Step Diffusion Model

Step diffusion models aim to accelerate the image and signal generation process inherent in traditional diffusion models by reducing the number of iterative denoising steps to a single step or a very small number of steps. Current research focuses on improving the quality of one-step outputs through techniques like knowledge distillation from multi-step models, adversarial training, and refined loss functions, often employing multi-layer perceptrons or rectified flow architectures. These advancements significantly enhance the efficiency of diffusion models, enabling real-time applications in areas such as image super-resolution, 3D motion prediction, and text-based image editing, while maintaining or even surpassing the quality of their multi-step counterparts.