Future of AI Generated Images - Stable Diffusion AI : Part 7

Introduction

In this article, we delve into using Stability AI's models within the AWS Bedrock platform, specifically focusing on generating images for marketing campaigns using SageMaker. This tutorial will guide you step-by-step through the process of setting up an environment, running Python scripts, and generating compelling images that can enhance your marketing efforts.

Setting Up Your Environment

To begin, we log in to the AWS portal and navigate to the Bedrock service to explore the capabilities of Stability AI's models. We choose the Stability AI SDXL model, which is suitable for our needs. Once this is confirmed, we proceed to set up a SageMaker notebook instance.

Creating a SageMaker Notebook

1. Go to the SageMaker section and select Notebooks.
2. Click on Create Notebook Instance, naming it appropriately for our project (e.g., "Cloud Guru Amit Marketing").
3. For the instance type, we select a minimal option since the workload is not resource-intensive.
4. After configuring the network settings—most defaults will suffice—click on Create Notebook Instance and wait for the status to change to "In Service."

Once the notebook instance is up and running, we’re ready to move on to executing Python scripts.

Running Python Scripts

In our architecture overview, we need to run a Python script to generate images. I've prepared several use cases that will be shared with Diamond members and above. Our first use case involves generating UI mockups for software projects.

Generating UI Mockups

1. Open Jupyter Notebook within the SageMaker.
2. Create a new Python 3 notebook.
3. Rename the notebook for clarity.
4. Paste the pre-prepared code for generating UI mockups.
5. The model ID used is stability diffusion Excel V1, and we define the output file name with a PNG extension.

Upon running the code, we anticipate the success message: "UI mockup generated successfully." If we receive this message, we verify that an image file has indeed been created.

After the script execution:

• Check the output directory for the generated file.
• View the image to evaluate the AI-generated UI mockup, which should align with the software project specifications.

Creating Smartphone Marketing Campaign Images

Next, we explore another use case—creating marketing images for smartphones. The procedure is similar:

1. Delete previous contents from the notebook.
2. Paste the new code for generating smartphone images.
3. Execute the code.

After running the script, we again look for the success message: "Marketing smartphone image generated successfully." We refresh our output folder to view and evaluate the generated smartphone image, highlighting the impressive capabilities of AI in image generation.

Recap of the Process

• We’ve utilized the Stability AI model from AWS Bedrock.
• Confirmed the creation of a SageMaker notebook instance.
• Executed Python scripts to generate and store images locally as PNG files.
• Successfully generated visual content tailored for both UI design and smartphone marketing.

Thank you for following along in this tutorial! If you’re interested in further resources or past videos on Generative AI, please visit the playlists section of our channel. For access to hands-on files or architecture diagrams, consider enrolling in the Diamond membership or above. Your support goes a long way.

Keywords

• AWS Bedrock
• Stability AI
• SageMaker
• Jupyter Notebook
• Image Generation
• Python Scripts
• UI Mockups
• Marketing Campaigns
• AI-generated images

FAQ

Q1: What is Stability AI used for in this context?
A: Stability AI is used to generate images based on prompts that can be applied in various marketing contexts, such as user interface designs or product imagery.

Q2: What AWS service is used to create the environment for image generation?
A: AWS SageMaker is used to create a notebook instance where we can run Python scripts for image generation.

Q3: How do you verify that the images were generated successfully?
A: Success is confirmed by receiving a success message after running the Python script, indicating that the file has been generated and stored in the output directory.

Q4: What types of images can be generated using this method?
A: The method can be used to generate a wide range of images, including UI mockups for software projects and marketing images for products like smartphones.

Q5: How can I access additional resources or templates?
A: Additional resources, such as hands-on files and architecture diagrams, are available through enrollment in the Diamond membership or higher levels on our channel.