ScFlow: Spectrum-Aware Correction for Stabilizing Training-Free Spatial Control in Rectified Flow Models

Rectified flow models (RFMs) have emerged as an alternative to score-based diffusion models (SDMs). Although training-free spatial-condition generation was successful in SDMs, so far, there has been no systematic study on RFMs. In this paper, we pinpoint that prior methods do not perform as well on RFMs. We focus on energy-guided sampling as a representative paradigm, and show that RFMs have a highly anisotropic latent-feature Jacobian compared to SDMs at early diffusion stage, which can unevenly emphasize or neglect different components of the control signal. We then develop Spectral Correction Flow (ScFlow), a training free spatial control framework targeting RFMs. ScFlow consists of two correction components, channel-wise whitening and prompt reweighting. Compared to the uncorrected baseline, ScFlow improves DINO self-similarity by 14.1% and CLIP score by 11.8% on average across evaluated RFMs. Extensive experiments demonstrate that ScFlow provides better controllability on RFMs and outperform state-of-the-art training-free methods.