Generate images from text with the stable diffusion model on Amazon SageMaker JumpStart

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. These features remove the heavy lifting from each step of the ML process, making it easier to develop high-quality models and reducing time to deployment.

This post is the fifth in a series on using JumpStart for specific ML tasks. In the first post, we showed how you can run image classification use cases on JumpStart. In the second post, we demonstrated how to run text classification use cases. In the third post, we discussed image segmentation use cases. In the fourth post, we showed how you can run text generation use cases.

In this post, we provide a step-by-step walkthrough on how to deploy pre-trained stable diffusion models for generating images from text. We explore two ways of obtaining the same result: via JumpStart’s graphical interface on Amazon SageMaker Studio, and programmatically through JumpStart APIs.

If you want to jump straight into the JumpStart API code we go through in this post, you can refer to the following sample Jupyter notebook: Introduction to JumpStart – Text to Image.

JumpStart overview

JumpStart helps you get started with ML models for a variety of tasks without writing a single line of code. Currently, JumpStart enables you to do the following:

  • Deploy pre-trained models for common ML tasks – JumpStart enables you to address common ML tasks with no development effort by providing easy deployment of models pre-trained on large, publicly available datasets. The ML research community has put a large amount of effort into making a majority of recently developed models publicly available for use. JumpStart hosts a collection of over 300 models, spanning the 15 most popular ML tasks such as object detection, text classification, and text generation, making it easy for beginners to use them. These models are drawn from popular model hubs such as TensorFlow, PyTorch, Hugging Face, and MXNet.
  • Fine-tune pre-trained models – JumpStart allows you to fine-tune pre-trained models without needing to write your own training algorithm. In ML, the ability to transfer the knowledge learned in one domain to another domain is called transfer learning. You can use transfer learning to produce accurate models on your smaller datasets, with much lower training costs than the ones involved in training the original model. JumpStart also includes popular training algorithms based on LightGBM, CatBoost, XGBoost, and Scikit-learn, which you can train from scratch for tabular regression and classification.
  • Use pre-built solutions – JumpStart provides a set of 17 solutions for common ML use cases, such as demand forecasting and industrial and financial applications, which you can deploy with just a few clicks. Solutions are end-to-end ML applications that string together various AWS services to solve a particular business use case. They use AWS CloudFormation templates and reference architectures for quick deployment, which means they’re fully customizable.
  • Refer to notebook examples for SageMaker algorithms – SageMaker provides a suite of built-in algorithms to help data scientists and ML practitioners get started with training and deploying ML models quickly. JumpStart provides sample notebooks that you can use to quickly use these algorithms.
  • Review training videos and blogs – JumpStart also provides numerous blog posts and videos that teach you how to use different functionalities within SageMaker.

JumpStart accepts custom VPC settings and AWS Key Management Service (AWS KMS) encryption keys, so you can use the available models and solutions securely within your enterprise environment. You can pass your security settings to JumpStart within Studio or through the SageMaker Python SDK.

Text-to-image and stable diffusion models

Text-to-image is the task of generating realistic images describing a raw input text. Stable diffusion is a popular model for this task. It’s a latent diffusion model where input text is first embedded into a latent space via a language model. Then, a series of noise addition and removal operations are performed in the latent space with U-Net architecture. Finally, denoised output is decoded into the pixel space.

For example, with the input “Garden painted in impressionist style,” we get the following output image.

Although primarily used to generate images conditioned on text, stable diffusion models can also be used for other tasks such as inpainting, outpainting, and generating image-to-image translations guided by text.

Solution overview

The following sections provide a step-by-step demo to perform inference, both via the Studio UI and via JumpStart APIs. We walk through the following steps:

  1. Access JumpStart through the Studio UI to deploy and run inference on the pre-trained model.
  2. Use JumpStart programmatically with the SageMaker Python SDK to deploy the pre-trained model and run inference.

Access JumpStart through the Studio UI and run inference with a pre-trained model

In this section, we demonstrate how to train and deploy JumpStart models through the Studio UI.

The following video shows you how to find a pre-trained text-to-image model on JumpStart and deploy it. The model page contains valuable information about the model and how to use it. You can deploy any of the pre-trained models available in JumpStart. For inference, we pick the ml.p3.2xlarge instance type, because it provides the GPU acceleration needed for low inference latency at a low price point. After you configure the SageMaker hosting instance, choose Deploy. It may take 5–10 minutes until your persistent endpoint is up and running.

After a few minutes, your endpoint is operational and ready to respond to inference requests!

To accelerate your time to inference, JumpStart provides a sample notebook that shows you how to run inference on your freshly deployed endpoint. Choose Open Notebook under Use Endpoint from Studio.

Use JumpStart programmatically with the SageMaker SDK

In the preceding section, we showed how you can use the JumpStart UI to deploy a pre-trained model interactively, in a matter of a few clicks. However, you can also use JumpStart’s models programmatically by using APIs that are integrated into the SageMaker SDK.

In this section, we go over a quick example of how you can replicate the preceding process with the SageMaker SDK. We choose an appropriate pre-trained model in JumpStart, deploy this model to a SageMaker endpoint, and run inference on the deployed endpoint. All the steps in this demo are available in the accompanying notebook Introduction to JumpStart – Text to Image.

Deploy the pre-trained model

SageMaker is a platform that makes extensive use of Docker containers for build and runtime tasks. JumpStart uses the available framework-specific SageMaker Deep Learning Containers (DLCs). We first fetch any additional packages, as well as scripts to handle training and inference for the selected task. Finally, the pre-trained model artifacts are separately fetched with model_uris, which provides flexibility to the platform. You can use any number of models pre-trained on the same task with a single inference script. See the following code:

model_id, model_version = "huggingface-txt2img-stable-diffusion-v1-4", "*"

# Retrieve the inference docker container uri
deploy_image_uri = image_uris.retrieve(
    framework=None,  # automatically inferred from model_id

# Retrieve the inference script uri
deploy_source_uri = script_uris.retrieve(model_id=model_id, model_version=model_version, script_scope="inference")

base_model_uri = model_uris.retrieve(model_id=model_id, model_version=model_version, model_scope="inference")

Next, we feed the resources into a SageMaker model instance and deploy an endpoint:

# Create the SageMaker model instance
model = Model(
    entry_point="",  # entry point file in source_dir and present in deploy_source_uri

# deploy the Model. Note that we need to pass Predictor class when we deploy model through Model class,
# for being able to run inference through the sagemaker API.
base_model_predictor = model.deploy(

After our model is deployed, we can get predictions from it in real time!

Run inference

The following code snippet gives you a glimpse of what the outputs look like. To send requests to a deployed model, input text needs to be supplied in a utf-8 encoded format.

def query(model_predictor, text):
    """Query the model predictor."""

    encoded_text = json.dumps(text).encode("utf-8")

    query_response = model_predictor.predict(
            "ContentType": "application/x-text",
            "Accept": "application/json",
    return query_response

The endpoint response is a JSON object containing the generated image and the prompt:

def parse_response(query_response):
    response_dict = json.loads(query_response['Body'].read())
    return response_dict['generated_image'], response_dict['prompt']
text = "Garden painted in impressionist style"
query_response = query(model_predictor, text)
img, prompt = parse_response(query_response)

We get the following image as our output.


In this post, we showed how to deploy a pre-trained text generation model using JumpStart. You can accomplish this without needing to write code. Try out the solution on your own and send us your comments. To learn more about JumpStart and how you can use open-source pre-trained models for a variety of other ML tasks, check out the following AWS re:Invent 2020 video.

About the Authors

Dr. Vivek Madan is an Applied Scientist with the Amazon SageMaker JumpStart team. He got his PhD from University of Illinois at Urbana-Champaign and was a Post Doctoral Researcher at Georgia Tech. He is an active researcher in machine learning and algorithm design and has published papers in EMNLP, ICLR, COLT, FOCS, and SODA conferences.

Santosh Kulkarni is an Enterprise Solutions Architect at Amazon Web Services who works with sports customers in Australia. He is passionate about building large-scale distributed applications to solve business problems using his knowledge in AI/ML, big data, and software development.

Leonardo Bachega is a Senior Scientist and Manager in the Amazon SageMaker JumpStart team. He’s passionate about building AI services for computer vision.

Read More