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Using Stable Diffusion with Python

Using Stable Diffusion with Python

By : Andrew Zhu (Shudong Zhu)
4.8 (5)
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Using Stable Diffusion with Python

Using Stable Diffusion with Python

4.8 (5)
By: Andrew Zhu (Shudong Zhu)

Overview of this book

Stable Diffusion is a game-changing AI tool that enables you to create stunning images with code. The author, a seasoned Microsoft applied data scientist and contributor to the Hugging Face Diffusers library, leverages his 15+ years of experience to help you master Stable Diffusion by understanding the underlying concepts and techniques. You’ll be introduced to Stable Diffusion, grasp the theory behind diffusion models, set up your environment, and generate your first image using diffusers. You'll optimize performance, leverage custom models, and integrate community-shared resources like LoRAs, textual inversion, and ControlNet to enhance your creations. Covering techniques such as face restoration, image upscaling, and image restoration, you’ll focus on unlocking prompt limitations, scheduled prompt parsing, and weighted prompts to create a fully customized and industry-level Stable Diffusion app. This book also looks into real-world applications in medical imaging, remote sensing, and photo enhancement. Finally, you'll gain insights into extracting generation data, ensuring data persistence, and leveraging AI models like BLIP for image description extraction. By the end of this book, you'll be able to use Python to generate and edit images and leverage solutions to build Stable Diffusion apps for your business and users.
Table of Contents (29 chapters)
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Free Chapter
1
Part 1 – A Whirlwind of Stable Diffusion
8
Part 2 – Improving Diffusers with Custom Features
15
Part 3 – Advanced Topics
21
Part 4 – Building Stable Diffusion into an Application

Using SDXL

We briefly covered loading the SDXL model in Chapter 6 and SDXL ControlNet usage in Chapter 13. You can find the sample codes there. In this section, we will cover more common SDXL usages, including loading community-shared SDXL models and how to use the image-to-image pipeline to enhance the model, using SDXL with community-shared LoRA models, and the unlimited length prompt pipeline from Diffuser (provided by the author of this book).

Use SDXL community models

Just months after the release of SDXL, the open source community has released countless fine-tuned SDXL models based on the base model from Stability AI. We can find these models on Hugging Face and CIVITAI (https://civitai.com/), and the number keeps growing.

Here, let’s load one model from HuggingFace, using the SDXL model ID:

import torch
from diffusers import StableDiffusionXLPipeline
base_pipe = StableDiffusionXLPipeline.from_pretrained(
    "RunDiffusion/RunDiffusion...

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