Diffusion Model
A generative model that creates images by iteratively removing noise from random input.
A diffusion model trains in two phases: a forward pass that gradually adds Gaussian noise to a training image until only noise remains, and a reverse pass where the model learns to denoise step-by-step. At inference only the reverse process runs, conditioned on a text prompt, enabling text-to-image generation. On the AIF-C01 exam, Amazon Bedrock hosts diffusion models such as Stability AI’s Stable Diffusion and Amazon Nova Canvas. Unlike GANs, which use a generator-discriminator adversarial loop, diffusion relies on iterative denoising, generally giving higher diversity and fewer mode-collapse artifacts.
PlayPrepHQ study notes are written and reviewed against primary exam sources. How we create & review content →