Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World
  Video Super-Resolution
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.