  Whenever invertible generative networks are needed for LHC physics,
normalizing flows show excellent performance. A challenge is their scaling to
high-dimensional phase spaces. We investigate their performance for fast
calorimeter shower simulations with increasing phase space dimension. In
addition to the standard architecture we also employ a VAE to compress the
dimensionality. Our study provides benchmarks for invertible networks applied
to the CaloChallenge.
