Abstract
We introduce a method for learning condition-dependent source distributions in flow matching frameworks. Standard flow matching uses a fixed Gaussian as the source distribution, leading to curved transport paths and slow convergence. By adapting the source distribution based on the target condition, our approach achieves straighter trajectories, reduced variance, and up to 3x faster convergence in FID compared to standard Gaussian source distributions.