🤖 AI Summary
This work addresses the performance degradation of large-scale text-to-video diffusion Transformers—such as Wan2.1-T2V-14B—under uniform quantization, which arises from significant heterogeneity in activation distributions between boundary and intermediate layers. The study presents the first systematic analysis of layer-wise activation distribution differences in video DiTs and introduces a data-driven, boundary-preserving post-training quantization (PTQ) strategy: the middle 35 modules are quantized to W8A8 HiFloat8, while the first and last five modules retain BF16 precision. Evaluated on Ascend 910B, this approach achieves lossless compression, matching or slightly surpassing the BF16 baseline across all five VBench dimensions, thereby validating the efficacy of boundary protection. Furthermore, under this configuration, quantization-aware training (QAT) demonstrates no significant advantage over PTQ.
📝 Abstract
We present a post-training quantization (PTQ) approach for Wan2.1-T2V-14B, a 14-billion-parameter text-to-video diffusion transformer, targeting the W8A8 HiFloat8 (HiF8) format on Ascend 910B NPUs. A central challenge in quantizing video DiT models is the heterogeneous activation distribution across transformer blocks: boundary blocks (the first and last few blocks) exhibit fundamentally different statistical properties from middle blocks, making uniform quantization ineffective. We conduct a systematic per-block activation analysis across all 40 WanAttentionBlocks and use the findings to motivate a boundary-protection strategy that retains the first two and last three blocks in BF16 while quantizing the remaining 35 blocks with W8A8 HiF8. The proposed PTQ method matches or marginally exceeds the BF16 baseline on all five VBench dimensions evaluated, indicating no measurable accuracy loss within the 5-prompt evaluation set. An ablation study over four protection configurations confirms that full boundary protection yields the highest average VBench score, validating the data-driven block selection. We additionally investigate quantization-aware training (QAT) as a complementary fine-tuning stage and analyze the conditions under which it fails to outperform plain PTQ on single-card hardware.