用滑模控制提升扩散模型的语义对齐，让生成更准更稳。
CFG-Ctrl: Control-Based Classifier-Free Diffusion Guidance
- 将无分类器指引重构成连续流上的控制问题，用误差信号调节生成速度场。
- 新方法在大引导系数下仍保持语义一致性和稳定性，优于传统方法。
- 适合需要高精度文本到图像生成的研究者与开发者。
无分类器指引（CFG）已成为增强基于流的扩散模型语义对齐的核心方法。本文提出统一框架 CFG-Ctrl，将 CFG 重新解释为对一阶连续时间生成流施加的控制，以条件-无条件差异作为误差信号来调节速度场。从这一视角出发，我们将原始 CFG 视为固定增益的比例控制器（P-control），而后续变体则基于此构建扩展控制律。然而，现有方法主要依赖线性控制，导致在大引导尺度下易出现不稳定性、超调和语义保真度下降。为此，我们引入滑模控制型无分类器指引（SMC-CFG），通过定义指数滑模面并引入切换控制项，实现非线性反馈校正，强制生成流快速收敛至滑模流形。同时提供李雅普诺夫稳定性分析，理论支持有限时间收敛。在 Stable Diffusion 3.5、Flux 与 Qwen-Image 等文本到图像生成模型上的实验表明，SMC-CFG 在语义对齐上优于标准 CFG，且在宽范围引导尺度下具备更强鲁棒性。
原文摘要 · Abstract (English)
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 semantic fidelity especially on large guidance scales. To address this, we introduce Sliding Mode Control CFG (SMC-CFG), which enforces the generative flow toward a rapidly convergent sliding manifold. Specifically, we define an exponential sliding mode surface over the semantic prediction error and introduce a switching control term to establish nonlinear feedback-guided correction. Moreover, we provide a Lyapunov stability analysis to theoretically support finite-time convergence. Experiments across text-to-image generation models including Stable Diffusion 3.5, Flux, and Qwen-Image demonstrate that SMC-CFG outperforms standard CFG in semantic alignment and enhances robustness across a wide range of guidance scales. Project Page: https://hanyang-21.github.io/CFG-Ctrl
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。