Information-Theoretic Classifier-Free Guidance with Adaptive Schedule Optimization
Diffusion models have achieved strong performance in image, text-to-image, and video generation, where conditional generation is often controlled by classifier-free guidance (CFG). CFG improves condition consistency by increasing a guidance weight, but stronger guidance typically reduces diversity and distributional coverage. It remains unclear how this consistency-coverage trade-off should be controlled across the reverse trajectory, since the distribution induced by CFG is not simply the fixed-time tilted distribution given by the guided score field. To address this issue, we propose an information-theoretic framework for CFG schedule optimization. Our approach uses a clean endpoint ref...
| Authors | Haobo Chen, Xiangxiang Xu, Yuheng Bu |
|---|---|
| Primary category | cs.LG |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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