TAG:Tangential Amplifying Guidance for Hallucination-Resistant Diffusion
  Sampling
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.
68e67c91975ac4c405ef2334
Hyunmin Cho
claimed_verified
2025-10-09T06:51:20.523Z
680ed019a9918bb1cbc66248
/avatars/904d243f2fad99341f11795e93788993.svg
Hyunmin Cho
user
hyeoncho01
68e67c91975ac4c405ef2335
Donghoon Ahn
68e67c91975ac4c405ef2336
Susung Hong
68e67c91975ac4c405ef2337
Jee Eun Kim
68e67c91975ac4c405ef2338
Seungryong Kim
68e67c91975ac4c405ef2339
Kyong Hwan Jin
TAG:Tangential Amplifying Guidance for Hallucination-Resistant Diffusion
  Sampling
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.
Tangential Amplifying Guidance (TAG) improves diffusion model sample quality by directly amplifying tangential components of estimated scores without modifying the model architecture.
diffusion models image generation semantic inconsistencies hallucinations inference-time guidance trajectory signals intermediate sample projection basis tangential components first-order Taylor expansion sampling trajectory higher-probability regions plug-and-play architecture-agnostic