High-Throughput Generative Video Super-Resolution on a Single Consumer GPU

Key points

• We study a different operating point: practical HD generative VSR on a single high-end consumer GPU without end-to-end retraining.1
• To this end, we present a zero-shot framework built entirely from pretrained components.1
• A time-distilled one-step diffusion backbone handles spatial upscaling, while temporal coherence is promoted by a lightweight pretrained flow-based interpolator that synthesizes intermediate frames between super-resolved keyframes.1
• To further improve scalability, we introduce a resolution-aware hybrid attention scheme that, in a zero-shot manner, replaces selected high-resolution global-attention layers with neighborhood attention while preserving global attention in semantically critical low-resolution layers, while also improving temporal consistency.1

Sources (1)

• [1]High-Throughput Generative Video Super-Resolution on a Single Consumer GPU
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 08:06 AM

  Generative video super-resolution (VSR) can recover visually compelling high-frequency detail by leveraging diffusion priors, but iterative sampling and heavy spatio-temporal coupling make practical deployments difficult.

  We study a different operating point: practical HD generative VSR on a single high-end consumer GPU without end-to-end retraining.

Extractive summary: sentences quoted from the sources.