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October 2, 2025Open Access

Beyond Scores: Proximal Diffusion Models

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The introduction of Proximal Diffusion Models provides a new method for efficient sampling from data distributions.
Theoretical proofs indicate that $\widetilde{O}(d/\sqrt{\varepsilon})$ steps allow for an $\varepsilon$-accurate distribution w.r.t. KL divergence.
Empirical results show faster convergence in sampling with ProxDM compared to traditional score-matching methods.
Utilizing proximal maps instead of the score leads to both theoretical and practical improvements in diffusion modeling.

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This paper demonstrates improved convergence in sampling from distributions using proximal maps, suggesting significant practical advantages.

Fang et al. (2025) studied this question.

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This paper demonstrates improved convergence in sampling from distributions using proximal maps, suggesting significant practical advantages.