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 mini5 mizes 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, account8 ing 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 O(n) to 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.
@inproceedings{lee2025accuquant,title={AccuQuant: Simulating Multiple Denoising Steps for Quantizing Diffusion Models},author={Lee, Seunghoon and Choi, Jeongwoo and Son, Byunggwan and Moon, Jaehyeon and Jeon, Jeimin and Ham, Bumsub},booktitle={Conference on Neural Information Processing Systems (NeurIPS)},year={2025},}
