提出动态调整扩散模型引导强度的新方法，提升生成质量与稳定性。
C$^2$FG: Control Classifier-Free Guidance via Score Discrepancy Analysis
- 基于得分差异分析，建立引导权重的理论上限
- 设计指数衰减控制函数，实现随时间变化的自适应引导
- 无需训练、即插即用，适用于多种生成任务
Classifier-Free Guidance（CFG）是现代条件扩散模型的核心，但其依赖固定或启发式引导权重，缺乏理论依据且忽略扩散过程的内在动态。本文对CFG进行严格的理论分析，基于扩散过程在不同时间步上建立了条件分布与无条件分布之间得分差异的严格上界。该发现解释了固定权重策略的局限性，并为时变引导提供了理论基础。受此启发，我们提出C$^2$FG：一种无需训练、可即插即用的新方法，通过指数衰减控制函数将引导强度与扩散动态对齐。大量实验表明，C$^2$FG在多种生成任务中均有效且通用，且与现有策略具有正交性。
原文摘要 · Abstract (English)
Classifier-Free Guidance (CFG) is a cornerstone of modern conditional diffusion models, yet its reliance on the fixed or heuristic dynamic guidance weight is predominantly empirical and overlooks the inherent dynamics of the diffusion process. In this paper, we provide a rigorous theoretical analysis of the Classifier-Free Guidance. Specifically, we establish strict upper bounds on the score discrepancy between conditional and unconditional distributions at different timesteps based on the diffusion process. This finding explains the limitations of fixed-weight strategies and establishes a principled foundation for time-dependent guidance. Motivated by this insight, we introduce \textbf{Control Classifier-Free Guidance (C$^2$FG)}, a novel, training-free, and plug-in method that aligns the guidance strength with the diffusion dynamics via an exponential decay control function. Extensive experiments demonstrate that C$^2$FG is effective and broadly applicable across diverse generative tasks, while also exhibiting orthogonality to existing strategies.
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