Rectified-CFG++ for Flow Based Models

Shreshth Saini; Shashank Gupta; Alan C. Bovik

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arXiv · 2510.07631

Rectified-CFG++ for Flow Based Models

Shreshth Saini·Shashank Gupta·Alan C. Bovik

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Abstract

Classifier-free guidance (CFG) is the workhorse for steering large diffusion models toward text-conditioned targets, yet its native application to rectified flow (RF) based models provokes severe off-manifold drift, yielding visual artifacts, text misalignment, and brittle behaviour. We present Rectified-CFG++, an adaptive predictor-corrector guidance that couples the deterministic efficiency of rectified flows with a geometry-aware conditioning rule. Each inference step first executes a conditional RF update that anchors the sample near the learned transport path, then applies a weighted conditional correction that interpolates between conditional and unconditional velocity fields. We prove that the resulting velocity field is marginally consistent and that its trajectories remain within a bounded tubular neighbourhood of the data manifold, ensuring stability across a wide range of guidance strengths. Extensive experiments on large-scale text-to-image models (Flux, Stable Diffusion 3/3.5, Lumina) show that Rectified-CFG++ consistently outperforms standard CFG on benchmark datasets such as MS-COCO, LAION-Aesthetic, and T2I-CompBench. Project page: https://rectified-cfgpp.github.io/

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Shreshth Saini, Shashank Gupta, Alan C. Bovik. 2025-10-09. Rectified-CFG++ for Flow Based Models. https://arxiv.org/abs/2510.07631

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