Conditional Latent Diffusion

Conditional latent diffusion models are a rapidly advancing class of generative models aiming to synthesize diverse data types, conditioned on various inputs, by leveraging the power of diffusion processes in a latent space. Current research focuses on applying these models to diverse tasks, including image harmonization, parameter generation for neural networks, and multi-modal data synthesis (e.g., MRI, human motion, and layout design), often incorporating autoencoders for efficient latent representation. This approach offers significant advantages in generating high-quality, controllable outputs across various domains, impacting fields ranging from medical imaging and robotics to computer vision and design.