Post-Training HiF4 W4A4 Quantization of Wan2.2-I2V-A14B for the ICME 2026 Low Bit-width Large Model Quantization Challenge

This technical report describes the ReopenAI team's post-training quantization (PTQ) solution for Sub-Challenge 1 of the ICME 2026 Low Bit-width Large Model Quantization Challenge. The target model is Wan2.2-I2V-A14B, an image-to-video generative model evaluated on OpenS2V-5M prompts with VBench-based quality metrics. The proposed method applies HiFloat4 W4A4 quantization to eligible linear layers in both low-noise and high-noise DiT experts, while keeping the first and last Transformer blocks in BF16. The pipeline uses calibration-only activation statistics, SmoothQuant-style equivalent scaling, activation and weight clipping, and reproducible quantization metadata export. The method requires no fine-tuning and preserves the official Wan2.2 generation path for reproducible evaluation with the official HiFloat4 simulator.