Adaptive Stochastic Coefficients for Accelerating Diffusion Sampling

2025-10-27

AI Summary

In diffusion sampling, ODE solvers suffer from accumulated gradient estimation errors, while SDE-based methods are highly sensitive to amplified discretization errors when the number of steps is limited. To address this trade-off, we propose AdaSDE—a novel single-step adaptive SDE solver that dynamically modulates error correction strength via a learnable scalar coefficient. This coefficient is estimated by a lightweight distillation module, incurring no additional network overhead and maintaining compatibility with mainstream solvers. Theoretically grounded and empirically validated, AdaSDE achieves state-of-the-art performance with only five sampling steps: FID = 4.18 on CIFAR-10, 8.05 on FFHQ, and 6.96 on LSUN Bedroom. By jointly preserving the computational efficiency of ODE solvers and enhancing robustness against discretization errors inherent to SDEs, AdaSDE significantly improves the speed–quality trade-off in diffusion sampling.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Ad search and search for Web retail
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Diffusion-based generative processes, formulated as differential equation solving, frequently balance computational speed with sample quality. Our theoretical investigation of ODE- and SDE-based solvers reveals complementary weaknesses: ODE solvers accumulate irreducible gradient error along deterministic trajectories, while SDE methods suffer from amplified discretization errors when the step budget is limited. Building upon this insight, we introduce AdaSDE, a novel single-step SDE solver that aims to unify the efficiency of ODEs with the error resilience of SDEs. Specifically, we introduce a single per-step learnable coefficient, estimated via lightweight distillation, which dynamically regulates the error correction strength to accelerate diffusion sampling. Notably, our framework can be integrated with existing solvers to enhance their capabilities. Extensive experiments demonstrate state-of-the-art performance: at 5 NFE, AdaSDE achieves FID scores of 4.18 on CIFAR-10, 8.05 on FFHQ and 6.96 on LSUN Bedroom. Codes are available in https://github.com/WLU-wry02/AdaSDE.

Problem

• Balancing computational speed with sample quality in diffusion sampling
• Addressing irreducible gradient errors in ODE-based diffusion solvers
• Mitigating amplified discretization errors in SDE methods with limited steps

Innovation

• AdaSDE uses learnable coefficient for error correction
• Lightweight distillation dynamically regulates correction strength
• Framework integrates with existing solvers to enhance performance