--- title: HunyuanVideo Foley emoji: 🎬 colorFrom: blue colorTo: indigo sdk: gradio sdk_version: 5.43.1 app_file: app.py pinned: false models: - tencent/HunyuanVideo-Foley ---

🎬 HunyuanVideo-Foley

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

Professional-grade AI sound effect generation for video content creators

[![Project Page](https://img.shields.io/badge/🌐_Project-Page-green.svg?style=for-the-badge)](https://szczesnys.github.io/hunyuanvideo-foley) [![Code](https://img.shields.io/badge/Code-GitHub-blue.svg?style=for-the-badge&logo=github)](https://github.com/Tencent-Hunyuan/HunyuanVideo-Foley) [![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg?style=for-the-badge&logo=arxiv)](https://arxiv.org/abs/2508.16930) [![Model](https://img.shields.io/badge/Model-Huggingface-yellow.svg?style=for-the-badge&logo=huggingface)](https://huggingface.co/tencent/HunyuanVideo-Foley)
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### πŸ‘₯ **Authors**
**Sizhe Shan**1,2* β€’ **Qiulin Li**1,3* β€’ **Yutao Cui**1 β€’ **Miles Yang**1 β€’ **Yuehai Wang**2 β€’ **Qun Yang**3 β€’ **Jin Zhou**1† β€’ **Zhao Zhong**1
🏒 1**Tencent Hunyuan** β€’ πŸŽ“ 2**Zhejiang University** β€’ ✈️ 3**Nanjing University of Aeronautics and Astronautics** *Equal contribution β€’ †Project lead
--- ### ✨ **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
--- ## πŸ“„ **Abstract**
**πŸš€ Tencent Hunyuan** proudly 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.*
### 🎯 **Core Highlights**
**🎬 Multi-scenario Audio-Visual Synchronization** Supports generating high-quality audio that is synchronized and semantically aligned with complex video scenes, enhancing realism and immersive experience for film/TV and gaming applications.
**βš–οΈ Multi-modal Semantic Balance** Intelligently balances visual and textual information analysis, comprehensively orchestrates sound effect elements, avoids one-sided generation, and meets personalized dubbing requirements.
**🎡 High-fidelity Audio Output** Self-developed 48kHz audio VAE perfectly reconstructs sound effects, music, and vocals, achieving professional-grade audio generation quality.
**πŸ† 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 Overview](assets/pan_chart.png) *πŸ“Š Performance comparison across different evaluation metrics - HunyuanVideo-Foley leads in all categories*
--- ## πŸ”§ **Technical Architecture** ### πŸ“Š **Data Pipeline Design**
![Data Pipeline](assets/data_pipeline.png) *πŸ”„ 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.
### πŸ—οΈ **Model Architecture**
![Model Architecture](assets/model_arch.png) *🧠 HunyuanVideo-Foley hybrid architecture with multimodal and unimodal transformer blocks*
**HunyuanVideo-Foley** employs a sophisticated hybrid architecture: - **πŸ”„ Multimodal Transformer Blocks**: Process visual-audio streams simultaneously - **🎡 Unimodal Transformer Blocks**: Focus on audio stream refinement - **πŸ‘οΈ Visual Encoding**: Pre-trained encoder extracts visual features from video frames - **πŸ“ 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
--- ## πŸ“ˆ **Performance Benchmarks** ### 🎬 **MovieGen-Audio-Bench Results**
> *Objective and Subjective evaluation results demonstrating superior performance across all metrics*
| πŸ† **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 | 3.36Β±0.78 | 3.54Β±0.88 | 3.46Β±0.95 | | V-AURA | 5.82 | 4.30 | 3.63 | 5.11 | 0.23 | 1.38 | 0.14 | 2.55Β±0.97 | 2.60Β±1.20 | 2.70Β±1.37 | | Frieren | 5.71 | 2.81 | 3.47 | 5.31 | 0.18 | 1.39 | 0.16 | 2.92Β±0.95 | 2.76Β±1.20 | 2.94Β±1.26 | | MMAudio | 6.17 | 2.84 | 3.59 | 5.62 | 0.27 | 0.80 | 0.35 | 3.58Β±0.84 | 3.63Β±1.00 | 3.47Β±1.03 | | ThinkSound | 6.04 | 3.73 | 3.81 | 5.59 | 0.18 | 0.91 | 0.20 | 3.20Β±0.97 | 3.01Β±1.04 | 3.02Β±1.08 | | **πŸ₯‡ HiFi-Foley (ours)** | **🟒 6.59** | **🟒 2.74** | **🟒 3.88** | **🟒 6.13** | **🟒 0.35** | **🟒 0.74** | **🟒 0.33** | **🟒 4.14Β±0.68** | **🟒 4.12Β±0.77** | **🟒 4.15Β±0.75** |
### 🎯 **Kling-Audio-Eval Results**
> *Comprehensive objective evaluation showcasing state-of-the-art performance*
| πŸ† **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 | | **πŸ₯‡ HiFi-Foley (ours)** | **🟒 6.07** | **🟒 202.12** | **🟒 1.89** | **🟒 8.30** | **🟒 6.12** | **🟒 2.76** | **🟒 3.22** | **🟒 5.53** | **🟒 0.38** | **🟒 0.54** | **🟒 0.24** |
**πŸŽ‰ Outstanding Results!** HunyuanVideo-Foley achieves the best scores across **ALL** evaluation metrics, demonstrating significant improvements in audio quality, synchronization, and semantic alignment.
--- ## πŸš€ **Quick Start** ### πŸ“¦ **Installation**
**πŸ”§ System Requirements** - **CUDA**: 12.4 or 11.8 recommended - **Python**: 3.8+ - **OS**: Linux (primary support)
#### **Step 1: Clone Repository** ```bash # πŸ“₯ Clone the repository git clone https://github.com/Tencent-Hunyuan/HunyuanVideo-Foley cd HunyuanVideo-Foley ``` #### **Step 2: Environment Setup**
πŸ’‘ **Tip**: We recommend using [Conda](https://docs.anaconda.com/free/miniconda/index.html) for Python environment management.
```bash # πŸ”§ Install dependencies pip install -r requirements.txt ``` #### **Step 3: Download Pretrained Models**
πŸ”— **Download Model weights from Huggingface** ```bash # using git-lfs git clone https://huggingface.co/tencent/HunyuanVideo-Foley # using huggingface-cli huggingface-cli download tencent/HunyuanVideo-Foley ```
--- ## πŸ’» **Usage** ### 🎬 **Single Video Generation**
Generate Foley audio for a single video file with text description:
```bash python3 infer.py \ --model_path PRETRAINED_MODEL_PATH_DIR \ --config_path ./configs/hunyuanvideo-foley-xxl.yaml \ --single_video video_path \ --single_prompt "audio description" \ --output_dir OUTPUT_DIR ``` ### πŸ“‚ **Batch Processing**
Process multiple videos using a CSV file with video paths and descriptions:
```bash python3 infer.py \ --model_path PRETRAINED_MODEL_PATH_DIR \ --config_path ./configs/hunyuanvideo-foley-xxl.yaml \ --csv_path assets/test.csv \ --output_dir OUTPUT_DIR ``` ### 🌐 **Interactive Web Interface**
Launch a user-friendly Gradio web interface for easy interaction:
```bash export HIFI_FOLEY_MODEL_PATH=PRETRAINED_MODEL_PATH_DIR python3 gradio_app.py ```
*πŸš€ Then open your browser and navigate to the provided local URL to start generating Foley audio!*
--- ## πŸ“š **Citation**
If you find **HunyuanVideo-Foley** useful for your research, please consider citing our paper:
```bibtex @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}, } ``` --- ## πŸ™ **Acknowledgements**
**We extend our heartfelt gratitude to the open-source community!**
🎨 **[Stable Diffusion 3](https://huggingface.co/stabilityai/stable-diffusion-3-medium)** *Foundation diffusion models* ⚑ **[FLUX](https://github.com/black-forest-labs/flux)** *Advanced generation techniques* 🎡 **[MMAudio](https://github.com/hkchengrex/MMAudio)** *Multimodal audio generation*
πŸ€— **[HuggingFace](https://huggingface.co)** *Platform & diffusers library* πŸ—œοΈ **[DAC](https://github.com/descriptinc/descript-audio-codec)** *High-Fidelity Audio Compression* πŸ”— **[Synchformer](https://github.com/v-iashin/Synchformer)** *Audio-Visual Synchronization*
**🌟 Special thanks to all researchers and developers who contribute to the advancement of AI-generated audio and multimodal learning!**
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