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HappyHorse 1.0

HappyHorse — Open-Source AI Video Generation, Reimagined

HappyHorse 1.0 is the official open-source AI video generation model from the Happy Horse team — a 15-billion-parameter unified Transformer that jointly produces video and synchronized audio from text or image prompts, with cinematic 1080p quality and seven-language lip-sync.

Use HappyHorse Now Explore Features
15B
Parameters
40
Transformer Layers
38s
5s @ 1080p on H100
7
Lip-Sync Languages

See Happy Horse in Action

Sample clips generated by Happy Horse 1.0 — click play to watch.

Sci-fi Scene

"A robot dancing on the moon with earth in the background"

Natural Scene

"An elder on a mountain peak overlooking the valley"

Urban Scene

"A cyberpunk city street at night with neon lights"

All samples are 5-8 second 1080p clips generated with Happy Horse 1.0

Core Capabilities of HappyHorse

A unified multimodal architecture purpose-built for joint video and audio generation.

Unified Transformer

40-layer self-attention network with 4 modality-specific layers on each end and 32 shared layers — single-stream processing with per-head gating for stable training.

Joint Video + Audio

Generates synchronized dialogue, ambient sound, and Foley alongside video frames — no post-production dubbing required.

8-Step DMD-2 Distillation

Reduces denoising to just 8 steps without classifier-free guidance, accelerated further by the in-house MagiCompiler runtime.

Multilingual Lip-Sync

Native support for English, Mandarin, Cantonese, Japanese, Korean, German, and French with industry-leading low Word Error Rate.

1080p Output

5–8 second clips at 1080p in standard aspect ratios (16:9, 9:16) — suitable for social, advertising, and cinematic use cases.

Open & Self-Hostable

Base model, distilled model, super-resolution module, and inference code released openly with commercial-use permission.

Benchmarks & Performance of HappyHorse

Based on 2,000 human-rated comparisons, Happy Horse 1.0 leads on visual quality, prompt alignment, and physical realism while delivering the lowest Word Error Rate among open competitors. Happy Horse was ranked #1 globally on the Artificial Analysis Video Arena with an Elo score of 1333.

Model Visual Alignment Physical WER (%)
OVI 1.1 4.73 4.10 4.41 40.45
LTX 2.3 4.76 4.12 4.56 19.23
Happy Horse 1.0 #1 4.80 4.18 4.52 14.60

Win rate: 80.0% vs OVI 1.1 · 60.9% vs LTX 2.3

Compared to Other Models

How Happy Horse 1.0 stacks up against the leading AI video generation models of 2026.

Model Developer Params Inputs License
Happy Horse 1.0 Happy Horse Team ~15B Text / Image Open + Commercial
Seedance 2.0 ByteDance Seed Undisclosed Text / Image / Audio / Video Proprietary
Ovi 1.1 Character AI & Yale ~11B Text (Image opt.) Open Source
LTX 2.3 Lightricks 22B Text / Image / Video / Audio Open Source
Open + Commercial
Open Source
Proprietary

Deploy HappyHorse 1.0

Happy Horse 1.0 runs on high-performance GPUs such as NVIDIA H100 or A100 (≥48GB VRAM recommended). FP8 quantization and the 8-step distilled checkpoint reduce memory footprint for single-GPU deployment.

Bash 
# Clone & install
git clone https://github.com/happy-horse/happyhorse-1.git
cd happyhorse-1
pip install -r requirements.txt

# Download weights
bash download_weights.sh

# Generate
python demo_generate.py --prompt "a robot dancing on the moon" --duration 5
Python 
from happyhorse import HappyHorseModel

model = HappyHorseModel.from_pretrained("happy-horse/happyhorse-1.0")

video, audio = model.generate(
    prompt="an elder on a mountain peak overlooking the valley",
    duration_seconds=5,
    fps=24,
    language="en",
)

video.save("output.mp4")
audio.save("output.wav")

GPU Memory

≥48GB VRAM (H100/A100)

Generation Speed

~38s for 5s clip on H100

Optimization

FP8 Quantization + 8-Step

Built by Researchers, Trusted by Builders

HappyHorse is published and maintained by the HappyHorse research team, with a transparent technical report covering architecture, training methodology, distillation, benchmark protocols, and known limitations. We publish reproducible inference code and are committed to the responsible release of generative video technology.

Expertise

Authored by practitioners working on multimodal Transformers, diffusion distillation, and large-scale video pretraining.

Transparency

Open weights, open inference code, and published benchmark methodology — verifiable by independent researchers.

Responsibility

We support content provenance, watermarking, and downstream moderation. Users are expected to comply with applicable AI regulations.

View our open-source repository

Blog

Latest updates, guides, and notes from the HappyHorse team.

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Frequently Asked Questions

Answers to common questions about Happy Horse 1.0.

What is Happy Horse 1.0?
Happy Horse 1.0 is a 15B-parameter open-source AI video generation model that jointly produces video and synchronized audio from text or image prompts.
Is Happy Horse free for commercial use?
Yes. Happy Horse is released as open source with commercial-use rights, including the base model, distilled model, super-resolution module, and inference code.
What hardware do I need to run Happy Horse?
An NVIDIA H100 or A100 GPU with at least 48GB VRAM is recommended. A 5-second 1080p clip generates in roughly 38 seconds on H100.
Which languages does Happy Horse support for lip-sync?
Seven languages: English, Mandarin, Cantonese, Japanese, Korean, German, and French — with industry-leading low Word Error Rate.
How does Happy Horse compare to OVI and LTX?
Happy Horse 1.0 outperforms OVI 1.1 (80.0% win rate) and LTX 2.3 (60.9% win rate) across visual quality, prompt alignment, and Word Error Rate.

Have more questions? Submit an issue on GitHub