How do contrastive diffusion models combine two unsupervised paradigms?

They combine diffusion-based generative modeling with contrastive representation learning objectives.

These hybrid models integrate score-based generative diffusion processes with contrastive losses to improve representation quality. While diffusion models learn data distribution through denoising, contrastive objectives ensure embeddings maintain semantic structure. This combination improves both generative fidelity and downstream representation usefulness.