Theory-Informed Improvements to Classifier-Free Guidance for Discrete Diffusion Models

Classifier-Free Guidance (CFG) is a widely used technique for conditional generation and improving sample quality in continuous diffusion models, and recent works have extended it to discrete diffusion. This paper theoretically analyzes CFG in the context of masked discrete diffusion, focusing on the role of guidance schedules. Our analysis shows that high guidance early in sampling (when inputs are heavily masked) harms generation quality, while late-stage guidance has a larger effect. These findings provide a theoretical explanation for empirical observations in recent studies on guidance schedules. The analysis also reveals an imperfection of the current CFG implementations. These implementations can unintentionally cause imbalanced transitions, such as unmasking too rapidly during the early stages of generation, which degrades the quality of the resulting samples. To address this, we draw insight from the analysis and propose a novel classifier-free guidance mechanism empirically applicable to any discrete diffusion. Intuitively, our method smoothens the transport between the data distribution and the initial (masked/uniform) distribution, which results in improved sample quality. Remarkably, our method is achievable via a simple one-line code change. The efficacy of our method is empirically demonstrated with experiments on ImageNet (masked discrete diffusion) and QM9 (uniform discrete diffusion).