Manifold-Optimal Guidance: A Unified Riemannian Control View of Diffusion Guidance
Classifier-Free Guidance (CFG) serves as the de facto control mechanism for conditional diffusion, yet high guidance scales notoriously induce oversaturation, texture artifacts, and structural collapse. We attribute this failure to a geometric mismatch: standard CFG performs Euclidean extrapolation in ambient space, inadvertently driving sampling trajectories off the high-density data manifold. To resolve this, we present Manifold-Optimal Guidance (MOG), a framework that reformulates guidance as a local optimal control problem. MOG yields a closed-form, geometry-aware Riemannian update that corrects off-manifold drift without requiring retraining. Leveraging this perspective, we further i...
| Authors | Zexi Jia, Pengcheng Luo, Zhengyao Fang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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