VISION XL is a framework that addresses high-definition video inverse problems using potential diffusion models. It optimizes video processing efficiency and time through a pseudo-batch consistency sampling strategy and batch consistency inversion methods, supporting multiple scales and high-resolution reconstructions. Key advantages of this technology include support for multi-scale and high-resolution reconstruction, memory and sampling time efficiency, and the use of the open-source potential diffusion model SDXL. By integrating SDXL, it achieves state-of-the-art video reconstruction across various spatio-temporal inverse problems, including complex frame averaging and combinations of spatial degradations such as deblurring, super-resolution, and restoration.