Using a large-scale generative model as the primary component in an image restoration system may have potential limitations and drawbacks. One drawback is the computational resources required to train and deploy such a model, which can be substantial. Additionally, large-scale models may suffer from overfitting if not properly regularized during training. To address these limitations, techniques such as regularization methods, data augmentation, and transfer learning can be employed to improve model generalization and efficiency. Moreover, continuous monitoring and optimization of the model architecture can help mitigate potential drawbacks associated with large-scale generative models.