Image generation is one of the important research directions in the field of computer vision, and Jay Alammar's latest work "Stable Diffusion" presents us with the principles and programming implementation of image generation in a high-quality illustration. This article will explain this work in detail and attach the corresponding source code to help readers fully understand the image generation process.
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Introduction
Image generation refers to the generation of realistic images through computer algorithms, which has a wide range of application prospects, such as computer games, virtual reality, movie special effects and other fields. Stable Diffusion is a stable image generation method that gradually generates images through the diffusion process and has good stability and generation quality. -
The core idea of image generation principle
Stable Diffusion is to simulate the diffusion process of image pixel values. The basic steps are as follows:
a. Initialization: Start with a random noise image as the initial state for generation.
b. Diffusion process: Through iterative calculation, the pixel values of the noise image are gradually diffused. In each iteration, the pixel value is diffused to its surrounding pixels, creating a smooth transition effect.
c. Stability control: By controlling the speed and intensity of diffusion, the diffusion process gradually becomes stable. This maintains image consistency and continuity during the generation process.
d. End condition: When the predetermined number of iterations is reached or a stable state is reached, the generation process ends and the final generated image is obtained. -
Implementation code
The following is a simple Python code example that demonstrates how to use Stable Diffusion to generate images:
import numpy as np
import matplotlib