Normalized Attention Guidance: Universal Negative Guidance for Diffusion
  Model
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 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!
6838c66dc60fb2fc462cec9f
Dar-Yen Chen
claimed_verified
2025-06-02T07:47:40.163Z
64d0eb731ed6649d70afb136
/avatars/4c591c86c575c82760126c39af5a02b4.svg
Chen Dar-Yen
user
ChenDY
6838c66dc60fb2fc462ceca0
Hmrishav Bandyopadhyay
claimed_verified
2025-06-02T13:40:02.354Z
638c81fa61eb51017518fa31
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/f0eCrzBrxz7Y9n25WkZ2v.png
Hmrishav Bandyopadhyay
user
Hmrishav
6838c66dc60fb2fc462ceca1
Kai Zou
6838c66dc60fb2fc462ceca2
Yi-Zhe Song
Normalized Attention Guidance: Universal Negative Guidance for Diffusion
  Model
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 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 (NAG) enhances diffusion models by providing effective negative guidance across regimes and modalities without retraining.
negative guidance diffusion models few-step sampling Classifier-Free Guidance (CFG) Normalized Attention Guidance (NAG) attention space L1-based normalization extrapolation fidelity CLIP Score FID PFID ImageReward UNet DiT image video model-agnostic inference-time approach