  Recently, video diffusion models (VDMs) have garnered significant attention
due to their notable advancements in generating coherent and realistic video
content. However, processing multiple frame features concurrently, coupled with
the considerable model size, results in high latency and extensive memory
consumption, hindering their broader application. Post-training quantization
(PTQ) is an effective technique to reduce memory footprint and improve
computational efficiency. Unlike image diffusion, we observe that the temporal
features, which are integrated into all frame features, exhibit pronounced
skewness. Furthermore, we investigate significant inter-channel disparities and
asymmetries in the activation of video diffusion models, resulting in low
coverage of quantization levels by individual channels and increasing the
challenge of quantization. To address these issues, we introduce the first PTQ
strategy tailored for video diffusion models, dubbed QVD. Specifically, we
propose the High Temporal Discriminability Quantization (HTDQ) method, designed
for temporal features, which retains the high discriminability of quantized
features, providing precise temporal guidance for all video frames. In
addition, we present the Scattered Channel Range Integration (SCRI) method
which aims to improve the coverage of quantization levels across individual
channels. Experimental validations across various models, datasets, and
bit-width settings demonstrate the effectiveness of our QVD in terms of diverse
metrics. In particular, we achieve near-lossless performance degradation on
W8A8, outperforming the current methods by 205.12 in FVD.
