  We present a super-resolution method for spin systems using a flow-based
generative model that is a deep generative model with reversible neural network
architecture. Starting from spin configurations on a two-dimensional square
lattice, our model generates spin configurations of a larger lattice. As a
flow-based generative model precisely estimates the distribution of the
generated configurations, it can be combined with Monte Carlo simulation to
generate large lattice configurations according to the Boltzmann distribution.
Hence, the long-range correlation on a large configuration is reduced into the
shorter one through the flow-based generative model. This alleviates the
critical slowing down near the critical temperature. We demonstrated 8 times
increased lattice size in the linear dimensions using our super-resolution
scheme repeatedly. We numerically show that by performing simulations for
$16\times 16$ configurations, our model can sample lattice configurations at
$128\times 128$ on which the thermal average of physical quantities has good
agreement with the one evaluated by the traditional Metropolis-Hasting Monte
Carlo simulation.
