Mitigating Diffusion Model Hallucinations with Dynamic Guidance 文章

arXiv:2510.05356v2 Announce Type: replace Abstract: Hallucinations in diffusion models are samples with structural inconsistencies that can emerge due to the excessive smoothing of the learned score function, which in turn leads to interpolations between modes of the data distribution. Since semantic interpolations are often desirable and contribute to sample diversity, we believe that a nuanced and targeted solution is required to address diffusion model hallucinations. In this work, we introduce Dynamic Guidance, which mitigates hallucinations by selectively sharpening the score function only along the pre-determined directions known to cause artifacts, while preserving valid semantic variations. This sharpening can be performed using either pre-determined classes or semantically coherent clusters that form pseudo-classes over the data distribution.