S $^{2}$ Q-VDiT$^+$: Accurate Quantized Video Diffusion Transformer with Multi-Resolution Sampling and Structural Distillation.
Large-scale video diffusion models (V-DMs) have achieved remarkable text-to-video generation quality, yet their massive computational complexity makes deployment costly. Post-Training Quantization (PTQ) offers an appealing route to accelerate inference without retraining, but existing diffusion PTQ methods remain fragile on modern V-DMs. A key reason is that contemporary V-DMs are intrinsically multi-resolution due to multi-stage training, while most prior PTQ pipelines calibrate at a fixed resolution, causing suboptimal calibration signals and biased distributions under resolution changes. To address this gap, we propose S $^{2}$ Q-VDiT $^+$, a multi-resolution co-design PTQ framework from data, supervision, and quantizer perspectives. First, Denoising-Prior Based Multi-Resolution Sampling constructs resolution-consistent noisy latents by mapping to the clean space and re-noising, together with a trajectory-aware resolution policy across timesteps. Second, Structure-Aware Multi-Resolution Distillation enhances structural alignment via window-wise distillation and transfers resolution-aware spatial dependencies via multi-scale attention distillation. Third, Debiased Modulated Quantization mitigates skewed distributions using asymmetric weight quantization and a fuseable activation debiasing scheme. Extensive experiments on multiple state-of-the-art video generation models demonstrate that S$^{2}$ Q-VDiT$^+$ consistently outperforms strong PTQ baselines under W4A6 and W4A4, delivers up to $2.08\times$ end-to-end speedup, and reduces model storage and inference memory by up to $3.8\times$ and $2.1\times$, respectively.