In this chapter, we explore the application of generative models to enhance the fidelity and utility of artificially generated images for data augmentation in digital pathology. We employ HistoGAN for synthetic augmentation, filtering images based on label congruence and feature resemblance to real specimens. This ensures that only the most accurate synthetic images are used. Additionally, we utilize reinforcement learning for automated quality checks, optimizing synthetic sample selection, and improving image classification outcomes. However, GANs have limitations, such as instability during training and the need for large annotated datasets for conditional generation. To address these issues, we transition to HistoDiffusion, a model that utilizes diffusion processes, which are more stable to train and reduce the risk of mode collapse common in GANs. Furthermore, unconditional diffusion models can be guided with smaller annotated datasets to enable conditional synthesis. HistoDiffusion generates more complex and nuanced images, enhancing realism and diversity. Through this exploration of generative AI techniques for synthetic augmentation, each model addresses the limitations of its predecessor, advancing the effectiveness of data augmentation in digital pathology.