Deep generative architectures (DGE) have revolutionized various fields by generating realistic imagined data. To maximize the performance of these models, researchers are constantly investigating new optimization algorithms. A common method involves fine-tuning hyperparameters through Bayesian optimization, aiming to reduce the objective function.