Lyapunov Guidance: A Unified Framework for Stabilizing Generative Flows
Researchers introduce LyaGuide, a Lyapunov-guided framework that formulates flow guidance as a control problem, unifying classifier, reward, and energy-based guidance methods with explicit stability guarantees. The approach uses a pseudo-projection operator to enforce Lyapunov conditions and supports both model-driven and data-driven settings. Experiments on synthetic benchmarks, image inverse problems, reinforcement learning, and energy-based modeling show consistent improvements in sample quality, guidance fidelity, and robustness, with minimal computational overhead.
Why it matters: This work provides a theoretical and practical foundation for reliably guiding pretrained flow models, potentially enabling more stable and efficient adaptation of generative models to new tasks without retraining.
Full story at: arXiv Machine Learning ↗