Stable Diffusion WebUI Basic 02–Model Training Related Principles

Foreword of Stable Diffusion WebUI Basic

In the world of AI image generation, there are many tools and methods to explore. If you’re interested in Midjourney, click there for a detailed tutorial. For a guide on ComfyUI, click there. This series focuses on teaching you how to use Stable Diffusion WebUI systematically. From basic operations to advanced techniques, we’ll guide you step by step to master WebUI’s powerful features and create high-quality AI-generated images.

This article aims to explain the principles of Stable Diffusion in a more accessible manner. By the end, you will understand the following topics:

1. How do machines understand images?
2. How are models trained?

Stable Diffusion WebUI Basic algorithm principle — Machines Recognize Images

Machines recognize images by pairing them with corresponding descriptions during training. Using image recognition techniques, natural language processing, and convolutional neural networks (CNN), the system learns to identify patterns in the images. The model trains on large labeled datasets, which helps it link visual features to concepts and accurately identify objects in new images. This process is known as supervised learning.

When training machine learning models to recognize images, we provide a large dataset, like pictures of dogs, along with labels such as “dog.” The machine processes this data repeatedly, learning to associate specific features in the images with the labels. Through billions of iterations, the model fine-tunes its ability to identify patterns and features, ultimately “recognizing” what makes a dog by using the characteristics it has learned.

Stable Diffusion WebUI Basic algorithm principle — Models trained

At this point, let’s assume the machine can recognize everything. However, the style of the generated images may not always meet our expectations due to the diversity of the images it has learned from. For example, after years of training, the machine can correctly identify the features of a “cute girl” without mistakes like three eyes or two mouths. When we input a “cute girl,” the machine generates image A. If the style of image A doesn’t match what we want, programmers adjust the algorithms and parameters until the output resembles image B more closely. After making these adjustments, the parameters are saved in a .ckpt file, which contains the trained model. This model ensures that future generated images will consistently reflect the desired set of characteristics.

The whole process is shown below:

When downloading models from C-Station, there are two types of files to be aware of:

• safetensors: These files are saved using NumPy, containing only tensor data without code. They are faster and safer to load.
• ckpt: These files use Pickle serialization, which can potentially contain malicious code. It’s important to be cautious if you don’t trust the source.

Therefore, it’s recommended to prioritize downloading .safetensors files for safety.

More Tutorial

If you’re excited to dive into the world of AI image generation, you’ve come to the right place! Want to create stunning images with Midjourney? Just click on our Midjourney tutorial and start learning! Interested in exploring ComfyUI? We’ve got a detailed guide for that too. Each guide is designed to be simple and fun, helping you master these powerful tools at your own pace. Here, you can learn all the AI knowledge you need, stay updated with the latest AI trends, and let your creativity run wild. Ready to start? Let’s explore the exciting world of AI together!