Adaptive CFG

CFG: Classifier-Free Guidance

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8 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Guidance Is a Field, Not a Scalar: Interval-Integrated Classifier-Free Guidance for One-Step Video Generation
Nov 5, 2026Zijie Meng, Xixin CaoAdaptive CFG Classifier-Free Guidance
Peking University
Opening the Black Box of Classifier-Free Guidance via Information Bottleneck
Oct 9, 2026Jiayang Gao, Tianyi Zheng, Jiayang Zou +5Adaptive CFG Classifier-Free Guidance
Shanghai Jiao Tong University, Shanghai, China · vivo BlueImage Lab, vivo Mobile Communication Co., Ltd., China · Stanford University, California, USA +1
PathGuide: Dynamic Classifier-Free Guidance via On-Policy Transport Alignment
Aug 29, 2026Avishag Nevo, Tamir HazanAdaptive CFG Inference-Time Guidance
Technion - Israel Institute of Technology
Adversarial Learning of Classifier-Free Guidance Schedules
Aug 14, 2026Ashwini Pokle, Alexandre Galashov, Arnaud Doucet +2Adaptive CFG Classifier-Free Guidance
Google DeepMind
Commitment Before Realization: When Classifier-Free Guidance Becomes Unnecessary in Masked Diffusion Language Models
Aug 8, 2026Fan Zhou, Weitian Wang, Tim Van de CruysClassifier-Free Guidance Controllable Text Generation
KU Leuven, Belgium · Ruhr University Bochum, Germany
VAGS: Velocity Adaptive Guidance Scale for Image Editing and Generation
May 15, 2026Yan Luo, Ahmadou Aidara, Jingyi Lu +3Classifier-Free Guidance Adaptive CFG
Harvard AI and Robotics Lab, Harvard University · School of Computing and Data Science, The University of Hong Kong · Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University
Guidance Is Not a Hyperparameter: Learning Dynamic Control in Diffusion Language Models
May 8, 2026Fan Zhou, Tim Van de CruysClassifier-Free Guidance Controllable Text Generation
KU Leuven
C $^2$ FG: Control Classifier-Free Guidance via Score Discrepancy Analysis
Mar 9, 2026Jiayang Gao, Tianyi Zheng, Jiayang Zou +9Adaptive CFG Classifier-Free Guidance
Shanghai Jiao Tong University, Shanghai, China · vivo BlueImage Lab, vivo Mobile Communication Co., Ltd., China

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Guidance Is a Field, Not a Scalar: Interval-Integrated Classifier-Free Guidance for One-Step Video Generation

Zijie Meng, Xixin Cao

Peking University

Adaptive CFG Classifier-Free Guidance One-Step Generative Modeling Single-Step Video Generation MeanFlow

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One-step generators built on MeanFlow synthesize a video in a single function evaluation, yet inherit a rigid treatment of classifier-free guidance (CFG): the guidance scale is frozen during training or, at best, exposed as a single scalar. This conflicts with mounting evidence that the optimal guidance is not a constant but a field that varies with the noise level, with spatial content, and, for video, with frame position. Every existing remedy presupposes a multi-step sampler along which guidance can be re-scheduled--an assumption one-step models violate by construction. We resolve this tension through a single observation: because the MeanFlow target is an integral over a noise interval, an arbitrary guidance field can be absorbed into the target itself and compiled into one evaluation. We formalize this as the interval-integrated CFG identity, prove that the compiled guidance carries no schedule-discretization error and can thus match or exceed any finite multi-step sampler, and instantiate it as AGAVE, which equips the average-velocity field with a learnable temporal schedule, a free motion-saliency spatial modulation, and a frame-position asymmetry. Adding fewer than ten scalars at zero inference cost, AGAVE surpasses all one-step baselines on VBench, UCF-101 and MSR-VTT while matching multi-step guided sampling. Project repository: https://github.com/YMlinfeng/AGAVE.