New preprint on diffusion model representations

Our new preprint, “No Alignment Needed for Generation: Learning Linearly Separable Representations in Diffusion Models,” is now available. In this work, we investigate how diffusion models naturally learn structured and linearly separable representations without requiring explicit alignment mechanisms.

Read more about the preprint on arxiv.org