Contemporary generative models leverage a two-stage approach, first extracting a latent representation of input signals via an autoencoder, then training a generative model on these latents. This method enhances efficiency by focusing on perceptually meaningful information while reducing the computational burden associated with processing raw pixel or waveform data. The article details the training process, the evolution of generative techniques, and the significance of latent representations in modern applications.