SF-V is a diffusion-based video generation model that optimizes a pre-trained model through adversarial training, achieving the capability of generating high-quality videos in a single step. This model significantly reduces the computational cost of the denoising process while maintaining the temporal and spatial dependencies of video data, paving the way for real-time video synthesis and editing.