S2Q-VDiT: Accurate Quantized Video Diffusion Transformer with Salient Data and Sparse Token Distillation

a stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage.

Figure 1: We present S 2 Q-VDiT, a post-training quantization method for video diffusion transformers. We quantize HunyuanVideo [24] to 4-bit weights and 6-bit activations without compromising visual quality. S 2 Q-VDiT can further achieve 3.9× model compression and 1.3× inference acceleration.