Wan2.2-I2V-14B: HiFi-Surgical-FP8 & BF16 (ComfyUI Optimized)
This model follows the Wan-AI Software License Agreement. Please refer to the original repository for usage restrictions.
This repository provides two high-performance versions of Wan2.2-I2V-14B, meticulously optimized for the ComfyUI ecosystem. We offer a standard BF16 version and a specialized HiFi-Surgical-FP8 mixed-precision version.
💎 The HiFi-Surgical Optimization Strategy
Unlike generic "one-click" quantization scripts that often cause visual degradation in Wan2.2, our HiFi-Surgical-FP8 version uses a data-driven, diagnostic-led approach to preserve cinematic quality.
1. Layer-Wise SNR Calibration
We performed a deep medical-grade scan on all 406 linear weight tensors of the FP32 Master. Only layers maintaining an SNR (Signal-to-Noise Ratio) > 31.5dB were converted to FP8. This ensures that the mathematical "soul" of the model remains intact.
2. High-Outlier Protection
Wan2.2 weights are notoriously "fragile" with sharp numerical peaks. Our strategy identifies layers with a high Outlier Index (Max/Std deviation > 12) and locks them in BF16. This specifically targets and eliminates the "sparkle" noise and flickering artifacts common in standard FP8 conversions.
3. Structural Integrity (Blocks 30-39)
We have physically isolated the Cross-Attention layers in the final blocks of the DiT architecture. By keeping these critical layers in BF16, we ensure that prompt adherence and temporal consistency are not compromised.
📊 Comparison & Specs
| Feature | Standard BF16 | HiFi-Surgical-FP8 (Recommended) |
|---|
| File Size | ~27.2 GB | ~22.4 GB |
| Precision | Pure Bfloat16 | Hybrid FP8-E4M3 / BF16 |
| VRAM Requirement | 24GB+ | 16GB - 24GB |
| Visual Fidelity | Reference Grade | 99% Reference Match |
| Inference Speed | Base Speed | Accelerated on Blackwell/Hopper |
🛠️ ComfyUI Integration & Usage
These models are specifically converted and tested for ComfyUI.
- Native Scaling Support: We have included the
scale_weight metadata for every quantized tensor. This allows ComfyUI loaders to utilize hardware-level scaling on NVIDIA Blackwell (RTX 50-series) and Hopper architectures for maximum speed.
- How to Use:
- Place the
.safetensors file in your `ComfyUI/models/diffusion_models/.
- Use the CheckpointLoaderSimple or the specialized UNETLoader.
- Ensure your ComfyUI is up-to-date to support the
float8_e4m3fn type.
📝 Diagnostic Methodology
Each weight in the HiFi version was selected based on the following diagnostic results:
- Total Layers Scanned: 406
- FP8 Layers: 184 (Non-sensitive FFN & Attention layers)
- BF16 Layers: 222 (Sensitive Cross-Attention & Outlier-heavy layers)
- Target Hardware: Optimized for RTX 4090, 5090, and H100/H200.
