What video upscaling use cases does this solution support?

Archives, museums, and broadcasters can restore and digitize historical footage at higher resolutions. Streaming services can upscale older TV shows and movies to 4K or higher resolutions. Creators can also upscale AI-generated videos, which often start at lower resolutions, turning computationally efficient rough drafts into polished, high-resolution final products.

What GPU instance type and infrastructure does the solution use?

The solution runs on ml.g5.4xlarge GPU instances within a three-tier AWS architecture that includes Amazon VPC for security, Amazon S3 for input and output storage, AWS Lambda to trigger processing jobs, and Amazon ECR to store the custom SeedVR2 container.

How long does deployment take and what are the prerequisites?

Deployment takes 15–20 minutes to complete. Prerequisites include Python 3.13+, the AWS Command Line Interface (AWS CLI), Docker, AWS Cloud Development Kit (AWS CDK) v2, an AWS account with appropriate permissions, and a service quota request for ml.g5.4xlarge in SageMaker processing jobs.

Amazon AI BlogJun 25, 2026Jun 26, 2026, 04:00 JST

AWS deploys ByteDance's SeedVR2 video upscaler on SageMaker

Send on LINE

3 Key Points

What happened
AWS released a technical walkthrough demonstrating how to deploy SeedVR2—a video restoration model developed by ByteDance's Seed team—on SageMaker AI infrastructure using ml.g5.4xlarge GPU instances. The solution uses a three-tier architecture with Amazon VPC for security, Amazon S3 for storage, AWS Lambda to trigger processing jobs, and ComfyUI as the inference framework.
Why it matters
Organizations with large libraries of older or lower-resolution video content can now upscale to higher resolutions without purchasing new content or generating it from scratch. Streaming services can enhance older TV shows and movies to 4K; museums and broadcasters can digitize historical footage; creators can turn computationally efficient AI-generated video drafts into high-resolution final products—all using a scalable, cost-efficient managed service rather than building custom infrastructure.
What to watch
The deployment process takes 15–20 minutes to complete and requires prerequisites including Python 3.13+, AWS CLI, Docker, AWS CDK v2, and a service quota request for ml.g5.4xlarge in SageMaker processing jobs. The sample code is available on GitHub at aws-samples/sample-sagemaker-video-upscaler.

Not sure about something? Ask the AI

Questions and answers are published on this page.

Summaries like this, in your inbox every morning.

Get news like this every morning, free

FAQ

What video upscaling use cases does this solution support?: Archives, museums, and broadcasters can restore and digitize historical footage at higher resolutions. Streaming services can upscale older TV shows and movies to 4K or higher resolutions. Creators can also upscale AI-generated videos, which often start at lower resolutions, turning computationally efficient rough drafts into polished, high-resolution final products.
What GPU instance type and infrastructure does the solution use?: The solution runs on ml.g5.4xlarge GPU instances within a three-tier AWS architecture that includes Amazon VPC for security, Amazon S3 for input and output storage, AWS Lambda to trigger processing jobs, and Amazon ECR to store the custom SeedVR2 container.
How long does deployment take and what are the prerequisites?: Deployment takes 15–20 minutes to complete. Prerequisites include Python 3.13+, the AWS Command Line Interface (AWS CLI), Docker, AWS Cloud Development Kit (AWS CDK) v2, an AWS account with appropriate permissions, and a service quota request for ml.g5.4xlarge in SageMaker processing jobs.

Amazon AI BlogRead Original Article

Send on LINE

AI news that matters for your work, delivered every morning.

Pick your industry and the AI tools you use, and get news related to your work every day.

Get it free

Free · 30 seconds with Google · unsubscribe anytimeWhat is AIToday? →

Ask AI

Ask AI anything about this article. The AI reads this article, earlier AIToday articles, and Wikipedia, and cites its sources. Q&As are published on this page for other readers too.

Next articleNVIDIA and Micron both posted blockbuster AI infrastructure quarters, but the market rewarded them opposite ways—NVIDIA's stock fell 10.38% while Micron surged 127.9%—reflecting different bets on where AI hardware spending lands in 2026.