What is the first step in using VAEs and U-nets in diffusion models?

In the context of Variational Autoencoders (VAEs) and U-Nets utilized in diffusion models, the initial step involves the VAE encoder encoding inputs into a latent space. This step is crucial because it transforms the input data into a compressed representation, capturing essential features while discarding less important information. By encoding the input into a latent space, the model prepares the data for subsequent processing, which allows for the generation of new samples or reconstructions through the decoder later in the process.

This foundational encoding helps facilitate the diffusion process by providing a meaningful and efficient representation that can be manipulated and analyzed to generate high-quality outputs. Without this initial encoding, the model would lack the necessary information structure required for effective learning and image reconstruction.