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This Guidance shows how to create super slow-motion videos by interpolating synthetic frames between real video frames. It uses generative artificial intelligence (AI) to generate new frames between two existing video frames, effectively slowing down the action while maintaining sharpness and detail. This approach can handle large motions between frames, making it well-suited for creating sports highlights or cinematic sequences. By combining the power of generative AI with the scalability and reliability of AWS services, this Guidance delivers a seamless and cost-effective way to unlock new creative possibilities and elevate your visual storytelling.
Please note: [Disclaimer]
Architecture Diagram
[Architecture diagram description]
Step 1
A DevOps engineer calls an Amazon API Gateway endpoint to create a model endpoint.
Step 2
API Gateway invokes an AWS Lambda function to process the request.
Step 3
The Lambda function uploads model artifacts. In this case, the Frame Interpolation for Large Motion (FILM) model and the endpoint configuration are uploaded to an Amazon Simple Storage Service (Amazon S3) bucket, creating an endpoint.
Step 4
On endpoint creation, Amazon SageMaker creates an asynchronous inference endpoint with an autoscaling feature.
Step 5
The operator uploads a short video to an Amazon S3 bucket for processing.
Step 6
An Amazon S3 event invokes an AWS Step Functions state machine through Amazon EventBridge to process the request.
Step 7
A Lambda function extracts frames from the video and stores them in an Amazon S3 bucket.
Step 8
A Lambda function creates an inference job by invoking the SageMaker Asynchronous Inference endpoint, where the FILM model interpolates new frames. The state machine processing is paused and waits for a job completion status.
Step 9
The SageMaker Asynchronous Inference endpoint sends the job status to Amazon Simple Notification Service (Amazon SNS).
Step 10
The state machine processing job resumes, where a Lambda function encodes all new frames to create a slow motion video, storing the frames in an Amazon S3 bucket.
Step 11
An Amazon S3 event sends the status to Amazon SNS to notify the operator that the slow-motion video is complete.
Step 1
A DevOps engineer calls an Amazon API Gateway endpoint to create a model endpoint.
Step 2
API Gateway invokes an AWS Lambda function to process the request.
Step 3
The Lambda function uploads model artifacts. In this case, the Frame Interpolation for Large Motion (FILM) model and the endpoint configuration are uploaded to an Amazon Simple Storage Service (Amazon S3) bucket, creating an endpoint.
Step 4
On endpoint creation, Amazon SageMaker creates an asynchronous inference endpoint with an autoscaling feature.
Step 5
The operator uploads a short video to an Amazon S3 bucket for processing.
Step 6
An Amazon S3 event invokes an AWS Step Functions state machine through Amazon EventBridge to process the request.
Step 7
A Lambda function extracts frames from the video and stores them in an Amazon S3 bucket.
Step 8
A Lambda function creates an inference job by invoking the SageMaker Asynchronous Inference endpoint, where the FILM model interpolates new frames. The state machine processing is paused and waits for a job completion status.
Step 9
The SageMaker Asynchronous Inference endpoint sends the job status to Amazon Simple Notification Service (Amazon SNS).
Step 10
The state machine processing job resumes, where a Lambda function encodes all new frames to create a slow motion video, storing the frames in an Amazon S3 bucket.
Step 11
An Amazon S3 event sends the status to Amazon SNS to notify the operator that the slow-motion video is complete.
Well-Architected Pillars
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
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Operational ExcellenceRead the Operational Excellence whitepaper
This Guidance uses SageMaker Asynchronous Inference and Amazon CloudWatch to reduce operational overhead while also making it easier to maintain and troubleshoot the video processing pipeline. Specifically, SageMaker Asynchronous Inference allows users to process multiple requests in parallel, providing a scalable and fault-tolerant architecture with its built-in queuing mechanism. This ensures the efficient and reliable handling of large volumes of video processing requests. CloudWatch, on the other hand, collects metrics and logs from AWS services like Lambda, Step Functions, and SageMaker Asynchronous Inference endpoints, enabling visibility into performance, health, and utilization. This proactive monitoring and alerting capability facilitates timely identification and resolution of issues, optimizes resource utilization, and enables data-driven decision-making for improved operations and cost efficiency.
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SecurityRead the Security whitepaper
API Gateway adds an essential security layer that supports robust authentication, authorization, and protection against common threats for secure and controlled access to the video processing pipeline. It provides built-in mechanisms for authenticating and authorizing API requests, allowing users to control access to their APIs using Amazon Cognito user pools, OAuth 2.0, or AWS Identity and Access Management (IAM) roles. From a data protection perspective, API Gateway ensures that data coming to the endpoint is SSL/TLS encrypted, safeguarding the confidentiality and integrity of the data in transit. Additionally, API Gateway supports API throttling, helping to protect the backend resources from excessive traffic or abuse, and mitigating the risk of distributed denial-of-service (DDoS) attacks.
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ReliabilityRead the Reliability whitepaper
By combining the capabilities of API Gateway, Lambda, SageMaker Asynchronous Inference, and Step Functions, this Guidance is capable of handling varying workloads to support reliable video processing, even in the face of traffic spikes or other potential disruptions. API Gateway provides built-in fault tolerance and automatic scaling capabilities, enabling it to handle traffic spikes seamlessly. Its integration with Lambda and SageMaker simplifies the process of building highly scalable and reliable serverless APIs.
Lambda offers automatic scaling and high availability, allowing code processing without concerns about the underlying infrastructure management, so video processing workloads can be processed reliably, even during periods of high demand.
SageMaker and its managed features are designed to deliver high reliability and availability for running machine learning workloads, so that the generative AI models used for creating slow-motion videos are consistently available and reliable.
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Performance EfficiencyRead the Performance Efficiency whitepaper
SageMaker offers a high-performance, low-latency inference feature specifically designed for hosting and serving machine learning models efficiently. It also has the capability to fine-tune the deployment configuration based on specific workload characteristics, helping to achieve optimal performance efficiency without over-provisioning resources. Users can easily configure the instance type, count, and other deployment configurations to right-size their inference workloads. This flexibility allows for optimizing the video processing performance based on factors such as latency requirements, desired throughput, and cost considerations.
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Cost OptimizationRead the Cost Optimization whitepaper
This Guidance uses serverless services that offer auto-scaling capabilities, allowing users to optimize their costs by paying only for the resources they consume. For example, SageMaker Asynchronous Inference supports auto-scaling down to zero instances when not in use, effectively eliminating compute costs during idle periods.
Similarly, Lambda and Step Functions follow a serverless compute model, where users are charged only for the compute time consumed while their code is running. This pay-per-use pricing model eliminates the need for provisioning and maintaining compute resources that run continually, leading to significant cost savings, especially during periods of low or intermittent workloads.
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SustainabilityRead the Sustainability whitepaper
The SageMaker Asynchronous Inference auto-scaling capability eliminates unnecessary compute resource consumption during idle periods. Additionally, Lambda and Step Functions follow a serverless compute model where resources are dynamically allocated based on demand, so no resources are wasted when not actively processing workloads.
By using the auto-scaling and serverless nature of these services, this Guidance promotes resource sharing and reuse, reducing the overall compute workload required to run the slow-motion video processing workload. This efficient utilization of resources helps minimize the environmental impact associated with running compute workloads.
Implementation Resources
A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment.
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
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Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.