ReopenAI/Wan2.2-I2V-14B-HiF4

Wan2.2-I2V-A14B HiFloat4 PTQ

https://github.com/Reopen-AI/Wan2.2-I2V-14B-HiF4

This repository contains the final optimized artifacts for the ICME 2026 Low-Bit-width Large-Model Quantization Challenge, Track 1.

Base Model

• Base model: Wan2.2-I2V-A14B BF16
• Task: image-to-video generation
• Quantization: HiFloat4 W4A4 quant-dequant simulation

Quantization Policy

• low_noise_model: quantized
• high_noise_model: quantized
• First and last Transformer blocks: BF16
• Other nn.Linear layers: HiFloat4 W4A4 QDQ
• Activations: HiFloat4 QDQ enabled
• Exported weights: BF16 tensors after HiFloat4 QDQ, with SmoothQuant and clip metadata

Files

i2v_hif4_quant_state.pt
i2v_hif4_quantized_weights_low_noise_model.pt
i2v_hif4_quantized_weights_high_noise_model.pt

i2v_hif4_quant_state.pt stores calibration metadata. The two exported-weight files store the low-noise and high-noise expert weights separately.

Usage

Clone the code repository and place or download these files under outputs/. Then run:

LOW_QUANT_WEIGHTS=outputs/i2v_hif4_quantized_weights_low_noise_model.pt \
HIGH_QUANT_WEIGHTS=outputs/i2v_hif4_quantized_weights_high_noise_model.pt \
OUT_DIR=/path/to/generated_videos \
bash opens2v_generate.sh

For full reproduction instructions, see the code repository README.