  Fast data generation based on Machine Learning has become a major research
topic in particle physics. This is mainly because the Monte Carlo simulation
approach is computationally challenging for future colliders, which will have a
significantly higher luminosity. The generation of collider data is similar to
point cloud generation with complex correlations between the points.
  In this study, the generation of jets with up to 30 constituents with
Normalising Flows using Rational Quadratic Spline coupling layers is
investigated. Without conditioning on the jet mass, our Normalising Flows are
unable to model all correlations in data correctly, which is evident when
comparing the invariant jet mass distributions between ground truth and
generated data. Using the invariant mass as a condition for the coupling
transformation enhances the performance on all tracked metrics. In addition, we
demonstrate how to sample the original mass distribution by interpolating the
empirical cumulative distribution function. Similarly, the variable number of
constituents is taken care of by introducing an additional condition on the
number of constituents in the jet.
  Furthermore, we study the usefulness of including an additional mass
constraint in the loss term. On the \texttt{JetNet} dataset, our model shows
state-of-the-art performance combined with fast and stable training.
