Generative machine learning can be used to augment and speed-up traditional physics simulations, i.e. the simulation of elementary particles in the detector of collider experiments. Like many physics data, these calorimeter showers can either be represented as images or as permutation-invariant lists of measurements, i.e. as point clouds. We advance the generative models for calorimeter showers on three frontiers: (1) increasing the number of conditional features for precise energy- and angle-wise generation with the bounded bottleneck auto-encoder (BIB-AE), (2) improving generation fidelity using a normalizing flow model, dubbed "Layer-to-Layer-Flows" (L2LFlows), (3) developing a diffusion model for geometry-independent calorimeter point cloud scalable to O(1000) points, called CaloClouds, and distilling it into a consistency model for fast single-shot sampling.