Diffusion Models in AI
Diffusion models are a class of generative AI models that learn to create data (images, audio, video, etc.) by learning to reverse a gradual noising process. The Core Idea The training process has two phases: Forward process (destroying data): Take a real image and progressively add Gaussian noise over many steps (say, 1000 steps) until it becomes pure random noise. This is fixed and requires no learning. Reverse process (learning to reconstruct): Train a neural network (usually a U-Net or Transformer) to predict and remove the noise at each step — essentially learning to “denoise.” At inference time, you start from pure noise and repeatedly apply this denoising to generate a new sample. ...