A foundation model is a machine learning model that has been pre-trained on a large amount of data, Machine learning (ML) methods have been a common and can then be fine-tuned for different downstream ingredient in particle physics research for a long tasks [61]. The idea behind utilizing pre-trained time, with neural networks being applied to object models is that their outputs can significantly enhance identification already in analyses at LEP [1]. Since the performance of downstream tasks, yielding then, the range of applications has grown drastically, better results than if the model were to be trained with ML methods being developed and used for from scratch. While the models mentioned above example in tagging [2-4], anomaly detection [5-8], have focused on exploring different tasks in specific individual reconstruction stages like particle tracking subdomains, like jet physics, a more ambitious goal [9-11] or even full event interpretation and reconstruction eventually would be to develop a foundation model [12]. Another important use case for for all tasks in all subdomains, including for example ML in high energy physics (HEP) is detector simulation.

Fast simulation of the energy depositions in high-granular detectors is needed for future collider experiments with ever increasing luminosities. Generative machine learning (ML) models have been shown to speed up and augment the traditional simulation chain in physics analysis. However, the majority of previous efforts were limited to models relying on fixed, regular detector readout geometries. A major advancement is the recently introduced CaloClouds model, a geometry-independent diffusion model, which generates calorimeter showers as point clouds for the electromagnetic calorimeter of the envisioned International Large Detector (ILD). In this work, we introduce CaloClouds II which features a number of key improvements. This includes continuous time score-based modelling, which allows for 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). We further distill the diffusion model into a consistency model allowing for accurate sampling in a single step and resulting in a $46\times$ ($37\times$) speed-up. This constitutes the first application of consistency distillation for the generation of calorimeter showers.