  The diffusion model has demonstrated promising results in image generation,
recently becoming mainstream and representing a notable advancement for many
generative modeling tasks. Prior applications of the diffusion model for both
fast event and detector simulation in high energy physics have shown
exceptional performance, providing a viable solution to generate sufficient
statistics within a constrained computational budget in preparation for the
High Luminosity LHC. However, many of these applications suffer from slow
generation with large sampling steps and face challenges in finding the optimal
balance between sample quality and speed. The study focuses on the latest
benchmark developments in efficient ODE/SDE-based samplers, schedulers, and
fast convergence training techniques. We test on the public CaloChallenge and
JetNet datasets with the designs implemented on the existing architecture, the
performance of the generated classes surpass previous models, achieving
significant speedup via various evaluation metrics.
