Abstract: Collider data generation via machine learning is gaining traction in particle physics due to the computational cost of traditional Monte Carlo simulations, especially for future high-luminosity colliders. This study presents a model using linearly scaling attention-based aggregation. The model is trained in an adversarial setup, ensuring input permutation equivariance respective invariance for the generator and critic, respectively. A feature matching loss is introduced to stabilise known unstable adversarial training. Results are presented for two different datasets. On the \textsc{JetNet150} dataset, the model is competitive but more parameter-efficient than the current state-of-the-art GAN-based model. The model has been extended to handle the CaloChallenge Dataset 2, where each point cloud contains up to $30\times$ more points than for the previous dataset. The model and its corresponding code will be made available upon publication.