Stable Diffusion Tutorial: Build a Generation Gallery with Chroma's Semantic Search

What is Stable Diffusion?

In recent years, a significant breakthrough in the realm of Artificial Intelligence has reshaped the landscape of digital art: AI-generated images. Among these pioneering technologies, one open-source image generation model stands out - Stable Diffusion.

Stable Diffusion quickly gained traction due to its impressive capabilities and openness, inspiring a new generation of models. With its ability to generate a wide variety of styles from short, human-readable prompts, Stable Diffusion has significantly lowered the barriers to creating AI art.

Unique Features of Stable Diffusion

What sets Stable Diffusion apart? It offers unique features like inpainting and outpainting:

• Inpainting: Allows users to edit within the image, enabling precise alterations and adjustments.
• Outpainting: Empowers users to extend the image beyond its original boundaries, perfect for creating panoramic views or expansive scenes.
• Image-to-Image Prompting: Users can create a new image based on a sourced one, akin to having a conversation with your AI.

Understanding Chroma and Embeddings

Let's explore an exciting technology called Chroma. Chroma is an open-source database designed for handling embeddings - a type of data representation widely used in AI, especially in the context of Large Language Models (LLMs).

Chroma facilitates the development of AI applications by providing a platform for storing, querying, and analyzing media embeddings, ranging from text to images, and in future releases, audio and video.

What are Embeddings?

Embeddings are a way of converting words or images into numerical vectors in a multi-dimensional space. This technique allows similar items to be placed close together, making embeddings a powerful tool for tasks like image recognition or recommendation systems.

Discovering the Flask HTTP Framework

Within the realm of web development, Flask stands out as a lightweight yet powerful Python-based web framework.

Flask is celebrated for its minimalist, pragmatic approach that does not dictate which libraries or patterns to use, allowing developers the freedom to choose what fits best for their projects.

Key Features of Flask

• Routing: Handle URLs elegantly to guide users through your site.
• Templates: Create dynamic HTML pages easily.
• User Data Management: Support for cookies and sessions to store user data.

Project Initialization

Setting Up Your Project

To begin, create a project directory named chroma-sd. Open your terminal and navigate to your project directory, then create and move into it.

Creating a Virtual Environment

As responsible Python developers, it’s essential to create a virtual environment to separate project dependencies:

1. For Windows:

   python -m venv env

2. For Linux or MacOS:

   python3 -m venv env

Activate the environment:

# Windows
.env\Scripts\activate

# Linux or MacOS
source env/bin/activate

Setting Up Required Libraries

Install necessary libraries using pip:

pip install Flask ChromaDB

Writing Project Files

Start coding your application in app.py, importing necessary modules like logging, os, flask, requests, and dotenv. Set up logging and define your environment variables for sensitive data.

Finalizing Endpoint Functions

Images Endpoint

Finalizing the images function to return a list of all image generation requests in JSON format.

Generate Endpoint

The generate function handles image generation requests and integrates error handling and logging.

Testing the Image Generation App

Run the application with the following command:

flask run

Navigate to localhost:5000 in your browser, input your text and click 'Generate' to create images.

Implementing Search Functionality

Add a search feature using ChromaDB to perform similarity searches based on embeddings, enhancing the application's functionality.

Conclusion

In conclusion, we've built an image generation gallery app using Stable Diffusion AI and ChromaDB for embedding storage. The possibilities for expansion are vast, and future features could include storing image embeddings and an inpainting functionality.