Score Decomposed Diffusion Model

Score-decomposed diffusion models (SDDMs) are a class of generative models that leverage the power of diffusion processes to generate high-quality data, particularly in image-to-image translation and video editing. Current research focuses on improving efficiency and controllability, for example, by decomposing the score function into separate components for denoising and content refinement, or by using lightweight architectures to guide the diffusion process. This approach offers advantages in terms of speed, memory efficiency, and the ability to manipulate specific attributes like motion in videos or content in images, leading to improved performance in various applications.