AccuQuant: Simulating Multiple Denoising Steps for Quantizing Diffusion Models

• URL: http://arxiv.org/abs/2510.20348v1
• Date: Thu, 23 Oct 2025 08:48:12 GMT
• Title: AccuQuant: Simulating Multiple Denoising Steps for Quantizing Diffusion Models
• Authors: Seunghoon Lee, Jeongwoo Choi, Byunggwan Son, Jaehyeon Moon, Jeimin Jeon, Bumsub Ham,
• Abstract summary: We present a novel post-training quantization (PTQ) method, dubbed AccuQuant, for diffusion models.<n>We show analytically and empirically that quantization errors for diffusion models are accumulated over denoising steps in a sampling process.<n>We demonstrate the efficacy and efficiency of AccuQuant across various tasks and diffusion models on standard benchmarks.
• Score: 19.061996414950098
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present in this paper a novel post-training quantization (PTQ) method, dubbed AccuQuant, for diffusion models. We show analytically and empirically that quantization errors for diffusion models are accumulated over denoising steps in a sampling process. To alleviate the error accumulation problem, AccuQuant minimizes the discrepancies between outputs of a full-precision diffusion model and its quantized version within a couple of denoising steps. That is, it simulates multiple denoising steps of a diffusion sampling process explicitly for quantization, accounting the accumulated errors over multiple denoising steps, which is in contrast to previous approaches to imitating a training process of diffusion models, namely, minimizing the discrepancies independently for each step. We also present an efficient implementation technique for AccuQuant, together with a novel objective, which reduces a memory complexity significantly from $\mathcal{O}(n)$ to $\mathcal{O}(1)$, where $n$ is the number of denoising steps. We demonstrate the efficacy and efficiency of AccuQuant across various tasks and diffusion models on standard benchmarks.

Related papers

• Gradient-Aligned Calibration for Post-Training Quantization of Diffusion Models [23.145275091022313]
  Post-training quantization emerges as a promising solution to accelerate sampling and reduce memory overhead for diffusion models.<n>Existing PTQ methods for diffusion models typically apply uniform weights to calibration samples across timesteps.<n>We propose a novel PTQ method that addresses these challenges by assigning appropriate weights to calibration samples.
  arXiv  Detail & Related papers  (2026-02-01T15:45:07Z)
• Inference-Time Scaling of Diffusion Language Models with Particle Gibbs Sampling [70.8832906871441]
  We study how to steer generation toward desired rewards without retraining the models.<n>Prior methods typically resample or filter within a single denoising trajectory, optimizing rewards step-by-step without trajectory-level refinement.<n>We introduce particle Gibbs sampling for diffusion language models (PG-DLM), a novel inference-time algorithm enabling trajectory-level refinement while preserving generation perplexity.
  arXiv  Detail & Related papers  (2025-07-11T08:00:47Z)
• Timestep-Aware Correction for Quantized Diffusion Models [28.265582848911574]
  We propose a timestep-aware correction method for quantized diffusion model, which dynamically corrects the quantization error. By leveraging the proposed method in low-precision diffusion models, substantial enhancement of output quality could be achieved with only negligible overhead.
  arXiv  Detail & Related papers  (2024-07-04T13:22:31Z)
• QNCD: Quantization Noise Correction for Diffusion Models [15.189069680672239]
  Diffusion models have revolutionized image synthesis, setting new benchmarks in quality and creativity. Post-training quantization presents a solution to accelerate sampling, aibeit at the expense of sample quality. We introduce a unified Quantization Noise Correction Scheme (QNCD) aimed at minishing quantization noise throughout the sampling process.
  arXiv  Detail & Related papers  (2024-03-28T04:24:56Z)
• EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models [8.742501879586309]
  Quantization can effectively reduce model complexity, and post-training quantization (PTQ) is highly promising for compressing and accelerating diffusion models.<n>Existing PTQ methods suffer from distribution mismatch issues at both calibration sample level and reconstruction output level.<n>We propose EDA-DM, a standardized PTQ method that efficiently addresses the above issues.
  arXiv  Detail & Related papers  (2024-01-09T14:42:49Z)
• Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation [53.04220377034574]
  We propose incorporating an analytical image attenuation process into the forward diffusion process for high-quality (un)conditioned image generation.<n>Our method represents the forward image-to-noise mapping as simultaneous textitimage-to-zero mapping and textitzero-to-noise mapping.<n>We have conducted experiments on unconditioned image generation, textite.g., CIFAR-10 and CelebA-HQ-256, and image-conditioned downstream tasks such as super-resolution, saliency detection, edge detection, and image inpainting.
  arXiv  Detail & Related papers  (2023-06-23T18:08:00Z)
• Parallel Sampling of Diffusion Models [76.3124029406809]
  Diffusion models are powerful generative models but suffer from slow sampling. We present ParaDiGMS, a novel method to accelerate the sampling of pretrained diffusion models by denoising multiple steps in parallel.
  arXiv  Detail & Related papers  (2023-05-25T17:59:42Z)
• PTQD: Accurate Post-Training Quantization for Diffusion Models [22.567863065523902]
  Post-training quantization of diffusion models can significantly reduce the model size and accelerate the sampling process without re-training. Applying existing PTQ methods directly to low-bit diffusion models can significantly impair the quality of generated samples. We propose a unified formulation for the quantization noise and diffusion perturbed noise in the quantized denoising process.
  arXiv  Detail & Related papers  (2023-05-18T02:28:42Z)
• Q-Diffusion: Quantizing Diffusion Models [52.978047249670276]
  Post-training quantization (PTQ) is considered a go-to compression method for other tasks. We propose a novel PTQ method specifically tailored towards the unique multi-timestep pipeline and model architecture. We show that our proposed method is able to quantize full-precision unconditional diffusion models into 4-bit while maintaining comparable performance.
  arXiv  Detail & Related papers  (2023-02-08T19:38:59Z)
• Pseudo Numerical Methods for Diffusion Models on Manifolds [77.40343577960712]
  Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. DDPMs require hundreds to thousands of iterations to produce final samples. We propose pseudo numerical methods for diffusion models (PNDMs) PNDMs can generate higher quality synthetic images with only 50 steps compared with 1000-step DDIMs (20x speedup)
  arXiv  Detail & Related papers  (2022-02-20T10:37:52Z)
• Noise Estimation for Generative Diffusion Models [91.22679787578438]
  In this work, we present a simple and versatile learning scheme that can adjust the noise parameters for any given number of steps. Our approach comes at a negligible computation cost.
  arXiv  Detail & Related papers  (2021-04-06T15:46:16Z)
• Denoising Diffusion Implicit Models [117.03720513930335]
  We present denoising diffusion implicit models (DDIMs) for iterative implicit probabilistic models with the same training procedure as DDPMs. DDIMs can produce high quality samples $10 times$ to $50 times$ faster in terms of wall-clock time compared to DDPMs.
  arXiv  Detail & Related papers  (2020-10-06T06:15:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.