guided Flow Matching (LeDiFlow), a novel scalable method for
training FM-based image generation models using a better-suited prior
distribution learned via a regression-based auxiliary model. By initializing
the ODE solver with a prior closer to the target data distribution, LeDiFlow
enables the learning of more computationally tractable probability paths. These
paths directly translate to fewer solver steps needed for high-quality image
generation at inference time. Our method utilizes a State-Of-The-Art (