Idempotent Generative Networks (IGNs) represent a novel class of deep generative models designed to project inputs onto data manifolds, effectively functioning as identity operators. These models aim to maintain idempotency, meaning that applying the network multiple times does not change the output beyond the initial application. However, despite their innovative approach, IGNs encounter significant training challenges, including mode collapse and instability. These difficulties largely stem from the adversarial components embedded within their training objectives, which complicate the optimization process. The presence of adversarial elements introduces complexities that hinder stable convergence and reliable generation. Consequently, while IGNs hold promise for idempotent generative modeling, addressing these training issues remains a critical area for further research and development. This context underscores the ongoing efforts to refine generative modeling techniques that balance innovation with training robustness.