[D] Works on flow matching where source distribution comes from dataset instead of Gaussian noise?

Flow matching is often discussed in the context of image generation from Gaussian noise.

In principle, we could model the flow from a complicated image distribution into another complicated image distribution (image to image).

Is that possible / well-understood in theoretical sense? Or are limited to the case where the source distribution is simple e.g. Gaussian?