Title: Absorbing Quantization Error by Deformable Noise Scheduler for Diffusion Models
Primary area: generative models
Keywords: Diffusion Model, Quantization, Distribution-perserving, Inference Effiency

Abstract: Diffusion models deliver state-of-the-art image quality but are expensive to deploy. Post-training quantization (PTQ) can shrink models and speed up inference, yet residual quantization errors distort the diffusion distribution (the timestep-wise marginal over $\vx_t$), degrading sample quality. We propose a distribution-preserving framework that absorbs quantization error into the generative process without changing architecture or adding steps.
(1) Distribution-Calibrated Noise Compensation (DCNC) corrects the non-Gaussian kurtosis of quantization noise via a calibrated uniform component, yielding a closer Gaussian approximation for robust denoising.
(2) Deformable Noise Scheduler (DNS) reinterprets quantization as a principled timestep shift, mapping the quantized prediction distribution $\vx_t$ back onto the original diffusion distribution so that the target marginal is preserved.
Unlike trajectory-preserving or noise-injection methods limited to stochastic samplers, our approach preserves the distribution under both stochastic and deterministic samplers and extends to flow-matching with Gaussian conditional paths. It is plug-and-play and complements existing PTQ schemes. On DiT-XL (W4A8), our method reduces FID from 9.83 to 8.51, surpassing the FP16 baseline (9.81), demonstrating substantial quality gains without sacrificing the efficiency benefits of quantization.

TL;DR: We propose a theoretically derived method to reduce quantization impact, which can be applied to diffusion and flow matching, and seamlessly integrate with a variety of other PTQs