Advanced Generative Model for Synthetic Data Generation

Create a sophisticated generative modeling framework capable of producing high-fidelity synthetic datasets that preserve complex statistical properties of original data. Implement advanced techniques including Generative Adversarial Networks (GANs), Variational Autoencoders, and diffusion models. Develop rigorous validation methods to ensure synthetic data maintains original dataset's statistical moments, correlation structures, and privacy constraints. Include automated evaluation metrics for synthetic data quality and utility.