Review of the Stable Diffusion Model: Generative Image Synthesis Using Latent Diffusion Models

This review critically evaluates the Stable Diffusion model, a generative framework leveraging latent diffusion techniques for high-quality image synthesis. By combining advances in variational autoencoders, diffusion processes, and cross-attention mechanisms, Stable Diffusion has demonstrated state-of-the-art results in creating diverse, detailed, and semantically meaningful images from text prompts. This review examines the model’s architecture, training methodology, performance benchmarks, and real-world applications while highlighting its contributions to the field of generative AI. Additionally, we discuss the limitations, computational demands, and ethical considerations associated with the model, offering insights for future research directions in diffusion-based generative models.