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Fast simulation of the energy depositions in high-granular detectors isneeded for future collider experiments with ever increasing luminosities.Generative machine learning (ML) models have been shown to speed up and augmentthe traditional simulation chain in physics analysis. However, the majority ofprevious efforts were limited to models relying on fixed, regular detectorreadout geometries. A major advancement is the recently introduced CaloCloudsmodel, a geometry-independent diffusion model, which generates calorimetershowers as point clouds for the electromagnetic calorimeter of the envisionedInternational Large Detector (ILD).  In this work, we introduce CaloClouds II which features a number of keyimprovements. This includes continuous time score-based modelling, which allowsfor a 25 step sampling with comparable fidelity to CaloClouds while yielding a$6\times$ speed-up over Geant4 on a single CPU ($5\times$ over CaloClouds). Wefurther distill the diffusion model into a consistency model allowing foraccurate sampling in a single step and resulting in a $46\times$ ($37\times$)speed-up. This constitutes the first application of consistency distillationfor the generation of calorimeter showers.

CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation