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[2602.12624] Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps
Summary
This paper presents a framework for optimizing sampling in diffusion-based generative models, addressing high sampling costs through adaptive solvers and Wasserstein-bounded timesteps.
Why It Matters
As diffusion-based generative models gain traction in various applications, optimizing their sampling efficiency is crucial for practical deployment. This research provides a systematic approach to enhance performance while reducing computational costs, making it relevant for researchers and practitioners in machine learning and computer vision.
Key Takeaways
- Introduces SDM, a framework aligning numerical solvers with diffusion trajectory properties.
- Demonstrates that low-order solvers are effective in early high-noise stages.
- Proposes a Wasserstein-bounded optimization framework for adaptive timesteps.
- Achieves state-of-the-art performance on benchmarks with fewer function evaluations.
- No additional training or architectural changes are required for implementation.
Computer Science > Machine Learning arXiv:2602.12624 (cs) [Submitted on 13 Feb 2026] Title:Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps Authors:Sangwoo Jo, Sungjoon Choi View a PDF of the paper titled Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps, by Sangwoo Jo and 1 other authors View PDF HTML (experimental) Abstract:Diffusion-based generative models have achieved remarkable performance across various domains, yet their practical deployment is often limited by high sampling costs. While prior work focuses on training objectives or individual solvers, the holistic design of sampling, specifically solver selection and scheduling, remains dominated by static heuristics. In this work, we revisit this challenge through a geometric lens, proposing SDM, a principled framework that aligns the numerical solver with the intrinsic properties of the diffusion trajectory. By analyzing the ODE dynamics, we show that efficient low-order solvers suffice in early high-noise stages while higher-order solvers can be progressively deployed to handle the increasing non-linearity of later stages. Furthermore, we formalize the scheduling by introducing a Wasserstein-bounded optimization framework. This method systematically derives adaptive timesteps that explicitly bound the local discretization error, ensuring the sampling pro...
