VAEs can overfit when trained on limited data, but using samples from a pre-trained diffusion model can help mitigate this issue. This method improves generalization, reduces the gap between training and testing performance, and enhances the robustness of VAEs on various datasets. Just a small number of samples from the diffusion model can lead to these improvements.