HunyuanVideo-Foley – Tencent Hunyuan’s Open-Source Video Sound Effects Generation Model

What is HunyuanVideo-Foley？

HunyuanVideo-Foley is an open-source, end-to-end video sound effects generation model developed by Tencent Hunyuan. The model can generate high-quality sound effects that precisely match the input video and text description, addressing the common issue of missing audio in AI-generated videos. Trained on a large-scale, high-quality text-video-audio dataset, it leverages an innovative multimodal diffusion transformer architecture and representation alignment (REPA) loss function to achieve strong generalization, balanced multimodal semantic responses, and professional-grade audio fidelity. It leads performance benchmarks across multiple evaluations and is widely applied in short video creation, film production, and other creative fields.

Key Features of HunyuanVideo-Foley

• Automatic Sound Effect Generation: Generates audio that perfectly matches video content and text prompts, turning silent AI videos into immersive auditory experiences.

• Multi-Scenario Application: Suitable for short videos, film production, advertising, and game development, helping creators efficiently produce context-aware sound effects and enhance content professionalism.

• High-Quality Audio: Produces professional-grade audio fidelity, accurately capturing fine details, such as a car driving on a wet road or an engine revving from idle to full throttle, meeting professional production standards.

• Balanced Multimodal Semantic Response: Understands video content while integrating text descriptions, automatically balancing information from multiple sources to create rich composite audio, avoiding over-reliance on text semantics and ensuring tight alignment with the scene.

Technical Principles of HunyuanVideo-Foley

• Large-Scale Dataset Construction: Built on an automatically annotated and filtered dataset of ~100,000 hours of text-video-audio (TV2A) data, providing strong support for model training and ensuring robust generalization.

• Multimodal Diffusion Transformer (MMDiT): Utilizes a dual-stream architecture with joint self-attention to model frame-level alignment between video and audio, while cross-attention injects text information to resolve modality competition and achieve precise alignment across video, audio, and text.

• Representation Alignment (REPA) Loss: Uses pre-trained audio features for semantic and acoustic guidance, maximizing cosine similarity between pre-trained and internal representations to improve audio quality, suppress background noise, and reduce inconsistencies.

• Audio VAE Optimization: Employs an enhanced variational autoencoder (VAE), replacing discrete audio representations with continuous 128-dimensional vectors, significantly improving audio reconstruction and overall sound effect quality.

Project Links

• Official Website: https://szczesnys.github.io/hunyuanvideo-foley/

• GitHub: https://github.com/Tencent-Hunyuan/HunyuanVideo-Foley

• HuggingFace Model Hub: https://huggingface.co/tencent/HunyuanVideo-Foley

• arXiv Paper: https://arxiv.org/pdf/2508.16930

• Online Demo: https://huggingface.co/spaces/tencent/HunyuanVideo-Foley

Application Scenarios of HunyuanVideo-Foley

• Short Video Creation: Quickly generate matching audio for short videos, e.g., the footsteps of a running pet, making content more lively.

• Film Production: Assist post-production sound design, e.g., generating spaceship roar effects in sci-fi films, improving efficiency.

• Advertising Creativity: Produce audio for ads, such as engine sounds for car commercials, enhancing appeal and impact.

• Game Development: Generate real-time in-game audio, e.g., bird chirps as characters walk through a forest, enhancing immersion.

• Online Education: Add engaging sound effects to educational videos, e.g., the rumble of a volcanic eruption, boosting learner interest.