Summary Happy Horse 1.0 targets AI video generation with native joint audio-video synthesis. It aims to provide synchronized video and audio output in a single pass, benefiting AI researchers and content creators seeking more coherent and aligned media generation.

How It Works The core is a reported 15B-parameter unified self-attention Transformer processing text, image, video, and audio tokens concurrently. This contrasts with multi-component approaches, aiming for parameter efficiency and deeper cross-modal reasoning via a sandwich layer layout (modality-specific outer layers, shared inner layers). DMD-2 distillation enables generation in ~8 denoising steps without classifier-free guidance, significantly reducing inference time compared to standard diffusion models.

Quick Start & Requirements Happy Horse 1.0 is pre-release; official model weights, inference code, and a repository are not yet published. Information is sourced from unverified community data. Inference is reported to require high-end hardware like an NVIDIA H100. Official demos and updates are available at happyhorses.io.

Highlighted Details

• Native joint audio-video generation, producing synchronized sound and video in one pass.
• Reported 1080p video generation in ~38 seconds on an NVIDIA H100 using an 8-step distillation process.
• Native lip-sync support for 6 languages: English, Mandarin, Japanese, Korean, German, and French.
• Unified text-to-video and image-to-video capabilities within a single model.

Maintenance & Community This repository is a personal information collection, not the official project. Official updates and demos are hosted at happyhorses.io.

Licensing & Compatibility The license is unannounced but expected to be open-source, potentially permitting commercial use. Definitive terms await official release.

Limitations & Caveats The project is pre-release; model weights, code, and an official repository are unavailable. All technical details and performance claims stem from unverified community sources and alleged leaks, lacking official confirmation or peer review. Reported generation metrics are not independently reproduced.