Using Stable Diffusion with Python: Leverage Python to control and automate high-quality AI image generation using Stable Diffusion

Title Page....2

Copyright and Credits....3

Dedication....4

Foreword....5

Contributors....6

Table of Contents....8

Preface....16

Part 1 – A Whirlwind of Stable Diffusion....22

Chapter 1: Introducing Stable Diffusion....24

Evolution of the Diffusion model....26

Before Transformer and Attention....26

Transformer transforms machine learning....27

CLIP from OpenAI makes a big difference....27

Generate images....28

DALL-E 2 and Stable Diffusion....29

Why Stable Diffusion....29

Which Stable Diffusion to use....29

Why this book....30

References....31

Chapter 2: Setting Up the Environment for Stable Diffusion....32

Hardware requirements to run Stable Diffusion....33

GPU....34

System memory....34

Storage....34

Software requirements....34

CUDA installation....34

Installing Python for Windows, Linux, and macOS....36

Installing PyTorch....38

Running a Stable Diffusion pipeline....39

Using Google Colaboratory....40

Using Google Colab to run a Stable Diffusion pipeline....41

Summary....43

References....43

Chapter 3: Generating Images Using Stable Diffusion....44

Logging in to Hugging Face....45

Generating an image....45

Generation seed....46

Sampling scheduler....47

Changing a model....50

Guidance scale....51

Summary....52

References....53

Chapter 4: Understanding the Theory Behind Diffusion Models....54

Understanding the image-to-noise process....55

A more efficient forward diffusion process....59

The noise-to-image training process....61

The noise-to-image sampling process....63

Understanding Classifier Guidance denoising....64

Summary....65

References....65

Chapter 5: Understanding How Stable Diffusion Works....68

Stable Diffusion in latent space....69

Generating latent vectors using diffusers....72

Generating text embeddings using CLIP....75

Initializing time step embeddings....77

Initializing the Stable Diffusion UNet....78

Implementing a text-to-image Stable Diffusion inference pipeline....79

Implementing a text-guided image-to-image Stable Diffusion inference pipeline....83

Summary....84

References....84

Additional reading....85

Chapter 6: Using Stable Diffusion Models....86

Technical requirements....86

Loading the Diffusers model....87

Loading model checkpoints from safetensors and ckpt files....88

Using ckpt and safetensors files with Diffusers....88

Turning off the model safety checker....89

Converting the checkpoint model file to the Diffusers format....90

Using Stable Diffusion XL....91

Summary....95

References....95

Part 2 – Improving Diffusers with Custom Features....98

Chapter 7: Optimizing Performance and VRAM Usage....100

Setting the baseline....100

Optimization solution 1 – using the float16 or bfloat16 data type....101

Optimization solution 2 – enabling VAE tiling....102

Optimization solution 3 – enabling Xformers or using PyTorch 2.0....103

Optimization solution 4 – enabling sequential CPU offload....104

Optimization solution 5 – enabling model CPU offload....106

Optimization solution 6 – Token Merging (ToMe)....107

Summary....108

References....108

Chapter 8: Using Community-Shared LoRAs....110

Technical requirements....111

How does LoRA work?....111

Using LoRA with Diffusers....112

Applying a LoRA weight during loading....114

Diving into the internal structure of LoRA....117

Finding the ​A​ and ​B​ weight matrix from the LoRA file....118

Finding the corresponding checkpoint model layer name....119

Updating the checkpoint model weights....121

Making a function to load LoRA....122

Why LoRA works....125

Summary....126

References....127

Chapter 9: Using Textual Inversion....128

Diffusers inference using TI....129

How TI works....131

Building a custom TI loader....132

TI in the pt file format....133

TI in bin file format....133

Detailed steps to build a TI loader....134

Putting all of the code together....136

Summary....138

References....139

Chapter 10: Overcoming 77-Token Limitations and Enabling Prompt Weighting....140

Understanding the 77-token limitation....141

Overcoming the 77-tokens limitation....142

Putting all the code together into a function....145

Enabling long prompts with weighting....149

Verifying the work....158

Overcoming the 77-token limitation using community pipelines....159

Summary....161

References....161

Chapter 11: Image Restore and Super-Resolution....162

Understanding the terminologies....163

Upscaling images using Img2img diffusion....164

One-step super-resolution....164

Multiple-step super-resolution....168

A super-resolution result comparison....169

Img-to-Img limitations....170

ControlNet Tile image upscaling....170

Steps to use ControlNet Tile to upscale an image....171

The ControlNet Tile upscaling result....173

Additional ControlNet Tile upscaling samples....174

Summary....179

References....179

Chapter 12: Scheduled Prompt Parsing....180

Technical requirements....180

Using the Compel package....180

Building a custom scheduled prompt pipeline....184

A scheduled prompt parser....184

Filling in the missing steps....187

A Stable Diffusion pipeline supporting scheduled prompts....189

Summary....197

References....197

Part 3 – Advanced Topics....198

Chapter 13: Generating Images with ControlNet....200

What is ControlNet and how is it different?....200

Usage of ControlNet....202

Using multiple ControlNets in one pipeline....206

How ControlNet works....208

Further usage....210

More ControlNets with SD....210

SDXL ControlNets....210

Summary....214

References....215

Chapter 14: Generating Video Using Stable Diffusion....216

Technical requirements....217

The principles of text-to-video generation....217

Practical applications of AnimateDiff....219

Utilizing Motion LoRA to control animation motion....221

Summary....222

References....223

Chapter 15: Generating Image Descriptions Using BLIP-2 and LLaVA....224

Technical requirements....224

BLIP-2 – Bootstrapping Language-Image Pre-training....226

How BLIP-2 works....226

Using BLIP-2 to generate descriptions....227

LLaVA – Large Language and Vision Assistant....228

How LLaVA works....228

Installing LLaVA....229

Using LLaVA to generate image descriptions....229

Summary....232

References....233

Chapter 16: Exploring Stable Diffusion XL....234

What’s new in SDXL?....234

The VAE of the SDXL....235

The UNet of SDXL....236

Two text encoders in SDXL....237

The two-stage design....240

Using SDXL....240

Use SDXL community models....240

Using SDXL image-to-image to enhance an image....241

Using SDXL LoRA models....243

Using SDXL with an unlimited prompt....245

Summary....247

References....247

Chapter 17: Building Optimized Prompts for Stable Diffusion....248

What makes a good prompt?....248

Be clear and specific....249

Be descriptive....251

Using consistent terminology....253

Reference artworks and styles....255

Incorporate negative prompts....256

Iterate and refine....257

Using LLMs to generate better prompts....258

Summary....269

References....269

Part 4 – Building Stable Diffusion into an Application....270

Chapter 18: Applications – Object Editing and Style Transferring....272

Editing images using Stable Diffusion....272

Replacing image background content....273

Removing the image background....276

Object and style transferring....278

Loading up a Stable Diffusion pipeline with IP-Adapter....279

Transferring style....280

Summary....282

References....282

Chapter 19: Generation Data Persistence....284

Exploring and understanding the PNG file structure....284

Saving extra text data in a PNG image file....286

PNG extra data storage limitation....289

Summary....289

References....289

Chapter 20: Creating Interactive User Interfaces....290

Introducing Gradio....291

Getting started with Gradio....291

Gradio fundamentals....294

Gradio Blocks....294

Inputs and outputs....295

Building a progress bar....296

Building a Stable Diffusion text-to-image pipeline with Gradio....297

Summary....300

References....300

Chapter 21: Diffusion Model Transfer Learning....302

Technical requirements....303

Training a neural network model with PyTorch....303

Preparing the training data....304

Preparing for training....304

Training a model....306

Training a model with Hugging Face’s Accelerate....307

Applying Hugging Face’s Accelerate....308

Putting code together....308

Training a model with multiple GPUs using Accelerate....309

Training a Stable Diffusion V1.5 LoRA....313

Defining training hyperparameters....314

Preparing the Stable Diffusion components....316

Loading the training data....317

Defining the training components....321

Training a Stable Diffusion V1.5 LoRA....323

Kicking off the training....328

Verifying the result....328

Summary....330

References....331

Chapter 22: Exploring Beyond Stable Diffusion....332

What sets this AI wave apart....333

The enduring value of mathematics and programming....334

Staying current with AI innovations....335

Cultivating responsible, ethical, private, and secure AI....337

Our evolving relationship with AI....338

Summary....338

References....339

Index....340

Other Books You May Enjoy....349

Stable Diffusion is a game-changing AI tool for image generation, enabling you to create stunning artwork with code. However, mastering it requires an understanding of the underlying concepts and techniques. This book guides you through unlocking the full potential of Stable Diffusion with Python.

Starting with an introduction to Stable Diffusion, you'll explore the theory behind diffusion models, set up your environment, and generate your first image using diffusers. You'll learn how to optimize performance, leverage custom models, and integrate community-shared resources like LoRAs, textual inversion, and ControlNet to enhance your creations. After 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 application. This book also delves 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.

What you will learn

• Explore core concepts and applications of Stable Diffusion and set up your environment for success
• Refine performance, manage VRAM usage, and leverage community-driven resources like LoRAs and textual inversion
• Harness the power of ControlNet, IP-Adapter, and other methodologies to generate images with unprecedented control and quality
• Explore developments in Stable Diffusion such as video generation using AnimateDiff
• Write effective prompts and leverage LLMs to automate the process
• Discover how to train a Stable Diffusion LoRA from scratch

Who this book is for

If you're looking to gain control over AI image generation, particularly through the diffusion model, this book is for you. Moreover, data scientists, ML engineers, researchers, and Python application developers seeking to create AI image generation applications based on the Stable Diffusion framework can benefit from the insights provided in the book.