To address this gap, we developed SpaiNN, an open-source Python tool for ML-accelerated photodynamics simulations that facilitates the use of both invariant and equivariant architectures, allowing for a direct comparison and evaluation of the benefits of equivariance. SpaiNN integrates the NAMD program SHARC 3.0 (Surface Hopping Including ARbitrary Couplings) with the neural network potentials of SchNetPack 2.0. (including SchNet 37 and PaiNN modules). This interface allows SpaiNN to facilitate ML-based surface hopping NAMD, leveraging SchNetPack 2.0's inherent strengths, such as an improved data pipeline and support for both invariant (SchNet) and equivariant (PaiNN) neural networks. We demonstrate the enhanced accuracy of SpaiNN models based on the PaiNN compared to SchNet architecture across various aspects, including PESs, which encompass energies and forces, as well as nonadiabatic couplings (NACs). Additionally, we highlight the improved computational efficiency of SpaiNN(PaiNN) with respect to SpaiNN(SchNet) models for both training and predictions. We showcase the performance of SpaiNN for a selection of alkenes and the methylenimmonium cation by accurately predicting energies, forces, and NACs. These molecules are known to undergo rotation around the double bond upon photoexcitation. Furthermore, we illustrate the acceleration of NAMD simulations for the methylenimmonium cation using SpaiNN models.