  The pursuit of understanding fundamental particle interactions has reached
unparalleled precision levels. Particle physics detectors play a crucial role
in generating low-level object signatures that encode collision physics.
However, simulating these particle collisions is a demanding task in terms of
memory and computation which will be exasperated with larger data volumes, more
complex detectors, and a higher pileup environment in the High-Luminosity LHC.
The introduction of "Fast Simulation" has been pivotal in overcoming
computational bottlenecks. The use of deep-generative models has sparked a
surge of interest in surrogate modeling for detector simulations, generating
particle showers that closely resemble the observed data. Nonetheless, there is
a pressing need for a comprehensive evaluation of their performance using a
standardized set of metrics. In this study, we conducted a rigorous evaluation
of three generative models using standard datasets and a diverse set of metrics
derived from physics, computer vision, and statistics. Furthermore, we explored
the impact of using full versus mixed precision modes during inference. Our
evaluation revealed that the CaloDiffusion and CaloScore generative models
demonstrate the most accurate simulation of particle showers, yet there remains
substantial room for improvement. Our findings identified areas where the
evaluated models fell short in accurately replicating Geant4 data.
