  In this paper we discuss a class of AutoEncoder based generative models based
on one dimensional sliced approach. The idea is based on the reduction of the
discrimination between samples to one-dimensional case. Our experiments show
that methods can be divided into two groups. First consists of methods which
are a modification of standard normality tests, while the second is based on
classical distances between samples. It turns out that both groups are correct
generative models, but the second one gives a slightly faster decrease rate of
Fr\'{e}chet Inception Distance (FID).
