  Building on the success of PC-JeDi we introduce PC-Droid, a substantially
improved diffusion model for the generation of jet particle clouds. By
leveraging a new diffusion formulation, studying more recent integration
solvers, and training on all jet types simultaneously, we are able to achieve
state-of-the-art performance for all types of jets across all evaluation
metrics. We study the trade-off between generation speed and quality by
comparing two attention based architectures, as well as the potential of
consistency distillation to reduce the number of diffusion steps. Both the
faster architecture and consistency models demonstrate performance surpassing
many competing models, with generation time up to two orders of magnitude
faster than PC-JeDi and three orders of magnitude faster than Delphes.
