Happy Horse

Happy Horse 1.0 is an open-source AI model designed to generate synchronized video and audio content from text or image prompts. It uses a unified Transformer architecture with 15 billion parameters, enabling it to produce cinematic-quality 1080p clips with natural multilingual lip-sync in seven languages. The model targets developers, researchers, and businesses who want to create high-quality video content with synchronized sound without relying on post-production dubbing.

The model's unique value lies in its joint video and audio generation capabilities, which include dialogue, ambient sounds, and Foley effects generated simultaneously. This integration reduces the need for separate audio editing and ensures better alignment between visuals and sound. Its open-source nature and commercial-use rights allow users to self-host, fine-tune, and deploy the model on their own infrastructure, providing flexibility and control.

Technically, Happy Horse 1.0 is built on a 40-layer self-attention Transformer with modality-specific layers at each end and shared layers in the middle. It employs an 8-step denoising distillation process that accelerates inference without sacrificing quality. The model supports FP8 quantization to reduce memory usage, enabling deployment on high-performance GPUs like NVIDIA H100 or A100 with at least 48GB VRAM.

Benchmarks show that Happy Horse leads in visual quality, prompt alignment, and physical realism compared to other open models, while achieving the lowest word error rate in lip-sync. It supports English, Mandarin, Cantonese, Japanese, Korean, German, and French, making it suitable for global applications. The team behind Happy Horse emphasizes transparency, publishing detailed technical reports and inference code to support reproducibility and responsible use.

Overall, Happy Horse 1.0 offers a powerful, flexible, and open solution for generating synchronized video and audio content, ideal for social media, advertising, and cinematic projects where quality and lip-sync accuracy are critical.