Faster Inference of Flow-Based Generative Models via Improved Data-Noise Coupling
LOOM-CFM is a new method that improves Conditional Flow Matching by optimizing how data and noise are coupled across training. It iteratively refines global data-noise assignments using minibatch optimal transport, leading to faster inference and better generation quality compared to previous methods.