What is HunyuanVideo-Foley
HunyuanVideo-Foley is an end-to-end video sound effect generation model open-sourced by the Tencent Hunyuan team. The model can generate high-quality sound effects that precisely match the video footage based on the input video and text description, solving the problem of missing sound effects in existing AI video generation. Trained on large-scale, high-quality text-video-audio datasets, the model uses an innovative multimodal diffusion transformer architecture and representation alignment loss function to achieve strong generalization ability, balanced multimodal semantic response, and professional-grade audio fidelity. It leads in performance on multiple evaluation benchmarks and is widely used in fields such as short video creation and film production.
Main Features of HunyuanVideo-Foley
- Automatic sound effect generation: Based on the input video and text description, it generates precisely matched sound effects for the video, giving silent AI videos an immersive auditory experience.
- Multi-scenario applications: Suitable for various scenarios such as short video creation, film production, advertising creativity, and game development, helping creators efficiently generate scenario-based sound effects and enhancing the appeal and professionalism of content.
- High-quality sound effect generation: The generated sound effects have professional-grade audio fidelity and can accurately reproduce various detailed textures, such as the details of a car driving over a wet and slippery road, the dynamic changes of an engine from idle to roar, etc., meeting the audio quality requirements of professional production.
- Balanced multimodal semantic response: It can understand the video footage and combine text descriptions, automatically balancing different information sources to generate rich layered composite sound effects, avoiding the problem of overly relying on text semantics and ignoring video semantics, making the sound effects highly consistent with the overall scene.
Technical Principles of HunyuanVideo-Foley
- Large-scale dataset construction: Based on audio-video data collected through automated annotation and filtering, a high-quality text-video-audio (TV2A) dataset of approximately 100,000 hours is constructed, providing strong data support for model training and giving the model strong generalization ability.
- Multimodal diffusion transformer architecture: It uses a dual-stream multimodal diffusion transformer (MMDiT) architecture, modeling frame-level alignment between video and audio through a joint self-attention mechanism, injecting text information through a cross-attention mechanism, solving the modality competition problem in multimodal data, and achieving precise alignment among video, audio, and text.
- Representation alignment (REPA) loss function: It uses pre-trained audio features to provide semantic and acoustic guidance for the modeling process. By maximizing the cosine similarity between pre-trained representations and internal representations, it significantly improves audio generation quality and stability, effectively suppressing background noise and inconsistent sound effect artifacts, and ensuring professional-grade audio fidelity.
- Audio VAE optimization: It uses an enhanced audio variational autoencoder (VAE) to replace discrete audio representations with continuous 128-dimensional representations, significantly improving audio reconstruction capability and further enhancing the quality of sound effect generation.
Project Addresses of HunyuanVideo-Foley
- Project official website：https://szczesnys.github.io/hunyuanvideo-foley/
- GitHub repository：https://github.com/Tencent-Hunyuan/HunyuanVideo-Foley
- HuggingFace model library：https://huggingface.co/tencent/HunyuanVideo-Foley
- arXiv technical paper：https://arxiv.org/pdf/2508.16930
- Online experience Demo：https://huggingface.co/spaces/tencent/HunyuanVideo-Foley
Application Scenarios of HunyuanVideo-Foley
- Short video creation: Quickly generate matching sound effects for short videos, such as the footsteps of a running pet, making the content more vivid.
- Film production: Assisting with sound effect design in film post-production, such as generating the roar of a spaceship in a science fiction film, improving production efficiency.
- Advertising creativity: Generating sound effects such as engine roars for car advertisements, enhancing the appeal and emotional impact of the advertisement.
- Game development: Generating sound effects for game scenes in real time, such as the birdsong when a character walks in a forest, enhancing immersion.
- Online education: Adding vivid sound effects to educational videos, such as the rumbling sound of a volcanic eruption, increasing interest in learning.