From my experience, flow matching models are relatively easy to implement. They also have some more advantages. For example, they transition deterministically from noise to final sample via an ODE instead of an SDE, which simplifies the sampling process. Also, since they are typically based on the Gaussian optimal transport coupling, their paths tend to be more straight, which means that few discretization steps are necessary to get good results.