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Using Stable Diffusion with Python

You're reading from   Using Stable Diffusion with Python Leverage Python to control and automate high-quality AI image generation using Stable Diffusion

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Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781835086377
Length 352 pages
Edition 1st Edition
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Author (1):
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Andrew Zhu (Shudong Zhu) Andrew Zhu (Shudong Zhu)
Author Profile Icon Andrew Zhu (Shudong Zhu)
Andrew Zhu (Shudong Zhu)
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Table of Contents (29) Chapters Close

Preface 1. Part 1 – A Whirlwind of Stable Diffusion FREE CHAPTER
2. Chapter 1: Introducing Stable Diffusion 3. Chapter 2: Setting Up the Environment for Stable Diffusion 4. Chapter 3: Generating Images Using Stable Diffusion 5. Chapter 4: Understanding the Theory Behind Diffusion Models 6. Chapter 5: Understanding How Stable Diffusion Works 7. Chapter 6: Using Stable Diffusion Models 8. Part 2 – Improving Diffusers with Custom Features
9. Chapter 7: Optimizing Performance and VRAM Usage 10. Chapter 8: Using Community-Shared LoRAs 11. Chapter 9: Using Textual Inversion 12. Chapter 10: Overcoming 77-Token Limitations and Enabling Prompt Weighting 13. Chapter 11: Image Restore and Super-Resolution 14. Chapter 12: Scheduled Prompt Parsing 15. Part 3 – Advanced Topics
16. Chapter 13: Generating Images with ControlNet 17. Chapter 14: Generating Video Using Stable Diffusion 18. Chapter 15: Generating Image Descriptions Using BLIP-2 and LLaVA 19. Chapter 16: Exploring Stable Diffusion XL 20. Chapter 17: Building Optimized Prompts for Stable Diffusion 21. Part 4 – Building Stable Diffusion into an Application
22. Chapter 18: Applications – Object Editing and Style Transferring 23. Chapter 19: Generation Data Persistence 24. Chapter 20: Creating Interactive User Interfaces 25. Chapter 21: Diffusion Model Transfer Learning 26. Chapter 22: Exploring Beyond Stable Diffusion 27. Index 28. Other Books You May Enjoy

Running a Stable Diffusion pipeline

Now that you have installed all the dependencies, it is time to run a Stable Diffusion pipeline to test whether the environment is correctly set up. You can use any Python editing tool, such as VS Code or Jupyter Notebook, to edit and execute Python code. Follow these steps:

  1. Install the packages for Hugging Face Diffusers:
    pip install diffusers
    pip install transformers scipy ftfy accelerate
  2. Start a Stable Diffusion pipeline:
    import torch
    from diffusers import StableDiffusionPipeline
    pipe = StableDiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        torch_dtype=torch.float16)
    pipe.to("cuda") # mps for mac

    If you are using a Mac, change cuda to mps. Even though macOS is supported and can generate images using the Diffusers package, its performance is relatively slow. As a comparison, an NVIDIA RTX 3090 can achieve about 20 iterations per second to generate one 512x512...

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