TAG: Tangential Amplifying Guidance for Hallucination-Resistant Sampling

Abstract

Diffusion models achieve state-of-the-art image generation but often produce semantic inconsistencies, or *hallucinations*. Existing inference-time guidance methods rely on external signals or architectural modifications, adding computational overhead. We propose **T**angential **A**mplifying **G**uidance **(TAG)**, a training-free, architecture-agnostic, plug-and-play guidance method that operates purely on trajectory signals. TAG uses an intermediate sample as a projection basis and amplifies the tangential components of the estimated score to correct the sampling trajectory. A first-order Taylor analysis shows that this steers the state toward higher-probability regions of the data manifold, reducing inconsistencies and improving fidelity while adding negligible overhead to existing samplers. Code is available at our Project Page (https://hyeon-cho.github.io/TAG/).