Error Propagation Mechanisms and Compensation Strategies for Quantized Diffusion

2025-08-16

AI Summary

Quantizing diffusion models is challenging due to their computationally intensive iterative denoising process, especially under resource constraints; while post-training quantization (PTQ) accelerates inference, quantization errors accumulate progressively across timesteps, severely degrading generation quality. This work presents the first closed-form characterization of cumulative quantization error in diffusion processes and establishes a timestep-aware error propagation model. Based on this analysis, we propose a dynamic compensation mechanism that introduces a timestep-dependent error correction term at each denoising step—requiring no retraining and fully compatible with standard PTQ pipelines. Evaluated across multiple image datasets, our method significantly mitigates error accumulation: under INT4 and INT8 precision, it substantially outperforms existing PTQ approaches in generation fidelity, achieving state-of-the-art performance.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Generation

Application Category

• 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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Diffusion models have transformed image synthesis by establishing unprecedented quality and creativity benchmarks. Nevertheless, their large-scale deployment faces challenges due to computationally intensive iterative denoising processes. Although post-training quantization(PTQ) provides an effective pathway for accelerating sampling, the iterative nature of diffusion models causes stepwise quantization errors to accumulate progressively during generation, inevitably compromising output fidelity. To address this challenge, we develop a theoretical framework that mathematically formulates error propagation in Diffusion Models (DMs), deriving per-step quantization error propagation equations and establishing the first closed-form solution for cumulative error. Building on this theoretical foundation, we propose a timestep-aware cumulative error compensation scheme. Extensive experiments across multiple image datasets demonstrate that our compensation strategy effectively mitigates error propagation, significantly enhancing existing PTQ methods to achieve state-of-the-art(SOTA) performance on low-precision diffusion models.

Problem

• Quantization errors accumulate in diffusion models
• Error propagation reduces output fidelity in generation
• Compensation strategy needed for low-precision diffusion models

Innovation

• Theoretical framework for error propagation equations
• Timestep-aware cumulative error compensation scheme
• Enhances post-training quantization for diffusion models