Geometry-Aware Attention Guidance for Diffusion Models via Modern Hopfield Dynamics
Classifier-Free Guidance (CFG) improves sample quality in diffusion models, but its dual-pass inference and reliance on null-condition training limit its use in few-step regimes. Attention-space guidance has emerged as a complementary paradigm that addresses this gap, yet why prior sparse-vs-dense attention guidance works remains elusive. We address this by analyzing attention extrapolation through Modern Hopfield dynamics, proving two directional properties of the sparse-dense discrepancy under shared conditioning that together certify it as a directionally consistent acceleration signal. Building on this, we propose Geometry-Aware Attention Guidance (GAG), a training-free, plug-and-play...
| Authors | Kwanyoung Kim |
|---|---|
| Primary category | cs.LG |
| Published | 3 March 2026 |
| Last updated at source | 17 May 2026 |
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