Quantization Method: FP8 E5M2 & E4M3FN weight-only quantization
Layers Quantized: Transformer block weights only (attention and FFN layers)
Preserved Precision: Normalization layers, embeddings, and biases remain in original precision
Expected VRAM Savings: ~30-40% reduction compared to BF16 original
Memory Usage: Enables running on <12GB GPUs when combined with other optimizations

Usage

ComfyUI (Recommended)

This model is specifically optimized for use with the ComfyUI-HunyuanVideo-Foley custom node, which provides:

VRAM-friendly loading with ping-pong memory management
Built-in FP8 support that automatically handles the quantized weights
Torch compile integration for ~30% speed improvements after first run
Text-to-Audio and Video-to-Audio modes
Batch generation with audio selection tools

Installation:

Install the ComfyUI node: ComfyUI-HunyuanVideo-Foley
Download this quantized model to ComfyUI/models/foley/
Enjoy <8GB VRAM usage with high-quality audio generation

Typical VRAM Usage (5s audio, 50 steps):

Baseline (BF16): ~10-12 GB
With FP8 quantization: ~8-10 GB
Perfect for RTX 3080/4070 Ti and similar GPUs

Other Frameworks

The FP8 weights can be used with any framework that supports automatic upcasting of FP8 to FP16/BF16 during computation. The quantized weights maintain compatibility with the original model architecture.

Files

hunyuanvideo_foley_fp8_e4m3fn.safetensors - Main model weights in FP8 format

Performance Notes

Quality: Maintains comparable audio generation quality to the original model
Speed: Conversion overhead is minimal; actual generation speed depends on compute precision
Memory: Significant VRAM reduction makes the model accessible on consumer GPUs
Compatibility: Drop-in replacement for the original model weights

Original Model

This quantization is based on tencent/HunyuanVideo-Foley. Please refer to the original repository for:

Model architecture details
Training information
License terms
Citation information

Technical Details

The quantization uses a conservative approach that only converts transformer block weights while preserving precision-sensitive components:

✅ Converted: Attention and FFN layer weights in transformer blocks
❌ Preserved: Normalization layers, embeddings, projections, bias terms

This selective quantization strategy maintains model quality while maximizing memory savings.

phazei/HunyuanVideo-Foley

HunyuanVideo-Foley FP8 Quantized

Quantization Details