ve 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
