PID-controlled Langevin Dynamics for Faster Sampling of Generative Models

2025/11/16 by Hongyi Chen, Chen, Hongyi, Jianhai Shu +7 · 1 voice
Computer Science · Mathematics · Physics and Astronomy · #Generative Adversarial Networks and Image Synthesis #Markov Chains and Monte Carlo Methods #Model Reduction and Neural Networks #cs.AI #cs.LG #eess.SY

paper · pdf · doi:10.48550/arxiv.2511.12603

openalex publication_date 2025/11/16 · openalex created_date 2025/11/19 · openalex updated_date 2026/07/28

Abstract

Langevin dynamics sampling suffers from extremely low generation speed, fundamentally limited by numerous fine-grained iterations to converge to the target distribution. We introduce PID-controlled Langevin Dynamics (PIDLD), a novel sampling acceleration algorithm that reinterprets the sampling process using control-theoretic principles. By treating energy gradients as feedback signals, PIDLD combines historical gradients (the integral term) and gradient trends (the derivative term) to efficiently traverse energy landscapes and adaptively stabilize, thereby significantly reducing the number of iterations required to produce high-quality samples. Our approach requires no additional training, datasets, or prior information, making it immediately integrable with any Langevin-based method. Extensive experiments across image generation and reasoning tasks demonstrate that PIDLD achieves higher quality with fewer steps, making Langevin-based generative models more practical for efficiency-critical applications. The implementation can be found at \hrefhttps://github.com/tsinghua-fib-lab/PIDLDhttps://github.com/tsinghua-fib-lab/PIDLD.

Citations

• Beyond Equilibrium: Non-Equilibrium Foundations Should Underpin Generative Processes in Complex Dynamical Systems
• Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
• Learning Iterative Reasoning through Energy Diffusion
• Score-based Generative Models with Adaptive Momentum
• Preconditioned Score-based Generative Models
• Accelerating Score-based Generative Models with Preconditioned Diffusion Sampling
• Learning Iterative Reasoning through Energy Minimization
• PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers
• Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
• Score-Based Generative Modeling through Stochastic Differential Equations
• Denoising Diffusion Implicit Models
• Denoising Diffusion Probabilistic Models
• Improved Techniques for Training Score-Based Generative Models
• Generative Modeling by Estimating Gradients of the Data Distribution
• SATNet: Bridging deep learning and logical reasoning using a\n differentiable satisfiability solver
• Neural Logic Machines
• A Style-Based Generator Architecture for Generative Adversarial Networks
• Recurrent Relational Networks
• Progressive Growing of GANs for Improved Quality, Stability, and Variation
• RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
• Improved Techniques for Training GANs
• Training Products of Experts by Minimizing Contrastive Divergence

Discussions

• Tired: control as inference Wired: inference as control arxiv.org/abs/2511.12603 [bsky, 5 points, 1 comments]

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• Generative Modelling with High-Order Langevin Dynamics
• Generative Modeling by Estimating Gradients of the Data Distribution
• Preconditioned Score-based Generative Models
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• Accelerating Score-based Generative Models with Preconditioned Diffusion Sampling