  We examine the capability of generative models to produce realistic galaxy
images. We show that mixing generated data with the original data improves the
robustness in downstream machine learning tasks. We focus on three different
data sets; analytical S\'ersic profiles, real galaxies from the COSMOS survey,
and galaxy images produced with the SKIRT code, from the IllustrisTNG
simulation. We quantify the performance of each generative model using the
Wasserstein distance between the distributions of morphological properties
(e.g. the Gini-coefficient, the asymmetry, and ellipticity), the surface
brightness distribution on various scales (as encoded by the power-spectrum),
the bulge statistic and the colour for the generated and source data sets. With
an average Wasserstein distance (Fr\'echet Inception Distance) of $7.19 \times
10^{-2}\, (0.55)$, $5.98 \times 10^{-2}\, (1.45)$ and $5.08 \times 10^{-2}\,
(7.76)$ for the S\'ersic, COSMOS and SKIRT data set, respectively, our best
models convincingly reproduce even the most complicated galaxy properties and
create images that are visually indistinguishable from the source data. We
demonstrate that by supplementing the training data set with generated data, it
is possible to significantly improve the robustness against domain-shifts and
out-of-distribution data. In particular, we train a convolutional neural
network to denoise a data set of mock observations. By mixing generated images
into the original training data, we obtain an improvement of $11$ and $45$ per
cent in the model performance regarding domain-shifts in the physical pixel
size and background noise level, respectively.
