  We present a simple method for assessing the predictive performance of
high-dimensional models directly in data space when only samples are available.
Our approach is to compare the quantiles of observables predicted by a model to
those of the observables themselves. In cases where the dimensionality of the
observables is large (e.g. multiband galaxy photometry), we advocate that the
comparison is made after projection onto a set of principal axes to reduce the
dimensionality. We demonstrate our method on a series of two-dimensional
examples. We then apply it to results from a state-of-the-art generative model
for galaxy photometry (pop-cosmos; arXiv:2402.00935) that generates predictions
of colors and magnitudes by forward simulating from a 16-dimensional
distribution of physical parameters represented by a score-based diffusion
model. We validate the predictive performance of this model directly in a space
of nine broadband colors. Although motivated by this specific example, the
techniques we present will be broadly useful for evaluating the performance of
flexible, non-parametric population models of this kind, and can be readily
applied to any setting where two sets of samples are to be compared.
