The field of deep generative image models is witnessing significant advancements, particularly in the areas of diffusion models and vision transformers. Recent innovations have focused on improving computational efficiency, generation quality, and the ability to handle diverse datasets. Key developments include the integration of advanced control mechanisms, such as ControlNet and regional attention systems, which enhance precision and customization in image synthesis. Additionally, there has been a notable shift towards energy-preserving guidance techniques that maintain natural image quality while enhancing semantic alignment. The use of generative models for augmenting small and imbalanced datasets, especially in medical imaging, has shown promising results, with diffusion models like DDPM outperforming traditional GANs in terms of realism and classification performance. Furthermore, the generation of large-content images through efficient fusion techniques and style alignment methods is gaining traction, addressing issues of artifact and inconsistency in patch-based approaches. In the medical domain, diffusion models are being rethought to synthesize critical imaging modalities like fundus fluorescein angiography from limited data, offering potential improvements in diagnostic accuracy and patient care. Overall, the field is progressing towards more efficient, scalable, and interpretable generative systems, with a strong emphasis on practical applications across various industries.