扩散模型

A class of generative models that create data such as images and video by iteratively denoising.

Diffusion models add noise to data in a forward pass, then learn to reverse it, generating new samples from random noise. Stable Diffusion, FLUX and Seedream all follow this paradigm, which also underpins text-to-video.