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Abstract:Classifier-Free Guidance (CFG) is a cornerstone of modern conditional diffusion models, yet its reliance on the fixed or heuristic dynamic guidance weight is predominantly empirical and overlooks the inherent dynamics of the diffusion process. In this paper, we provide a rigorous theoretical analysis of the Classifier-Free Guidance. Specifically, we establish strict upper bounds on the score discrepancy between conditional and unconditional distributions at different timesteps based on the diffusion process. This finding explains the limitations of fixed-weight strategies and establishes a principled foundation for time-dependent guidance. Motivated by this insight, we introduce \textbf{Control Classifier-Free Guidance (C$^2$FG)}, a novel, training-free, and plug-in method that aligns the guidance strength with the diffusion dynamics via an exponential decay control function. Extensive experiments demonstrate that C$^2$FG is effective and broadly applicable across diverse generative tasks, while also exhibiting orthogonality to existing strategies.
| Comments: | Accepted to CVPR 2026 (Highlight) |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.08155 [cs.LG] |
| (or arXiv:2603.08155v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08155 arXiv-issued DOI via DataCite |
From: Jiayang Gao [view email]
[v1]
Mon, 9 Mar 2026 09:37:17 UTC (1,733 KB)
[v2]
Thu, 9 Apr 2026 04:45:55 UTC (1,741 KB)
[v3]
Wed, 20 May 2026 06:27:00 UTC (1,740 KB)
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