Diffusion Models

Diffusion models generate images, audio, and video by learning to reverse a gradual noising process: they start from pure noise and denoise step by step into a sample, steered by a text prompt through cross-attention. Latent diffusion runs this in a compressed space for speed, and classifier-free guidance trades diversity for prompt adherence. FDE loops probe diffusion because the steps-versus-quality-versus-latency trade-off and how it differs from autoregressive generation come up in any media-generation deployment.