Signed Rectified Flow: Negativity-Controlled Generation
Researchers propose Signed Rectified Flow (Signed RF), a generalization of Rectified Flow that enables generative models to both promote desired distributions and suppress undesired ones by leveraging a signed measure. The method provides a principled way to incorporate negative information and exclusion constraints into generative modeling. Experiments show that Signed RF improves the fidelity-diversity trade-off on ImageNet, reduces memorization, and decreases nudity in Stable Diffusion 3.5 without degrading output quality.
Why it matters: This work introduces a novel framework for controlling generative models with exclusion constraints, offering practical advances in safety and content moderation.
Full story at: arXiv Machine Learning ↗