Diffusion ODE

Diffusion ODEs represent a continuous-time formulation of diffusion probabilistic models, aiming to accelerate the slow sampling process inherent in these powerful generative models. Current research focuses on developing efficient and accurate ODE solvers, including high-order methods and those incorporating error-robust strategies, to reduce the number of function evaluations needed for high-quality sample generation. These advancements are significantly impacting image generation and related tasks by enabling faster and more stable sampling, improving both the speed and quality of results in applications such as image super-resolution and anomaly detection in medical imaging. The improved efficiency also opens up possibilities for deploying these models in resource-constrained environments.