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Building Data Science Applications with FastAPI - Second Edition

You're reading from  Building Data Science Applications with FastAPI - Second Edition

Product type Book
Published in Jul 2023
Publisher Packt
ISBN-13 9781837632749
Pages 422 pages
Edition 2nd Edition
Languages
Author (1):
François Voron François Voron
Profile icon François Voron

Table of Contents (21) Chapters

Preface 1. Part 1: Introduction to Python and FastAPI
2. Chapter 1: Python Development Environment Setup 3. Chapter 2: Python Programming Specificities 4. Chapter 3: Developing a RESTful API with FastAPI 5. Chapter 4: Managing Pydantic Data Models in FastAPI 6. Chapter 5: Dependency Injection in FastAPI 7. Part 2: Building and Deploying a Complete Web Backend with FastAPI
8. Chapter 6: Databases and Asynchronous ORMs 9. Chapter 7: Managing Authentication and Security in FastAPI 10. Chapter 8: Defining WebSockets for Two-Way Interactive Communication in FastAPI 11. Chapter 9: Testing an API Asynchronously with pytest and HTTPX 12. Chapter 10: Deploying a FastAPI Project 13. Part 3: Building Resilient and Distributed Data Science Systems with FastAPI
14. Chapter 11: Introduction to Data Science in Python 15. Chapter 12: Creating an Efficient Prediction API Endpoint with FastAPI 16. Chapter 13: Implementing a Real-Time Object Detection System Using WebSockets with FastAPI 17. Chapter 14: Creating a Distributed Text-to-Image AI System Using the Stable Diffusion Model 18. Chapter 15: Monitoring the Health and Performance of a Data Science System 19. Index 20. Other Books You May Enjoy

Generating images from text prompts with Stable Diffusion

Recently, a new generation of AI tools has emerged and fascinated the whole world: image-generation models, such as DALL-E or Midjourney. Those models are trained on huge amounts of image data and are able to generate completely new images from a simple text prompt. These AI models are very good use cases for background workers: they take seconds or even minutes to process, and they need lots of resources in the CPU, RAM, and even the GPU.

To build our system, we’ll rely on Stable Diffusion, a very popular image-generation model that was released in 2022. This model is available publicly and can be run on a modern gaming computer. As we did in the previous chapter, we’ll rely on Hugging Face tools for both downloading the model and running it.

Let’s first install the required tools:

(venv) $ pip install accelerate diffusers

We’re now ready to use diffuser models thanks to Hugging Face.

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