Denoising Step

Denoising steps are crucial in diffusion models, a class of generative models that create data by iteratively removing noise from a random sample. Current research focuses on accelerating this process, primarily by reducing the number of denoising steps needed to generate high-quality outputs, employing techniques like optimized ODE solvers, timestep skipping, and feature reuse within model architectures such as U-Nets. These advancements aim to improve the efficiency and practicality of diffusion models, making them more suitable for resource-constrained applications and expanding their use in diverse fields like image generation, medical imaging, and 6D object pose estimation.