Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution
Authors: Shangchen Zhou, Peiqing Yang, Jianyi Wang, Yihang Luo, Chen Change Loy

Text-based diffusion models have exhibited remarkable success in generation and editing showing great promise for enhancing visual content with their generative prior. However applying these models to video super-resolution remains challenging due to the high demands for output fidelity and temporal consistency which is complicated by the inherent randomness in diffusion models. Our study introduces Upscale-A-Video a text-guided latent diffusion framework for video upscaling. This framework ensures temporal coherence through two key mechanisms: locally it integrates temporal layers into U-Net and VAE-Decoder maintaining consistency within short sequences; globally without training a flow-guided recurrent latent propagation module is introduced to enhance overall video stability by propagating and fusing latent across the entire sequences. Thanks to the diffusion paradigm our model also offers greater flexibility by allowing text prompts to guide texture creation and adjustable noise levels to balance restoration and generation enabling a trade-off between fidelity and quality. Extensive experiments show that Upscale-A-Video surpasses existing methods in both synthetic and real-world benchmarks as well as in AI-generated videos showcasing impressive visual realism and temporal consistency.