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Adaptive Stochastic Coefficients for Accelerating Diffusion Sampling

generative-ai › diffusion-models

📄 Abstract

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.

Authors (5)

Ruoyu Wang

Beier Zhu

Junzhi Li

Liangyu Yuan

Chi Zhang

Submitted

October 27, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

Introduces AdaSDE, a novel single-step SDE solver for diffusion models that accelerates sampling by dynamically regulating error correction strength using a learnable coefficient estimated via lightweight distillation. This aims to combine the efficiency of ODE solvers with the error resilience of SDE solvers.

Business Value

Enables faster generation of high-quality synthetic data, accelerating workflows in creative industries, data augmentation for training ML models, and content generation applications.

Paper Metadata

Innovation Type

Algorithm/Solver

Deployment Feasibility

High feasibility. The method improves the efficiency of existing diffusion models, making them more practical for deployment.

Limitations Addressed

Irreducible gradient error in ODE solvers and amplified discretization errors in SDE solvers when step budget is limited, leading to slow sampling or reduced sample quality in diffusion models.

Performance Gains

Achieves state-of-the-art FID scores at significantly fewer NFEs compared to existing methods.

Technical Tags

diffusion modelsODE solversSDE solversaccelerated samplingadaptive coefficientsgenerative processessample qualitycomputational speed

Research Topics

Generative AIDiffusion ModelsNumerical MethodsMachine LearningComputer Vision

Methods & Architectures

Adaptive Stochastic Differential Equation (SDE) solverSingle-step solverLightweight distillationLearnable coefficient AdaSDE solver

Applications & Tasks

Image Generation Data Augmentation Content Creation Slow Diffusion SamplingBalancing Speed and QualityError Accumulation in Solvers Accelerating diffusion samplingImproving sample quality with fewer stepsUnifying efficiency of ODEs with error resilience of SDEs

Datasets & Benchmarks

Datasets

CIFAR-10

Benchmarks

CIFAR-10: FID score of 4.18 at 5 NFE (Number of Function Evaluations)

FID scoreNFE (Number of Function Evaluations)

Related Fields

Generative ModelsDiffusion ModelsNumerical AnalysisMachine LearningComputer Vision

Keywords

diffusion modelsgenerative AIsampling accelerationSDE solversODE solversadaptive methodsimage generationNFEFID scorenumerical methodsdeep learning

Academic Context

#Generative AI#Diffusion Models#Numerical Methods#Machine Learning#Computer Vision

Technology Stack

Frameworks & Libraries

PyTorch

Programming Languages

Python

Commercial Potential

Potential Products

Faster diffusion model inference enginesTools for generating high-quality synthetic dataCreative AI content generation platforms

Target Industries

Media & EntertainmentGamingAdvertisingE-commerceAI Research

Use Case Examples

Rapid generation of diverse image assets for games or marketingAccelerated creation of synthetic datasets for training other AI modelsFaster prototyping of generative art

Competitive Edge

Offers a significant speed-up in diffusion model sampling while maintaining or improving sample quality, outperforming existing acceleration techniques.

Market Opportunity

Rapidly growing market for generative AI and diffusion models.

Revenue Models

Licensing of accelerated sampling technologyintegration into SaaS platforms.

Resource Requirements

Compute Needs

Moderate, as it aims to reduce computation (NFEs) required for sampling.

Data Requirements

Requires standard image datasets for evaluating generative model performance (e.g., CIFAR-10).

Deployment Constraints

Integration into existing diffusion model pipelines. Ensuring stability and generalization of the learned coefficient.

Scalability

The method improves the efficiency of sampling, thus enhancing scalability for generating large numbers of samples.

Regulatory Considerations

Ethical considerations regarding the generation of synthetic media.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for integration into diffusion model libraries and platforms.

Patent Potential

Moderate, related to novel solvers and adaptive techniques for diffusion models.

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