Guidance Breaks the Fitted Operator: A Terminal-Fitted Repair for Classifier-Free Guidance
On the map: Diffusion models → Diffusion foundations and sampling
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 10 of its 11 references that are in the map. First posted on arXiv 8 Jul 2026.
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10 of 11 references.
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- 2026C2FG: Control Classifier-Free Guidance via Score Discrepancy Analysis9 Mar
- 2025CFG-Zero*: Improved Classifier-Free Guidance for Flow Matching Models24 Mar
- 2024Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models3 Oct
- 2024What does guidance do? A fine-grained analysis in a simple setting19 Sep
- 2024Classifier-Free Guidance is a Predictor-Corrector16 Aug
- 2024CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models12 Jun
- 2024Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models11 Apr
- 2022Classifier-Free Diffusion Guidance26 Jul
- 2022Elucidating the Design Space of Diffusion-Based Generative Models1 Jun
- 2020Denoising Diffusion Implicit Models6 Oct
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- 2025What Exactly Does Guidance Do in Masked Discrete Diffusion Models
- 2025Efficient Distillation of Classifier-Free Guidance using Adapters
- 2026Improving Classifier-Free Guidance of Flow Matching via Manifold Projection
- 2023Bridging the Gap: Addressing Discrepancies in Diffusion Model Training for Classifier-Free Guidance