Abstract: Diffusion-based generative models have demonstrated exceptional promise in super-resolution (SR) tasks, achieving a substantial advancement in detail generation relative to prior methods. However, these approaches face significant computational efficiency challenges. When the input is video, the problem becomes even more pronounced. For instance, current techniques may require tens of minutes to super-resolve a mere 2-second, 1080p video. In this paper, we present TurboVSR, an ultra-efficient diffusion-based video super-resolution model. Our core design comprises three key aspects: **(1)** We employ an autoencoder with a high compression ratio of 32$\times$32$\times$8 to reduce the number of tokens. **(2)** Highly compressed latents pose substantial challenges for training. We introduce factorized conditioning to mitigate the learning complexity: we first learn to super-resolve the initial frame; subsequently, we condition the super-resolution of the remaining frames on the high-resolution initial frame and the low-resolution subsequent frames. **(3)** We convert the pre-trained diffusion model to a shortcut model to enable fewer sampling steps, further accelerating inference.As a result, TurboVSR performs on par with state-of-the-art VSR methods, while being 100+ times faster, taking only 7 seconds to process a 2-second long 1080p video. TurboVSR also supports image resolution by considering image as a one-frame video. Our efficient design makes SR beyond 1080p possible, results on 4K (3648$\times$2048) image SR show surprising fine details.