[2603.03692] Error as Signal: Stiffness-Aware Diffusion Sampling via Embedded Runge-Kutta Guidance

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.03692 (cs) [Submitted on 4 Mar 2026] Title:Error as Signal: Stiffness-Aware Diffusion Sampling via Embedded Runge-Kutta Guidance Authors:Inho Kong, Sojin Lee, Youngjoon Hong, Hyunwoo J. Kim View a PDF of the paper titled Error as Signal: Stiffness-Aware Diffusion Sampling via Embedded Runge-Kutta Guidance, by Inho Kong and 3 other authors View PDF HTML (experimental) Abstract:Classifier-Free Guidance (CFG) has established the foundation for guidance mechanisms in diffusion models, showing that well-designed guidance proxies significantly improve conditional generation and sample quality. Autoguidance (AG) has extended this idea, but it relies on an auxiliary network and leaves solver-induced errors unaddressed. In stiff regions, the ODE trajectory changes sharply, where local truncation error (LTE) becomes a critical factor that deteriorates sample quality. Our key observation is that these errors align with the dominant eigenvector, motivating us to leverage the solver-induced error as a guidance signal. We propose Embedded Runge-Kutta Guidance (ERK-Guid), which exploits detected stiffness to reduce LTE and stabilize sampling. We theoretically and empirically analyze stiffness and eigenvector estimators with solver errors to motivate the design of ERK-Guid. Our experiments on both synthetic datasets and the popular benchmark dataset, ImageNet, demonstrate that ERK-Guid consistently outperforms state-of-...