Gradient Guidance

Gradient guidance is a technique used to improve the efficiency and quality of generative models, particularly diffusion models, by directing the sampling process towards desired outcomes. Current research focuses on applying gradient guidance to enhance various tasks, including robot control, image generation (including 3D models and image-to-image translation), and audio synthesis, often employing techniques like kinematic constraint guidance, ODE solvers with timestep-skipping strategies, and latent consistency models. These advancements lead to more efficient and controllable generation processes, impacting fields ranging from robotics and autonomous driving to medical imaging and creative content production.