HappyHorse Generator: Unified Video + Audio Generation
15B-parameter single Transformer architecture, just 8 inference steps, natively generates video and audio together. Text-to-video, image-to-video, SFX, ambient audio, and narration — 7 languages supported. Fully open-source. Ranked #1 on the Artificial Analysis Video Arena (Elo 1333).
Coming Soon
HappyHorse is fully open-source: base model, distilled model, super-resolution, and inference code.
HappyHorse's Six Breakthroughs
Unified architecture, native audio, ultra-fast inference — one of the most powerful video generation paradigms in the open-source world.
Unified Multi-Modal Generation
Text-to-video and image-to-video unified in a single model. A single inference pass generates both visual frames and an audio track — no post-production dubbing or stitching required.
Creators, game developers, ad production, short-form content — complete output in one generation.
8-Step Ultra-Fast Inference (No CFG)
Uses a single Transformer Transfusion paradigm with no Classifier-Free Guidance. Only 8 steps to complete inference — far faster than traditional diffusion models with significantly lower compute requirements.
Real-time creation, rapid iteration, edge device deployment, low-cost batch generation.
Native Synchronized Audio Generation
Sound effects, ambient audio, and narration are natively synchronized with video during the generation process — not layered in post. Physics-driven sound design tightly matches scene visuals.
Social media short videos, game cinematics, documentaries, voiced advertising content.
Six-Language Audio Support
Natively supports narration and dialogue generation in Chinese, English, Japanese, Korean, German, and French — no manual translation or post-dubbing required.
Global content distribution, multilingual marketing, international education, cross-border e-commerce videos.
Fully Open-Source Ecosystem
Base model, distilled model, super-resolution module, and complete inference code are all open-source. Researchers can reproduce, developers can deploy locally, the community can freely extend.
Academic research, enterprise private deployment, model fine-tuning, secondary development and commercial integration.
720p@24fps High-Quality Output
Generates 1280×720, 24fps, 5-second videos with sharp and smooth visuals. The built-in super-resolution module can further enhance output quality.
Social platform publishing, product demos, prototype validation, content batch production.
HappyHorse Featured Examples
Covering text-to-video, image-to-video, sound effect generation, multilingual narration, and more.
HappyHorse Technical Specifications
Understand the core parameters to plan local deployment and use cases.
HappyHorse vs Leading Video Generation Models
Happy Horse 1.0 compared against leading AI video generation models in 2026.
| Happy Horse 1.0 | Seedance 2.0 | Sora | LTX 2.3 | |
|---|---|---|---|---|
| Developer | Happy Horse Team | ByteDance Seed | OpenAI | Lightricks |
| Parameters | ~15B | Undisclosed | Undisclosed | 22B |
| Native Audio | ✓ SFX / Ambient / Narration | ✓ | ✗ | ✓ |
| Inference Steps | 8 steps (no CFG) | Undisclosed | Undisclosed | Undisclosed |
| Input Modalities | Text / Image | Text / Image / Audio / Video | Text / Image / Video | Text / Image / Video / Audio |
| Resolution | 1080p | Undisclosed | Up to 1080p | 1080p |
| License | Open-source (commercial) | Proprietary | Proprietary | Apache 2.0 |
Benchmark Evaluation
Based on 2,000 human ratings across visual quality, text alignment, physical realism, and word error rate.
| 模型 | Visual Quality | Text Alignment | Physical Realism | WER % (lower is better) |
|---|---|---|---|---|
| Happy Horse 1.0 | 4.8 | 4.18 | 4.52 | 14.60 |
| LTX 2.3 | 4.76 | 4.12 | 4.56 | 19.23 |
How to Use HappyHorse's Audio Capabilities
HappyHorse's native audio system generates in sync with video frames — no post-production dubbing steps required.
Three Audio Generation Modes
Sound Effects (SFX)
Sounds produced by object interactions in the scene — hoofbeats, water flow, wind, footsteps, etc.
A brown horse galloping across the prairie, the sound of hooves on wet grass clearly audible, birdsong in the distanceDescribe specific physical actions in your prompt — the AI will automatically infer and generate corresponding sound effects
Ambient Audio
Background sounds that create spatial presence and immersion — forest birdsong, city noise, ocean waves, etc.
A bamboo forest at dawn, a gentle breeze rustling the leaves, a distant stream babbling, occasional birdsongDescribe the time, location, and natural environment of the scene — the AI will automatically match appropriate ambient audio
Narration
Character dialogue or voiceover narration, natively generated in Chinese, English, Japanese, Korean, German, and French.
A man in a suit faces the camera and says in Mandarin: Welcome to the world of tomorrowSpecify the language and spoken content in your prompt, e.g., 'say in Japanese...' or 'English narration introducing...'
Best Practices
- Explicitly describe the audio type you want in your prompt (SFX / ambient / narration)
- Use specific action descriptions rather than abstract terms, e.g., 'hooves striking dirt' instead of 'horse sounds'
- Place the narration language tag at the start, e.g., '[English Narration] A chef introduces...'
- The more your ambient audio matches the visual scene, the higher the generation quality
- Avoid requesting too many simultaneous audio elements in a single prompt
HappyHorse Prompt Best Practices
Master the technique of joint video+audio prompting for more precise generation results.
Video + SFX Combined Template
[Visual] [Scene description], [subject] [action] in [environment]
[SFX] [specific sound 1], [specific sound 2], [background sound]
[Camera] [movement], [shot type]Why it works: Layering visual, sound, and camera descriptions separately lets the AI accurately target each dimension of generation
Best for: nature scenes, action sequences, product showcases
Multilingual Narration Template
[Language tag] e.g. [English Narration] / [中文旁白] / [日本語ナレーション]
[Character] [appearance description], facing camera, expression [description]
Says: [exact dialogue content]
Background: [scene description]Why it works: Placing the language tag first ensures the model prioritizes language recognition; more specific dialogue content leads to more accurate generation
Best for: product introductions, educational content, multilingual marketing, roleplay
Environmental Immersion Template
[Time] at [location], [visual description]
[Ambient layer 1]: [specific description]
[Ambient layer 2]: [specific description]
[Overall atmosphere], [emotional tone]Why it works: Describing ambient audio in layers creates spatial depth — the generated audio has more three-dimensional presence
Best for: mood videos, meditation content, ASMR-style, scene building
Image-to-Video + Audio Template
Based on [reference image description], generate dynamic video
Animation: [specific motion description]
Audio to match: [corresponding sound description]
Camera: [movement style]
Preserve the [color/style/composition] of the reference imageWhy it works: Explicitly stating the direction of change from image to motion, paired with matching audio requirements, gives the AI clear targets
Best for: animating illustrations, product image demos, art image video conversion
HappyHorse Frequently Asked Questions
What is HappyHorse?
HappyHorse is a fully open-source unified video and audio generation model. It uses a single Transformer Transfusion architecture to support text-to-video and image-to-video, natively generating synchronized sound effects, ambient audio, and narration. Just 8 inference steps produce a 720p@24fps 5-second video.
How is it different from other open-source video models?
Three key differentiators: (1) Native unified audio generation — video and audio are produced simultaneously, no post-dubbing required; (2) 8-step no-CFG inference — approximately 6× faster than traditional diffusion models; (3) Fully open-source — not just weights, but also distilled model, super-resolution module, and complete inference code.
How does native audio generation work?
HappyHorse uses the Transfusion unified architecture, co-modeling visual frames and audio waveforms in a single inference pass. Both share the Transformer's attention mechanism, ensuring strict audio-visual synchronization. It's not video-first-then-dub — it's true co-generation.
Which languages does narration generation support?
Currently supports native narration and dialogue generation in seven languages: Chinese (Mandarin), Cantonese, English, Japanese, Korean, German, and French. Add a language tag to your prompt (e.g., [Chinese Narration] or [English Narration]) to specify the language.
How do I run HappyHorse locally?
HappyHorse is fully open-source. Download the base model weights, distilled model, and inference code from the official GitHub repository. Recommended config: GPU with at least 16GB VRAM (the distilled model can run on lower specs). Full environment setup documentation is provided officially.
Is commercial use free?
HappyHorse is fully open-source, with the base model and inference code freely available for both academic research and commercial use. For the specific license, refer to the LICENSE file in the official GitHub repository.