Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

Understand

Since their introduction, diffusion models have quickly become the prevailing approach to generative modeling in many domains.

• They can be interpreted as learning the gradients of a time-varying sequence of log-probability density functions.
• This interpretation has motivated classifier-based and classifier-free guidance as methods for post-hoc control of diffusion models.
• In this work, we build upon these ideas using the score-based interpretation of diffusion models, and explore alternative ways to condition, modify, and reuse diffusion models for tasks involving compositional generation and guidance.