This article will delve into the field of image generation, focusing on diffusion models. By integrating cutting-edge technologies such as the working principles of DALL·E and Stable Diffusion, we aim to understand their practical applications and development potential.
Diffusion models are a type of generative model that creates new images through a reverse diffusion process. Starting from Gaussian noise, diffusion models progressively infer the original image. This process can be seen as simulating a gradual “denoising” procedure for image generation. It is a very powerful and efficient method of generation that has received widespread attention in recent years.
The working principle of Stable Diffusion is based on the reverse process of diffusion models. The fundamental idea of diffusion models is to start with a completely random noise image and progressively infer a clear image. This process is divided into two stages:
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Forward Diffusion Process During the forward diffusion process, given a real image, noise is gradually added until it becomes a completely random noise image. This process can be understood as the “destructive” step of the image. Noise is typically added incrementally through a Gaussian distribution, with each step making the image more blurred and noisy.
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Reverse Diffusion Process The reverse diffusion process is the core functioning part of Stable Diffusion. Given a completely random noise image, the model learns how to progressively denoise it to restore the original image. The reverse process is the opposite of the forward process; it uses a pre-trained neural network model to infer a clearer image at each step. Each step of the reverse process involves the model predicting the magnitude of the current noise and subtracting it from the current image to obtain a less noisy image. Ultimately, after multiple reverse steps, the model can generate a clear and high-resolution image from pure noise.
Image source：https://sushant-kumar.com/blog/ddpm-denoising-diffusion-probabilistic-models
We can quickly deploy Stable Diffusion WebUI on Jetson devices using the jetson-examples tool. This project allows users to load the Stable Diffusion model and configure workflows through a graphical interface.
Step 1. Install jetson-examples on your Jetson device Open a terminal on your Jetson device and enter:
pip3 install jetson-examplesStep 2. Use the jetson-examples tool to install the stable-diffusion-webui project with one command:
reComputer run stable-diffusion-webuiStep 3. Open your browser and go to http://<ip-jetson>:7860, and you can start generating images with Stable Diffusion.
