Hello everyone, I am Wei Xue AI. Today I will introduce to you Deep Learning Practice 51 - Detailed explanation of image generation principles and project practice based on the Stable Diffusion model. Do you know how the beautiful pictures of beautiful ladies posted on various platforms are generated? The underlying principle of these generations uses the Stable Diffusion model. Stable Diffusion is a deep learning-based image generation method designed to generate high-quality, realistic images. This project utilizes the process of steady diffusion, a process of image generation by gradually blurring and sharpening the image. This method has a wide range of applications in the field of image generation, including artistic creation, virtual scene generation, data enhancement, etc.
I. Introduction
In the field of deep learning, image generation has always been a popular research direction. In this field, a new model has recently emerged - Stable Diffusion. This article will introduce the depth principle of the Stable Diffusion model in detail, and demonstrate how to use PyTorch to build the model and generate images through actual combat.
2. Depth principle of Stable Diffusion model
2.1 Model Overview
Stable Diffusion model, a name that sounds extremely scientific and enigmatic. However, this complex process becomes vivid and visual if we compare it to cooking a dish.
Imagine you are preparing to make a delicious soup. You need a variety of ingredients: vegetables, meats, spices, and more. These raw ingredients are like our initial data distribution. All ingredients are served raw without any seasoning or processing before cooking begins.
Next, you start putting various ingredients into the pot and add water (this is like we add Gaussian noise). Then you start to slowly heat the pan