CFG-Ctrl: Control-Based Classifier-Free Diffusion Guidance
Classifier-Free Guidance (CFG) has emerged as a central approach for enhancing semantic alignment in flow-based diffusion models. In this paper, we explore a unified framework called CFG-Ctrl, which reinterprets CFG as a control applied to the first-order continuous-time generative flow, using the conditional-unconditional discrepancy as an error signal to adjust the velocity field. From this perspective, we summarize vanilla CFG as a proportional controller (P-control) with fixed gain, and typical follow-up variants develop extended control-law designs derived from it. However, existing methods mainly rely on linear control, inherently leading to instability, overshooting, and degraded s...
| Authors | Hanyang Wang, Yiyang Liu, Jiawei Chi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 March 2026 |
| Last updated at source | 11 March 2026 |
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