Guidance 打破拟合算子:无分类器引导的终端拟合修复

Guidance Breaks the Fitted Operator: A Terminal-Fitted Repair for Classifier-Free Guidance

精选理由

这篇论文找到了CFG在高引导下翻车的数学原因,还给了个零成本修复,CIFAR-10和SD1.5上点FID全胜,做采样的人值得看看。

AI 摘要

无分类器引导(CFG)在大强度下导致扩散采样过饱和。作者通过数值分析指出,DDIM步是未引导终端层的唯一拟合算子,而引导使判别子空间以指数1+w重新变硬。他们提出单系数零额外NFE修复:将CFG的w(r-1)替换为r^(1+w)-r。在CIFAR-10和Stable Diffusion 1.5 DDIM上,该方法以9/9点FID优于CFG,并减少残差放大和饱和,但并非通用质量提升。

原文 · arXiv cs.LG

Guidance Breaks the Fitted Operator: A Terminal-Fitted Repair for Classifier-Free Guidance

Classifier-free guidance (CFG) is the standard way to strengthen class-conditioning in diffusion and flow-matching samplers, yet at large guidance it oversaturates and destabilizes, symptoms practitioners suppress with more steps or limited-interval schedules. We analyze CFG through an asymptotic-preserving, numerical-analysis lens. Building on a recent result that the deterministic DDIM step is the unique fitted operator for the unguided terminal layer, exact on the final small-sigma stretch of sampling, we show that guidance re-stiffens exactly the discriminative subspace to an anomalous exponent 1+w. DDIM is therefore no longer fitted there, and on coarse meshes its guided residual diverges as sigma_min goes to zero. We prove a guided clock barrier with three ordered step-size thresholds, and read one-step oversaturation as its endpoint: a solver artifact on the calibration model rather than the continuous guided law. The same analysis yields a one-coefficient, zero-extra-NFE repair: replace CFG's w(r-1) by r^(1+w)-r on the guidance direction. On the calibration model's discriminative crossover, this removes CFG's sigma_min-divergent blow-up and is first-order accurate against the exact guided flow as sigma_min goes to zero. On learned CIFAR-10 checkpoints, and as a cross-domain smoke test on Stable Diffusion 1.5 DDIM, it acts as a high-guidance stabilizer at no extra cost rather than a universal quality knob: it cuts residual amplification and saturation, gives 9/9 point-FID wins over CFG on the tested grid, and preserves classifier-proxy target accuracy in the hard-cell blocks. We report the limits alongside: it is not a universal image-quality win, and against a dense vanilla-CFG reference it is not a uniformly better integrator of that field.