This pattern demonstrates how to generate images with a serverless workflow using Amazon Bedrock (Stable Diffusion XL) and save the images to Amazon S3. The pattern has a default prompt to generate images of an rusted oil pipeline as pictured below.

Note: this pattern includes a layer with a custom version of boto3. Using an outdated boto3 version will result in an "unknown service error" Note: this patten also incldues a custom layer for Pillow

Important: this application uses various AWS services and there are costs associated with these services after the Free Tier usage - please see the AWS Pricing page for details. You are responsible for any AWS costs incurred. No warranty is implied in this example.

• Create an AWS account if you do not already have one and log in. The IAM user that you use must have sufficient permissions to make necessary AWS service calls and manage AWS resources.
• AWS CLI installed and configured
• Git Installed
• AWS Cloud Development Kit installed
• Amazon Bedrock Stability Diffusion XL Access

You must request access to a model before you can use it. If you try to use the model (with the API or console) before you have requested access to it, you receive an error message. For more information, see Model access.

1. In the AWS console, select the region from which you want to access Amazon Bedrock. At the time of writing, Amazon Bedrock is available in us-east-1 (N. Virginia) and us-west-2 (Oregon) regions.

   

2. Find Amazon Bedrock by searching in the AWS console.

   

3. Expand the side menu.

   

4. From the side menu, select Model access.

   

5. Select the Edit button.

   

6. Use the checkboxes to select the models you wish to enable. Review the applicable EULAs as needed. Click Save changes to activate the models in your account. For this pattern we only need StabilityAI / model id: stability.stable-diffusion-xl-v1.

1. Create a new directory, navigate to that directory in a terminal and clone the GitHub repository:

   git clone https://github.com/aws-samples/serverless-patterns
   

2. Change directory to the pattern directory:

   cd serverless-patterns/apigw-lambda-bedrock-s3-cdk-python
   

3. Create virtual environment for Python

   python3 -m venv .venv
   

   For a Windows platform, activate the virtualenv like this:

   .venv\Scripts\activate.bat
   

   For a OSX / Linux platform, activate the virtualenv like this:

   source .venv/bin/activate
   

4. Install the Python required dependencies:

   pip install -r requirements.txt
   

5. Run the command below to bootstrap your account. CDK needs it to deploy

   cdk bootstrap
   

6. Review the CloudFormation template CDK generates for you stack using the following AWS CDK CLI command:

   cdk synth
   

7. From the command line, use AWS CDK to deploy the AWS resources.

   cdk deploy
   

8. After deployment completes, take a look at the Outputs section. There will be an entry containing the URL of the API Gateway resource you just created. Copy that URL as you'll need it for your tests.

   The format of the URL will be something like https://{id}.execute-api.{region}.amazonaws.com/prod

CDK will create an API Gateway, along with a resource and a POST method. There's a AWS Lambda function that will be taking the prompt and invoking Amazon Bedrock synchronously.

We'll be making requests to the image_gen endpoint with a desired prompt.

Follow the example below and replace {your-api-url} with your api url from step 8.

```
curl -X POST \
{your-api-url}/prod/image_gen \
-H "Content-Type: application/json" \
-d '{"prompt": "Rusted oil pipeline weathered, damaged, old"}'
```

Go to Amazon S3, and navigate to the S3 bucket. It will have a name similar to 'apigwlambdabedrockstack-mybucket...'. In the S3 bucket you should see image-{num}.png.

Here is an example generated image:

1. Run below script in the apigw-lambda-bedrock-s3-cdk-python directory to delete AWS resources created by this sample stack.

   cdk destroy

• Bedrock Api Reference

Copyright 2023 Amazon.com, Inc. or its affiliates. All Rights Reserved.

SPDX-License-Identifier: MIT-0