Extension for Stable Diffusion on Amazon Web Services
Unlock the Power of Generative AI in the Cloud with Ease and Speed
Extension for Stable Diffusion on Amazon Web Services
Unlock the Power of Generative AI in the Cloud with Ease and Speed
Overview
The Extension for Stable Diffusion on Amazon Web Services solution helps customers migrate their existing Stable Diffusion model training, inference, and finetuning workloads from on-premises servers to Amazon SageMaker using extension and CloudFormation template. By leveraging elastic resources in the cloud, it accelerates model iteration and avoids performance bottlenecks associated with single-server deployments.
Benefits
This solution leverages CloudFormation for easy deployment of Amazon Web Services middleware. Combined with the installation of the native Stable Diffusion WebUI (WebUI) features and third-party extensions, users can quickly utilize Amazon SageMaker's cloud resources for inference, training and finetuning tasks.
This solution leverages CloudFormation for easy deployment of Amazon Web Services middleware. Combined with the installation of the native Stable Diffusion WebUI (WebUI) features and third-party extensions, users can quickly utilize Amazon SageMaker's cloud resources for inference, training and finetuning tasks.
This solution is implemented as an extension, allowing users to seamlessly use their existing WebUI without any changes. Additionally, the solution's code is open source and follows a non-intrusive design, enabling users to keep up with community-related feature iterations, such as popular plugins like Dreambooth, ControlNet, and LoRa.
This solution is implemented as an extension, allowing users to seamlessly use their existing WebUI without any changes. Additionally, the solution's code is open source and follows a non-intrusive design, enabling users to keep up with community-related feature iterations, such as popular plugins like Dreambooth, ControlNet, and LoRa.
This solution decouples the WebUI interface from the backend, allowing the WebUI to launch on supported terminals without GPU restrictions. Existing training, inference, and other tasks can be migrated to Amazon SageMaker through the provided extension functionalities, providing users with elastic computing resources, cost reduction, flexibility, and scalability.
This solution decouples the WebUI interface from the backend, allowing the WebUI to launch on supported terminals without GPU restrictions. Existing training, inference, and other tasks can be migrated to Amazon SageMaker through the provided extension functionalities, providing users with elastic computing resources, cost reduction, flexibility, and scalability.
Technical details
The diagram below presents the architecture you can automatically deploy using the solution's implementation guide and accompanying Amazon CloudFormation template.
1. Users in WebUI console will trigger the requests to API Gateway with assigned API token for authentication. Note that no Amazon Web Services credentials are required from WebUI perspective.
2. Amazon API Gateway will route the requests based on URL prefix to different functional Lambda to implement util jobs (for example, model upload, checkpoint merge), model training and model inferencing. In the meantime, Amazon Lambda will record the operation metadata into Amazon DynamoDB (for example, inferencing parameters, model name) for successive query and association.
3. For training process, the Amazon Step Functions will be invoked to orchestrate the training process including Amazon SageMaker for training and SNS for training status notification.
For inference process, Amazon Lambda will invoke the Amazon SageMaker to implement async inference. Training data, model and checkpoint will be stored in Amazon S3 bucket delimited with difference prefix.
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