In this lesson, we are going to delve into a use case of AI technology in the creative domain of children's picture book creation in a project called "FableForge", leveraging both OpenAI GPT-3.5 LLM for writing the story and Stable Diffusion for generating images for it.
Introduction
This lesson’s project, FableForge, is an application that generates picture books from a single text prompt. It utilizes the power of OpenAI's language model GPT-3.5 to write the story. Then, the text is transformed into visual prompts for Stable Diffusion, an AI that creates corresponding images, resulting in a complete picture book. The data, both text and images, are then stored in a Deep Lake dataset for easy analysis.
The article guides us through the steps of building FableForge, detailing the challenges, successes, and methodologies adopted. You will learn how the team leveraged the “function calling” feature newly introduced by OpenAI, which is used in this project specifically to structure text data suitable for Stable Diffusion, a task that initially proved difficult due to the model's tendency to include non-visual content in the prompts. We’ll see how to overcome this by using a function providing structured, actionable output for external tools.
We'll delve into each component of FableForge, including the generation of text and images, combining them into a book format, storing the data into Deep Lake, and finally presenting it all through a user-friendly interface with Streamlit. We'll explore the process of text generation, extracting visual prompts, assembling PDFs, and uploading the data to Deep Lake.
By the end of this lesson, you'll gain a comprehensive understanding of how various AI tools and methodologies can be effectively integrated to overcome challenges and open new frontiers in creative domains.
Congratulations on finishing this module! You can now test your new knowledge with the module quizzes. The next module will be about chains, which are the concept that gives the name to LangChain.