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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

Converting the checkpoint model file to the Diffusers format

Loading checkpoint model data from a .ckpt or safetensors file is slow compared with the Diffusers format because every time we load a .ckpt or safetensors file, Diffusers will unpack and convert the file to the Diffusers format. To save the conversion every time we load a model file, we may consider converting checkpoint files to the Diffusers format.

We can use the following code to convert a .ckpt file to the Diffusers format:

ckpt_checkpoint_path = r"D:\temp\anythingV3_fp16.ckpt"
target_part = r"D:\temp\anythingV3_fp16"
pipe = download_from_original_stable_diffusion_ckpt(
    ckpt_checkpoint_path,
    from_safetensors = False,
    device = "cuda:0"
)
pipe.save_pretrained(target_part)

To convert a safetensors file to the Diffusers format, simply change the from_safetensors parameter to True as shown in the following sample...

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