Q-Drift: Quantization-Aware Drift Correction for Diffusion Model Sampling

Post-training quantization (PTQ) is a practical path to deploy large diffusion models, but quantization noise can accumulate over the denoising trajectory and degrade generation quality. We propose Q-Drift, a principled sampler-side correction that treats quantization error as an implicit stochastic perturbation on each denoising step and applies the paired drift adjustment prescribed by generalized marginal-preserving SDEs. Unlike prior sampler-side corrections, which aim to cancel the quantization error of each individual sample, Q-Drift aims to preserve the distribution. Q-Drift estimates a timestep-wise variance statistic from calibration, in practice requiring as few as 10 paired full-precision/quantized calibration runs. The resulting sampler correction is plug-and-play with common samplers, diffusion models, and PTQ methods, while incurring negligible overhead at inference. Across six diverse text-to-image models (spanning DiT and U-Net), three samplers (Euler, flow-matching, DPM-Solver++), and two PTQ methods (SVDQuant, MixDQ), Q-Drift improves FID over the corresponding quantized baseline in most settings, with up to 4.79 FID reduction on PixArt-$\Sigma$ (SVDQuant W3A4), while preserving CLIP scores.