Diffusion-Based Synthetic Data

Diffusion-based synthetic data generation has become one of the most prominent modern approaches, especially in terms of output quality and controllability. Data is first gradually noised and then reconstructed through a reverse process. This structure can learn complex distributions with greater stability. It offers important opportunities for diversity and sample quality in synthetic data workflows. However, generation cost, evaluation difficulty, and privacy leakage risks still require careful handling.