README.md

Multimodal Diffusion with Representation Alignment for High-Fidelity Foley Audio Generation

Professional-grade AI sound effect generation for video content creators

👥 Authors

• [2025.9.29] 🚀 HunyuanVideo-Foley-XL Model Release - Release XL-sized model with offload inference support, significantly reducing VRAM requirements.
• [2025.8.28] 🌟 HunyuanVideo-Foley Open Source Release - Inference code and model weights publicly available.

Experience the magic of AI-generated Foley audio in perfect sync with video content!

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🤝 Community Contributions

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✨ Key Highlights

🎭 Multi-scenario Sync High-quality audio synchronized with complex video scenes

🧠 Multi-modal Balance Perfect harmony between visual and textual information

🎵 48kHz Hi-Fi Output Professional-grade audio generation with crystal clarity

🚀 Tencent Hunyuan open-sources HunyuanVideo-Foley an end-to-end video sound effect generation model!

A professional-grade AI tool specifically designed for video content creators, widely applicable to diverse scenarios including short video creation, film production, advertising creativity, and game development.

🏆 SOTA Performance Achieved

HunyuanVideo-Foley comprehensively leads the field across multiple evaluation benchmarks, achieving new state-of-the-art levels in audio fidelity, visual-semantic alignment, temporal alignment, and distribution matching - surpassing all open-source solutions!

📊 Performance comparison across different evaluation metrics - HunyuanVideo-Foley leads in all categories

🔄 Comprehensive data processing pipeline for high-quality text-video-audio datasets

The TV2A (Text-Video-to-Audio) task presents a complex multimodal generation challenge requiring large-scale, high-quality datasets. Our comprehensive data pipeline systematically identifies and excludes unsuitable content to produce robust and generalizable audio generation capabilities.

🧠 HunyuanVideo-Foley hybrid architecture with multimodal and unimodal transformer blocks

HunyuanVideo-Foley employs a sophisticated hybrid architecture:

• 🔄 Multimodal Transformer Blocks: Process visual-audio streams simultaneously
• 🎵 Unimodal Transformer Blocks: Focus on audio stream refinement
• 👁️ Visual Encoding: Pre-trained encoder extracts visual features from video frames
• 📝 Text Processing: Semantic features extracted via pre-trained text encoder
• 🎧 Audio Encoding: Latent representations with Gaussian noise perturbation
• ⏰ Temporal Alignment: Synchformer-based frame-level synchronization with gated modulation

Objective and Subjective evaluation results demonstrating superior performance across all metrics

🏆 Method	PQ ↑	PC ↓	CE ↑	CU ↑	IB ↑	DeSync ↓	CLAP ↑	MOS-Q ↑	MOS-S ↑	MOS-T ↑
FoleyGrafter	6.27	2.72	3.34	5.68	0.17	1.29	0.14	3.36±0.78	3.54±0.88	3.46±0.95
V-AURA	5.82	4.30	3.63	5.11	0.23	1.38	0.14	2.55±0.97	2.60±1.20	2.70±1.37
Frieren	5.71	2.81	3.47	5.31	0.18	1.39	0.16	2.92±0.95	2.76±1.20	2.94±1.26
MMAudio	6.17	2.84	3.59	5.62	0.27	0.80	0.35	3.58±0.84	3.63±1.00	3.47±1.03
ThinkSound	6.04	3.73	3.81	5.59	0.18	0.91	0.20	3.20±0.97	3.01±1.04	3.02±1.08
HunyuanVideo-Foley (ours)	6.59	2.74	3.88	6.13	0.35	0.74	0.33	4.14±0.68	4.12±0.77	4.15±0.75

Comprehensive objective evaluation showcasing state-of-the-art performance

🏆 Method	FD_PANNs ↓	FD_PASST ↓	KL ↓	IS ↑	PQ ↑	PC ↓	CE ↑	CU ↑	IB ↑	DeSync ↓	CLAP ↑
FoleyGrafter	22.30	322.63	2.47	7.08	6.05	2.91	3.28	5.44	0.22	1.23	0.22
V-AURA	33.15	474.56	3.24	5.80	5.69	3.98	3.13	4.83	0.25	0.86	0.13
Frieren	16.86	293.57	2.95	7.32	5.72	2.55	2.88	5.10	0.21	0.86	0.16
MMAudio	9.01	205.85	2.17	9.59	5.94	2.91	3.30	5.39	0.30	0.56	0.27
ThinkSound	9.92	228.68	2.39	6.86	5.78	3.23	3.12	5.11	0.22	0.67	0.22
HunyuanVideo-Foley (ours)	6.07	202.12	1.89	8.30	6.12	2.76	3.22	5.53	0.38	0.54	0.24

🎉 Outstanding Results! HunyuanVideo-Foley achieves the best scores across ALL evaluation metrics, demonstrating significant improvements in audio quality, synchronization, and semantic alignment.

🔧 System Requirements

• CUDA: 12.4 or 11.8 recommended
• Python: 3.8+
• OS: Linux (primary support)
• VRAM: 20GB for XXL model (or 12GB with --enable_offload), 16GB for XL model (or 8GB with --enable_offload)

# 📥 Clone the repository
git clone https://github.com/Tencent-Hunyuan/HunyuanVideo-Foley
cd HunyuanVideo-Foley

💡 Tip: We recommend using Conda for Python environment management.

# 🔧 Install dependencies
pip install -r requirements.txt

🔗 Download Model weights from Huggingface

# using git-lfs
git clone https://huggingface.co/tencent/HunyuanVideo-Foley

# using huggingface-cli
huggingface-cli download tencent/HunyuanVideo-Foley

Generate Foley audio for a single video file with text description:

# Use XXL model (default, best quality)
python3 infer.py \
    --model_path PRETRAINED_MODEL_PATH_DIR \
    --single_video video_path \
    --single_prompt "audio description" \
    --output_dir OUTPUT_DIR \
    # --enable_offload  

# Use XL model (memory-friendly)
python3 infer.py \
    --model_path PRETRAINED_MODEL_PATH_DIR \
    --model_size xl \
    --single_video video_path \
    --single_prompt "audio description" \
    --output_dir OUTPUT_DIR \
    # --enable_offload

Process multiple videos using a CSV file with video paths and descriptions:

# Download sample test videos
bash ./download_test_videos.sh

# Batch processing
python3 infer.py \
    --model_path PRETRAINED_MODEL_PATH_DIR \
    --csv_path assets/test.csv \
    --output_dir OUTPUT_DIR \
    # --enable_offload

Launch a user-friendly Gradio web interface for easy interaction:

# Launch with XXL model (default)
export HIFI_FOLEY_MODEL_PATH=PRETRAINED_MODEL_PATH_DIR
python3 gradio_app.py

# Launch with XL model (memory-friendly)
export HIFI_FOLEY_MODEL_PATH=PRETRAINED_MODEL_PATH_DIR
MODEL_SIZE=xl python3 gradio_app.py

# Optional: Enable offload to reduce memory usage
ENABLE_OFFLOAD=true python3 gradio_app.py

🚀 Then open your browser and navigate to the provided local URL to start generating Foley audio!

If you find HunyuanVideo-Foley useful for your research, please consider citing our paper:

@misc{shan2025hunyuanvideofoleymultimodaldiffusionrepresentation,
      title={HunyuanVideo-Foley: Multimodal Diffusion with Representation Alignment for High-Fidelity Foley Audio Generation}, 
      author={Sizhe Shan and Qiulin Li and Yutao Cui and Miles Yang and Yuehai Wang and Qun Yang and Jin Zhou and Zhao Zhong},
      year={2025},
      eprint={2508.16930},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2508.16930}, 
}

We extend our heartfelt gratitude to the open-source community!

🌟 Special thanks to all researchers and developers who contribute to the advancement of AI-generated audio and multimodal learning!

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