SimpleGVR: A Simple Baseline for Latent-Cascaded Video Super-Resolution
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.
685b5e05d2ee4fac76521ddd
Liangbin Xie
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2025-06-25T13:32:05.492Z
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Liangbin Xie
user
Liangbin
685b5e05d2ee4fac76521dde
Yu Li
685b5e05d2ee4fac76521ddf
Shian Du
685b5e05d2ee4fac76521de0
Menghan Xia
685b5e05d2ee4fac76521de1
Xintao Wang
685b5e05d2ee4fac76521de2
Fanghua Yu
685b5e05d2ee4fac76521de3
Ziyan Chen
685b5e05d2ee4fac76521de4
Pengfei Wan
685b5e05d2ee4fac76521de5
Jiantao Zhou
685b5e05d2ee4fac76521de6
Chao Dong
SimpleGVR: A Simple Baseline for Latent-Cascaded Video Super-Resolution
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.
Researchers propose design principles for cascaded video super-resolution models to improve high-resolution video generation by introduces degradation strategies, timestep sampling, noise augmentation, and interleaving temporal units with sparse local attention.
latent diffusion models video generation cascaded video super-resolution VSR degradation strategies timestep sampling noise augmentation interleaving temporal unit sparse local attention