Denoising Diffusion Implicit Model

Denoising Diffusion Implicit Models (DDIMs) are a class of generative models accelerating the sampling process of diffusion probabilistic models, aiming to produce high-quality images and 3D models more efficiently. Current research focuses on improving DDIM's speed and stability for various applications, including image inpainting, text-to-3D generation, and image translation across different sensors, often employing techniques like coarse-to-fine sampling and improved noise approximation methods. These advancements are significant because they enable faster and more robust generation of realistic images and 3D content, impacting fields such as computer vision, medical imaging, and digital content creation.