Title: Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation
Primary area: generative models
Keywords: Diffusion Models, Contrastive Learning, Latent Space Structuring, Controllable Generation, Nonlinear Latent Manifolds, Latent Interpolation, Representation Learning

Abstract: Diffusion models excel at generation, but their latent spaces are not explicitly organized for interpretable control. We introduce ConDA (Contrastive Diffusion Alignment), a framework that applies contrastive learning within diffusion embeddings to align latent geometry with system dynamics. Motivated by recent advances showing that contrastive objectives can recover more disentangled and structured representations, ConDA organizes diffusion latents such that traversal directions reflect underlying dynamical factors. Within this contrastively structured space, ConDA enables nonlinear trajectory traversal that supports faithful interpolation, extrapolation, and controllable generation. Across benchmarks in fluid dynamics, neural calcium imaging, therapeutic neurostimulation, and facial expression, ConDA produces interpretable latent representations with improved controllability compared to linear traversals and conditioning-based baselines. These results suggest that diffusion latents encode dynamics-relevant structure, but exploiting this structure requires contrastive organization and traversal along the latent manifold.

TL;DR: We present ConDA, a contrastive framework that structures diffusion latents for nonlinear latent interpolation, enabling interpretable and controllable generation.