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

• URL: http://arxiv.org/abs/2603.03692v1
• Date: Wed, 04 Mar 2026 03:37:57 GMT
• Title: Error as Signal: Stiffness-Aware Diffusion Sampling via Embedded Runge-Kutta Guidance
• Authors: Inho Kong, Sojin Lee, Youngjoon Hong, Hyunwoo J. Kim,
• Abstract summary: In stiff regions, the ODE trajectory changes sharply, where local truncation error (LTE) becomes a critical factor that deteriorates sample quality.<n>We propose Embedded Runge-Kutta Guidance (ERKGuid), which exploits detected stiffness to reduce LTE and stabilize sampling.<n>Our experiments on both synthetic datasets and the popular benchmark dataset, ImageNet, demonstrate that ERKGuid consistently outperforms state-of-the-art methods.
• Score: 35.84939959436188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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-the-art methods. Code is available at https://github.com/mlvlab/ERK-Guid.

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