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Copy file name to clipboardExpand all lines: README.md
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|[SageMaker model deployment (JumpStart)](./src/patterns/gen-ai/aws-model-deployment-sagemaker/README_jumpstart.md)| Deploy a foundation model from Amazon SageMaker JumpStart to an Amazon SageMaker endpoint. | Amazon SageMaker |
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|[SageMaker model deployment (Hugging Face)](./src/patterns/gen-ai/aws-model-deployment-sagemaker/README_hugging_face.md)| Deploy a foundation model from Hugging Face to an Amazon SageMaker endpoint. | Amazon SageMaker |
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|[SageMaker model deployment (Custom)](./src/patterns/gen-ai/aws-model-deployment-sagemaker/README_custom_sagemaker_endpoint.md)| Deploy a foundation model from an S3 location to an Amazon SageMaker endpoint. | Amazon SageMaker |
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|[Content Generation](./src/patterns/gen-ai/aws-contentgen-appsync-lambda/README.md)| Generate images from text using Amazon titan-image-generator-v1 or stability.stable-diffusion-xl model. | AWS Lambda, Amazon Bedrock, AWS AppSync |
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|[Content Generation](./src/patterns/gen-ai/aws-contentgen-appsync-lambda/README.md)| Generate images from text using Amazon titan-image-generator-v1 or stability.stable-diffusion-xl-v1 model. | AWS Lambda, Amazon Bedrock, AWS AppSync |
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|[Web crawler](./src/patterns/gen-ai/aws-web-crawler/README.md)| Crawl websites and RSS feeds on a schedule and store changeset data in an Amazon Simple Storage Service bucket. | AWS Lambda, AWS Batch, AWS Fargate, Amazon DynamoDB |
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|[Amazon Bedrock Monitoring (Amazon CloudWatch Dashboard)](./src/patterns/gen-ai/aws-bedrock-cw-dashboard/README.md)| Amazon CloudWatch dashboard to monitor model usage from Amazon Bedrock. | Amazon CloudWatch |
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3. Lambda function first implement text moderation using Amazon Comprehend to check for inappropriate content.
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4. The functions then generate an image from the text using Amazon Bedrock with the stability.stable-diffusion-xl/amazon.titan-image-generator-v1 model.
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4. The functions then generate an image from the text using Amazon Bedrock with the stability.stable-diffusion-xl-v1/amazon.titan-image-generator-v1 model.
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5. Next, image moderation is performed using Amazon Rekognition to further ensure appropriateness.
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This construct builds a Lambda function from a Docker image, thus you need [Docker desktop](https://www.docker.com/products/docker-desktop/) running on your machine.
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Make sure the model (stability.stable-diffusion-xl/amazon.titan-image-generator-v1) is enabled in your account. Please follow the [Amazon Bedrock User Guide](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html) for steps related to enabling model access.
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Make sure the model (stability.stable-diffusion-xl-v1/amazon.titan-image-generator-v1) is enabled in your account. Please follow the [Amazon Bedrock User Guide](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html) for steps related to enabling model access.
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AWS Lambda functions provisioned in this construct use [Powertools for AWS Lambda (Python)](https://github.com/aws-powertools/powertools-lambda-python) for tracing, structured logging and custom metrics creation.
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Where:
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- job_id: id which can be used to filter subscriptions on client side.
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- status: this field will be used by the subscription to update the status of the image generation process.
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- model_config: configure model id amazon.titan-image-generator-v1/stability.stable-diffusion-xl.
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- model_config: configure model id amazon.titan-image-generator-v1/stability.stable-diffusion-xl-v1.
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- model_kwargs: Image generation model driver for Stable Diffusion models and Amazon Titan generator on Amazon Bedrock.
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