  This paper presents a novel approach for directly generating full events at
detector-level from parton-level information, leveraging cutting-edge machine
learning techniques. To address the challenge of multiplicity variations
between parton and reconstructed object spaces, we employ transformers,
score-based models and normalizing flows. Our method tackles the inherent
complexities of the stochastic transition between these two spaces and achieves
remarkably accurate results. The combination of innovative techniques and the
achieved accuracy demonstrates the potential of our approach in advancing the
field and opens avenues for further exploration. This research contributes to
the ongoing efforts in high-energy physics and generative modelling, providing
a promising direction for enhanced precision in fast detector simulation.
