As display technologies advance to higher resolutions, many organizations face a common challenge: their existing video libraries contain lower-resolution content that appears pixelated or blurry on modern high-definition displays. Traditional video upscaling approaches often struggle with computational limits, inconsistent quality, and scalability issues when processing large video collections. Many existing solutions also lack the techniques needed to restore fine details, sharpen edges, and reduce noise artifacts.

SeedVR2 is an open-source video restoration model developed by ByteDance’s Seed team. Running SeedVR2 on Amazon SageMaker AI addresses these challenges by providing a scalable solution for upscaling and video quality enhancement, also known as super resolution. This approach analyzes visual information frame by frame to restore details and improve video quality, so you don’t need to repurchase content in higher resolutions. With SageMaker managed infrastructure, you can process video collections at scale while maintaining cost efficiency and performance.

In this post, we demonstrate how to implement video upscaling using SeedVR2 on SageMaker AI. We cover the solution architecture, walk through the deployment steps, and show performance comparisons that highlight the quality improvements and processing efficiency you can achieve. By the end of this post, you’ll have the practical knowledge needed to implement this super resolution solution.