Revolutionizing Image Editing with Generative AI

Generative AI adoption is revolutionizing image editing across various industries, including graphic design, marketing, and social media. Traditionally, specialized tools were required for image editing, making custom solutions complex. However, using AWS services, you can quickly deploy a serverless solution for editing images, giving your teams access to image editing foundation models (FMs) through Amazon Bedrock.

Amazon Bedrock, a fully managed service, offers FMs from leading AI startups and Amazon through an API. This allows you to choose from a wide range of FMs to find the one best suited for your use case. Being serverless, Amazon Bedrock enables quick deployment, private customization of FMs with your data, and seamless integration and deployment into your applications using AWS tools without the need to manage infrastructure.

One of the AI FMs available with Amazon Bedrock is the Amazon Titan Image Generator G1, which allows you to generate an image from text or upload and edit your own image, focusing on inpainting and outpainting as key features.

Solution Overview

By utilizing AWS services like AWS Amplify, Amazon Cognito, Amazon API Gateway, AWS Lambda, and Amazon Bedrock with the Amazon Titan Image Generator G1 model, you can build a web application for image editing. This article delves into the inner workings of this solution to provide a comprehensive understanding of how each service functions and how they connect to offer a complete image editing solution.

The architecture of the solution involves Amazon Cognito for user authentication, Amplify for hosting the frontend, API Gateway and Lambda for backend services, and Amazon Bedrock for integrating with the FM model to enable image editing through prompts.

Prerequisites

In order to implement the solution detailed in this post, ensure you have the following prerequisites in place:

Deploying the Solution

When deploying the AWS CloudFormation template, the following resources are created:

• Amazon Cognito resources
• Lambda resources
• AWS Identity Access Management (IAM) resources
  • IAM role: <stack name>-ImageEditBackendRole-<auto-generated/>
  • IAM inline policy: AmazonBedrockAccess
• API Gateway resources
  • Rest API: ImageEditingAppBackendAPI
  • Methods: OPTIONS, POST
  • Authorization: Through Amazon Cognito using CognitoAuthorizer

After deploying the CloudFormation template, copy the necessary details from the Outputs tab for the deployment of Amplify:

• userPoolId
• userPoolClientId
• invokeUrl

Deploying the Amplify Application

To deploy the Amplify application, follow these steps:

1. Download the frontend code from the GitHub repo.
2. Unzip the downloaded file and navigate to the folder.
3. In the config.js file, replace the values of userPoolId, userPoolClientId, and invokeUrl with the values collected from the CloudFormation stack outputs.

Make sure to compress the files correctly before deployment as outlined in the provided instructions. Once deployed, you will receive a domain to access the application.

Conclusion

This solution showcases the seamless integration of AWS serverless services and generative AI models for image editing. By leveraging Amazon Bedrock and the Amazon Titan FM, users can efficiently edit images using prompts without worrying about managing infrastructure. Start deploying this sample solution today to experience the benefits of automated image editing with AWS.