The current research into the issue known as mode collapse has underrepresented the relationship between hyperparameters and mode collapse in GANs. This study aimed to research this further by investigating the effects of four prominent hyperparameters on the manifestation of mode collapse in unconditioned and conditional DCGANs. The investigation was performed via the use of quasi-experiments where the independent variables were the four hyperparameters and the dependent variable was the FID score. Supplemental evaluation was also done by counting classes present with classifiers and visual inspections. The results showed that the number of epochs and the size of the latent dimension only displayed signs of mode collapse at impractically low values. Meanwhile, the learning rate and batch size both had a large effect on mode collapse for higher values. The results showed no significant differences in the hyperparameters’ effect on mode collapse between the unconditioned and conditional DCGANs. The main exception was that the conditional DCGANs were more unstable during training. The conditional DCGANs also occasionally were seen to suffer a phenomenon this study refers to as training failure rebound. This phenomenon had a strong effect on the outputs of the models and is potentially a valuable avenue for future research.