  LHCb is one of the major experiments operating at the Large Hadron Collider
at CERN. The richness of the physics program and the increasing precision of
the measurements in LHCb lead to the need of ever larger simulated samples.
This need will increase further when the upgraded LHCb detector will start
collecting data in the LHC Run 3. Given the computing resources pledged for the
production of Monte Carlo simulated events in the next years, the use of fast
simulation techniques will be mandatory to cope with the expected dataset size.
In LHCb generative models, which are nowadays widely used for computer vision
and image processing are being investigated in order to accelerate the
generation of showers in the calorimeter and high-level responses of Cherenkov
detector. We demonstrate that this approach provides high-fidelity results
along with a significant speed increase and discuss possible implication of
these results. We also present an implementation of this algorithm into LHCb
simulation software and validation tests.
