Step Sampling

Step sampling in diffusion models aims to accelerate the image and video generation process, reducing computational cost while maintaining or improving output quality. Current research focuses on developing novel algorithms, such as those leveraging consistency models or classifier-based feature distillation, to achieve efficient one-step or few-step sampling without sacrificing performance. This work is significant because faster inference speeds enable wider application of diffusion models in various fields, from image editing and video synthesis to more efficient data processing and machine translation. Addressing limitations like singularities at the endpoints of time intervals and improving the robustness of sampling methods are also key research directions.