Mitigating Diffusion Model Hallucinations with Dynamic Guidance

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. The latter allows for a principled extension of Dynamic Guidance to text-to-image generation, where we select modes to correspond to fine-grained contextual differences in textual descriptions. To our knowledge, this is the first approach that addresses hallucinations at generation time rather than through post-hoc filtering. Dynamic Guidance substantially reduces hallucinations on both controlled and natural image datasets, significantly outperforming baselines.