Diffusion Loss is a loss function associated with diffusion models, which is used to guide the model to learn how to gradually remove noise and restore the original structure of the data during the training process. Diffusion models operate in two stages: forward diffusion process and reverse diffusion process. In the forward diffusion process, the model gradually adds noise to the data until the data is completely transformed into noise; in the reverse diffusion process, the model learns how to remove noise and restore the data.

The purpose of the diffusion loss function is to minimize the prediction error of the denoising process, that is, the difference between the denoising result predicted by the model and the actual data. This loss function is usually defined based on the mean squared error (MSE) or variational inference principle.