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 sampler-side correction that aims to preserve the intended sampling marginals through a deterministic drift adjustment motivated by generalized marginal-preserving SDEs. Q-Drift uses the calibrated conditional residual variance of quantization error to determine a correction factor at each step. Our SDXL study shows that calibration with as few as 10 paired full-precision/quantized runs remains effective. 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 all seven settings of our main evaluation, with up to 4.79 FID reduction on PixArt-Sigma (SVDQuant W3A4), while preserving CLIP scores. Code is available at https://github.com/sooyoung-ryu/Q-Drift.

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