Diffusion Model

Diffusion models power the AI image generation revolution: Stable Diffusion, DALL-E 3, Midjourney, and Google's Imagen all use this approach. The core idea: start with pure random noise, then iteratively "denoise" it, guided by a text prompt, until a coherent image emerges. It is like sculpting: you start with a block of marble (noise) and chisel away (denoise) until the shape (image) appears.

The technical process: (1) During training, the model learns to predict the noise that was added to real images. (2) During generation, it starts with pure noise and applies its denoising knowledge step by step, conditioned on the text prompt. (3) Each step makes the image slightly more coherent. Typically 20-50 steps are needed for a good result.

Diffusion models have expanded beyond images to video (Sora, Veo), audio (Stable Audio), and 3D objects. Key parameters for builders: guidance scale (how closely to follow the prompt: higher = more literal, lower = more creative), steps (more = better quality but slower), and seed (for reproducible results). API services like OpenAI's DALL-E and Google's Imagen abstract these details behind simple text-to-image endpoints.