Using Stable Diffusion with Python, Zhu A., 2024

Using Stable Diffusion with Python, Zhu A., 2024.
     
   Artificial intelligence has ushered in a new era of creativity, with generative models offering a glimpse into what was once futuristic. Stable Diffusion stands out as an innovative leap forward, blending technical sophistication with practical application in a way that empowers creators across diverse domains. Andrew Zhu's book is a comprehensive resource for understanding Stable Diffusion's technical underpinnings. He provides an in-depth exploration of its foundational principles, contrasts it with alternative generative models, and demonstrates how to apply it to varied creative fields.




Introducing Stable Diffusion.
Stable Diffusion is a deep learning model that utilizes diffusion processes to generate high-quality artwork from guided instructions and images.

In this chapter, we will introduce you to AI image generation technology, namely Stable Diffusion, and see how it evolved into what it is now.

Unlike other deep learning image generation models, such as OpenAI’s DALL-E 2, Stable Diffusion works by starting with a random-noise latent tensor and then gradually adding detailed information to it. The amount of detail that is added is determined by a diffusion process, governed by a mathematical equation (we will delve into the details in Chapter 5). In the final stage, the model decodes the latent tensor into the pixel image.

Since its creation in 2022, Stable Diffusion has been used widely to generate impressive images. For example, it can generate images of people, animals, objects, and scenes that are indistinguishable from real photographs. Images are generated using specific instructions, such as A cat running on the moon’s surface or a photograph of an astronaut riding a horse.

Contents.
Preface.
Part 1 – A Whirlwind of Stable Diffusion.
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.
Part 2 – Improving Diffusers with Custom Features.
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.
Part 3 – Advanced Topics.
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.
Part 4 – Building Stable Diffusion into an Application.
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.
Index.
Other Books You May Enjoy.



Бесплатно скачать электронную книгу в удобном формате, смотреть и читать:
Скачать книгу Using Stable Diffusion with Python, Zhu A., 2024 - fileskachat.com, быстрое и бесплатное скачивание.

Скачать pdf
Ниже можно купить эту книгу, если она есть в продаже, и похожие книги по лучшей цене со скидкой с доставкой по всей России.Купить книги


Скачать - pdf - Яндекс.Диск.




Теги: учебник по программированию :: программирование :: Zhu