Information-Theoretic Classifier-Free Guidance with Adaptive Schedule Optimization

• URL: http://arxiv.org/abs/2606.24025v1
• Date: Tue, 23 Jun 2026 00:04:04 GMT
• Title: Information-Theoretic Classifier-Free Guidance with Adaptive Schedule Optimization
• Abstract summary: Diffusion models have achieved strong performance in image, text-to-image, and video generation.<n>We propose an information-theoretic framework for CFG schedule optimization.<n>On ImageNet-512 with EDM-XXL and COCO with SD-XL, the learned schedules achieve competitive or improved trade-offs over constant guidance.
• Score: 15.35957804795103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 reference to specify the desired consistency-coverage trade-off, while optimizing the actual distribution induced by the guided sampler toward this reference. We derive trajectory-level formulas to estimate the objective from samples and score evaluations, avoiding explicit density estimation. On ImageNet-512 with EDM-XXL and COCO with SD-XL, the learned schedules achieve competitive or improved trade-offs over constant guidance and allocate guidance selectively across noise levels.

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