Image Generation Basics

Generation

Part of: Multimodal AI: Vision & Images

Reading an image and making one are opposite jobs done by opposite machines. A vision model takes pixels and gives you text. An image generator takes text and gives you pixels. It does not "paint", it starts with pure visual static and removes the noise, step by step, until a picture that matches your words emerges. Understanding that backwards-from-noise idea is the key to this whole lesson. What it is Image generation is producing a new picture from a text prompt . The dominant approach today is the diffusion model : it learns to turn random noise into an image that fits a description. You type "a red bicycle in the rain, watercolor," and it conjures a matching image that never existed before. The trick is counterintuitive. During training the model watches clean images get destroyed into static, and it learns to reverse that destruction. At generation time it runs the reversal: start from noise, denoise toward your prompt. How it works Generation runs as a loop of small cleanups, each guided by your text: 1. Start from noise. Begin with a canvas of random static, no picture yet. 2. Predict the noise to remove. Guided by your prompt, the model estimates what part of the current i

Challenge: The Denoising Scheduler