Motivated by the computational limitations of simulating interactions of particles in highly-granular detectors, there exists a concerted effort to build fast and exact machine-learning-based shower simulators. This work reports progress on two important fronts. First, the previously investigated Wasserstein generative adversarial network and bounded information bottleneck autoencoder generative models are improved and successful learning of hadronic showers initiated by charged pions in a segment of the hadronic calorimeter of the International Large Detector is demonstrated for the first time. Second, we consider how state-of-the-art reconstruction software applied to generated shower energies affects the obtainable energy response and resolution. While many challenges remain, these results constitute an important milestone in using generative models in a realistic setting.