General and Efficient Steering of Diffusion Models

Qingsong Wang; Mikhail Belkin; Yusu Wang

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arXiv · 2602.11395

General and Efficient Steering of Diffusion Models

Qingsong Wang·Mikhail Belkin·Yusu Wang

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Abstract

Steering diffusion models toward conditions unseen during training typically requires either retraining with conditional inputs or per-step gradient computations, both of which incur substantial computational overhead. We present Noise-Aligned RFM Steering (NA-RFM), a general recipe for efficiently steering diffusion models without gradient guidance during inference, enabling fast controllable generation. The method combines two offline-computed signals: noise alignment, a high-noise correction from PCA statistics of the target examples and the full data, and Recursive Feature Machine (RFM) activation steering, which learns a target-discriminative direction from labeled forward-process activations. During sampling, noise alignment provides coarse control at high noise, while the RFM direction is reused over intermediate/late timesteps through lightweight activation edits. Experiments on CIFAR-10, ImageNet, CelebA, and fine-grained bird species show improved target accuracy over gradient-based post-hoc guidance baselines, improved FID on the class-guidance benchmarks, and substantial inference speedups. Code: https://github.com/isotrivial/na-rfm.

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Qingsong Wang, Mikhail Belkin, Yusu Wang. 2026-02-11. General and Efficient Steering of Diffusion Models. https://arxiv.org/abs/2602.11395

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