How do diffusion models define cost functions implicitly?

They learn to reverse a noise process using a denoising objective instead of explicit likelihood loss.

Diffusion models train by adding noise to data and learning to reverse this corruption process. The cost function typically minimizes the difference between predicted and actual noise. This indirect objective avoids explicit density estimation while still enabling high-quality generative modeling.