Normalized Attention Guidance: Universal Negative Guidance for Diffusion Models
Authors: Dar-Yen Chen
Hmrishav Bandyopadhyay
Kai Zou
Yi-Zhe Song
Venue: NeurIPS 2025 poster ()
Keywords: Diffusion models
negative guidance
guidance
text2image
text2video
Primary area: deep_learning

Abstract: Negative guidance -- explicitly suppressing unwanted attributes -- remains a fundamental challenge in diffusion models, particularly in few-step sampling regimes. While Classifier-Free Guidance (CFG) works well in standard settings, it fails under aggressive sampling step compression due to divergent predictions between positive and negative branches. We present Normalized Attention Guidance (NAG), an efficient, training-free mechanism that applies extrapolation in attention space with L1-based normalization and refinement. NAG restores effective negative guidance where CFG collapses while maintaining fidelity. Unlike existing approaches, NAG generalizes across architectures (UNet, DiT), sampling regimes (few-step, multi-step), and modalities (image, video), functioning as a \textit{universal} plug-in with minimal computational overhead. Through extensive experimentation, we demonstrate consistent improvements in text alignment (CLIP Score), fidelity (FID, PFID), and human-perceived quality (ImageReward). Our ablation studies validate each design component, while user studies confirm significant preference for NAG-guided outputs. As a model-agnostic inference-time approach requiring no retraining, NAG provides effortless negative guidance for all modern diffusion frameworks -- pseudocode in the Appendix!

Normalized Attention Guidance: Universal Negative Guidance for Diffusion Models
Dar-Yen Chen 1.2 Hmrishav Bandyopadhyay'
Kai Zou?
Yi-Zhe Song'
"SketchX, CVSSP, University of Surrey NetMind
{d. chen, h. bandyopadhyay, y.song) ac.uk kz@netmind,
https://chendaaryen githnn............... io/NAG.o/nooo getteb.ittuu....
Abstract
Negative guidance- explicitly suppressing unwanted attributes remains funda- mental challenge in diffusion models, particularly in few-step sampling regimes. While Classifier-Free Guidance (CFG) works well standard settings, it fails under aggressive sampling step compression due divergent predictions between positive and negative branches. We present Normalized Attention Guidance (NAG), efficient, training- free mechanism that applies extrapolation in attention space with L1-based normalization and refinement. NAG restores effective negative guidance where CFG collapses while maintaining fidelity. Unlike existing ap- proaches, NAG generalizes across architectures (UNet, DìT), sampling regimes (few-step, multi-step), and modalities (image, video), functioning universal plug-in with minimal computational overhead. Through extensive experimentation, we demonstrate consistent improvements in text alignment (CLIP Score), fidelity (FID, PFID), and human- human-perceived quality (ImageReward), Our ablation studies validate each design component, while user studies confirm significant preference for NAG-guided outputs. As model-agnostic inference ditrennce-tmme approach requiring no retraining, NAG provides effortless negative guidance for all modern diffusion frameworks pseudocode in the Appendix!
Negative Prompting
Portrait of researcher
Sketch
Glasses
Male
over pyramid
Realistic, complex
Black and white
Blurry, low contrast
A tiger
Vigerr
Cow
Photo of aurora
Green
Figure 1: Negative prompting 4-step Flux-Schnel (1]. CFG fails in few-step models. NAG restores effective negative prompting, enabling direct suppression of visual, semantic, and stylistic attributes, such "glasses," "tiger, "realistie," " "blurry." This enhances controllability and expands creative freedom across composition, style, and quality-includig prompt-based debiasing.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Diffusion models have revolutionized visual synthesis, setting new standards for photorealism in image 4 and video [] generation. Despite advances in quality and efficiency, critical limitation persists: effective negative guidance suppressing unwanted attributes particularly in few-step sampling regimes 778). This capability is crucial for content safery, quality control, creative expression, and debiasing applications in real-world deployments.
The prevailing approach to diffusion model control, Classifier-Free Guidance (CFG) (9. enables negative guidance by extrapolating between positive and negative conditional outputs at each denois- ing step However, in few-step regimes, CFG's assumption of consistent structure between diffusion branches breaks down, as these branches diverge dramatically at early steps. This divergence causes severe artifacts rather than controlled guidance, precisely when negative guidance is most needed for high-efficiency inference scenarios.
Although several recent CFG variants [00-1 mitigate specific limitations, they inherit the same fundamental constraint:
reliance on structural similarity between extrapolated outputs. Attempts to circumvent this limitation, such as NASA [3], modify attention activations directly but lack proper constraints, potentially leading to out-of manifolddl feature drift, instability and feature collapse
particularly in modern DiT architectures 14 15] where feature spaces are more complex and sensitive to perturbation.
In this paper, we propose Normalized Attention Guidance (NAG), a simple and effective approach to negative guidance that works universally across sampling regimes, model architectures, and generation domains with minimal overhead. The core insight of NAG is to apply extrapolation directly in attention feature space, complemented by LI-norm-base normalization and feature refinement to constrain feature magnitude while preserving directional guidance. This enables effective suppression of undesired attributes without compromising fidelity across diverse settings and generation tasks.
Methodologically, NAG operates by computing attention outputs from both positive and negative
prompts, then performing controlled extrapolation in this feature space: 7 = 2+ + db (Z+ Z-). Unlike previous approaches that directly impose guidance on model predictions, this maintains semantic coherence even when conditional branches diverge significantly. Intuitively, NAG computes vector away from undesired attributes (e.g.. "glasses", "blurry") and moves attention features along this direction, while constraining them to avoid straying from meaningful representations. This is achieved through two key stabilization mechanisms: (1) Ll-norm-based feature normalization that acts as a "guardrail", preserving directional information, and (2) feature refinement that pulls extreme features back toward familiar territory through interpolation. As detailed in Seccion 4.2) this creates structured trajectory through attention space, preventing the instability inherent to direct subtraction approaches like NASA [13].
The universality of NAG is demonstrated across three dimensions: (i) Sampling regimes - NAG works in few-step models (1-8 steps) where CFG fails and in multi-step models alongside existing methods; (ii) Model architectures - NAG functions across UNet and DiT architectures without architecture- specific adjustments: (iii) Generation domains - NAG extends to both image and video, improving alignment and temporal coherence. Ablation studies confirm each component's contribution and the method's robustness across hyperparameters. NAG requires no retraining and integrates simple plug-in to existing pipelines. One immediate benefit? A simple change lets Al researchers break free from the stereotypical representation! (Figure
Our contributions are as follows: (i) We introduce NAG, a universal, training-free attention guidance method that provides stable, controllable negative guidance across the diffusion model ecosystem. (ii) We restore effective negative guidance in few-step diffusion models where traditional CFG fails completely, while also enhancing negative control in multi-step diffusion when integrated with existing guidance methods. (iii) We demonstrate consistent improvements across diverse architectures (UNet, DiT), sampling regimes (1-25+ steps), and metrics (CLIP Score (16), FID [17]. PFID (7). IewaeReward (18). (iv) We validate NAG's generalization to video diffusion without domain-specific modifications, improving both semantic alignment and motion characteristics through effective negative guidance.
CFG serves as both powerful alignment mechanism for text-to- sot-to--mmae generation and an essential tool for negative guidance in real-world applications.
2

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2 Related Works
Diffusion models. Diffusion models 3 19 form the foundation of modern generative modeling, driving advances in image 2 4
20 21 and video synthesis [5 22 Early methods rely on
stochastic differential equations (SDEs)
26 |7] to learn a reverse denoising process from noise to
data. Recently, deterministic formulations based on ordinary differential equations (ODEs) [28 34),
such as Rectified Flow [29] and Flow Matching [35]. have emerged as efficient alternatives, learning continuous trajectories that transform noise into data. These methods accelerate convergence and improve stability, especially in large-scale models. In parallel, architectural innovation has shifted
from UNet backbones to Diffusion Transformers (DiT) [ 14 15 36
offering greater
scalability [14 42 43
To improve inference efficiency in large-scale models, recent efforts compress the sampling trajectory to 1-8 steps 8 29 54). These few-step models encompass both UNet and DiT families, facilitating low-latency generation. However, aggressive step reduction disables classifier-fre guidance (CFG) [13] limiting control. Our work bridges this gap with a training- feining-free attention- space method that restores controllability in few-step models.
Sampling guidance. Sampling guidance directs generation toward target semantics while improving fidelity. Classifier Guidance [19] uses classifier gradients, but requires auxiliary models and additional training. Classifier-Free Guidance (CFG) (9] avoids this by interpolating between conditional and unconditional predictions. Several advancements (10 55 56) address CFG's limitations, such as fidelity degradation and inaccurate estimation [5]. Parallel works modify attention mechanism to guide generation without negative conditioning. Self-Attentn
Guidance (SAG) (58) and Perturbed Attention Guidance (PAG) [59] work on self-attention layers, offering enhanced sample structure. PLADIS (60) focus on cross tross-attention layers to improve text alignment. However, most approaches assume semantic alignment across branches, an assumption that breaks under few-step sampling. In few-step sampling, conditional and unconditional predictions diverge, making extrapolation unreliable and causing collapse [13). NASA (13) attempts to mitigate this by adjusting cross-attentio features directly, but suffers from instability due to the feature aat-of-manifold issue, especially in DiT models.
We analyze attention feature manipulation and study how to stabilize this process via extrapolation, normalization, and refinement. This leads to robust and generalizable guidance mechanism that operates consistently across UNet and DiT architectures, and across both few-step and multi-step diffusion sampling.
3 Background
We briefly review the fundamentals of diffusion models for text-to-image generation; background on flow-based models is provided in Appendix B
3.1
Tmxteto-Image Diffusion Models
Diffusion models (3] synthesize images by iteratively refining noisy samples through a learned reverse process. Training involves simulating forward Markovian process in which clean images zn are progressively perturbed by adding Gaussian noise No I) over T' discrete timesteps, producing intermediate noisy states 2z.
5 = Va,to + V1- (1,1,...,7)
where o. corresponds to coefficients from a noising schedule, controlling the signal-to-noise ratio (SNR) [3 26) across timesteps. vp is reconstructed through training a neural network Go to estimate the added noise G)( and plugging back into Eq. (1)
FD
(2)
V 0
In text-to-image tasks, the model estimates noise as غ = Go (xe, c), where represents text condition- ing. Cross-attention units (2) integrate text embeddings into the reverse process, enabling semantic alignment between prompts and generated images.
3.2 Classifier-Free Guidance
Classifier guidance [19 enhances diffusion models by using gradients from a pretrained classifier to steer generation toward a target class. Classifier-Free Guidance (CFG) 99) removes the classifier dependency by extrapolating between predictions conditioned negative and positive prompts.
3

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Given noisy sample Te, the model predicts noise ět Go ( c*) using the positive condition ct and è- Go(z,, c for the negative condition. CFG then applies guidance by extrapolating beyond the positive prediction:
ECR = + ( E-),,
where Φ 2 is the guidance scale controlling the trade-off between fidelity and diversity. At inference, the estimated noise ęCR replaces é in Eq. (2). biasing generation toward the positive condition, and away from the negative condition.
3.3 Negative-Away Steer Attention
NASA (13] introduces attention-spar space guidance by directly modifying cross-attention outputs. For image query Q and text key-value pairs (K, V), attention is generally computed as:
QK
Z Softmax
V.
Vd
To steer generation, NASA independently obtains Z and Z using (K+, V+) and (K-, V-) from positive and negative prompts, then subtracts scaled Z guide attention:
NASAA Z a Z
(5)
This enables directional control without manipulating estimated noise.
DIT, Flux Flux-Schnell, 4 steps
Methodology
Baseline h 0.25
6=1
b
CFG applies an extrapolation on noise pre-
NASA NAG (Ours)
dictions, which can be rewritten in terms of zo by substituting Eq. (2 into Eq. (3
CFG 10 = o. (x) _n
(6)
where To and 2o reconstructed from posi- tive and negative conditions, respectively. CFG inherently assumes a multi-step denoising pro- cess to keep these branches aligned, where sam- ples are gradually refined across iterations. In few-step models, however, zi and zo diverge significantly after a single inference step, mak- ing the extrapolation fundamentally iol-posed. As shown on the left [Figure 3 directly apply-
A pet. Cat.
UNeet, NitroSD- -eallism, 2 steps
Baseline
Φ 0.25
4
NASA NAG (Ours)
ing CFG introduces severe artifacts. NASA (13) sidesteps output-space extrapolation by modify- ing cross-attention features. However, the ab-
sence constraints leads to out-of-manifoll shifts and degraded synthesis quality (Figure 2). We observe that this instability is further ampli- fied in DiT architectures, motivating the need for a more stable guidance strategy.
A kid.
Boy.
Figure 2: Comparison of NAG against NASA.
4.1 First-Order Analysis of Guidance
To analyze why CFG [9] degrades under few-step sampling, we use first-order Taylor view. Let one denoising update at time be defined by
2: c+ Co(
A Taylor expansion around e yields
1 (ATTV.)) z(c) + (ACTV.)2( + (V))(.)
<(c+ Ac)
(7)
Keeping only first-order terms gives the output-space finite difference
A= 2(c+ + Ac) 2(c) Voz(c) Ac.
(8)

---

This relation underlies CFG's output-space extrapolation, by treating A as a first-order estimate of the gradient with respect to c.
Ac) remains within a smooth locally linear neighbourhood of z(c), the higher-order terms in Equation (7) are negligible and the single-shot estimator Az is reliable. In few-step regimes the required denoising is large and varies sharply with e which makes higher-order terms non-negligible and causes CFG break down.
In multi-step sampling each denoising step is small. When 2
Although the end-to-end mapping c + z(c) is not locally linear, individual layers often operate more linearly. We factor the network at an intermediate layer into a prefix Gj that produces features and a tail Gg that maps those features to the output
Fi(c) Gg(a,, c),
=(c) F
where Fi(e) is the feature of layer 1. By the chain rule, we have
O= (F()))( dc dc
OF aG
(10)
de
where J denotes the Jacobian. By repeatedly selecting an intermediate layer 1' of G and applying aG1 the chain rule to de we obtain the layer-summed form
0z =
OF de
[Ja(File))
(11)
Applying a first-order approximation in all I, define
aFL (c)
AFi A Fi Ac) Fi(c)
Ac. Σ Jaj(Fi) AF x (oz(c) Ac,
(12)
dc
This gives the corresponding output increment via feature steps
Feetture
(13)
which provides alternative first-order update: sum of small, layer-wise edits propagated by Jacobians.
4.2 Normalized Attention Guidance
Normalized Attention Guidance (NAG) addresses instability in attention- guidance by applying extrapolation in attention feature space, followed by Normalization and Refinement. Each component (i.e. extrapolation, normalization and refinement) constrains out-of offamoolflldd shifts while preserving the semantic direction of guidance (Figure 3).
Extrapolation in attention space. Motivated by the first-order feature-space estimator in Equa tion (13) we move CFG-style extrapolation from output space to attention features and apply
(14) where ø is the guidance scale and Z+ Z are features under positive and negative conditions. This is the feature-space analogue of CFG and it distributes guidance across layers. In few-step regimes editing features tends to stay closer to local linearity than editing the output directly. Large Φ can still drive features off the manifold, so the following normalization and refinement stages bound the guidance and maintain stability.
- Z + dZ+ Z-).
L1-Based Normalization. To constrain the extrapolated features, we compute the point-wise LI norm ratio between 7 and Z+:
2 1+ +Il
R
iE (I,...,!),
(15)
where Z E R is the attention output of sequence length and dimension d. We then apply clipping and rescaling:
min( [2())
7 i (I,....,.
(16)
R The threshold limits feature magnitude, preventing extreme activations while maintaining direction- ality. We adopt the LI norm to normalize attention outputs, as it preserves low-magnitude activations that often encode subtle semantics. Since attention features undergo linear projections, even small components can significantly influence the final representation. Unlike L2 or max norms, which disproportionately shrink these values, L1 retains essential structure for precise guidance.
5

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(a) CFG
(b) NAG (Ours)
Normalized Attention Guidance
Guidance Seale:
.V
Guidance Scale: Ø
Positice Text Feorares
Image Feutures
Negatve Text Foatures
Blending Scale: a
procape
Gogh
Positive Van Gogh
V K
K
Negative The Stary Night
Nugueive Proept
The Starry Night
x(1 + 9)
X| +9
o)
2
Norm Z
(--)
x(1 6)
NAG
Z5g
Figure 3: Comparison of CFG and NAG in single-step sampling. Left: Classifier-Free Guidance (CFG) (9] generates onj and Tn from positive and negative prompts, then applies output-space extrapolation. In few-step models, on and so differ significantly due to coarse denoising, leading to severe artifacts rather than controlled guidance. Right: Normalized Attention Guidance (NAG) operates attention space by extrapolating positive and negative features Z and followed by Ll-based normalization and a a-blending, This constrains feature deviation, suppresses out-of- manifold drift, and achieves stable, controllable guidance.
Feature Refinement. Though normalization constrains magni- tude, it may still disrupt alignment with the original distribution. To mitigate this, we blend Z with the positive baseline:
NAG a (1- Z+
(17)
This blending serves as a regularizer, pulling features toward the stable manifold Z+ and ensuring that the guidance remains bounded both magnitude and semantics.
Z NAG
Z'
Z
Geometric interpretation. Figure 4 visualizes the trajectory
of guided features in NAG. Raw extrapolation produces Z, which may extend beyond the distribution of valid features. Normalization produces Ž by scaling into a bounded re- gion (Guidance Boundary), mitigating out-of-manifold risk.
Mefinement/Manioole
Guidance Boundary
The final refinement produces NAG by blending Z with Z+, contracting the feature into the Refinement Manifold. This progressive regularization preserves directional guidance from Z while maintaining distributional consistency with Z
Figure 4: Visualization of NAG.
Experiments
Unless otherwise specified, experiments are conducted on Flux-Schnell [1] with 4-step sampling, using an NVIDIA A100 GPU. We evaluate NAG the COCO-5K dataset [6] using CLIP Score [16]. Fréchet Inception Distance (FID) [17], Patch FID (PFID) (7). and ImageReward [18]. which reflects human aesthetic preference. Following NASA (13), we use "Low resolution, blurry" as a universal negative prompt for quantitative comparison. Implementation and default settings are detailed in Appendix C
5.1 Evaluating NAG in Few-Step Sampling
Few-step diffusion models facilitate rapid inference, but generally lack support for CFG, making
negative guidance ineffective. We evaluate NAG on both DiT-based SANA-Sprint (53), Flux-
Schnell [1], LDD..-LLaree-Tunn
52), and UNet-based NitroSD-Reali
[8] DMD2-SDXI. (50)
SDXIL-Lightning [7]. We also include 25-step Flux-Dev (T), which lacks CFG support. Figure T
and Figure 5 show that NAG enables guidance across concepts, ranging from removing undesired

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Table 1: Quantitative results of NAG.
CLIP (*)
FID (U)
PFID (J)
ImageReward (1)
Arch
Model
Steps
Base.
NAG
Base.
NAG
Base.
NAG
Base.
NAG
SANA-Spriat
31.4 31.9
30.29 28.31 (-198)
37.56
33.29 (-4.27)
L,008
1,075 (40.067)
DIT
Flux- Soxx-Shhelll
31.4 32.0 (-0,)
25.47
24.46 (-100)
38.26
34,95 (-3.31)
1.029
1,099 (+0.070)
SD3 S-are--ogeoo
318
29.97
29.81 (-018)
44.37
41.87 (-2.00)
0.944
1.118 (+0.174)
Flux-Der
25
30,9 31,5 (+0.))
31.04
28.11
(-293)
43.22
39.01
(-4.21)
1,066
1.166 (+0,100)
1.Nct
NitoSD Realism
31.8 32.4
26.21 23,98 (-2.23)
30.53
28.73 (-1800)
0.847
0.948 (c0.101)
DMD2-SDXI.
31.6 32.2 (-0.6)
24.79 23.32 (-1.47)
27.11
25.61 (-1.50)
0.876
0.960 (+0.084)
SDXI-Lighting
31.8 (+0.7)
27.01 24.99 (-2.02)
34,02 31.70 (-2.32)
0.730 0.842 (+0.112)
DIT
UNet SDXL-Lighanig stegs
Flux-Dev
SD8.5-Lage-TTrbo steps
Flux-Schnell steps
Sprint steps
DMD2-SDXL
NitroSD-Realise
steps,
steps
step
Baseline
NAG
beautiful cyborg.
Robot.
Baseline
NAG
A baby phoenix made of fire and flames is born from the smoking ashes. - Low resolution, blurry.
Figure 5: Qualitative results of NAG. NAG enhances controllability in models lacking CFG,
improving semantic alignment and visual quality across architectures and sampling steps.
objects to refining perceptual quality. the Cyborg vs. Robot example, NAG attenuates robotic features like metallic textures, producing more human-like depictions. In the Phoenix sample, using the universal negative prompt "Low resolution, blurry" results enhanced sharpness and gradient contrast, yielding more vivid flame structures.
Table reports consistent and significant improvements across all models and metrics. CLIP gains indicate enhanced prompt alignment, while FID) and PFID reductions reflect improved fidelity. Notably, ImageReward increases across the board, validating perceptual gains through aesthetic preference metrics. These results show that NAG restores effective negative prompting in few-step models without retraining, achieving guidance previously unachievable in this regime.
5.2 Comparison with NASA
We compare NAG against NASA 13 in Figure 2 On DiT-based Flux-Schnell []. NASA exhibits strong instability even at low scale, producing unnatural textures and broken images. On UNet- based NitroSD-Realisen [8], NASA performs reasonably at low & but degrades as scale increases, contrast, NAG consistently maintains output quality while enforeing
introducing distortions. negative constraints. Across architectures, it enables stronger guidance without compromising fidelity high scale. This reflects the effectiveness of normalized feature-space extrapolation, and highlights NAG's s stability, generality in regimes where prior methods collapse.
7

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Table 2: Quantitative results of NAG with CFG and PAG.
CLIP (T)
FID (4)
PFID
ImageReward
Arch
Steps
Setting
w/o
NAG
w/o
NAG
w/o NAG
NAG
DIT
Larrge
25
CFG
31.8
(+0.2)
25.07 25.42 (+0.35)
31.68 31,63 (-0.05)
1,029
1.130
(+0.101)
CFG PAG
31.5
318 (+0.)
24.49 24.35 (-0.4)
37,93 39.09 (+1.1)
0.939 1.063 (+0.124)
UNot
SDXL.
25
31,9
(40.8
(-2.55)
(-2.11)
0.791
(40.115)
CFG PAG
31.5
32.3 (408)
26,25 23.53 (-2.77)
35.58 (-3.0)
0.748
0,914 (40.166)
Baseline
NAG
An anthropomorphic cat thoughtfully paints
An origami fox running in the forest. The fox is made polygons... Static, low resolution, blurry.
oil self-portrait on canvas...
Human face, low resolution, blurry.
Figure 6: Qualitative video results for Wan2.1-T2V-14BB
5.3 Integrating NAG with Other Guidance To evaluate compatibility with existing guid- ance, we integrate NAG into 25-step SD3.5- Large [15] and SDXL [62] models alongside CFG 9 and PAG [12 in Figure 7 In the Ilama- bird hybrid case, NAG disambiguates concepts, guiding generation toward the intended bird anatomy. In the astronaut case, it enhances clar- ity and accentuates prompt-relevant attributes without distorting composition. Results in Ta- ble confirm that NAG complements existing methods: Although FID and PFID vary. CLIP scores and Rewre consistently improve. These results demonstrate that NAG is general enhancement to standard guidance strategies, of- fering ad advancements in multi-step models.
|DiT, SD3.5-5 25 steps UNet, SDXT, 25 steps
CPG PAG
CPG
wrle NAG
NAG
A Alama-bird hybrid creature flying the sky
Llama head, bird body.
Llama body.
win win NAG
5.4 Video Diffusion Models
To validate cross-modal generality. we apply NAG to the video model Wan2.1-T2V 14B (6]
NAG
for generating 5-second, 480p videos. Figure 6 demonstrates that NAG enables effective nega-
tiny astronaut hatching from egg the moon.
tive prompting in video generation. In the first example, NAG suppresses unintended facial fea- tures and enhances detail in the fur and back- ground. In the second example, including "static" in the negative prompt produces outputs with stronger motion dynamics. These results show that NAG extends robust control to video synthesis, enabling content suppression and quality enhancement. It highlights NAG's capacity as general- purpose mechanism for diffusion control across spatial and temporal domains.
-Loow resolution, blurry.
Figure 7: NAG integration with CFG and PAG.
5.5
Computational Cost
Unlike CFG [9]. which requires doubling the computation of sampling steps, NAG only ap- plies additional computation to cross-altention layers or MM-DiT [5] blocks. Table 3 reports the per-step latency across families. In Flux (T). NAG incurs a similar cost to CFG, whereas in SD3.5-Large (15). SANA (63), SDXL (62) and 1 (6). it introduces significantly lower ad- ditional inference time.
Table 3: Per-step sampling latency.
Model Family
Baseline
Flux
487ms
4488ms (e 100%)
+426ms (+87%)
S-3.S-Large
231ms
+219ms (495%)
+109ms (+43%)
SANA
39ms
+35ms (+90%)
+Sms (+13%)
SDXL
75ms
+25ms (+34%)
(+22%)
Wan2.1
10.74
+10.7s (+ (009))
+1.35 (+12%)
8

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5.6 User Study
Table 4: User preferences study.
We conduct a user study to assess perceived im- provements in three aspects: text alignment, visual appeal, and for video, motion dynamics. Text align- ment measures how well outputs match the prompt semantics. Visual appeal reflects aesthetic quality, and motion dynamics captures temporal coherence and realism in video. Details provided in Appendix H We compare generations using NAG against the same models without NAG in Table 4 Following Lin al. (54), the preference score is computed as (P / S NV) where P. N, and S denote preferred, non-preferred, and similar votes. A score of 0% indicates equal preference, while +100% and -100% signify complete preference for or against NAG, respectively.
Model
Modal
Steps
CPG
Texx
Visual
Motion
Flux Schnell
Image
4
X
+25.0%
+33.9%
SD3.5 Large
Image
25
/
+9.29%
+15.5%
Wan2.1-14B
Video
25
/
+20.5%
+8.7%
+1.3%
On stw- Flux-Schnell [T. which lacks CFG, users clearly prefer NAG for both text alignment and visual quality. For SD3.5- Large [T3] with CFG, improvements are smaller but still consistent.
For the video model Wan2.1-141 [6]. NAG achieves notable preference in text relevance and motion quality. Overall, NAG improves user-perceived quality across models and modalities.
5.7 Ablation Study
Effectiveness of components. To assess the contribution of each design choice, we perform an ablation by removing the refinement and normalization operations. As shown on the left of FFigure 8 full NAG maintains stable performance across scales, achieving continuously improving CLIP as well as optimal FID and IeageReward. Omitting refinement and normalization causes rapid degradation beyond Φ > 5, confirming their necessity. Visual results on the right of Figure support quantitative findings. Full NAG preserves structure and color at high guidance strength (ø 20), suppressing undesired concepts without sacrificing fidelity. Without refinement, mild distortion 10, and results in failure
appears at é 5, increases artifacts emerge early and intensify with higher guidance, due to unbounded scaling. These results confirm that normalization and refinement both critical for stable and effective negative guidance.
20. Without normalization.
NAG (Full)
Baseline
Φ 0-5
Φ = 10
Φ 20
w/o Refine
(Ful)
w/o Refine & Norm
CLIP (1)
32
0-0
$-5
6-
w/o Refine.
FID (1)
$=5
1=
wio Refine. w/o Norm.
Reward (t)
sustainable future city with plants on Mars. MMas.
Figure 8: Left: Ablation study of NAG. Right: Visual results from ablation study.
Impact of guidance scale. We evaluate NAG's performance under varying guidance seales in Figure 9. As scale increases, negative prompt influence strengthens: in the woman example, the figure is gradually removed, isolating the flowing dress; in the wolf example, higher scales reveal sharper structure and more fine-grained details, until excessive guidance introduces distortions and saturation issues. These trends illustrate both the strength and limits of NAG: moderate scales enable refined control, while extreme scales risk artifacts unnatural stylization.
Figure 10 further analyzes performance across different inference steps. Notably, CLIP increases monotonically, while FID and ImageReward peak at moderate scales (4-8), indicating tradeoffs

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0.5
C
An elegant dress fluttering under the sea. Woman.
majestic wolf howling inside the silhouette of mountain, double exposure photography style, surreal and dreamlike. Low resolution, blury.
Figure 9: Impact of NAG scale.
stop - 4 steps
CLIP (1)
FID
ImageReward (1)
12.5
25
32.0
L.5
1,00
Figure 10: Quantitative comparison of NAG scale.
between alignment and quality. Sampling with more steps tolerates higher scales before severe degradation, allowing stronger guidance without compromising stability.
Baseline
NAG
Bascline
NAG
Limitations and Future Work
Photo of future car.
Photo of flying dragon.
While NAG exhibits effective negative guidance, some failure cases remain. As shown in Fig- ure 11, it may struggle to fully suppress certain concepts. Despite extrapolation is constrained, excessive guidance or poorly formulated prompts may still trigger instability or texture collapse. Future work may explore finer-grainet interventions within the attention mechanism improve sensitivity to stylistic cues and suppress global artifacts. Adaptive attention weighting or token-wise modulation could improve responsiveness to nuanced under-specified negative prompts. Addi- tionally. refining feature calibration or incorporating local structural priors may offer more precise control over semantic and stylistic attributes.
Color photography.
Low resolution, blurry.
Figure 11: Examples of NAG failure cases.
Conclusion
We present Normalized Attention Guidance (NAG), training-free mechanism that restores and enhances controllability in diffusion models via stable attention-space guidance. Unlike existing approaches, NAG addresses the the limitations of negative guidance in few-step models, where output- space extrapolation methods like CFG (9) fails due divergent predictions. By applying extrapolation directly in attention features and stabilizing it with L1-based normalization and refinement, NAG mitigates out-of-manifold shifts while preserving guidance direction. NAG generalizes across architectures (UNet 33. DIT (14), sampling strategies (few-step. multi-step), and generation domains (image, video), serving as a universal inference- time plug-in. integrates seamlessly with CFG and PAG [12), enhancing controllability without retraining, Extensive experiments demonstrate consistent improvements in text alignment, visual fidelity, and numan-percein quality. These findings establish NAG simple yet powerful solution for universal control in modern diffusion models negative prompting that actually works!
10

---

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Table of Contents
Introduction
2
Related Works
3
Background
3.1
Text-to-lmage Diftusion Models
3.2
Massifier-Free Guidance
3.3
Negative-Away Steer Attention
4
Methodology
First-Order Analysis Guidance
4.
4.2
Normalized Attention Guidance
5
Experiments
5.1
Evaluating NAG in Few-Step Sampling
5.2
Comparison with NASA
5.3
Integrating NAG with Other Guidance
5.4
Video Diftusion Models
5.5
Computational Cost
5.6
User Study
5.7
Ablation Study
6
Limitations and Future Work
10
7
Conclusion
10
A
Broader Impact
14
B
Flow-Based Models
14
C
Implementation Details
15
D
Extended Evaluation
16
Extended Comparison with NASA
17
Ablation Study on 7 and
17
Early Stopping of NAG
18
User Study Details
20
Additional Qualitative Results
21
J
Prompt Details
27
J.1
Prompts for Figures
27
J.2 Prompts for User Study
28
A Broader Impact
By improving the effectiveness of negative prompting, NAG's flexibility enables positive impact across creative fields, including graphic design, film production, and educational content, through more reliable visual generation in both image and video domains. However, the same controllability can be misused. NAG could potentially facilitate more precise deepfake creation. amplification of harmful biases, or content manipulation by making easier to suppress or alter specific visual traits. Its training-fret free nature reduces the barrier for adapting it to sensitive or malicious applications.
We strongly advocate for responsible usage and recommend that future work explore safeguards such as output detection mechanisms. While NAG expands the practical utility of diffusion models, its ethical deployment remains essential.
B Flow-Based Models
Flow-based generative models establish continuous transformation between base distribution and the data distribution using time-dependent trainable velocity field ug(x,, c). Given pair of data points (æo0 21) (Phase; ), intermediate states are constructed by linear interpolation:
Tz (1 - f)xp + tæ t e [].
(18)
where is sampled from known distribution such as A I). and æ1 is a real data sample. At inference time, a new sample is generated by solving the ordinary differential equation:
dr.
di 0g (are: c)2
(19)
which moves the initial point 2o along learned trajectory toward the data distribution as 1.
14

---

z_negative = soccale_dot_proddee prteporruut_ttttetiin query, key_negative, C Implementation Details
We provide the default NAG hyperparameters for different model families in Table 5 Algorithm provides Syyrch-style pseudocode for integrating NAG into cross-attention layers.
Table 5: Default NAG hyperparameters.
Architecture
Model Family
Ø
Flux
4
2.5
0.25
LDrge-Large
4
2.5
0.125
DiT
SANA
4
2.5
0.375
PixArt-E
4
2.5
0.375
Wan2.1
4
2.5
0.25
SDXL
2
2.5
0.5
UNet
Playground
2
2.5
0.5
SD1.5
2
2.5
0.375
Algorithm 1 Cross-Attention with NAG.
class McChoosattnProcesor::
def __init__ (self, nag_ aag_scale=2.0, tau=2.5, alpha=0.5)
self self.nag_scan self. tau = tau self allha = alpha
nag_scale
def c_lll__ self, attn, emb, text_enb cert_emb_ppate enb_ttsiiiiee, text_ amb_negativee )
query attn. to_ (image_ennt emb)
Compute key K and value for both positive and negative prompt key-positive attn. to_k (ttetttemeppotttttteeeee
value_positive = attn. to (te_tteet.....e. key_negative attn. (teettext_____ee.teott emb_negative) value_negative attn. to_
attn.to_v (ttettttee_mmmee__eee _eme_mmeebive)
Compute attention output Z for both positive and negative prompt
z-positive = F.scaled_ dotaaleddott_pooducc..oo prdductt_tteetionl query, key-positive, value_positive,
Eqqutioo 7 2_tilde = z_positive + self nag_scale (z_positive z_negative)
Equation 8
norm_possi corm_positive worch.norm(z_poore norm_ tilde = torch. norech.ommm__tideee p=1, dim=-1, keepdim=True) ratio norm_tilde norm_positive
p=1, dim=-1, keepdin=True)
Equation 9 2 hat www.h.weeeeeeeeeeeo where(ratio self. tau, tau, ratio) / ratio z_tilde
Equation 10 z_nag self alpha zah self alpha) z_positive
return hidden_states
15

---

D Extended Evaluation
We expand our evaluation to encompass additional diffusion models. For few-step generation, we include Seper-Flux--eee [48). Flux- Turbo-Alpha (64), and Hyper-SDXL [88] For standard sampling with CFG and PAG, we test SANA 1.6B [63], PixArt-E 37]. SD1.5 2 and Playground v2.5 [65]
Quantitative results in Table 6 and qualitative comparisons in Figure 12 further validate the effective- ness and generality of NAG across diverse architectures and sampling regimes.
Table 6: Additional quantitative results of NAG.
CLIP (f)
FID (
PFID (U)
ImgReward (1)
Arch
Model
Steps
CFG/PAG
Base.
NAG
Basc.
NAG
Base.
NAG
Basc.
NAG
Hyper-Flux-Des
31.7
32.3 (-06)
24.65
23.08
28.12 25,51 (-2.6)
1,025 1.096 (-0001))
DIT
Flux-TurbeAApha.
x
31.2
31.8 (-0.6)
29.53
27.28 (-2.55)
36.14 33.74 (-240)
0.899 1.012 (+0.113)
166
CHG+PAG
(-2.5)
(-2.2)
(+0.205)
PixAn-E
25
CRG
31.4
31.7 (-03)
31.01
30.36 (-0.55)
42.27
40.50 (-1.77)
0.920
1,024 (+0.109)
UNot
Hyper-SDXI.
31.5
32.0
32.36
31,08
39.90
38.11 (-1.79)
0.931
1,021 (40.90)
SD1.5
25
CFG
31.2
31.7 (-0.5)
23.04
23.18 (+0,14)
25.19 26.48 (+1.29)
0.191 0.337 (+0.146)
Playground
CHO
1.163
DIT
UNet Playground v2.5 steps,
PixArt-E
SANA 1.6B
Flux-Tubo-Alpha Hyper-Flux-Dev
Hyper-SDi SDX steps
stpp,
steps, CPG, PAG
steps
steps
steps,
Base line
NAG
Photo of a man from the 1960s.
Black and white.
Baseline
NAG
Macro shot of blooming cherry blossom, hyperrealistic, 8k. - Low resolution, blurry.
Figure 12: Additional qualitative results of NAG.
16

---

E Extended Comparison with NASA
We quantitatively compare NAG against NASA 13 Table
Since NASA fails with DiT architecture, we focus on UNet- based models: NitroSD- 18], DMD2-SDXL [50], and SDXL- Lightning 77. Across all metrics, NAG consistently outperforms NASA, demonstrating superior text alignment (CLIP), fidelity (FID, PFID), and human-pen auman-perceived quality (ImageReward),
Furthermore, conduct user study for direct evaluation of perceptual quality. To guarantee thorough assessment, we expand the original six positive-negative prompt pairs from Nguyen er al. [[3 to sixteen pairs, detailed in in/Appendix J.2] For text alignment, participants are instructed to consider both the positive prompt, which specifies the desired content, and the negative prompt, which defines the attribute to be suppressed.
summarized in (Table 8) users exhibit strong preference for NAG-guides outputs, reflecting improvements in both text alignment and visual appeal. The results demonstrate effectiveness of NAG in practical scenarios.
Table 7: Quantitative results of NAG against NASA.
CLII (1)
FID (
PFID ())
ImugeReward (f)
Arch
Model
Steps
NASA
NAG
NASA
NAG
NASA
NAG
NASA
NAG
UNet
NitmSD Realism
32.0 32.4 (10A)
25.86 23,98
30.01 28.73 (-128)
0.900 0,948 (-0.048)
DMD2-SDXI.
4
31.9 32.2 (40.3)
24.44
23.32 (-1.12)
26.74 25.61 (-1.33
0.915 0.960 (-0.045)
SDXL-Lightning
31.6 31.8 (+0.2)
26.38 24.99 (-139)
32.39
31.70
0.805
(-0.077)
Table 8: User preferences study of NAG against NASA.
Model
Steps Text Alignment Visual Appeal
DMD2-SDXL
+46.0%
+56.8%
F Ablation Study on T and a
Besides the guidance scale s, NAG introduces two additional hyperparameters: the normalization threshold and the refinement factor While our default values generalize well across model families and use cases, this section investigates how each affects generation behavior. 7 controls the upper bound of the normalized attention magnitude. On the left of Figure 13, lower r overly restrict guidance strength, while higher values may introduce instability and artifacts artufacts-especiallyy especially under extreme guidance scales. In practice, moderate threshold achieves balanced trade-off between stability and effectiveness. a regulates how much of the guidance is retained. As depicted in the right Figure 13] lower improves robustness by maintaining closeness to the original positive features, but may weaken guidance. With 0=0.115, the model fails to fully suppress the red tone specified in the negative prompt. Larger values enable stronger guidance, but risk of collapse high s.
Baseline
20
Baseline
$ 5
10
$ = 20
a 0.5
F-23
0.25
-
ut 0.125
Minimalist ubstract line drawing: face portrait of girl with long hair. Complex, detail.
cathedral built on floating island space.
nebula the background.
Red.
Figure 13: Left: Impact of T. Right: Impact of oc.
17

---

G Early Stopping of NAG
To understand the impact of NAG across timesteps, we visualize intermediate outputs during the sampling process in Figure 14 The modification from negative guidance is most prominent at early stages, with diminishing influence as denoising proceeds.
Motivated by this, investigate early stopping of NAG. Specifically, define threshold 0 < 1 and apply NAG only to the first portion the denoising steps. Figure presents qualitative results for different 0. Even with 0.25, outputs remain competitive. For example, in right columns, where the negative prompt targets global structure (e.g., close-up), all settings produce similar results. In contrast, higher / improves details for fine-grained edits like the smoke representation of lion's
Flux-Schnelly 4 steps
Sampling without NAG
250
==(=
Sampling with NAG
=0(#pe",c)
A
A corgi with flower crown. Pink and white flowers.
Photo meerkat pirate captain, hyperrealistic, Low resulution, blurry.
Flux- Dev, 25 steps
Sampling without NAG 1=100
959
909
826
7:45
598
116
S8-4P
S8-B
Sampling with NAG
=o(=meet<>)
ct)
A
Photo of penguin wearing sunglasses sunbathing on beach lounger. Low resolution, blurry.
Figure 14: Visualization of the sampling process with NAG.
18

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face. Quantitative results in Table support this. On few-step models like Flux-Schnell [ and DMD2-SDXL [50]. 8 0.25 performs comparably to the full application with 0 1.0, significantly
reducing inference time. On 25-step sampling such as PixArt-E 37 and SDXI. 62 lower slightly
reduces performance, but results remain strong.
In summary, early stopping of NAG preserves most benefits while reducing computation. For various use cases, limiting NAG application the initial steps represents practical balance between quality and computational efficiency.
DIT
UNet
DIT PixArt-E 25 stops, CFG
UNet
PixArt-E
Flux-Schned steps
SDXL
DMD2-SDXL stops
Flux-Schnell stops
SDXL 25 steps, CFG
DMD2-SDXL steps
25 steps,
stops,
Buseline
0.25
0=03
0= 1.0
Surreal shot of lion head made entircly of drifting smoke.
Portrait of firefighter. Close-up, zoomed-in, facial focus, detailed portrait, headshot, cropped view, subject-centered.
Dark background, visually striking and artistic effect. Lion head, mane, face,
Figure 15: Qualitative results of early stopping.
Table Quantitative comparison of early stopping.
Architecture
Model
Steps
CFG
CLIP (t)
FID (L)
PFID (1)
Image Reward (1)
Latency (1)
31.4
25.47
38.26
1.029
2.11s
Flux-Schnell
X
32.0 (+066
24.44 (-1.33
34.94 (3332)
1.098 (+0.069)
2.95% (+10%)
0.5
32.0 (+0.6
24.49 13.990000 (098))
34.98 (3.27)
1.100 (+0.711)
3.368 (+5918)
32.0 (60.6)
24.46 (-1.11)
34.95 (-3.31)
1.099 (40.070)
3.75% (478%))
DIT
31.4
31.01
42.27
0.920
2.78s
PixAnt
(+0.2)
(.0.6)
(-1.27)
(+0.045)
(#4%))
0.5
31.7 (=0.3)
30.31 (-0.7))
40.49 (-1.78)
1.013 (40.003)
2.97% (47%)
31.7 (-0.3)
30.36 (-0,55)
40.50 (-1.77)
(04)
1..0s (+129)
31.6
24.79
27.11
0.876
0.53s
DMD2-SDXL.
(+0.5)
25.53 (-1.58)
(+0.074)
0.60% (+141))
0.5
32.2 (10.6)
23.40 (-1.39)
25.40 (-2.71)
0.958 (40.082)
0.62s (4179))
UNer
32.2 (40.6)
23.32 (-1.47)
25.61 (-1.50)
(+0.084)
0.66s (425%)
31.9
23.25
30.01
0.791
2.72s
0.25
32.5
21.79
28.92 (-1.09)
0.888 (40.097)
2,81s
SDXL
25
0.5
32.6 (40.7)
21.17 (-2.08)
28.39 (-1.62
0.908 (40.117)
2,86s (45%))
(40.)
(-3.35)
27.90 (-2.11)
(+0.115)
(49%)
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H User Study Details
To perform the human preference study for the eext-to-image task, we randomly sample 100 prompts from the Pick-a-Pic v2 test dataset [66], excluding sensitive content. For text-to-video synthesis, select 50 prompts from the Wan2.1 community. All generated samples undergo manual screening to ensure safety present risks to participants.
Each comparison involves two samples: one generated with NAG and the other without NAG. Participants were asked to select the sample with superior quality according to three criteria: (1) alignment with the prompt, (2) visual appeal, and (3) natural motion dynamics (for video). The user study interface is displayed in Figure 16
prompt meporrcnn
Aerbil view. Vidday, spradingg wrarge with anroepheeed pa styeredtyeepppeee perched on nanow wwstnggleerrrmmm.oooooooooo
coseettddoommmooodddeoooo cossected wan.esssaartettteeeeeeeree thanss Sokeisyde
Algement
Wilcwidrs ligh more anaittyy with peorpet
Virall Appoot
Rilccidec austree redereeddd plassin!!
Right
Medion
Left
Drew
Right
Next
Figure 16: User preference study interface. Images and videos are presented in random order.
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I Additional Qualitative Results
We provide additional qualitative examples in this section. Additional text-to-video results are shown in Figure 17; We present NAG applied Wan2.1-12V-145 (6] for image-to to- to-vo generation in Figure 18] Figures 19 and 20 demonstrate side-by-side comparisons highlighting fine ome-grained details;
Figures 21 and 22 provide uncurated results to illustrate NAG's general behavior.
Wan2.1-72V-14B, 25 steps
Baseline
NAG
Enormous glowing jellyfish float slowly across sky filled with soft clouds. Their tentacles shimmer with iridescent light as they drift above peaceful mountain landscape. Magical and dreamlike, captured in wide shot. Surreal realism style with detailed textures. Low resolution, blurry.
Baseline
NAG
The video begins with roll of seemingly ordinary toilet paper modern bathroom countertop. As the camera zooms in, hand holding a knife slices through the roll, revealing it to be a cake. The cut exposes multiple layers of cake and filling inside. This visual illusion art is both surprising and delightful, blending everyday objects with creative craftsmanship for an engaging and fun reveal. Low resolution, blurry.
Baseline
NAG
this vidco, statue stands in picturesque outdoor setting. At the start, its wings are motionless but gleam under the light.
As the video progresses, the statue begins to transform- gradually shatters into countless fragments, creating breathtaking visual spectacle. Static, low resolution, blurry.
appear, glowing effects intensify, and
Figure 17: Additional qualitative results NAG for text-to-video generation.
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Wan2.1- 25 steps
Reference Image
Prompt
A vibrant red fox with thick fur dashes through ruin, its bright eyes focused and ears pinned back in full sprint. The illustration, rendered in fantasy style, uses dynamic blur to simulate camera tracking the fox's rapid movement, capturing the trail of dust kicked up in its wake. The sense of speed and mystery. This scene is filled with motion and energy.
Static, low resolution, blurry
Baseline
NAG
Golden sunset bathes snowfield, blending with icy white for stunning scene. A black sled motorcycle speeds like lightning, its engine roaring through the quier cold. Snow flies as leaves tracks behind. The driver, in heavy black suit, gazes ahead with determination. Distant mountains glow softly under the sun, merging stillness and motion. Close-up captures speed and passion dynamically. Static, low resolution, blurry
lise
NAG
A tiny ginger sits open field. Suddenly, UFO appears above, emitting soft, glowing blue beam of light that envelops the the cat is slowly lifted off the ground into the hovering spacecraft. The UFO ascends and disappears, leaving the empty field in silence. Moody, dramatic lighting, auospperic realism, cinematic visual storytelling.
Cat, static, low resolution, blurry
Baseline
NAG
Figure 18: Qualitative results of NAG for image-to-video.
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Flux-Dev, 25 steps
Baseline
NAG
NAG
NAG
ATTENTION GUDANCE
NESATA SHENECT OPISION
A realisticc neon sign night in a futuristic city. The big letters "NAG" and the small words "Normalized Attention Guidance: Universal Negative Guidance for Diffusion Models." " Low resolution, blurry.
SD3.5-Large-Turbo 8 steps
Baseline
NAG
Close-up portrait of a woman illuminated by soft, warm afternoon light streaming through window blinds, creating striking shadow patterns across her face. She has natural, glowing complexion with dewy skin. The background features muted teal walls that enhance the warmth of the scene. She wears dark, pinstriped blazer, adding touch of elegance. The overall aesthetic is hyperrealistic, capturing intricate details like the texture of her skin and the delicate play of light and shadow, with a color palette of warm neutrals and soft pastels. Low resolution, blurry.
Figure 19: Detailed qualitative analysis of Flux-Dev and SD3.5-Large-Turbo 15 52
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Flux-Schnell, 4 steps
Baseline
NAG
A surreal, modern, small fluffy couch made entirely of pale peach fluffy cloud, shaped like cumulus cloud.
The chuir has four minimalist legs, standing seamless solid muted coral background.
Clean and minimalistic, with soft lighting. Ultra high resolution, product design aesthetic, concept art. Shot by Sony a7R IV, contrast with highlight, casting realism sunlight, sharp focus, clear detailed, cinematic, glamorous, editorial shoot, 16K, rich detail. Low resolution, blurry.
SprinS, 2 steps
Baseline
NAG
protagonist, at the beginning of their story, about to embark their journy.. Low resolution, blurry.
Figure 20: Detailed qualitative analysis of Flux-Schnell and SANA-Sprint 53
24

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Flux-Dev, 25 steps
Baseline
NAG
Baseline
NAG
Baseline
NAG
Baseline
NAG
SD3.5-Large-Turban 8 steps
Baseline
NAG
Baseline
NAG
Baseline
NAG
Baseline
NAG
Figure 21: Uncurated qualitative results for Flux-Dev [ and SD3.5-Large-Tuus 15 All prompts are detailed in Anppppddxx J.I NAG-guided sample utilizes the negative prompt "oomp"Low- "Low- resolution, blurry."
52
25

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Flux-Schnell, 4 steps
Baseline
NAG
Baseline
NAG
Baseline
NAG
Baseline
NAG
al
15
SANNASprrin, steps
Baseline
NAG
Baseline
NAG
Baseline
NAG
Baseline
NAG
Figure 22: Uncurated qualitative results for Flux-Schnell and SANA-Sprint 53 All prompts are detailed in Appendix J.I NAG-guided sample utilizes the negative prompt "Low-resolution, blurry."
26

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J Prompt Details
J.1 Prompts for Figures
Figure 6
1. An anthropomorphic cat thoughtfully paints an oil self-portrait on canvas, capturing its likeness with delicate brushstrokes inside warmly lit, artistically cluttered studio.
2. An origami fox running in the forest. The fox is made of polygons. Speed and passion. Realistic.
Figure 21 and Figure 22
First row:
1. A cabinet in which all the planets of the solar system are collected
2. a futuristic interpretation of a dodo bird. Cyborg bird. Amazing colorful. Artstation, hyperrealistic
3. happy female wizard surrounded by pieces of paper flying in the air around her
4. a recruitment consultant, sitting before a screen full of analysis diagram, carrying mobile device, fuji film style, like moss in wandering earth
Second row:
1. Large birthday cake for cardiothoracic surgeon.
2. anthropomorphic white rabbit, male wizard face, dressed in black and white, fine winning intricate, elegant, sharp focus, cinematic lighting. highly detailed, digital painting, 8 k concept art, art by guweiz and gu, masterpiece, trending artstation, 8 k
3. owl transforms into an eagle
4. a photorealistic dragon pup
Third row:
1. in a room a MGb car smashing through hole in the wall .sparks dust rubble bricks ,tudio lighting, white walls, mg logo
2. werewolf reading book
3. Black and white 1905 year portrait of futuristic professional photographer with camera in hand sadly seating deep in dark pit covered by splash of dust
4. panda riding motorcycle
Fourth row:
1. wide angle photo of roman soldiers in front of courtyard roman buildings.technicllor film ,roman soldier in foreground masculine features nose helmet and silver sword eyes,clear sky, arches grass steps field Camooram,,Canaletton detailed photo, julius Caesar ceremonial, parade, roma, detailed photo, temples roads hes- tia,single point perspective, colonnade, necropolis, ultra realism, imperium .by claude-joseph vernet and thomas cole .pediment sky clouds,stones in the foreground,dusty volumetric lighting
floor, vanishing ben-hr flags
2. cuddly stuffed dinosaur talking to microphone
3. blue apple, red banana
4. Anime cute little girl
Fifth row:
1. a lonely man inside a old bucolic house surrounded by cats by Richard Billingham
2. full body space suit with boots, futuristic, character design, cinematic lightning, epic fantasy, hyper realistic, detail 8k
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3. young danish traveller standing at an immigration counter in ho chi minh city
4. Amigurumi figure of a little pig wearing a red sweater, professional photography, close up, vintage, 8k, product photo
J.2 Prompts for User Study
Table 10: Positive-negative prompt pairs used in the user study comparing NAG and NASA.
Positive prompt
Negative prompt
Male
A photo of person
Female
Young
A photo of person
Old
Dark
A photo of a person
Bright
Cat
photo of a pet
Dog
Black and white
A photo from the 1960s
Color
Apples
A still painting of fruits
Grapes
Trees
A landscape
Mountains
Bed
A room with furniture
Window
28

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Reviews:
[{"review_id": "Nk3JVcs5Qg", "reviewer": "Reviewer_oLD6", "summary": "This paper proposes Normalized Attention Guidance (NAG), a novel inference-time approach for negative prompting in diffusion models. Unlike traditional Classifier-Free Guidance (CFG) or Perturbed Attention Guidance (PAG) methods that operate in the output space, NAG directly performs extrapolation within the attention feature space between positive and negative conditioned attention maps. To ensure stable extrapolation, the authors introduce L1-based normalization and refinement blending, constraining attention vectors within stable manifolds. Comprehensive experiments demonstrate the effectiveness and generalizability of NAG across multiple model architectures (UNet, DiT), sampling regimes (few-step, multi-step, guidance distilled models), and modalities (image, video).", "questions": "Q1. Beyond geometric interpretation, can authors provide a formal theoretical justification or probabilistic interpretation for extrapolating directly in attention feature space? Is there any formal rationale for why this manipulation maintains semantic coherence and stability?\n\nQ2.Considering PLADIS (ICCV 2025) already introduced attention-space extrapolation, how precisely does NAG differ methodologically from PLADIS? Is NAG essentially a special case of PLADIS, with sparse positive and dense negative attention maps, or are there fundamental differences beyond the application task?\n\nQ3. What specific hyperparameter values (particularly the refinement blending factor α) are used across experiments, and why were these particular choices made? How sensitive is performance to variations in α?\n\nQ4. Given the empirical observations in Figures 8 and 9 showing peak performance at relatively low guidance scales (around 2–4), what is the practical advantage of employing the additional complexity of normalization and refinement mechanisms to enable higher guidance scales?\n\nQ5. How does NAG perform independently without CFG or PAG? Can standalone NAG achieve competitive results, or is the method inherently complementary rather than independently effective?\n\nQ6. While NAG is designed explicitly for negative prompting, can the authors extend or generalize the NAG approach to standard (positive-only) text-to-image generation scenarios, without explicitly provided negative prompts?", "limitations": "- Theoretical underpinnings beyond geometric intuition are completely absent, weakening interpretability.\n\n- Novelty claims must be revised considering PLADIS’s prior introduction of attention-space extrapolation.\n\n- Lack of standalone evaluations limits the clarity of intrinsic method effectiveness.\n\n- Incomplete quantitative assessments (particularly video evaluation) and specialized metrics are significant gaps.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "\\** Strength\n\n\\#1 Attention-space Extrapolation:\nNAG’s core innovation, extrapolating directly in attention feature space, offers rich semantic control over diffusion models, surpassing output-space extrapolation approaches in terms of stability and flexibility.\n\n\\#2 Strong Empirical Generalization:\nExtensive experiments show NAG's generalizability across architectures, sampling scenarios (including aggressive few-step regimes and distilled models), and modalities such as images and videos, which are clearly valuable contributions.\n\n\\#3 Practicality and Ease of Integration:\nNAG is computationally lightweight, training-free, and easily integrated into existing diffusion pipelines. These practical benefits significantly enhance the approach's applicability.\n\n\\** Major Weakness\n\n\\#1 Lack of Theoretical Grounding beyond Geometric Interpretation\n\n- While the authors provide a helpful geometric interpretation of attention-space extrapolation, they overly rely on this intuition without offering formal theoretical analysis or probabilistic justification.\n\n- CFG and PAG clearly interpret extrapolation in output space within a well-understood diffusion probabilistic framework. In contrast, extrapolation in attention space fundamentally differs and lacks an equivalent probabilistic interpretation.\n\nThe authors exclusively rely on empirical observations and geometric explanations without theoretical guarantees or detailed formal analysis, making the underlying principles unclear.\n\n\\#2  Inaccurate Novelty Claim regarding Attention-Space Extrapolation \nThe authors claim NAG is the first method performing extrapolation in attention feature space. However, the method presented in:\n- PLADIS: Pushing the Limits of Attention in Diffusion Models at Inference Time by Leveraging Sparsity (ICCV 2025)\nalready introduced attention-space extrapolation explicitly. Thus, novelty claims must be significantly narrowed or revised clearly.\n\n- Indeed, NAG seems conceptually a special case of PLADIS, where positive attention maps are sparse and negative attention maps are dense. Explicit citation of PLADIS and detailed methodological differences are crucial, not just differences in tasks or applications.\n\n\\#3 Insufficient Justification of Normalization and Refinement Components\nWhile the normalization and blending (refinement) steps are central to NAG’s success, explicit hyperparameter details—particularly the alpha (α) value for refinement blending—are not clearly reported in the main text.\n\n- The manuscript fails to explain intuitively or theoretically why normalization and refinement specifically enhance stability.\n\n- Although the authors argue these mechanisms allow higher guidance scales, results in Figure 8 show performance peaks around relatively modest scales (2–4), questioning the practical value of further increasing the guidance scale (e.g., to 16). Qualitatively, Figure 9 similarly indicates diminishing returns after moderate scales, undermining this core claimed advantage.\n\n\\#4 No Standalone Performance Evaluation for NAG\nAll provided experimental results combine NAG with other guidance methods (CFG or PAG).\n\n- Critical information on NAG's standalone performance without additional guidance methods is absent, limiting understanding of NAG’s intrinsic value and effectiveness.\n\n\\** Minor Weakness\n\n\\#1  Lack of Specialized Quantitative Metrics:\nThe current evaluation relies heavily on general-purpose metrics like ImageReward. More specialized metrics specifically designed for diffusion and text-image alignment (e.g., PickScore, HPSv2) would provide deeper insights into alignment quality.\n\n\\#2 Lack of Quantitative Video Evaluation:\nVideo generation results rely solely on qualitative assessments, lacking quantitative metrics such as VBench. Providing quantitative results would strengthen reliability and interpretability.", "was_revised": true, "final_justification": "During the rebuttal period, the author provided both experimental and theoretical findings to address my remaining concerns. Therefore, I am voting for a positive rating.", "rebuttal": "We’re grateful for the thorough reviews and recognition of our strong empirical generalization. \n\n### Q1: Beyond geometric interpretation, can authors provide a formal theoretical justification or probabilistic interpretation for extrapolating directly in attention feature space? Is there any formal rationale for why this manipulation maintains semantic coherence and stability?\n\nBelow, we provide a theoretical interpretation for NAG attention space extrapolation and will add it to the paper.\n\n**Implicit Classifier Gradient**\n\nFor a pretrained diffusion network $G_\\theta$, and timestep $t$ we denote the conditional and unconditional noise predictions as  \n\n$\\hat\\epsilon = G_\\theta(x_t, c),\\qquad\\hat\\epsilon^\\varnothing =G_\\theta(x_t, \\varnothing).$  \n\nBy Bayes’ rule, the implicit classifier over $x_t$ can be defined as  \n\n$p^i(c\\mid x_t) \\propto \\frac{p(x_t\\mid c)}{p(x_t\\mid \\varnothing)}.$  \n\nIts gradient can be approximated with  \n\n$\\nabla_{x_t}\\log p^i(c\\mid x_t)= \\nabla_{x_t}\\log p(x_t\\mid c) - \\nabla_{x_t}\\log p(x_t\\mid \\varnothing) \\approx -\\bigl(\\hat\\epsilon - \\hat\\epsilon^{\\varnothing}\\bigr).$\n\nCFG then performs a heuristic gradient ascent step, leading to the guidance scaled by $\\phi$\n\n$\\tilde\\epsilon= \\hat\\epsilon + \\phi \\bigl(\\hat\\epsilon - \\hat\\epsilon^\\varnothing\\bigr).$ \n\n**Generalizing to Intermediate Features**\n\nRather than only adjusting the final noise estimate, we can view each intermediate feature map $F_l$ of layer $l$ as also carrying implicit classifier gradient. Denote\n\n$F_l = G_\\theta^l(x_t, c),\\qquad F_l^{\\varnothing} = G_\\theta^l(x_t, \\varnothing).$\n\nAnalogously, we approximate\n\n$\\nabla_{x_t}\\log p^i(c\\mid x_t)\\approx -\\bigl(F_l - F_l^{\\varnothing}\\bigr),$  \n\nso an ascent step in feature space yields\n\n$\\widetilde F_l= F_l + \\phi \\bigl(F_l - F_l^{\\varnothing}\\bigr).$\n\n**Negative Guidance**\n\nTo steer generation *away* from unwanted attributes, we replace the null condition with negative condition $c^-$. Given  \n\n$F_l^+ = G_\\theta^l(x_t, c^+),\\qquad F_l^- = G_\\theta^l(x_t, c^-),$\n\nthe unified negative guidance becomes\n\n$\\widetilde F_l = F_l^+ + \\phi \\bigl(F_l^+ - F_l^-\\bigr).$  \n\nThis directs $F_l$ both toward the positive semantics and away from negative features in one update.\n\n**Layer‑Wise Updates for Stability**\n\nCFG and NAG can be viewed as two special cases of where to apply the unified guidance.\n\n- CFG (output‑space) applies a single global adjustment at the final layer. Under aggressive few‑step sampling, the positive and negative predictions have already drifted apart, causing severe artifacts.  \n- NAG (attention‑space) instead interleaves guidance steps in attention layers. Because $F_l^+$ and $F_l^-$ remain closer in feature space early on, each update is well‑conditioned and prevents runaway divergence.\nThe cumulative effect of many modest, layer‑wise adjustments retains semantic coherence and provides stable negative guidance even with very few denoising steps.\n\nBy viewing each attention map as contributing an implicit gradient, NAG generalizes CFG’s single output guidance into a sequence of feature space guidance.\n\n\n### Q2: Considering PLADIS (ICCV 2025) already introduced attention-space extrapolation, how precisely does NAG differ methodologically from PLADIS? \n\nWe appreciate the pointer to PLADIS (published in March 2025) and we will include it and clarify the distinctions in our related work.\n\nWhile PLADIS also operates in the cross‑attention domain, it contrasts dense versus sparse attention maps extracted from a single prompt. PLADIS treats the dense map as a perturbation of its sparse counterpart, in a manner reminiscent of Perturbed‑Attention Guidance (PAG).\n\nBy contrast, NAG explicitly exploits the semantic difference between a positive prompt and a negative prompt, providing targeted suppression of undesired attributes during generation, a capacity that PLADIS and PAG do not support. When a negative prompt is absent, NAG reduces to the perturbation‑style mechanism by treating the null condition as the denser attention reference. This dual capability sits naturally within our unified negative‑guidance framework.\n\n\n\n### Q3: What specific hyperparameter values (particularly the refinement blending factor α) are used across experiments, and why were these particular choices made? How sensitive is performance to variations in α?\n\nAll default NAG hyperparameters, including the refinement blending factor $\\alpha$ and normalization threshold $\\tau$, are listed in Appendix C (lines 521–523). Defaults $\\tau = 2.5$, $\\alpha \\in [0.125, 0.5]$ vary by model family. In Appendix F (lines 542–552), we present an ablation study over $\\alpha$ and $\\tau$, demonstrating that performance remains stable for values of $\\alpha$ below $0.5$ and $\\tau$ around $2.5$.\n\nAlthough a full grid search across every model family would be computationally prohibitive, we conducted smaller‑scale explorations to identify an effective range, then chose the defaults based on visual effect. In practice, users may begin with these settings and adjust them as needed to suit personal preferences.\n\n### Q4: What is the practical advantage of employing the additional complexity of normalization and refinement mechanisms to enable higher guidance scales?\n\nHigh guidance scales often introduce visual artifacts or even collapse, limiting how aggressively users can steer generation. NAG’s normalization and refinement steps prevent these artifacts. As a result, users can apply much more aggressive negative guidance without risk, achieving robust suppression of unwanted attributes.\n\n### Q5: How does NAG perform independently without CFG or PAG?\n\nNAG was designed to work as a standalone negative guidance in few‑step (1–8 steps) sampling where CFG and PAG fail (lines 54-55).\n\nIn our few‑step experiments (Section 5.1) on both UNet and DiT backbones, applying NAG by itself (without CFG or PAG) demonstrates substantial gains in CLIP alignment, FID/PFID, and ImageReward compared to the baseline.\nIn multi‑step regimes (Section 5.3), NAG can be combined with CFG or PAG for even stronger guidance, but on few‑step models NAG is the only method that delivers universal negative guidance.\n\n### Q6: While NAG is designed explicitly for negative prompting, can the authors extend or generalize the NAG approach to standard (positive-only) text-to-image generation scenarios, without explicitly provided negative prompts?\n\n**Text-to-Image Flux‑Schnell on COCO‑5K**\n\nGood point! NAG can be applied with positive‑only generation simply by using an empty negative prompt (i.e. \"\" instead of \"Low resolution, blurry\").\nEven with the empty negative prompt, NAG boosts CLIP and ImageReward, confirming enhanced alignment and perceptual quality.\n\n\n| Flux-Schnell 4-step | CLIP ↑ | FID ↓ | PFID ↓ | ImageReward ↑ |\n|:--------------------:|:---:|:---:|:----:|:-----------:|\n| Baseline            |   31.4  |  25.47   |   38.26   |      1.029       |\n| NAG (negative)      |   32.0 (+0.6)  |  24.46 (-1.01)   |   34.95 (-3.31)   |      1.099 (+0.70)       |\n| NAG (empty)         |   31.8 (+0.4)  |  26.62 (+1.15)   |   37.94 (-0.32)   |      1.083 (+ 0.54)       |\n\n**Class-conditional MeanFlow‑B/4 on ImageNet**\n\nWe also evaluate NAG for class‑conditional generation on ImageNet using the single‑step MeanFlow‑B/4 [1] model. By viewing the null class as the negative condition, NAG reduces FID and significantly improves IS.\n\n| MeanFlow-B/4 1-step | FID ↓ | IS ↑ |\n|:--------------------:|:---:|:---:|\n| Baseline            |  13.33   |  135.69   |\n| NAG         |   12.02 (-1.32)  |  155.97 (+20.28)   |\n\nThese results confirm that NAG seamlessly supports positive‑only guidance.\n\n\n[1] Mean Flows for One-step Generative Modeling. arXiv 2025\n\n### Additional Quantitative Metrics\n\nThanks you for the suggestions. We provide the specialized quantitative metrics HPSv2 and PickScore for few‑step models.\n\nIn experiments, HPSV2 exhibits consistent gains with NAG across all models, while PickScore shows mixed results. Across the three primary human‑preference metrics (ImageReward in Table 1, HPSV2, and PickScore), NAG produces reliable improvements in most of them (both ImageReward and HPSV2).\n\nMetrics reported for Baseline / NAG.\n\n|  Arch | Model             | Steps | HPSV2 ↑                   |        PickScore ↑        |\n| :---: | :----------------: | :---: | :------------------------: | :-----------------------: |\n|  DiT  | SANA‑Sprint       |   2   | 27.86 / **28.50** (+0.64) | 22.89 / **22.80** (−0.09) |\n|  DiT  | Flux‑Schnell      |   4   | 30.16 / **31.00** (+0.84) | 22.64 / **22.57** (−0.07) |\n|  DiT  | SD3.5‑Large‑Turbo |   8   | 29.66 / **30.22** (+0.56) | 22.83 / **22.95** (+0.12) |\n| U‑Net | NitroSD‑Realism   |   1   | 29.46 / **29.69** (+0.23) | 22.49 / **22.45** (−0.04) |\n| U‑Net | DMD2‑SDXL         |   4   | 29.85 / **29.98** (+0.11) | 22.83 / **22.82** (−0.01) |\n| U‑Net | SDXL‑Lightning    |   8   | 28.83 / **28.95** (+0.12) | 22.65 / **22.64** (−0.01) |"}, {"review_id": "97biqSoo8g", "reviewer": "Reviewer_P6ev", "summary": "This paper builds on NASA [1], which is an attention guidance/extrapolation method for negative prompting, by applying two additional operations: (1) feature normalization by clipping and re-scaling the extrapolated feature, and (2) feature blending via interpolating between the re-scaled feature and the positive guidance feature. These constraints restrict the features to be within the manifold of meaningful representations, while maintaining the effectiveness of negative guidance. It is a training-free, simple, and effective method. Experiments are conducted on different architectures (UNet and DiT), different sampling steps, and on both image and video generations, demonstrating the adaptability of the proposed method.\n\n[1] SNOOPI: Supercharged One-step Diffusion Distillation with Proper Guidance. Arxiv 2024.", "questions": "I am happy to raise the score if the authors can sufficiently address the following questions and the concerns in the weaknesses section:\n\n1. Any performance gaps when the guidance scale $\\phi<1$ (i.e., doing interpolation)? From Figure 2, it seems the gap only exists when the scale is large, while one can still have effective results by using the baseline NASA method by simply setting this hyperparameter properly. \n2. Have you experimented with the proposed method on stronger few-step generation DiT and UNet backbones to show consistent performance enhancement to support the claim that NAG can be uniformly applied?\n3. Can you provide analyses of the hyperparameters additionally introduced by NAG?\n4. Can you provide additional analysis of the proposed two stabilization mechanisms to demonstrate the technical contribution in this paper in addition to NASA [1]?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n1. The writing of the paper is clear and well-structured, making the paper easy to follow and understand. The visual examples are helpful in illustrating the limitation of the standard CFG and the baseline NASA method.\n2. The proposed NAG is training-free, simple and effective according to several metrics such as CLIP Score, FID, PFID, and user study.\n3. The proposed NAG is adaptable to different settings of diffusion models, such as different architectures (UNet and DiT), different sampling steps (one-step and few-steps), and different modality (image and video generation).\n\nWeaknesses\n\n1. The improvements over the NASA [1] baseline is somewhat incremental, as only the last two operations (normalization and blending) are additionally proposed in NAG compared to NASA. Also, these two operations are more like tricks instead of technical improvements. Specifically, the normalization step is essentially doing a clipping/rescaling, while the blending step (Equation 10) seems to up-weight the positive branch from Equation 7, whose effect may be achieved by additional hyperparameter tuning in Equation 7. In such case, I would suggest the authors conduct more analysis (include but not limited to why L1 can retain essential structure for precise guidance as claimed) to show the technical contribution of these two operations, which should be emphasised more than the design of attention-based guidance by applying extrapolation in attention feature space, which NASA uses. Currently, such analysis is quite insufficient, as shown in the short methodology section 4.1.\n2. The proposed method introduces an additional blending scale hyperparameter $\\alpha$. It would be good to see additional analysis of the performance sensitivity to this when it is set to 0.1, 0.3, … 0.9, which is currently missing. Also, during the normalization step, an additional clipping threshold $τ$ is employed to limit feature magnitude. The sensitivity analysis of such hyperparameter is also missing.\n3. In Figure 1, the caption includes “CFG fails in few-step models”, which is, however, not reflected in the Figure. It’d be better if the authors could include the failure examples of CFG for these specific examples for qualitative comparison, or simply remove the caption to reduce the emphasis on comparing with CFG and make it a standalone plot for demonstrating the result of the proposed NAG.\n4. The denominator R in Equation 9 is not defined. Should it be the previously defined R[i]?", "was_revised": true, "final_justification": "The rebuttal has well addressed my listed concerns. I am now more inclined to recommend acceptance of this paper.", "rebuttal": "We appreciate the suggestion to illustrate CFG failures in Figure 1. And we will correct the notation around $R[i]$ to ensure clarity.\n\n\n### Q1: Any performance gaps when the guidance scale $\\phi < 1$ (i.e., doing interpolation)? From Figure 2, it seems the gap only exists when the scale is large, while one can still have effective results by using the baseline NASA method by simply setting this hyperparameter properly.\n\nFor the UNet SDXL‑DMD2 model, we include an extended comparison against NASA in Appendix F (lines 530–541), covering both quantitative metrics and a user study, which illustrate improvements of NAG over NASA across all evaluated criteria.\n\nAdditionally, we evaluate DiT Flux‑Schnell under small guidance scales here. Importantly, $0<\\phi<1$ does not strictly correspond to interpolation—it simply applies a lighter guidance strength. In the experiments, NASA at low $\\phi$ does improve CLIP score but degrades FID and ImageReward, and crash in $\\phi = 0.5$. In contrast, NAG demonstrates stable enhancements and achieves the best overall metrics on $\\phi = 4$, boosting both alignment and image quality. As a result, NAG is robust to a wide range of $\\phi$, significantly reducing tuning overhead.\n\nMetrics reported for Baseline / NAG.\n\n| Flux-Schnell 4-step | CLIP ↑ | FID ↓ | PFID ↓ | ImageReward ↑ |\n|:--------------------:|:---:|:---:|:----:|:-----------:|\n| Baseline        |  31.4  |  25.47   |   38.26   |      1.029       |\n| $\\phi$=0.125        |  31.7 / 31.5  |  27.10 / 25.48   |   36.49 / 38.15   |      0.929 / 1.035       |\n| $\\phi$=0.25         |  31.7 / 31.5   |  33.04 / 25.53   |   36.65 / 38.11   |      0.591 / 1.042       |\n| $\\phi$=0.5          |  21.7 / 31.5   |  297.36 / 25.50   |   305.3 / 37.75   |      -2.08 / 1.057       |\n| $\\phi$=4            |  - / **32.0**   |  - / **24.46**   |   - / **34.95**   |      - / **1.099**       |\n\n### Q2: Have you experimented with the proposed method on stronger few-step generation DiT and UNet backbones to show consistent performance enhancement to support the claim that NAG can be uniformly applied?\n\nBeyond the experiments in Section 5.1 of the main paper, we have evaluated NAG on a wide range of few‑step backbones in Appendix D (lines 524–529). Overall, we include SOTA models from academia and the community, such as DiT architecture Flux, SD3.5-Turbo (SIGGRAPH Asia 2024) and SANA-Sprint (ICCV 2025), as well as UNet architecture NitroFusion (CVPR 2025), DMD2 (NeurIPS 2024) and HyperSD (NeurIPS 2024). On average, we can observe +0.5 CLIP, −1.5 FID, -2.5 PFID, and +0.1 ImageReward gains.\n\nAdditionally, in the rebuttal to the Reviewer oLD6, we demonstrate improved performance, -1.3 FID and +20 IS, for the newly released single-step class‑conditional MeanFlow.\n\n### Q3: Can you provide analyses of the hyperparameters additionally introduced by NAG?\n\nWe include an ablation study on hyperparameters in Appendix F (lines 542–552). In particular, Figure 13 visually analyzes the performance sensitivity of normalization threshold $\\tau$ and refinement factor $\\alpha$. We hope this analysis, along with visual results, clarifies NAG’s sensitivity to hyperparameter choices.\n\n### Q4: Can you provide additional analysis of the proposed two stabilization mechanisms to demonstrate the technical contribution in this paper in addition to NASA?\n\nUnlike NASA, which directly subtracts negative attention maps, NAG applies extrapolation grounded in the implicit classifier view (please refer the Q1 of reviewer oLD6 for more details). This yields more stable editing in attention space. The subsequent normalization and refinement further reinforce this stability.\n\nThese operations share two critical properties:\n\n1. Firm Nonexpansiveness: Each update can only bring features closer together, never amplifying deviations beyond the model’s learned manifold.\n2. Direction Preservation: By projecting onto norm‑balls or taking convex combinations, the original edit direction remains intact, ensuring semantic coherence.\n\nWhile these properties guarantee features stay near the learned manifold, they do not ensure that NAG's attention space edits will always yield a stable guidance signal.\n\nEmpirically, however, our ablations confirm that both normalization and refinement are indispensable for stability. In practice, NAG remains a simple yet powerful mechanism for robust and controllable negative guidance across architectures and sampling regimes.\n\nWe believe NAG is just the beginning, and encourage future research to explore alternative attention space constraint formulations to achieve more stable guidance."}, {"review_id": "XFpPrZT688", "reviewer": "Reviewer_63rU", "summary": "This paper introduces Normalized Attention Guidance (NAG), a plug-in technique for attention-based diffusion models that achieves effects similar to negative prompting through simple attention modulation. The method employs L1 normalization and linear combination to process attention maps from both unconditional and conditional branches.", "questions": "- Why does modulating self-attention outputs preserve generation quality and not corrupt the learned data manifold?\n- Why specifically L1 normalization? Do you have any ablation studies comparing other normalization variants? There are also variants that have as benign an effect as L1 normalization on low magnitude signals.\n- If NAG still requires CFG, what fundamental classes of problems does it solve that CFG alone cannot? If there's no such distinction, why not study the prediction of classifier-free guidance and then modulate attention layers within it?", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 1, "strengths_and_weaknesses": "## Strengths\n- This paper provides a straightforward approach through normalization and linear combination of attention maps.\n- Since the method is based on simpler assumptions, this paper shows broad applicability across diffusion model variants, including single-step and video generation tasks.\n- NAG can handle edge cases where CFG with negative prompts cannot.\n\n## Weaknesses\n- Incremental contribution is of major concern. The core innovation appears to be adding a normalization step to NASA, which represents an incremental rather than fundamental contribution.\n- Modulating self-attention outputs fundamentally alters the learned data manifold, yet the paper lacks rigorous analysis of why these modifications preserve generation quality. Figure 4 somewhat addresses this, and I understand that L1 normalization constraints may limit extreme deviations, but this doesn't fully justify why such modulation is admissible.\n- Unlike Classifier-Free Guidance (CFG), which derives from well-established Classifier Guidance principles enabling principled extrapolation of noise predictions, this approach lacks comparable theoretical grounding.\n- The technique still requires CFG integration rather than serving as a standalone replacement, raising questions about its fundamental necessity.\n- Ablation studies comparing L2 normalization and max normalization variants are absent, limiting understanding of the method's sensitivity to normalization choices.", "was_revised": true, "final_justification": "After reading other reviewers' comments and the authors' responses, several of my concerns have been resolved. However, concerns about the incremental contribution remain. I have decided on a final rating of borderline reject but do not strongly oppose acceptance.", "rebuttal": "Thank you for recognizing NAG’s broad applicability and for these insightful questions.\n\n### Q1: Why does modulating self-attention outputs preserve generation quality and not corrupt the learned data manifold?\n\nTo clarify, NAG operates on cross-attention or multimodal DiT (MMDiT) layers, not on self-attention layers; the notation $Z$ in our paper denotes the cross-attention features (Equation 4).\n\nAs detailed in our response to Reviewer oLD6, NAG can be viewed as a variant of CFG’s implicit classifier gradient applied at intermediate cross-attention layers rather than only at the final output. NAG performs a series of small, implicit classifier‑guided steps where the positive and negative features remain semantically aligned, preventing runaway divergence from the manifold. The subsequent normalization and refinement bounds feature magnitudes near the model’s learned feature space, alleviating the risk of manifold corruption.\n\nMoreover, these modifications are continuously smoothed by the model’s linear projections, LayerNorm, nonlinear activations, and other layers before the next attention operation. These architectural “filters” mix, re-center, and softly cap value spikes, preventing outlier attention features from cascading into large distributional shifts. distributional shifts.\n\n### Q2: Why specifically L1 normalization? Do you have any ablation studies comparing other normalization variants?\n\nThank you for pointing this out! We have run ablations comparing L1 against L2 and max-norm, but did not include them in the submission; we will add these results and accompanying discussion in the paper.\n\nWe find that swapping to L2 yields visually similar results but with slightly worse metrics, while max‑norm will soften fine details.\nOverall, any norm scales the attention features, providing constraints without changing semantic direction, so NAG’s performance is not really sensitive to the exact choice.\n\nL1 was chosen because it conceptually best preserves proportional relationships and subtle semantics (lines 167 - 170), provides the best quantitative results, and it’s the most efficient to compute.\n\n| Flux-Schnell 4-step | CLIP ↑ | FID ↓ | PFID ↓ | ImageReward ↑ |\n|:--------------------:|:---:|:---:|:----:|:-----------:|\n| NAG (L1)            |   **32.0**  |   **24.46**  |   **34.95**   |      1.099       |\n| NAG (L2)            |   31.9  |   25.20 |   36.93   |       **1.104**      |\n| NAG (Max)           |   31.9  |   25.35  |   37.18   |      1.089       |\n\n### Q3: If NAG still requires CFG, what fundamental classes of problems does it solve that CFG alone cannot? If there's no such distinction, why not study the prediction of classifier-free guidance and then modulate attention layers within it?\n\nWe apologize for the lack of clarity. NAG works completely independently under few‑step sampling (Figure 6, Table 5), where CFG collapses (Left of Figure 3). In essence, NAG serves as a standalone alternative to CFG for negative guidance in few-step sampling.\n\nUnder multi-step sampling, NAG can also be used in conjunction with CFG. When paired with CFG, NAG further enhances qualitative and quantitative results (Figure 7, Table 6). This dual capability highlights NAG’s universality."}, {"review_id": "r6EVvvVzVn", "reviewer": "Reviewer_zEeN", "summary": "This paper introduces Normalized Attention Guidance (NAG), a novel method for negative guidance in diffusion models. It addresses the limitations of existing approaches, such as Classifier-Free Guidance (CFG), which fail in few-step sampling regimes. NAG operates directly within the attention feature space, applying controlled extrapolation complemented by L1-norm-based normalization and feature refinement to prevent feature drift issues. Extensive experiments demonstrate its robustness and ability to generalize effectively.", "questions": "None", "limitations": "- While NAG operates effectively in attention feature space, its applicability may be limited to models or architectures that leverage attention mechanisms. It may not generalize to non-attention-based architectures or other types of generative models.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n- This paper is well written and easy to follow.\n- The authors conduct comprehensive experiments across a wide range of models (UNet, DiT), sampling regimes (few-step to 25-step), and modalities (image and video). Metrics used include CLIP Score, FID, PFID, and ImageReward, providing multi-faceted validation.\n- NAG adds minimal computational overhead and does not require retraining, making it highly practical for deployment.\n\nWeaknesses\n- As shown in Figure 8, the effect of normalization does not appear to be significant. Additionally, the experiment \"w/o Norm & with Refine\" is missing.\n- While generally effective, the paper acknowledges that NAG struggles with very aggressive prompts or stylistic suppression, which may lead to instability or visual artifacts (Figure 11).", "was_revised": true, "final_justification": "The rebuttal has satisfactorily addressed my concerns, and I lean toward accepting the paper.", "rebuttal": "We thank for acknowledging the paper’s clear presentation and comprehensive evaluation of NAG. Below we address the two points with new ablations and expanded applicability experiments.\n\n### As shown in Figure 8, the effect of normalization does not appear to be significant. Additionally, the experiment \"w/o Norm & with Refine\" is missing.\n\nThank you for catching the missing ablation. We have now added the “w/o Norm” ablative study alongside the Full NAG:\n\n**$\\phi = 4$**\n\n| Flux-Schnell 4-step | CLIP ↑ | FID ↓ | PFID ↓ | ImageReward ↑ |\n|:--------------------:|:---:|:---:|:----:|:-----------:|\n| Full NAG            |   32.0  |   24.46  |   34.95   |      1.099       |\n| w/o Norm.           |   31.9  |   25.78  |   37.32   |      1.107       |\n\n**$\\phi = 8$**\n\n| Flux-Schnell 4-step | CLIP ↑ | FID ↓ | PFID ↓ | ImageReward ↑ |\n|:--------------------:|:---:|:---:|:----:|:-----------:|\n| Full NAG            |   32.2  |   24.13  |   33.92   |      1.069       |\n| w/o Norm.           |   32.1  |  26.12   |   37.22   |      1.081       |\n\nRemoving normalization degrades both fidelity metrics (FID, PFID), and leads to even larger drops at higher scales. The degraded fidelity indicates that the guidance becomes unstable and disrupts the feature flow through the network. Normalization therefore acts as a critical safeguard by bounding feature magnitudes.\n\n### While NAG operates effectively in attention feature space, its applicability may be limited to models or architectures that leverage attention mechanisms.\n\nActually NAG is not inherently tied to attention mechanism. Because it exploits the semantic difference between two conditions, the same guidance algorithm can be applied to any conditioning module.\n\nWe examine two conditioning mechanisms in SDXL: cross‑attention and adaptive normalization.\n\n- Adaptive‑Norm Only: applying NAG just in adaptive normalization layer already yields consistent gains.\n- Attention + Adaptive‑Norm: There is no obvious improvement compared to attention only NAG, implying that guidance in adaptive normalization contributes minimal additional benefit over attention space NAG.\n\nWe focused our paper on attention layers because it is the dominant conditioning mechanism in today’s generative models, but these results clearly demonstrate that NAG can be ported to different conditioning mechanisms without modification of the core algorithm.\n\n| SDXL-DMD2 4-step | CLIP ↑ | FID ↓ | PFID ↓ | ImageReward ↑ |\n|:-----------------:|:---:|:---:|:----:|:-----------:|\n| Baseline         |  31.6   |  24.79   |   27.11   |         0.876    |\n| NAG (adaptive)   |  31.8   |  24.73   |   26.79   |         0.892    |\n| NAG (attention)  |  **32.2**   |  23.32   |   **25.61**   |         **0.960**    |\n| NAG (attention + adaptive)     |  **32.2**   |   **23.27**  |   25.62   |    0.945         |"}]

Author rebuttal:

Meta-review: In this work, authors tackle the problem of steering the visual generation process better in adherence to a negative prompt. They propose a method based on attention space manipulation during generation and show its efficacy over CFG. Reviewers were overall in agreement of acceptance of the paper and the discourse between the authors and the reviewers also elicited additional theoretical motivations beyond the intuition and experimental results. The proposed algorithm is intuitive, useful, practical and I recommend acceptance.
Decision: Accept (poster)