Conditioned Diffusion

Conditioned diffusion models are a class of generative models that leverage the principles of diffusion processes to create new data instances conditioned on a given input. Current research focuses on applying these models to diverse tasks, including image and video editing, 3D shape generation, and multi-modal data synthesis, often employing U-Net architectures and incorporating attention mechanisms for improved performance. These advancements are significantly impacting various fields, enabling high-quality image and video manipulation, improved medical image analysis, and more realistic simulations in computer graphics and robotics. The ability to generate diverse and high-fidelity outputs conditioned on specific inputs makes conditioned diffusion models a powerful tool across numerous scientific and practical applications.