SimpleGVR: A Simple Baseline for Latent-Cascaded Video Super-Resolution
Authors: name: Liangbin Xie, name: Yu Li, name: Shian Du, name: Menghan Xia, name: Xintao Wang, name: Fanghua Yu, name: Ziyan Chen, name: Pengfei Wan, name: Jiantao Zhou, name: Chao Dong

Latent diffusion models have emerged as a leading paradigm for efficient
video generation. However, as user expectations shift toward higher-resolution
outputs, relying solely on latent computation becomes inadequate. A promising
approach involves decoupling the process into two stages: semantic content
generation and detail synthesis. The former employs a computationally intensive
base model at lower resolutions, while the latter leverages a lightweight
cascaded video super-resolution (VSR) model to achieve high-resolution output.
In this work, we focus on studying key design principles for latter cascaded
VSR models, which are underexplored currently. First, we propose two
degradation strategies to generate training pairs that better mimic the output
characteristics of the base model, ensuring alignment between the VSR model and
its upstream generator. Second, we provide critical insights into VSR model
behavior through systematic analysis of (1) timestep sampling strategies, (2)
noise augmentation effects on low-resolution (LR) inputs. These findings
directly inform our architectural and training innovations. Finally, we
introduce interleaving temporal unit and sparse local attention to achieve
efficient training and inference, drastically reducing computational overhead.
Extensive experiments demonstrate the superiority of our framework over
existing methods, with ablation studies confirming the efficacy of each design
choice. Our work establishes a simple yet effective baseline for cascaded video
super-resolution generation, offering practical insights to guide future
advancements in efficient cascaded synthesis systems.