When Stable Diffusion was released on Aug 22, 2022, this Diffusion-based image generation model quickly caught the attention of the whole world. Both its model and source code are completely open-source and hosted on GitHub. With millions of community participants and users, numerous new and mixed models have been released. Tools such as Stable Diffusion Webui and InvokeAI have been created.
While the Stable Diffusion WebUI tool can generate fantastic images driven by the diffusion model, its usability is limited for everyone. The open-sourced Diffusers package from Hugging Face allows users to have full control over Stable Diffusion using Python. However, it lacks many key features such as loading custom LoRA and Textual Inversion, utilizing community-shared models/checkpoints, scheduling and weighted prompts, unlimited prompt tokens, image high-resolution fixing and upscaling. The book will assist you in overcoming the limitations of Diffusers and implementing the advanced features to create a fully customized and industrial-level Stable Diffusion application.
By the end of this book, you will not only be able to use Python to generate and edit images, but also leverage the solutions provided in the book to build Stable Diffusion applications for your business and users.

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Spis treści

Using Stable Diffusion with Python. Leverage Python to control and automate high-quality AI image generation using Stable Diffusion eBook -- spis treści

• 1. Introducing Stable Diffusion
• 2. Setting Up the Environment for Stable Diffusion
• 3. Generating Images Using Stable Diffusion
• 4. Understanding the Theory Behind Diffusion Models
• 5. Understanding How Stable Diffusion Works
• 6. Using Stable Diffusion Models
• 7. Optimizing Performance and VRAM Usage
• 8. Using Community-Shared LoRAs
• 9. Using Textual Inversion
• 10. Overcoming 77-Token Limitations and Enabling Prompt Weighting
• 11. Image Restore and Super-Resolution
• 12. Scheduled Prompt Parsing
• 13. Generating Images with ControlNet
• 14. Generating Video Using Stable Diffusion
• 15. Generating Image Descriptions using BLIP-2 and LLaVA
• 16. Exploring Stable Diffusion XL
• 17. Building Optimized Prompts for Stable Diffusion
• 18. Applications - Object Editing and Style Transferring
• 19. Generation Data Persistence
• 20. Creating Interactive User Interfaces
• 21. Diffusion Model Transfer Learning
• 22. Exploring Beyond Stable Diffusion