Create Stunning Videos
Powered by HappyHorse
Bring your vision to life with AI video generation. Type a prompt or drop an image — get cinema-grade video with synchronized audio in minutes.
Core Capabilities
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.
Joint Video + Audio
Generates synchronized dialogue, ambient sound, and Foley alongside video frames.
8-Step DMD-2
Reduces denoising to just 8 steps without classifier-free guidance.
Multilingual Lip-Sync
Native support for 7 languages: English, Mandarin, Cantonese, Japanese, Korean, German, French.
1080p Output
5–8 second clips at 1080p in standard aspect ratios for social, advertising, and cinematic use.
Open & Self-Hostable
Base model, distilled model, super-resolution module, and inference code released openly.
How it works
From idea to cinematic video in three simple steps.
Write your prompt
Describe the video you want — or upload an image as a starting frame. Our AI understands cinematic direction, lighting, and mood.
Choose your settings
Pick the AI model, aspect ratio, resolution, and duration. Enable prompt enhancement for even better results.
Generate & share
Your video is ready in minutes. Download in 1080p, publish to the community gallery, or share via a unique link.
"HappyHorse 1.0" is the preferred video generation model on ZNIX.ai
HappyHorse 1.0 is a 15-billion parameter open-source AI video generation model that jointly produces video and synchronized audio from text or image prompts.
Joint Audio-Video Synthesis
Generate video and perfectly synchronized audio in a single pass — no post-processing needed.
15B Parameters
Massive model capacity enables cinema-grade visual quality with rich details and natural motion.
7-Language Lip-Sync
Native support for Chinese, English, Japanese, Korean, German, French, and Cantonese lip synchronization.
1080p Output
Full HD resolution output with 8-step DMD-2 distillation for fast, high-quality generation.
Why "HappyHorse 1.0" Was Chosen as the Preferred Model for ZNIX.ai
#1 in Text-to-Video
Achieved Elo 1333 in T2V (no audio), outperforming all competitors by +60 Elo points.
#1 in Image-to-Video
Achieved Elo 1392 in I2V (no audio), leading the benchmark with superior visual fidelity.
Open Source
Fully open-source model weights and code, enabling transparency and community innovation.
Unified Architecture
40-layer Transformer with sandwich layout — first/last 4 layers use modality-specific projections, middle 32 layers share parameters.
Video Showcase
Real examples generated by AI models on ZNIX.ai.
1990s action cartoon style — A young martial artist performs a firebending kata. The flames are hand-drawn with thick outlines and bold orange-yellow gradients. Dynamic camera swoops around the character.
Artificial Analysis Video Arena
Ranked by blind user voting — the standard public benchmark using Elo ratings.
T2V Leaderboard
| Rank | Model | Elo | Released |
|---|---|---|---|
| 🏆 #1 | HappyHorse-1.0 | 1333 | Apr 2026 |
| #2 | Seedance 2.0 | 1273 | Mar 2026 |
| #3 | SkyReels V4 | 1245 | Mar 2026 |
| #4 | Kling 3.0 Pro | 1241 | Feb 2026 |
| #5 | PixVerse V6 | 1240 | Mar 2026 |
Frequently Asked Questions
Answers to common questions about 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. It reached #1 globally in both Text-to-Video (Elo 1333) and Image-to-Video (Elo 1392) under blind user voting.
Users submit a prompt, the system generates outputs from two models, and users pick the one they prefer — without knowing which model made which. Votes feed into an Elo rating system based entirely on aggregate human preference under blind conditions.
As of early April 2026: Text-to-Video (no audio) #1 at Elo 1333, Image-to-Video (no audio) #1 at Elo 1392, Text-to-Video (with audio) #2 at Elo 1205, Image-to-Video (with audio) #2 at Elo 1161.
Seven languages: Chinese, English, Japanese, Korean, German, French, and Cantonese.
A unified self-attention Transformer with 40 layers in a sandwich layout. The first and last 4 layers use modality-specific projections, while the middle 32 layers share parameters across all modalities. DMD-2 distillation reduces sampling to 8 steps.
An NVIDIA H100 or A100 GPU with at least 48GB VRAM is recommended. Reported inference speeds are roughly 38 seconds for 1080p on an H100.