S²Flow: Towards Fast and Authentic Training-Free High-Resolution Video Generation

Rectified flow models have shown strong potential in highfidelity video generation, yet extending them to highresolution remains challenging due to the high cost of full attention and error accumulation in the ODE-solving process. In this paper, we propose S 2 Flow, a training-free framework that enables efficient and authentic high-resolution video generation by jointly exploring Flow-guided Sparse attention and Second-order ODE solution. Specifically, S 2 Flow exploits and transfers the semantic and structural information from the low-resolution flow trajectory to guide the high-resolution flow in two aspects. First, S 2 Flow dynamically captures the sparse patterns of the spatio-temporal attention maps from low-resolution videos to construct localized 3D windows, enabling efficient window attention in high-resolution inference. This can significantly reduce redundant computation while preserving contextual dependencies. Second, S 2 Flow adopts a second-order ODE solver based on Taylor expansion, where the high-order derivative is approximated via central difference from the low-resolution flow, facilitating accurate high-resolution denoising. Extensive experiments on VBench dataset demonstrate that S 2 Flow outperforms prior methods in both visual quality and inference speed, enabling 4× acceleration on 2560 × 1536 video generation.