title: Normalized Attention Guidance: Universal Negative Guidance for Diffusion
    Model
summary: 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!
paper_authors._id: 6838c66dc60fb2fc462cec9f
paper_authors.name: Dar-Yen Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:47:40.163Z
paper_authors._id: 64d0eb731ed6649d70afb136
paper_authors.avatarUrl: /avatars/4c591c86c575c82760126c39af5a02b4.svg
paper_authors.fullname: Chen Dar-Yen
paper_authors.type: user
paper_authors.user: ChenDY
paper_authors._id: 6838c66dc60fb2fc462ceca0
paper_authors.name: Hmrishav Bandyopadhyay
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T13:40:02.354Z
paper_authors._id: 638c81fa61eb51017518fa31
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/f0eCrzBrxz7Y9n25WkZ2v.png
paper_authors.fullname: Hmrishav Bandyopadhyay
paper_authors.type: user
paper_authors.user: Hmrishav
paper_authors._id: 6838c66dc60fb2fc462ceca1
paper_authors.name: Kai Zou
paper_authors._id: 6838c66dc60fb2fc462ceca2
paper_authors.name: Yi-Zhe Song
paper_title: Normalized Attention Guidance: Universal Negative Guidance for Diffusion
    Model
paper_summary: 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!
paper_ai_summary: Normalized Attention Guidance (NAG) enhances diffusion models by providing effective negative guidance across regimes and modalities without retraining.
paper_ai_keywords: 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