KDE Sampling with negative density and/or class-specific weighting

I have a dataset which contains two overlapping distributions/classes of points. I have been trying to sample from just one of these distributions/classes using the scikit learn Kernel Density class, but I am finding this does not work well in overlapping regions. Is there a way to do this sort of KDE sampling that also takes into account/avoids areas where these two distributions overlap? Ideally I would like to sample more often in non-overlapping areas or, when this is not possible, from overlapping areas that contain the lowest density of points from the undesired class. Could this be done with the scikit learn KDE library or is there a different tool that would work better here?