How Diffusion Models Create Unique Images
Diffusion models are tools (like Midjourney) used to generate images from random noise. Here's a simple explanation of how they work: 1. Starting with Random Noise These models begin with what can be described as static, or random noise. This noise serves as the initial point for the image creation. 2. Understanding the Process of Adding Noise Imagine you have a clear picture, like a photograph of a cat. If you add more and more noise to this picture, it becomes less clear. This process is known as diffusion. 3. Reversing the Process to Create Images The core of the diffusion model is reversing the above process: a. The model looks at the noisy image and figures out how to make it slightly clearer. b. It repeats this step until a recognizable image is formed. 4. Training the Model To enable the model to turn noise into images, it needs to be trained. This is done by showing the model many pictures to help it learn the patterns, and by employing advanced math to produce similar images. 5. Generating Different Images Since the starting noise is random, the images produced by the model vary each time. Additionally, the model makes predictions based on this noise at every step, guiding the image's formation. It's akin to having unique building blocks and a new blueprint each time you want to construct something. Therefore, the diffusion model doesn't work like a collage from sample images; instead, it relies heavily on mathematics.