asynchronous-stable-diffusion-image-api

This project contains AWS resources to support the generation of images using an existing Sagemaker Endpoint and API Gateway and AWS Lambda.

Prerequisiites

This project assumes that a Sagemaker Endpoint is already running in the target account. To create a stable diffusion endpoint you can use Sagemaker Jumpstart.

Architecture

Resources Deployed

• Rest API
  • POST /generate: Begins asynchronous workflow for image generation using a prompt strign as the payload.
• Websocket API
  • $default: Triggers Lambda to store the connection:Step Function execution pair and waits for the callback message containing the image data payload.
• GenerateImageSFN
  • State machine that contains two Lambda Functions. One that calls the Sagemaker Endpoint with the image prompt payload and then passes the image data to a callback function which issues the callback to the websocket API.
• Socket Connections Table
  • DynamoDb table responsible for storing StepFunction Execution ARNs alongside the websocket connection ID for callback.

Deploy the sample application

The Serverless Application Model Command Line Interface (SAM CLI) is an extension of the AWS CLI that adds functionality for building and testing Lambda applications. It uses Docker to run your functions in an Amazon Linux environment that matches Lambda. It can also emulate your application's build environment and API.

To use the SAM CLI, you need the following tools.

• SAM CLI - Install the SAM CLI
• Python 3 installed
• Docker - Install Docker community edition

To build and deploy your application for the first time, run the following in your shell:

sam build --use-container
sam deploy --guided

The first command will build the source of your application. The second command will package and deploy your application to AWS, with a series of prompts:

• Stack Name: The name of the stack to deploy to CloudFormation. This should be unique to your account and region, and a good starting point would be something matching your project name.
• AWS Region: The AWS region you want to deploy your app to.
• Confirm changes before deploy: If set to yes, any change sets will be shown to you before execution for manual review. If set to no, the AWS SAM CLI will automatically deploy application changes.
• Allow SAM CLI IAM role creation: Many AWS SAM templates, including this example, create AWS IAM roles required for the AWS Lambda function(s) included to access AWS services. By default, these are scoped down to minimum required permissions. To deploy an AWS CloudFormation stack which creates or modifies IAM roles, the CAPABILITY_IAM value for capabilities must be provided. If permission isn't provided through this prompt, to deploy this example you must explicitly pass --capabilities CAPABILITY_IAM to the sam deploy command.
• Save arguments to samconfig.toml: If set to yes, your choices will be saved to a configuration file inside the project, so that in the future you can just re-run sam deploy without parameters to deploy changes to your application.

You can find your API Gateway Endpoint URL in the output values displayed after deployment.

Call the API to Generate an Image

See client.py for an example generation script

Cleanup

To delete the sample application that you created, use the AWS CLI. Assuming you used your project name for the stack name, you can run the following:

sam delete --stack-name image-gpt-api

Resources

See the AWS SAM developer guide for an introduction to SAM specification, the SAM CLI, and serverless application concepts.

Next, you can use AWS Serverless Application Repository to deploy ready to use Apps that go beyond hello world samples and learn how authors developed their applications: AWS Serverless Application Repository main page

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.