Model reference · open weights
HunyuanVideo-Foley is an open-weight audio or speech model from tencent. HunyuanVideo-Foley (BF16) weighs 10.3 GB; the smallest configuration that runs it is RTX 4060 Ti 16 GB.
What it is
| Released by | tencent |
|---|---|
| Type | Audio & music |
| Task | Music / audio |
| Runs with | hunyuanvideo-foley |
| Released | 2025-08-21 |
| Popularity | 514 downloads / month |
| Weights | 10.3 GB (HunyuanVideo-Foley (BF16), file size) |
| Licence | Its own licence terms |
What it runs on
Weights 10.3 GB (file size) · overhead about 1.6 GB.
| Card | One stream | Counted memory |
|---|---|---|
| RTX 3060 12 GB | does not fit | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
Estimates, not measurements: the weights are the build's file size. A speech model's decoder keeps a small cache for every stream it transcribes, so memory grows with the streams and beams at once. Counted memory is 92 % of what CUDA reports for the card.
From the model card
Sizhe Shan1,2* • Qiulin Li1,3* • Yutao Cui1 • Miles Yang1 • Yuehai Wang2 • Qun Yang3 • Jin Zhou1† • Zhao Zhong1
🏢 1Tencent Hunyuan • 🎓 2Zhejiang University • ✈️ 3Nanjing University of Aeronautics and Astronautics
*Equal contribution • †Project lead
🎭 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.
🎬 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.
⚖️ 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.
🏆 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:
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
Quoted from the model card on Hugging Face — the full card is behind the Hugging Face link above.
How it works