"Global Transport Couplings for Classifier-Free Guided Flows" Flow matching models typically pair random noise with training images, but these pairings can produce unstable trajectories under strong classifier-free guidance (CFG). This paper instead uses optimal transport to match noise with images across the entire batch, ignoring class labels. This reduces the gap between conditional and unconditional predictions, so you can have stronger guidance without degrading generation quality. On…