plug-and-plas solution for video diffusion.
ABLATION STUDY
Comparison of Acquisition Functions. Using CogVideoX-2BB we compare BANSA with random sampling, entropy-based selection, and two variants: BANSA (B) with Bernoulli masking and BANSA (D) with dropout-based stochasticity (Table 7 All methods improve over the baseline, but BANSA (B) consistently achieves the best scores across quality, semantic, and total metrics. This suggests that Bernoulli masking better captures attention-level uncertainty than dropout, underscoring the importance of modeling uncertainty in line with the model's 's structure.
Effect of Ensemble Size K. We investigate how the number stochastic forward passes K influences subject and background consistency (Table Both metrics improve K increases from 1 to 10, indicating that larger ensembles provide more reliable noise evaluation. Performance saturates the default in all experiments.
K 10. which
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Table 7: Comparison of different acquisition functions for noise selection.
Table 8: Effect of varying the number of K.
K
Subject Consistency
Background Consistnecy
Method
Quality Score
Semantic Score
Total Score
1
0.9618
0.9788
Random
$2.08
76.83
81.03
0,9623
0,9793
Entropy
82 23
76.23
$1.13
5
0.9632
0,9798
BANSA
82.43
76.91
81.33
7
0,9638
0,9802
BANSA
$2.56
81.66
10
0.9641
0.9811
Table Quantitative comparison of reversed BANSA scoring on CogVideoX This presents results when selecting samples using the highest BANSA scores, compared to the default selection.
Methed
Subyect Consistoncy
Bockground Consistency
Flickering
Motion
Aesthetic Quility
Quality
Dynamic Degrec
Quality Score
0.9616
0.6195
$2.08
Ours (reverse)
0.9625
06181 0.6202
Chirs
0,9641
0,9811
0.9725
0.9746
RU6276
IJ6511
82.56
Table 10: Component analysis of Bernoulli masking parameters Wan2.1 1.3B.
Backbone Model
Berncali
Subject Consistency
Motion Smonthness
Aesthetic Quality
Imaging Quality
Overall
Consistencyy
0.0
0.9254
0.9751
0.6464
0.6459
0.2731
0.2
0,9310
0.978
0.6589
0.6516
0.2763
Win2.1 1,3
0.5
0.9308
0.9779
0.6543
0.6533
0.2727
0.9302
0.2726
1.0
0,9232
0.9753
0.6451
0.6508
0.2721
Table 11: Quantitative evaluation on the IPOC-2B after post-training and Wan2.1 14B model.
Bacibhone Model
Methal
Subject Consistency
Background Comsistency
Temporal Flickering
Metion Smowlhness
Aestbetic Quality
Imaging Quality
Dynamic
IPOC-28
Vanilla
0,9375
0,9535
0U918
0.9741
0.6544
0.664
0,7527
Ours
0,9281
0,9534
0.9921
IL9745
0.6661
0.6644
0,7721
War2.1 148
Vanilla
0.3290
0.9587
0.9931
0.9759
0.6522
0.6582
0.5916
Ours
0.0302
0.9596
0.9938
0.9772
0.6601
0.6529
0.6277
Effect of Noise Pool Size M. We analyze the effect noise pool size M, which controls the diversity of candidate seeds in BANSA. Larger M improves the chance of finding high-quality seeds but increases inference cost. As shown in Figure & (Appendix), performance saturates around M = 10 for CogVideoX22B and -5B, which adopt the default for each model.
Reversing the BANSA Criterion. To further validate BANSA, we conduct a control experiment where the noise seed with the highest BANSA score is selected -ie., choosing the seed associated with the greatest model uncertainty. As shown in Table / this reversal results in degradation of quality-related metrics, confirming that lower BANSA scores are predictive of perceptually stronger generations and supporting the validity of our selection strategy.
Effect of Bernoulli masking probability p. We conduct a sensitivity analysis on the Bernoulli masking probability p using the Wanx 2.1 (1.3B) model. As shown in Table T0, both disabling masking (p = 0) and fully random masking (p = 1) degrade performance, indicating that the masking probability has meaningful impact on the results. The method remains stable within the intermediate range E [0.2, 0.7]. where overall performance varies only slightly. Among these configure oonigurattions, p 0.2 delivers the best performance, which supports our choice of the default setting.
Evaluation strong video backbones and post-RL models. We further evaluate our method on stronger settings, including the large-capacity Wan2.1 14B backbone and the post-trained IPOC-2B model. IPOC-2B is refined with DPO-related preference alignment method (Yang al.) 2025). making it a competitive post-RL baseline. As shown in Table our method consistently improves the vanilla models across most VBench dimensions, demonstrating generalization larger architectures and robustness under preference-aligeee post-RL training.
User Study, We conducted a human preference study in which evaluators compared paired videos generated with and without method along two criteria: overall quality and video alignment. As shown in Fig. (a), our method is consistently preferred on both aspects, indicating improvements in perceptual quality and semantic alignment. The details are provided in Appendix K

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Published as a conference paper at ICLR 2026
Gal
Image Qualin
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Corraation:
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Concees- can
Copicss 50
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Prompn Nigginntt
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Figure 5: (a) User study showing consistent human preference for our method in overall quality and text-video alignment. (b) Correlation analysis where BANSA scores exhibit strong negative trends with key VBench metrics, indicating that lower scores correspond to higher-quality generations.
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Figure 6: Failure case and limitation of our method. Although the BANSA score indicates low uncertainty, the resulting video still contains unnatural content. This represent a limitation of ours: we select optimal seeds but do not alter the generation process itself.
Analysis on correlation with actual quality. We examined how BANSA scores relate to quality by sampling diverse prompts and noise inputs and visualizing their score score-quality trends in Fig. (]... Pearson correlations show strong negative relationships with Subject Consistency (-0.6672), Back- ground Consistency (-0.7492), and Motion Smoothness (-0.6769), indicating that lower BANSA scores reliably associate with higher-quality and more stable generations.
DISCUSSION AND LIMITATIONS
Our method focuses on noise-seed selection based on model uncertainty, which introduces several limitations. As shown in Figure even seeds with low BANSA scores may still produce unnatural results because ANSE does not modify the subsequent sampling process. Moreover, BANSA captures uncertainty the attention level, which does not fully account for semantic or aesthetic quality, Although evaluating multiple candidates with stronger quality metrics could reduce this gap, such an approach is computationally prohibitive. A promising direction for future work is to integrate ANSE with post-traiee tes-training refinement methods such as Self-Forcing (SF) (Huang et al.) 2025) that directly enhance sample quality. Notably, ANSE already works effectively with post-Rl refinement methods like IPoC, and extending this compatibility to SF represents a natural next step. Such combinations may yield complementary gains in both quality and robustness beyond seed selection alone.
CONCLUSION
We present ANSE, a framework for active noise selection in video diffusion models. At its core is BANSA, an acquisition function that leverages attention-derived uncertainty to identify noise seeds yielding confident, consistent attention and thus higher-quality generations. BANSA adapts the BALD principle the generative setting by operating in attention space, with efficient deployment enabled through Bernoulli-mann -maked attention and lightweight layer selection. Experiments across multiple T2V backbones show that ANSE improves video quality and prompt alignment with minimal inference overhead. This introduces an inference-time scaling paradigm, enhancing generation not by altering the model or sampling steps, but through informed seed selection.
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ACKNOWLEDGEMENT
This work was partially supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (Artificial Intelligence Graduate School Program (GIST)) (No. 2019-0-01842).
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A SUPPLEMENTARY SECTION
In this supplementary document, we present the following:
Proof of Proposition 1 from the main paper regarding the BANSA Zero condition in Section
Implementation details of ANSE and evaluation metrics in Section
Further explanation of layer selection through cumulative BANSA correlation in Section D
Additional ablation studies, including full-layer BANSA score analysis (Section temporal scope effects (Section F). and attention-space effects (Section
Additional analysis, including effect of CFG scale (Section equivalent computation
budget (Section J statistical validation (Section]). J. and user study protocol (Section
Additional qualitative results demonstrating the impact of BANSA Score in Section
B PROOF PROPOSITION 1
Proposition (BANSA Zero Condition). Let A(z,c,t) -.,,),..., be a set of row- stochastic attention maps. Then:
CANSA(Z,c,t)=2 =0 + A
(k))
Proof. BANSA is defined the difference between the average entropy and the entropy of the average:
K (z,
BANSA(Z.c,t) c, =
Σ
( (2,
c.t (2,c,))
H(A (z,
K
k=1
Since the Shannon entropy H(-)) is strictly concave over the probability simplex. Therefore, by Jensen's 's inequality:
K
H
A
(R)
H(A
K k=1
K 1=1
with equality if and only if (11) maps are identical.
AK)) Thus, BANSA(Z,c, 0 and only if all attention
Remark 1. (Interpretation) This result confirms that the BANSA score quantifies disagreement among sampled atlention maps. A BANSA score of zero occurs only when all stochastic attention realizations collapse to single deterministic map-iee. the model exhibits no epistemic uncertainty in its attention distribution. Higher BANSA values indicate greater variation across samples, and thus, higher uncertainty. In this sense, BANSA acts as a Maasen-Shannon-tpe divergence over attention maps, capturing their dispersion under stochastic masking.
C FURTHER DETAILS ON EVALUATION METRICS AND IMPLEMENTATION
To evaluate performance on Vbench, we use the Vbench-long version, where prompts are augmented using GPT-4o across all evaluation dimensions. This version is specifically designed for assessing videos longer than 4 seconds.
Evaluation Metries
We rigorously evaluate our generated videos following the official evaluation protocol. The Quality
Score is weighted average of the following aspects: subject consistency, background consistency, temporal flickering, motion smoothness, aesthetic quality, imaging quality, and dynamic degree.
The Semantic Score is weighted average of the following semantic dimensions: object class, multiple objects, human action, color, spatial relationship. scene, appearance style, temporal style, and overall consistency.
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CogVideoX-28
CogVideox-5B
CogVideoX-26
CogVideoX-5B
$1.5
0.8
03
52.3
81.00
BANBA
BANSA
Boselime
01:00
Baselime
34-
0.2
Threshold (0.7)
92-
Thresho
13.2
01.53
Layer
Layer Index
Nolse Pool Size
Noise Pool Size
Correlation analysis between cu- Figure 8: Ablation study on noise pool size M. mulative BANSA score and full-layer scores. We evaluate total scores across three text-to-videe video The 0.7 threshold is reached around layer 14 for diffusion models with varying M, and select suit- Cog VideoX-25 and layer 19 for Cog VideoX-5B. able values based on computational cost.
Figure 7:
The Total Score is then computed weighted combination of the Quality Score and Semantic Score:
Wi Quality Score
W2
Total Score
Semantic Score
+ W2
Wi +
where w1 4 and W2 1, following the default setting in the official implementation.
Implementation We compute our BANSA score using the BALD-style formulation, which yields non-negative values. For clearer visualization, we normalize the BANSA scores from their original range (minimum: 0.45, maximum: 0.60) to the [0. I] interval. This normalization is used solely for visual clarity figures and plots, and does not affect the noise selection process, which operates on the raw BANSA scores. All models are evaluated using their default inference configurations: AnimateDiff (50 steps), Cog Video-X (50 steps), Wan2.1 (50 steps), and Hunyuan Video (50 steps).
D FURTHER DETAIL OF BANSA LAYER-WISE CORRELATION ANALYSIS
We evenly sampled 100 prompts from the four official VBench categories: Subject Consistency, Overall Consistency, Temporal Flickering, and Scene. Each category contains 25 prompts, selected to ensure diversity in motion, structure, and semantics.
Prompt construction.
Below representative examples:
Subject Consistency (c.g., "A young man with long, flowing hair sits on rustic wooden stool in a cozy room, strumming an acoustic guitar...")
Overall Consistency (e.g.. "A mesmerizing splash of turquoise water erupts extreme slow motion, each droplet suspended in mid-air...")
Temporal Flickering (e.g.. "A cozy restaurant with flickering candles and soft music. Patrons dine peacefully as snow falls outside...")
Scene (e.g., "A university campus transitions from lively student life golden sunset behind the clock tower...")
Prompt sampling was stratified to ensure coverage of diverse visual and temporal patterns. The full list of prompts will be made publicly available upon code release.
BANSA score computation and correlation analysis. For each prompt, we generated 10 videos using different random noise seeds and computed BANSA scores each attention layer. This yielded one full-layer BANSA score and set of layer-wise scores per seed. To obtain stable estimates, averaged the per-layer and full-layer BANSA scores across the 10 seeds, reducing noise-specific variance and capturing consistent uncertainty patterns.
We then computed Pearson correlations between the cumulative BANSA scores (summed from layer 1 to d) and the official quality scores. The optimal depth d' was defined as the smallest at which the correlation exceeded 0.7, a widely used threshold indicating a strong positive relationship. As shown in Figure 7 this point reached at d' 14 for Cog VideoX-2B and d' 19 for Cog VideoX-5B.
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For AnimateDiff, the correlation crossed the threshold earlier at d' 10, while for Hunyuan Video and Wanx 2.1 it consistently appeared around d' = 20. These model-specific depths were applied throughout all experiments. Such consistency across architectures empirically supports our choice of the 0.7 threshold and ensures that the correlation analysis reflects generalizable, noise-agnostic patterns of baaed uncertainty.
Table 12: Comparison between full-layer and truncated BANSA score.
Backbone Model
Method
Comsistency
Subject
Background Consistency
Temporal Flickering
Motion Smoothmss
Aesthetic Quality
Imaging Quality
Dynamic Degree
Quality Soore
Inference
Time L
CogvideoX-28
Pull-layer
0.9639
0.9810
0,9801
0:9743
0.6198
0.6244
0.6516
82.58
903.7
Truncated
0.9641
0
0.9755
0.9746
0.6202
0.6276
0.6511
82.56
269.3
CogvidooX-BB
Pull-laer
Truncated
0.9658
0.9660
0.96.3
0.9630
0.9861
0.9863
0
0.9708
0.6179
0.6168
0.6290
0.6290
0.6979
0.6918
82.71
82:70
754.3
810.3
E EFFECTIVENESS OF TRUNCATED BANSA SCORE
To reduce the computational overhead of BANSA evaluation, we adopt a truncated score that aggregates attention uncertainty only up fixed depth d', rather than summing over all layers. To evaluate the effectiveness of this approximation, we compared the final generation quality when selecting noise seeds using either the full-layer or truncated BANSA scores.
As shown in Table 12 both approaches yield highly similar results across all seven dimensions of the VBench evaluation protocol (subject consistency, background consistency, aesthetic quality, imaging quality, motion smoothness, dynamic degree, and temporal flickering). Importantly, the overall quality scores are preserved despite the substantial reduction in attention layers used.
This demonstrates that truncated BANSA is sufficient to capture the key uncertainty signals for reliable noise selection while reducing inference time. The strong alignment in quality stems from the fact that our method relies relative ranking rather than absolute values, allowing for efficient yet robust selection with significantly lower computational cost, We attribute this effectiveness to the fact that most informative attention behaviors emerge early in the denoising process, allowing accurate uncertainty estimation without full-layer computation.
Table 13: Effect of temporal scope in BANSA score on generation quality.
Subject Consisteney
Temporal Flickering f
Motion Smoothness 1
Aesthetic Quality
Imaging Quality
Dynamic Degree
Inference
BANSA Scope
Time 4
1-step
0.9639
0.9801
0.9743
0.6198
0.6244
0.6516
1
25-step
0.9651
0.9798
0.9746
0.6202
0.6271
0.6511
x 25
50-step avg
0.9652
0.9799
0.9751
0.6203
0.6276
0.6514
x 50
F EFFECT OF TEMPORAL SCOPE IN BANSA SCORE
While our method computes the BANSA score only at the first denoising step to minimize cost, is natural to ask whether incorporating more timesteps improves its predictive power for noise selection. To investigate this, we compute the average BANSA score across the first 1, 25, and 50 denoising steps and compare their effectiveness in predicting video quality.
Table 13 reports the VBench scores for subject consistency, aesthetic quality, imaging quality, motion smoothness, dynamic degree, and temporal flickering when using BANSA computed over different temporal scopes. Although using more timesteps results slightly better quality, the gains are marginal. This indicates that most of the predictive signal for noise quality is embedded early in the generation trajectory.
More importantly, since BANSA is used solely to assess the uncertainty of the initial noise seed-not not to track full-step generation behavior- coavior-ouur-oo our 1-step computation is sufficient to capture the core uncertainty signal. In contrast, computing BANSA over all steps requires running multiple attention forward passes across the full trajectory, resulting in substantial computational overhead that limits its practicality for real-world applications.
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Table 14: Quantitative results VBench using AnimateDift, CogVideoX-2B and -5B.
Backhone Model
Method
Quality Score
Semantic Score
Total Score
Vanilla
80.22
69.03
77.98
AnimateDiff
Guo et al. 2024b
+ Ours (Self-Attenton)
81.68
70.72
79.19
Ours (Cross-Attention)
81.66
71.09
79.33
G EFFECT OF ATTENTION SPACE IN BANSA SCORE
To further examine whether BANSA scores capture quality- or semantic-related uncertainty, we conducted additional experiments with the AnimateDiff backbone, which separates self- and cross- attention layers. Unlike Video and Wanx, which adopt an MMDiT architecture where multimodal attention entangles both components, DnimateDiff provides a clearer lens for disentangling semantic effects. As shown in Table [4 applying BANSA to self-attention primarily improved perceptual quality scores, whereas applying it to cross-attention enhanced semantic alignment. This indicates that uncertainty over noise manifests differently depending the attention type, supporting the interpretation that BANSA reflects both quality- and semantics- variations. In contrast, MMDiT-based models such Cog Video and Wanx integrate these dimensions within their unified attention structure, making them more suitable for overall noise selection in the main paper's experiments.
Table 15: Ablation study on the interaction between higher CFG scale and ours on Wan2.1(1.3B).
Backbone Model
CFG Scale
Subject Consistency f
Motion Smoothness t
Aesthetic Quality
Imaging Quality
Method
Vanilla
5.0
0.9285
0.9748
0.6465
0.6519
Wanx 2.1 1.3B
Ours
5.0
0.9310
0.9786
0.6559
0.6516
Vanilla
7.5
0.9266
0.9740
0.6447
0.6357
Ours
7.5
0.9276
0.9755
0,6519
0.6409
H EFFECT OF CFG SCALE ON OUR METHOD
Classifier-fres free guidance (CFG) modifies the conditional conditionalluuccndiiionn interpolation only at the final stage inference, whereas our method selects noise seeds at initialization based on attention-level uncertainty. Since they operate at different points in the pipeline, their effects are largely independent: CFG adjusts the extrapolation strength, while our method chooses seeds that lead to more stable carly-stage behavior.
To assess potential interactions, we increased the CFG scale from the default value of 5,0 to 7.5 on the Wanx 2.1 (1.3B) model, shown in Table 15 Higher CFG values generally degrade performance by pushing sampling beyond the model's calibrated range. Nevertheless, our method consistently outperforms the vanilla baseline under both settings, indicating that its benefits remain stable across different CFG scales.
Table 16: Ablation study under equivalent computation budget. Increasing the baseline sampling steps does not consistently improve performance, whereas our method achieves better results with fewer effective steps.
Backbone Model
Subject Consistency 1
Motion Smoothness
Aesthetic Quality t
Imaging Quality f
Method
Steps
Vanilla
50
0.9285
0.9748
0.6465
0.6519
Wan2.1 1.3B
Vanilla
60
0.9274
0.9745
0.6476
0.6492
Ours
< 60
0.9310
0.9786
0.6559
0.6516
18

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Published as a conference paper at ICLR 2026
I EFFECT OF EQUIVALENT COMPUTATION BUDGET
Our method evaluates M 10 noise candidates, but the actual computation is lower than 50 10 steps because many candidates terminate early during partial denoising. To assess whether the baseline could benefit from additional computation, we increased its sampling steps from 50 to 60. As shown in Table [6. this results in only marginal changes and even degrades some metrics, likely because the model is not calibrated for longer sampling schedules.
In contrast, our method consistently improves performance while requiring fewer effective steps than the 60-step baseline, suggesting that the gains stem from selecting more stable noise initializations rather than from additional compute. This highlights the efficiency advantage of our approach under equivalent or lower computation budgets.
Table 17: Comparison of VBench Quality Scores With and Without BANSA. Scores are reported as mean (95% confidence interval, CI: upper).
Mustoees
Maki
Menioal
Comoicky HCU
Hedigraadl
racknnn
Qudar 14D
Apdies
Quala: T4L1) BKONT OVATeR kame
Degse ILD
Dywae:
Hockee
coiuus seas conday
0.390 (LaeEs 0.200 -..99)
0J066 235653 (3867) (UN63 (1964)
0J963 (8:835 (0989)
(D0mBe 407188
0.5148 0UMB9 (0357)
4.120
Table 18: Comparison of VBench Semantic Scores With and Without BANSA. Scores are reported as mean (95% confidence interval, CI: lower upper).
Eigg
p15011 1aoo (eati 83033
Table 19: Comparison of VBench Quality Scores for Hunyuan Video and 1an2. Scores are reported as mean (95% confidence interval, CI: lower upper).
Backbose
Model
Mehho
Coniionncy (CD)
Backspoard Coovectincy
Smoothere:
Quality (CT)
Quality (C1)
Flikkringg
Huryua
Vasillo
0.5522 10.551 019533)
U1N65 9645 0:5667)
0:5550 63 9838
0.5802)
0.676
18
0.6354)
0.633 ILSA5 06815)
0,501 10-5900 9440)
Video
Oam
6.9112 (8-963 0.961)
0.9681
(0.9559 0.9672)
0,9888 089847--
9.999)
0.636
(0.69)--0 0.645)
0,6511 nuuns- 6.127)
6,9930 (0.9919 0.9957)
Winz.1
Veill
Oas
0,9012 (8.9611 - 0.963)
8.9%6 (88955 - 09971)
0.9663
0196666 9614 (.900))
(0.9649 - 0.9673)
0.489) 19
0,9651 (8 (8.9866-.
0.997))
9991)
IL6276
0.6268
(8,5211
(0.6199 0.636)
0.61 (0 06811)
0.6151 (0,0733 0.629)
015907 01-9654 - 99655)
0,9956 (8.9935 - 0.9677)
J STATISTICAL VALIDATION OF RESULTS
Our VBench evaluation uses 4,750 videos (five samples per prompt) across 17 fine-grained dimen- sions, where even small absolute gains are meaningful. To assess statistical significance, we computed 95% confidence intervals using 10 independent runs, each based on random subsets of 100 videos. As shown in Tables 17 18, and[1] our method consistently improves or maintains performance across quality-, semantic-, and motion-related metrics without exhibiting bias toward any particular subset. These statistical results provide strong evidence of the robustness and reliability of our approach.
K
USER STUDY PROTOCOL
As presented in Fig. F (a), we conduct a human evaluation study to complement automated metrics and directly assess whether ANSE improves video quality and prompt coherence. We evaluate four representative video diffusion models Cog Viogeed 2BB VideoX-5B,, Wan 2.1, and Hunyuan- Video by comparing their outputs with and without ANSE. To construct fair evaluation protocol, we randomly sample 30 prompts from the Subject Consistency and Overall Consistency dimensions of the VBench benchmark. For each prompt, we generate paired videos using identical noise seeds for both the baseline and the ANSE-equipped models.
We recruit 12 independent human evaluators, all of whom remain fully blind to the models and are restricted to participating only once. Each evaluator is shown two videos side-by-side (baseline vs. ANSE) and responds two questions:
1. Video Quality: "Which video appears more stable and visually pleasing?"
2. Prompt Alignment: "Which video better represents the given text prompt?"
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Published as a conference paper at ICLR 2026
The order of prompts and the arrangement of video pairs are fully randomized. Across all backbones and prompts, models equipped with ANSE are consistently preferred terms of both video quality and prompt alignment, demonstrating the practical effectiveness and robustness of ANSE.
L ADDITIONAL QUALITATIVE COMPARISON
More qualitative results. Figures 10 and 13 present additional examples generated using our noise selection framework including diverse backhone such as, AnimateDiff, Cog VideoX2B and 5B, Hunyang Video, and 1an2.1. Across diverse prompts, the selected seeds yield improved spatial detail, aesthetic quality, and semantic alignment, further validating the robustness of our approach. These examples complement our quantitative findings by illustrating the visual impact of BANSA-basee noise selection.
Effect of BANSA score on generation quality. Figures 14 provide qualitative comparison of outputs generated using three types of noise seeds: randomly sampled seed, the seed with the highest BANSA score (lowest quality), and the seed with the lowest BANSA score (highest quality). All videos were generated using 50 denoising steps with the Cog VideoX-5B backbone. The lowest- BANSA seed consistently produces sharper, more coherent, and semantically faithful videos, whereas the highest-BANSA seed often leads structural artifacts temporal instability. These results highlight the practical value of BANSA-guided noise selection.
20

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Published as a conference paper at ICLR 2026
Animalabit
Dif
cow benddigg down water river."
cute raccoon playing guitar
Arimoteeaaf
AnimereSity
"A couple formal evening wear with umbrellos."
"A dog playing in park."
FreePrirr
FreePrior
FreoPror
FreePriot
"A cute happy Corgi playing park, sunset, black white"
giraffe taking peaceful wolk."
FreoPror
Freg
FreoPrior Cors
Fireskrior OB1Z
"A motorcycle accelerating to gain speed for cornering."
"A train accelerating to gain speed."
Figure 9: Effect of ANSE on semantic fidelity and motion stability in AnimateDiff and FreqPrior: Each block compares baseline generations with those using ANSF-selected noise. In addition, while FreqPrior serves as noise-refinement baseline, our method is fully compatible and achieves further improvements when combined.
21

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Published as a conference paper at ICLR 2026
CogVidesX-28 BANSA Score
2B + Ours
BANSA Score 1.4.5
charming Parisian café, animated panda sits at quaint wooden table, sipping coffee from delicate porcelcin cup. The panda, wearing styl beret and striped scorf, gazes out the wincow at the bustling Paris streets...."
CogVidacX 28 BANSA Score 0.54
CogVideoX 28 OuTs BANSA Spore 1: 0.38
"A cool cot, sporting sleek black sunglosses and a red lifeguard vest, sits confidentlly on a high lifeguard chair overlooking a sparkling pool. feline's orange and with authority."
CogVideox-BB BANSA Score L: 0.59
CogVideoX-B5 BANSA Score L: 0.52
"A young womon with flowless skin and a serene expression sits at a vanity, bathed soft morning light. She begins ns by opplying light her fingers moving gently her face. Next, she foundation orush blend sheer.""
CogVideoX-oB BANSA Score 0.58
CogVideoX BANSA Score 0.4.1
"An astronout 'n pristine white spacesult, floats effortlessly against the vost, star-studdec expanse space, As the comera zooms out, the Intricate details of the suit, including the life-support backpack and tether."
Figure 10: Effect of ANSE on semantic fidelity and motion stability in CogVideoX outputs. Each block compares baseline generations with those using ANSE-selected noise. Across both Cog VideoX-2B and 5B, ANSE improves semantic alignment to the prompt and reduces artifacts such as temporal flickering and object distortion. White numbers show BANSA scores for the same prompt.
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Published as a conference paper at ICLR 2026
CogVideoX-28 BANSA Score 0:51
GogVideoX-28 RONSA Score 4: 0.51
sophisticated couple, cressed elegant evening attire, walks down dimly street, their formal man, In rollored block tuxedo, and the woman, flowing gown, share black umbrellc as the rain coptures their synchronized steps." .
CogVideoX Score 0.58
CogVideoX-2B + Ours
"A caiptures breathtaking of festive celebration square, centered around towering, brilliantyy It Christmas tree adomedd with twinkling lights and ornaments The scene alive with vibrant fireworks."
CogVideoX SB BANSA Score J: 0.61
Cogvideol 5B Ours
BANSA Score 0.25
"In whimsical forest clearing, with mischievnus eye stands stump. holding electric guitar, The raccoon, weoring a tiny leather jacket, strums the guitar with surprising skill, its tiny pows moving daftly over the strings..."
CogMideoX-58 BANSA Score (
CopvideoX 58 ours
BANSA Score 0.55
"A mojestic elephant strolls gracefully through lush, verdant forest, its mossive feet gently pressing into the soft eorth. The sunlight filters through the dense conopy, costing dappled shodows on its wrinkled, grey skin...... ..."
Figure 11: Additional qualitative comparison of Cog variants with and without ANSE.
Results from Cog VideoX-2B are shown in the first two rows; the rest show VogVideoX-5B With ANSE, videos exhibit improved visual quality, better text alignment, and smoother motion transitions compared to the baseline. White numbers show BANSA scores for the same prompt.
23

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Published as a conference paper at ICLR 2026
HunjongViides
Hungonngiiee 0uss
BANSA Scorel (:02
"A fluffy white sheep, woolly leisurely strolls through picturesque meadow, dotted with vibrant wildfoowrs and lush green grass. The sheep's colm demeanor and slow, deliberate steps exude tranquility as it meanders along narrow dirt path.
HunggnngViie Video BANGA Score :0081
HunuangV BANGA 0:36
majestic chestnut horse glossy coat leisurely through sun-dappled meadow, its mane gently swaying in breeze. As it walks, the horse's hooves softly tread on the lush, green grass, creating rhythmic,."
HungongVVdde BANSA 0:773
HungongWiide Ours
BANSA 1:022
"A dynamic athlete, soors through the air in packed, electrifying arena. with sweat giistening on their deterriined face, grips busketball tightly. they approach hoop, emphosicing groce the jurnp.
HungangVideg RANSA S..7
HungangVidder 5:03
young woman long, hair dimiy face illuminated by glow nearby lamp. Tears stream down her cheeks, Th. comero coptures her quivering lips and the silent sobs thot shoke her shoulders.,
Additional qualitative comparison of Hunyang Video with and without ANSE. With ANSE, the generated videos achieve sharper visual fidelity, stronger alignment between text and content, and smoother motion progression than the bascline. White numbers show BANSA scores for the same prompt.
Figure 12:
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Published as a conference paper at ICLR 2026
Wo.2211.38
BANSA 3:.7
Wanz 1-11B OutZ
BANSA 0:28
"A determined individual sleek black tank and gray othletic shorts performs push-ups pristine wooden floor minimalist, sunlit room. The camera captures the sweat glistening on their forehead, emphasizing their intense focus
Won2
Sccore Ouee
k1-2 Gurz BANSA Score :: 0.20
"A mojestic steam train, with its vintage black and red carriages, chugs along winding mountainside track, enveloped in cloud train's powerful engine, adorned bross gleams sunlight ascends..."
Score
1-.2.1-1.38 BANSA Score d.2z
"A vibrant, multi-colored cream cone sits on rustic wooden table, its creamy swirls beginning soften under the warm sunlight streaming through nearby window. The camera zooms capture the intricate details of the melting ice cream"
Scoro.:0.3
vibrant clownfish, its striking orange stripes, gracefully navigates through a coral teeming The fish weoves between the intricate branches of colorful corals, which range from deep purples to bright yellows,
Figure 13: Additional qualitative comparison of Wan2.1 with and without ANSE. ANSE enables videos to deliver higher visual quality, more accurate adherence to the given text, and more seamless motion dynamics compared to the baseline. White numbers show BANSA scores for the same prompt.
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Published as a conference paper at ICLR 2026
Pandom
BANSA (WorlD
SANGA (best)
lone bicycle th slack frome and black tires, glides effortiessly through a vast, sovwecovereed under a pale winter sky. The rider, bundled in red parka, black gloves, and woolen hat, pesals steadily, leaving delicate trail the pristine snow. The scene coptures the quist the continues, the serenity of the landscape, snowllokes gently falling the discant allhouette of bare trees lning
Random
BANSA (woors))
BANSA (BesO
surrounded lush greenery grasses sun casts golden glow, highlighting giratte's patterned coat and gentie riplles water. Nearby, tamilyy of zobros grazes pancefully, adaing to the tranquil scene. Finds flutee above, thair ratlart ons dancing on the water's surface. The g raffe's delicate movements create sense of harmony w nature, as the rivor flows flows gently, reflecting the viorant colors of the surrounding lardscope..
mojestic giraffe,
gracefully orching, bends
from
Figure 14: Qualitative comparison of generations from different noise seeds. We compare outputs generated from a randomly sampled seed (top), the seed with the highest BANSA score (middle), and the seed with the lowest score (bottom), using the same prompt and model. BANSA-selected seeds produce more coherent structure, stable motion, and stronger semantic alignment than both random and high-uncertainty seeds.
26


Reviews:
[{"review_id": "4X8fzEBN0b", "reviewer": "Reviewer_CXmX", "summary": "The paper proposes ANSE (Active Noise Selection for Generation), an inference-time framework that improves text-to-video diffusion by selecting the initial noise seed that minimizes a new attention-based epistemic uncertainty measure, BANSA (Bayesian Active Noise Selection via Attention). This method improves video quality and temporal coherence across various T2V backbones and is approximated efficiently via Bernoulli-masked attention and layer truncation chosen by correlation analysis to keep overhead low.", "questions": "1. The paper mentions that BANSA can be applied to cross-, self-, or temporal attention. Did the authors investigate which type of attention map is most predictive of final video quality?\n2. The Classifier-Free Guidance (CFG) scale is a crucial parameter for controlling prompt alignment in diffusion models. How does the choice of noise seed, as guided by BANSA, interact with different CFG values? Is it possible that a high BANSA score could be improved by using a higher CFG scale, or does the initial noise fundamentally constrain the quality regardless of the guidance strength?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The central idea of using attention-based uncertainty as an internal signal to guide noise selection is innovative. It shifts the paradigm from relying on external, often heuristic-based priors to a principled method that is aware of the model's own confidence.\n2. Through approximations (Bernoulli masking, layer truncation), authors make the consistent quality gains with a reasonable inference overhead. Demonstrates robust generalization and real-world viability.", "weaknesses": "1. Lack of statistical confidence reporting. Improvements are modest in absolute terms; without standard deviations/CI or significance tests, it’s hard to assess robustness and effect sizes across prompts/seeds.\n2. Lack of Sensitivity Analysis for Key Hyperparameters. The method relies on several key hyperparameters, notably the Bernoulli masking probability p (set to 0.2) . The paper does not provide a sensitivity analysis for these values. It is unclear how the optimal choices were determined and how robust the method's performance is to variations in these parameters.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8O3UKtuQfw", "reviewer": "Reviewer_KjHH", "summary": "This paper addresses noise selection in video diffusion models by introducing a metric called BANSA, which utilizes attention information to estimate model confidence and consistency. It also proposes a Bernoulli-masked approximation to efficiently compute scores from a single diffusion step.", "questions": "Please refer to the strengths and weaknesses", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "+ A novel metric is defined to guide noise selection.\n+ The paper is well-organized and presents the idea clearly.", "weaknesses": "- Would the method remain effective when applied to a powerful base video model (e.g., WAN 2.2/2.1 14B), or when the video model has undergone post-training procedures such as RL or DPO?\n- There is a lack of visualizations showing the score distributions for different prompts and samples from different noise inputs.\n- How does the Bernoulli masking affect the score distribution? Is there a significant difference between using and not using the mask?\n- It would be helpful to show generated samples from the same prompt but with different scores, to help validate the effectiveness of the proposed metric.\n- What is the underlying reason that using this score leads to improvements in motion, composition, and artifact reduction? More insight into the mechanism would be valuable.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "T5d2vFZQZD", "reviewer": "Reviewer_YgYi", "summary": "The paper presents ANSE, a framework for active noise selection in video diffusion models using BANSA, an attention-based acquisition function that identifies noise seeds yielding consistent and confident generations. BANSA adapts the BALD principle to attention space with lightweight, efficient inference. Experiments across multiple text-to-video backbones show that ANSE may enhance video quality and prompt alignment with slight overhead.", "questions": "Please see the [Weakness] section.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "* The paper is clearly written.\n\n* The accompanying website includes excellent figures and animations, which are much appreciated.\n\n* The experiments and ablation studies are detailed and thorough.", "weaknesses": "1. **Ensemble Size**  \n   - In Table 7, the metrics consistently improve as the ensemble size $K$ increases.  \n   - Could the authors provide results with even larger $K$ values?  \n\n2. **Denoising Steps**  \n   - What is the number of denoising steps used for each model?  \n   - It appears to be 50 based on the appendix, but this is only explicitly mentioned for the CogVideoX-5B backbone (L839).  \n\n3. **Computation Budget (NFE)**  \n   - From a neural function evaluation (NFE) perspective, the total cost is effectively $M + 50$ (or the actual number of denoising steps).  \n   - What would happen if the baseline methods were also given an equivalent $M + 50$ computation budget?  \n\n4. **Ablation on Bernoulli-Masked Attention**  \n   - Please include ablation results for the Bernoulli-masked attention mechanism, particularly analyzing the effect of the masking probability parameter $p = 0.2$.  \n\n5. **Evaluation Metrics**  \n   - Consider including additional motion-specific metrics, such as FVMD [1], to better assess the claimed improvements.  \n   - The sample videos on the website look good and visibly outperform the baseline. A human evaluation could further validate these qualitative improvements, as VBench—while comprehensive—may have limitations in capturing perceptual quality.  \n\nRef:\\\n[1] Liu, J., Qu, Y., Yan, Q., Zeng, X., Wang, L. and Liao, R., 2024. Fr'echet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos. arXiv preprint arXiv:2407.16124.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)