用求解器误差做信号，让扩散模型在复杂区域生成更稳定。
Error as Signal: Stiffness-Aware Diffusion Sampling via Embedded Runge-Kutta Guidance
- 检测求解器误差方向，将其作为引导信号优化采样路径。
- 在ImageNet上比现有方法显著提升生成质量，尤其在难采样区域。
- 适合关注生成稳定性与高质量图像合成的研究者。
无分类器引导（CFG）为扩散模型的条件生成奠定了基础，表明设计良好的引导代理能显著提升生成效果。自引导（AG）扩展了这一思路，但依赖辅助网络，且未解决求解器引入的误差问题。在刚性区域，微分方程轨迹剧烈变化，局部截断误差（LTE）成为影响样本质量的关键因素。我们观察到，这些误差与主导特征向量对齐，因此提出嵌入式龙格-库塔引导（ERK-Guid），利用检测到的刚性信息降低LTE并稳定采样过程。通过理论与实证分析刚性及特征向量估计器与求解器误差的关系，指导了ERK-Guid的设计。在合成数据集和ImageNet基准上的实验表明，该方法持续优于当前最优方法。代码已公开于https://github.com/mlvlab/ERK-Guid。
原文摘要 · Abstract (English)
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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