TAG:Tangential Amplifying Guidance for Hallucination-Resistant Diffusion
  Sampling
Authors: name: Hyunmin Cho, name: Donghoon Ahn, name: Susung Hong, name: Jee Eun Kim, name: Seungryong Kim, name: Kyong Hwan Jin

Recent diffusion models achieve the state-of-the-art performance in image
generation, but often suffer from semantic inconsistencies or hallucinations.
While various inference-time guidance methods can enhance generation, they
often operate indirectly by relying on external signals or architectural
modifications, which introduces additional computational overhead. In this
paper, we propose Tangential Amplifying Guidance (TAG), a more efficient and
direct guidance method that operates solely on trajectory signals without
modifying the underlying diffusion model. TAG leverages an intermediate sample
as a projection basis and amplifies the tangential components of the estimated
scores with respect to this basis to correct the sampling trajectory. We
formalize this guidance process by leveraging a first-order Taylor expansion,
which demonstrates that amplifying the tangential component steers the state
toward higher-probability regions, thereby reducing inconsistencies and
enhancing sample quality. TAG is a plug-and-play, architecture-agnostic module
that improves diffusion sampling fidelity with minimal computational addition,
offering a new perspective on diffusion guidance.