Generative AI using AWS SageMaker Jumpstart
Published: 2023-04-16 10:26:53

## Table of Contents

- [Introduction](#introduction)
- [Stable Diffusion](#stablediffusion)
- [AWS SageMaker Jumpstart](#jumpstart)
  - [Deploy a pre-trained Stable Diffusion model](#deploy)
  - [Fine-tune a Stable Diffusion model](#finetune)
- [Amazon Bedrock](#bedrock)
- [Conclusion](#conclusion)
- [References](#references)

<a name="introduction"></a>
## Introduction

Generative AI has become an increasingly popular topic in the field of machine learning, with applications in areas such as computer vision, natural language processing, and creative arts. Unlike traditional machine learning, which focuses on finding patterns in data, generative AI aims to create new data that mimics the patterns found in the training data. This can be useful for tasks such as image and text generation, as well as for creating realistic simulations of real-world scenarios.

One approach to Generative AI that has shown promising results is Stable Diffusion. Stable Diffusion is a type of generative model that uses a diffusion process to transform a noise vector into a sequence of image-like samples. This approach has several advantages over other generative models, including improved sample quality and stability.

In this blog post, we will explore how to use Stable Diffusion for Generative AI on AWS SageMaker Jumpstart, a cloud-based platform for building, training, and deploying machine learning models. We will start by introducing Stable Diffusion and its advantages for Generative AI, then move on to exploring how to deploy pre-trained models and fine-tune models on custom collections of images. Finally, we will discuss how Amazon Bedrock can be used to democratize Generative AI and scale projects. By the end of this post, you will have a practical understanding of how to use Stable Diffusion on AWS SageMaker Jumpstart for your own Generative AI projects.

<a name="stablediffusion"></a>
## Stable Diffusion

Stable Diffusion is a generative model that uses a diffusion process to transform a noise vector into a sequence of image-like samples. Unlike other generative models such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), Stable Diffusion does not require a discriminator or an encoder, making it simpler and more stable to train.

In Stable Diffusion, the noise vector is iteratively transformed by a diffusion process that adds noise to the current sample. The diffusion process is controlled by a diffusion coefficient, which determines the amount of noise added at each iteration. The resulting sequence of samples is then transformed back into a single image using a reverse process.

Stable Diffusion has several advantages over other generative models. One key advantage is its stability during training, which can be a challenge for GANs and VAEs. Stable Diffusion does not require a discriminator to be trained against the generator, which can lead to instability and mode collapse. Instead, Stable Diffusion directly optimizes the likelihood of the data given the noise vector.

Another advantage of Stable Diffusion is its ability to generate high-quality samples. By iteratively adding noise to the samples, Stable Diffusion generates images that have fine details and realistic textures. This can be especially useful for tasks such as image generation or style transfer.

Stable Diffusion has been applied to a variety of tasks, including image generation, video prediction, and text generation. It has also been used in creative applications, such as generating realistic paintings or music.

In the next section, we will explore how to use Stable Diffusion on AWS SageMaker Jumpstart for Generative AI.

![High level steps of text-to-image generation](https://lilianweng.github.io/posts/2021-07-11-diffusion-models/unCLIP.png)

<figcaption>High level steps of text-to-image generation</figcaption>  

<figcaption itemprop="High level steps of text-to-image generation"><a href="https://arxiv.org/abs/2204.06125">Ramesh et al. 2022</a></figcaption>

<a name="jumpstart"></a>
## AWS SageMaker Jumpstart

AWS SageMaker Jumpstart provides a variety of tools and resources for developers and data scientists, including pre-built machine learning models, notebooks, and data sets. Jumpstart also includes a range of features for deploying and scaling machine learning models, such as automatic model tuning and model hosting.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/y8rud7q6nibt6d2ob9pi.png)
<figcaption>SageMaker Studio</figcaption>  

One of the benefits of using AWS SageMaker Jumpstart for Generative AI is the ability to quickly deploy pre-trained models. AWS provides a number of pre-trained models for Stable Diffusion, which can be used to generate high-quality images with minimal training. These pre-trained models can be deployed and used in a variety of applications, such as image generation or style transfer.

Another advantage of using AWS SageMaker Jumpstart for Generative AI is the ability to fine-tune models on custom collections of images. Jumpstart provides a range of tools for fine-tuning models, including automatic hyperparameter tuning and data augmentation. This allows developers and data scientists to quickly and easily train models that are tailored to their specific use case.

Overall, AWS SageMaker Jumpstart provides a powerful platform for building, training, and deploying Generative AI models. With its range of pre-built models, notebooks, and tools, Jumpstart makes it easy for developers and data scientists to get started with Generative AI and to build models that are tailored to their specific use case.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ltxjrhzrokfl058m54qs.png)

<figcaption>SageMaker Jumpstart in Studio</figcaption>  

<a name="deploy"></a>
## Deploy a pre-trained Stable Diffusion model

To deploy a pre-trained Stable Diffusion model on AWS SageMaker Jumpstart, follow these steps:

1. Open the AWS SageMaker Studio console and select the "Stable Diffusion 2.1 base" model from the list of pre-built models in SageMaker Jumpstart. Once you click on the model card you will directed to the deploy and train page where you can deploy a pre-trained model easily.
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/vzjnlr29m25l3t6o9m1b.png)
2. Choose the pre-trained Stable Diffusion model that best matches your use case. AWS provides a range of pre-trained models for Stable Diffusion, each of which is optimized for a specific type of generative AI task.
3. Once you have selected the pre-trained model, choose the instance type and number of instances that you want to use for deployment. AWS SageMaker Jumpstart provides a range of instance types, from small instances suitable for testing and development to large instances suitable for production deployments.
4. After selecting the instance type, configure the deployment settings for the pre-trained model. This includes settings such as the endpoint name, and other configurations. Once you have configured the deployment settings, deploy the pre-trained model to the selected instance type. AWS SageMaker Jumpstart will automatically provision the necessary resources and deploy the model to the specified instance type.
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/vkgpqaybgytssm0hv7vc.png)
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/8g0pe601p42usx0naamx.png)
5. After the model is deployed, you can use the AWS SageMaker Jumpstart console to test the model and generate images. AWS provides a range of tools for testing and evaluating generative AI models, including tools for visualizing the generated images and evaluating the quality of the generated output.

Overall, deploying a pre-trained Stable Diffusion model on AWS SageMaker Jumpstart is a straightforward process that can be completed quickly and easily. With a range of pre-built models and deployment options, AWS SageMaker Jumpstart provides a powerful platform for building and deploying generative AI models.

<a name="finetune"></a>
## Fine-tune a Stable Diffusion model
In addition to deploying pre-trained models, AWS SageMaker Jumpstart also provides tools for fine-tuning Stable Diffusion models on custom collections of images. This can be useful for tasks such as generating images that are specific to a particular use case or domain. In this section, we will explore how to fine-tune a Stable Diffusion model on AWS SageMaker Jumpstart for a custom collection of images.
Before fine-tuning a Stable Diffusion model on a custom collection of images, you will need to prepare the data. This includes selecting a set of high-quality images that are representative of the domain you want to generate images in. For this example, we will fine-tune the model on a set of Koala bear images.
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/whmyx93jkgl9uuawpr9c.jpg)
<figcaption>Collection of Koala images for fine-tuning</figcaption>  
To fine-tune a Stable Diffusion model on a custom collection of images, you can use the example notebook provided by AWS SageMaker Jumpstart. This allows you to fine-tune pre-trained models on your custom collection of images. Clone the notebook, create a directory ‘training_images’, upload the Koala bear images to the directory, change the ‘use_local_images’ = True and run all cells. SageMaker spins up a training job for fine-tuning the model on the Koala bear images. After the training is completed, the model can be deployed for generating new images. 

Once you have fine-tuned the Stable Diffusion model on your custom collection of images, you can use it to generate new images. 

Overall, fine-tuning a Stable Diffusion model on AWS SageMaker Jumpstart is a powerful tool for generating high-quality images that are tailored to your specific use case. By selecting a custom collection of images and fine-tuning the model, you can generate images that are specific to your domain and that meet your specific requirements.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/2ro7ui221kvpg75azau7.png)

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ncwcep8j63v1e71th62g.png)

<a name="bedrock"></a>
## Amazon Bedrock
Amazon Bedrock is a new service that offers customers access to powerful foundation models (FMs) from various providers through an API. It democratizes access to FMs, making it easier for customers to build and scale generative AI-based applications. It allows customers to find the right model for their needs, customize FMs with their own data, and integrate and deploy them using AWS tools without managing any infrastructure. Bedrock is a scalable, reliable, and secure AWS managed service. It simplifies the development of generative AI applications without the need for managing physical infrastructure or high operational overhead.

![High level steps of text-to-image generation](https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2023/04/13/building-generative-ai-on-aws.png)

<figcaption>Building with Generative AI on AWS</figcaption>  

<a name="conclusion"></a>
## Conclusion
In this blog post, we explored Stable Diffusion, a type of generative model that uses a diffusion process to transform a noise vector into a sequence of image-like samples. We discussed its advantages over other generative models, including improved stability and sample quality. We then moved on to explore how to use AWS SageMaker Jumpstart, a cloud-based platform for building, training, and deploying machine learning models, for Generative AI. We discussed how to deploy pre-trained models and fine-tune models on custom collections of images. Finally, we discussed Amazon Bedrock, a new service that offers customers access to powerful foundation models (FMs) from various providers through an API, making it easier for customers to build and scale generative AI-based applications. With these tools, developers and data scientists can quickly and easily build and deploy Generative AI models that are tailored to their specific use case.

<a name="references"></a>
## References
- "Stable Diffusion 2.1." GitHub, [https://github.com/ermongroup/stable-diffusion](https://github.com/ermongroup/stable-diffusion)
- "Amazon SageMaker Jumpstart." AWS, [https://aws.amazon.com/sagemaker/jumpstart/](https://aws.amazon.com/sagemaker/jumpstart/)
- "Amazon Bedrock." AWS, [https://aws.amazon.com/bedrock/](https://aws.amazon.com/bedrock/)
- Text to image on AWS SageMaker Jumpstart, [https://aws.amazon.com/blogs/machine-learning/generate-images-from-text-with-the-stable-diffusion-model-on-amazon-sagemaker-jumpstart/](https://aws.amazon.com/blogs/machine-learning/generate-images-from-text-with-the-stable-diffusion-model-on-amazon-sagemaker-jumpstart/)
- Fine tune text to image on AWS SageMaker Jumpstart, [https://aws.amazon.com/blogs/machine-learning/fine-tune-text-to-image-stable-diffusion-models-with-amazon-sagemaker-jumpstart/](https://aws.amazon.com/blogs/machine-learning/fine-tune-text-to-image-stable-diffusion-models-with-amazon-sagemaker-jumpstart/)

> Disclaimer: This blog post was generated with the assistance of AI tools. While the author has edited and reviewed the content, some portions of the article may have been generated or suggested by the AI tool. The opinions and views expressed in this post are solely those of the author.