  Detector simulations are an exciting application of modern generative
networks. Their sparse high-dimensional data combined with the required
precision poses a serious challenge. We show how combining Conditional Flow
Matching with transformer elements allows us to simulate the detector phase
space reliably. Namely, we use an autoregressive transformer to simulate the
energy of each layer, and a vision transformer for the high-dimensional voxel
distributions. We show how dimension reduction via latent diffusion allows us
to train more efficiently and how diffusion networks can be evaluated faster
with bespoke solvers. We showcase our framework, CaloDREAM, on datasets 2 and 3
of the CaloChallenge.
