AWS Machine Learning Blog

Introducing Stable Diffusion 3.5 Large in Amazon SageMaker JumpStart

We are excited to announce the availability of Stability AI’s latest and most advanced text-to-image model, Stable Diffusion 3.5 Large, in Amazon SageMaker JumpStart. This new cutting-edge image generation model, which was trained on Amazon SageMaker HyperPod, empowers AWS customers to generate high-quality images from text descriptions with unprecedented ease, flexibility, and creative potential. By adding Stable Diffusion 3.5 Large to SageMaker JumpStart, we’re taking another significant step towards democratizing access to advanced AI technologies and enabling businesses of all sizes to harness the power of generative AI.

In this post, we provide an implementation guide for subscribing to Stable Diffusion 3.5 Large in SageMaker JumpStart, deploying the model in Amazon SageMaker Studio, and generating images using text-to-image prompts.

Stable Diffusion 3.5 Large capabilities and use cases

At 8.1 billion parameters, with superior quality and prompt adherence, Stable Diffusion 3.5 Large is the most powerful model in the Stable Diffusion family. The model excels at creating diverse, high-quality images across a wide range of styles, making it an excellent tool for media, gaming, advertising, ecommerce, corporate training, retail, and education. For ideation, Stable Diffusion 3.5 Large can accelerate storyboarding, concept art creation, and rapid prototyping of visual effects. For production, you can quickly generate high-quality 1-megapixel images for campaigns, social media posts, and advertisements, saving time and resources while maintaining creative control.

Stable Diffusion 3.5 Large offers users nearly endless creative possibilities, including:

  • Enhanced creativity and photorealism – You can generate exceptional visuals with highly detailed 3D imagery that include fine details like lighting and textures.
  • Exceptional multi-subject proficiency – It offers unrivaled capabilities in generating images with multiple subjects, which is ideal for creating complex scenes.
  • Increased efficiency – Fast, accurate, and quality content production streamlines operations, saving time and money. Despite its power and complexity, Stable Diffusion 3.5 Large is optimized for efficiency, providing accessibility and ease of use across a broad audience.

Solution overview

With SageMaker JumpStart, you can choose from a broad selection of publicly available foundation models (FMs). ML practitioners can deploy FMs to dedicated SageMaker instances from a network isolated environment and customize models using Amazon SageMaker for model training and deployment. You can now discover and deploy the Stable Diffusion 3.5 large model with a few clicks in SageMaker Studio or programmatically through the SageMaker Python SDK, enabling you to derive model performance and MLOps controls with SageMaker features such as Amazon SageMaker Pipelines, Amazon SageMaker Debugger, or container logs. The model is deployed in an AWS secure environment and under your virtual private cloud (VPC) controls, helping provide data security.

The Stable Diffusion 3.5 Large model is available today in the following AWS Regions: US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Osaka, Hong Kong), China (Beijing), Middle East (Bahrain), Africa (Cape Town), and Europe (Milan, Stockholm).

SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models. For more details on how to get started and set up SageMaker Studio, refer to Amazon SageMaker Studio.

Prerequisites

Make sure that your AWS Identity and Access Management (IAM) role has AmazonSageMakerFullAccess. To successfully deploy the model, confirm that your IAM role has the following three permissions and you have authority to make AWS Marketplace subscriptions in the AWS account used:

  • aws-marketplace:ViewSubscriptions
  • aws-marketplace:Unsubscribe
  • aws-marketplace:Subscribe

Subscribe to the Stable Diffusion 3.5 Large model package

You can access SageMaker JumpStart through the SageMaker Studio Home page by selecting JumpStart in the Prebuilt and automated solutions section. The JumpStart landing page allows you to explore various resources including solutions, models, and notebooks. You can search for a particular provider. In this following screenshot, we are looking at all the models by Stability AI on SageMaker JumpStart.

Each model is presented with a model card containing key information such as the model name, fine-tuning capability, provider, and a brief description. To find the Stable Diffusion 3.5L model, you can either browse the Foundation Model: Image Generation carousel or use the search function. Select Stable Diffusion 3.5 Large.

Next, we will subscribe to Stable Diffusion 3.5 Large, follow these steps:

  1. Open the model listing page in AWS Marketplace using the link available from the example notebook in SageMaker JumpStart.
  2. On the listing, choose Continue to subscribe.
  3. On the Subscribe to this software page, review and choose Accept Offer if you and your organization accept the EULA, pricing, and support terms.
  4. Choose Continue to configuration to start configuring your model.
  5. Choose a supported Region, and you will see the model package Amazon Resource Name (ARN) that you need to specify when creating an endpoint.

Note: If you don’t have the necessary permissions to view or subscribe to the model, reach out to your AWS administrator or procurement point of contact. Many enterprises may limit AWS Marketplace permissions to control the actions that someone can take in the AWS Marketplace Management Portal.

Deploy the model in SageMaker Studio

Now you’re prepared to follow the notebook example from Stability AI’s GitHub repository to create an endpoint (with the model package ARN from AWS Marketplace) and create a deployable ModelPackage.

For Stable Diffusion 3.5 Large, you’ll need to deploy on an Amazon Elastic Compute Cloud (Amazon EC2) ml.p5.48xlarge instance.

Generate images with a text prompt

Refer to the Stable Diffusion 3.5 Large documentation for more details. From the example notebook, the code to generate an image is as follows:

sm_runtime = boto3.client("sagemaker-runtime")

params = {
    "prompt": " Photography, pink rose flowers in the twilight, glowing, tile houses in the background.",
    "seed": 101,
    "aspect_ratio": "21:9",
    "output_format": "jpeg",
}

payload = json.dumps(params).encode("utf-8")

response = sm_runtime.invoke_endpoint(
    EndpointName=endpoint_name,
    ContentType="application/json",
    Accept="application/json",
    Body=payload,
)

out = json.loads(response["Body"].read().decode("utf-8"))
try:
    base64_string = out["body"]["images"][0]
    image_data = base64.b64decode(base64_string)
    image = Image.open(io.BytesIO(image_data))
    display(image)

except:
    print(out)

The following are examples of images generated from different prompts.

Prompt:

Photography, pink rose flowers in the twilight, glowing, tile houses in the background.

Prompt:

The word “AWS x Stability” in a thick, blocky script surrounded by roots and vines against a solid white background. The scene is lit by flat light, creating a reflective scene with a minimal color palette. Quilling style.

Prompt:

Expressionist painting, side profile of a silhouette of a student seated at a desk, absorbed in reading a book. Her thoughts artistically connect to the stars and the vast universe, symbolizing the expansion of knowledge and a boundless mind.

Prompt:

High-energy street scene in a neon-lit Tokyo alley at night, where steam rises from food carts, and colorful neon signs illuminate the rain-slicked pavement.

Prompt:

3D animation scene of an adventurer traveling the world with his pet dog.

Clean up

When you’ve finished working, you can delete the endpoint to release the EC2 instances associated with it and stop billing.

Get your list of SageMaker endpoints using the AWS Command Line Interface (AWS CLI) as follows:

!aws sagemaker list-endpoints

Then delete the endpoints:

deployed_model.sagemaker_session.delete_endpoint(endpoint_name)

Conclusion

In this post, we walked through subscribing to Stable Diffusion 3.5 Large in SageMaker JumpStart, deploying the model in SageMaker Studio, and generating of a variety of images with Stability AI’s latest text-to-image model.

Start creating amazing images today with Stable Diffusion 3.5 Large on SageMaker JumpStart. To learn more about SageMaker JumpStart, see SageMaker JumpStart pretrained models, Amazon SageMaker JumpStart Foundation Models, and Getting started with Amazon SageMaker JumpStart.

If you’d like to explore advanced prompt engineering techniques that can enhance the performance of text-to-image models from Stability AI and facilitate the creation of compelling imagery, see Understanding prompt engineering: Unlock the creative potential of Stability AI models on AWS.

About the Authors

Tom Yemington is a Senior GenAI Models Specialist focused on helping model providers and customers scale generative AI solutions in AWS. Tom is a Certified Information Systems Security Professional (CISSP). Outside of work, you can find Tom racing vintage cars or teaching people how to race as an instructor at track-day events.

Isha Dua is a Senior Solutions Architect based in the San Francisco Bay Area working with GENAI Model providers and helping customer optimize their GENAI workloads on AWS. She helps enterprise customers grow by understanding their goals and challenges, and guides them on how they can architect their applications in a cloud-native manner while ensuring resilience and scalability. She’s passionate about machine learning technologies and environmental sustainability.

Boshi Huang is a Senior Applied Scientist in Generative AI at Amazon Web Services, where he collaborates with customers to develop and implement generative AI solutions. Boshi’s research focuses on advancing the field of generative AI through automatic prompt engineering, adversarial attack and defense mechanisms, inference acceleration, and developing methods for responsible and reliable visual content generation.