HappyHorse 1.0

open-sourcemultilinguallip-sync

ATH-AI (ex-Alibaba Taotian Lab) · Unified Single-Stream Transformer · v1.0verifiedVerified

$0.000/sec

Resolution

1080p

Duration

5–10s

Providers

—

Text-to-VideoImage-to-VideoAudioMulti-ShotLipsync

Why HappyHorse 1.0?

thumb_upStrengths

  • AA Arena #1 in both Text-to-Video (ELO 1,347) and Image-to-Video (ELO 1,406) — largest gap over #2 in leaderboard history
  • Fully open-source with commercial licensing — 15B parameter weights, distilled models, and inference code on GitHub
  • Native 7-language lip-sync (CN/EN/JP/KR/DE/FR) generated in a single pass with video
  • Fast inference — 1080p video in ~38 seconds on a single H100 via 8-step denoising
  • Unified single-stream Transformer architecture handles audio + video without separate pipelines

infoLimitations

  • No public API or verified third-party provider pricing yet — FAL.ai, WaveSpeed, and Replicate have no confirmed support
  • Shorter max duration (10s) than competitors like Kling v3 (15s) and SkyReels V4 (15s)
  • No video-to-video editing, camera control presets, or motion brush capabilities
  • Very new (April 2026) with limited community testing — real-world reliability unproven at scale
  • Weights announced but not yet publicly released as of April 10, 2026 — GitHub repo shows 'coming soon'

auto_fix_highPrompt Guide

  1. 1Front-load key visuals — the model applies disproportionate attention to the first ~40 tokens. Place camera direction and primary subject before secondary details.
  2. 2Keep prompts concise — HappyHorse responds better to shorter, clearer prompts than long-winded creative descriptions. Aim for 20-50 tokens.
  3. 3Describe observable elements — use literal positioning, lighting, and movement instead of abstract emotional language. 'Wide tracking shot through pine trees with morning side light' beats 'a magical forest scene.'
  4. 4Leverage native audio — indicate ambient sounds, dialogue, and tone in the prompt. The model generates synchronized audio in a single pass without separate audio steps.
  5. 5Use reference images for consistency — for image-to-video, the model intelligently animates stills with natural motion and camera movement. Provide high-quality reference frames.
  6. 6Iterate rapidly — generation takes ~38 seconds on H100, so experiment with small word swaps and reordering to compare outputs quickly.

✓ Do this

  • Structure prompts as: Camera/Framing + Subject + Action + Environment + Audio/Mood
  • For lip-sync across languages (CN/EN/JP/KR/DE/FR), specify language and vocal quality in brackets: [Speaker, warm female voice, Japanese]
  • Landscapes and simple environments produce the most consistent results in early testing
  • For multi-shot storytelling, maintain consistent character and environment descriptions across shots
  • Use negative descriptions sparingly — the model handles exclusion less reliably than inclusion

✗ Avoid this

  • No public API yet — only accessible via official demo at happyhorse-ai.com with daily generation limits
  • Complex multi-character scenes with overlapping motion may produce artifacts
  • Max 10-second duration limits narrative development compared to 15-second competitors
  • Text rendering within video frames is unreliable
  • Camera control is less precise than dedicated camera-control models like Kling v3

Example Prompts

Landscape / Nature

“Cinematic drone shot of mountain landscape at sunset, camera slowly descending over a misty lake, golden hour light reflecting off water surface, ambient sounds of wind and distant birds.”

Character / Multilingual Lip-sync

“Close-up of a woman in a red coat walking through a snowy Tokyo street at night, neon reflections on wet pavement, shallow depth of field. [Woman, soft Japanese]: 'Yuki ga futte iru.'”

Product / Commercial

“Product shot of a ceramic coffee mug on a wooden table, steam rising from the cup, morning sunlight streaming through a window, the quiet hum of a coffee shop.”

Based on the official prompt guide →

FAQexpand_more

How much does HappyHorse 1.0 cost?

From $0.000/sec. A 5-second video ≈ $0.00.

How do I get good results with HappyHorse 1.0?

Front-load key visuals — the model applies disproportionate attention to the first ~40 tokens. Place camera direction and primary subject before secondary details. See the prompt guide below.