Conditional Diffusion Framework for Hybrid Image Synthesis: Towards Photorealistic and Geometry Aware Generation
Image synthesis has become a central problem in generative AI, with applications spanning virtual reality, medical imaging, autonomous systems, and creative content generation. Diffusion-based generative models have substantially advanced the field by producing high-fidelity, visually consistent outputs, yet a fundamental tension remains: hybrid synthesis tasks require photorealism and geometric consistency to be achieved together, so that perceptual quality does not come at the cost of structural accuracy. This review surveys recent progress in conditional diffusion frameworks, with particular attention to geometry-aware conditioning mechanisms that connect appearance and structure. Our contributions are a taxonomy of 18 conditioning frameworks, a six-parameter controllability analysis (Table II), formal definitions of the evaluation metrics most commonly reported in the literature (FID, SSIM, LPIPS, IS, Precision, Recall, Dice) with a consolidated reference table, an explicit architectural comparison of DDPM, DDIM, and LDM together with transformer-based generative models (DiT, VQGAN+Transformer), a structured comparison of diffusion models and GANs across deployment-relevant criteria, and a discussion of open challenges that includes the limitations of current experimental validation practice.