From Silent Footage to Cinema-Grade Sound

A Practical Guide to HunyuanVideo-Foley for Global Creators

“My video looks great, but it’s dead silent—how do I add believable sound without hiring a Foley artist?”
“Can an AI really create skateboard-wheel screeches that sync perfectly with the picture?”
“Is there a one-click tool to batch-dub short-form content with high-quality audio?”

If any of those questions sound familiar, this guide is for you. HunyuanVideo-Foley (HVF for short) is an open-source “text-video-to-audio” system released by Tencent’s Hunyuan team. Feed it any silent clip plus a short description, and it returns broadcast-ready 48 kHz audio that lines up in time and meaning with both the visuals and your words.

Below you’ll find everything you need—how it works, why it works, and exactly how to run it today—written for readers with at least a junior-college level of technical confidence.

1. What Problem Are We Solving?

2. Three Reasons HVF Sounds Better

1. Massive, Clean Dataset
   100 000 hours of video-audio-text triplets were automatically filtered for silence, poor quality, and mis-alignment.
2. Two-Stage Attention

   • First, align audio time-steps with video frames.
   • Then inject the text prompt so it guides content without overpowering the visuals.
3. REPA Fine-Tuning
   A self-supervised “teacher” network whispers extra acoustic details into the diffusion model, boosting fidelity and reducing noise.

3. Under the Hood—Without the Jargon

Think of HVF as two small factories working in a row.

3.1 Data Factory (Pipeline)

Result: A gigantic, squeaky-clean training set that teaches the model “what goes with what.”

3.2 Model Factory (Architecture)

1. Multimodal Transformer (18 blocks)

   • Vision lane: SigLIP-2 turns every video frame into a vector.
   • Audio lane: DAC-VAE compresses raw audio into 50 fps latent codes.
   • Cross-Attention: Interleaved RoPE lets the two lanes swap timing cues.
2. Unimodal Transformer (36 blocks)
   Pure-audio refinement.
3. REPA Loss
   Match intermediate features to a pre-trained ATST-Frame encoder → crisper transients, cleaner spectrum.

4. Quick-Start Checklist

4.1 Hardware & OS

• Linux (Ubuntu 20.04+ tested)
• Python 3.8–3.11
• CUDA 11.8 or 12.4
• 16 GB GPU RAM minimum (RTX 4090 / A100)

4.2 Install

# 1. Pull source
git clone https://github.com/Tencent-Hunyuan/HunyuanVideo-Foley
cd HunyuanVideo-Foley

# 2. Dependencies
pip install -r requirements.txt

# 3. Model weights (git-lfs must be installed)
git lfs install
git clone https://huggingface.co/tencent/HunyuanVideo-Foley pretrained

4.3 Single Clip

python3 infer.py \
  --model_path ./pretrained \
  --config_path ./configs/hunyuanvideo-foley-xxl.yaml \
  --single_video ./clips/skateboard.mp4 \
  --single_prompt "skateboard rolling on rough concrete, light wheel noise" \
  --output_dir ./results

Produces an 8-second 48 kHz WAV named with a timestamp.

4.4 Batch Mode

Create jobs.csv

clips/001.mp4,light rain on umbrella
clips/002.mp4,cat purring softly

then

python3 infer.py \
  --model_path ./pretrained \
  --csv_path ./jobs.csv \
  --output_dir ./batch_results

4.5 Web Demo

export HIFI_FOLEY_MODEL_PATH=./pretrained
python3 gradio_app.py

Open http://localhost:7860, drag-and-drop, type a prompt, hit Generate.

5. Performance Snapshot

Higher is better for MOS-Q and PQ; lower is better for FD.

6. Frequently Asked Questions

Q1: How long can my input video be?

• Training max: 10 s. Inference default: 8 s. For longer footage, slice into 8 s chunks and stitch.

Q2: Must prompts be in English?

• English gives the most predictable results. Chinese prompts also work but may vary.

Q3: Commercial usage?

• Code is Apache-2.0. Model weights carry additional Tencent terms—read the license before monetizing.

Q4: GPU memory too low?

• Launch with --fp16 or set --batch_size 1 to fit 12 GB cards.

Q5: Can I skip the video and use text only?

• No. HVF is video-driven. For text-only, try TangoFlux or AudioLDM.

7. Pro Tips for Better Sound

8. Real-World Use Cases

• Short-form creator
  Batch-dub 30 skate reels overnight; upload next morning.

• Indie game dev
  Export 8-second character animation → auto footsteps + cloth rustle.

• Ad agency
  Script “car racing in rain” + stock footage → instant engine + downpour mix.

9. Next Steps

1. Domain Fine-Tuning
   Record your own footstep library, LoRA-fine-tune for signature sounds.

2. Long-Form Stitching
   Concatenate 8-second chunks with cross-fades for 60-second ambience.

3. Real-Time Fork
   Community TensorRT branch reaches 0.8 s latency—perfect for live streaming.

10. Key Takeaway

HunyuanVideo-Foley combines:

• 100 000 hours of curated data → no off-topic noise
• Two-stage attention → visuals and text stay balanced
• REPA fine-tuning → broadcast-level clarity

Whether you’re a TikTok editor, indie dev, or ad producer, one command turns silent pixels into 48 kHz cinema-grade sound today.

@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}, 
}