Getting Started with Happy Horse

From zero to your first AI-generated video.

Note:As of April 8, 2026, Happy Horse weights and inference code are listed as “Coming Soon.” This guide is based on published documentation and will be updated once the full release is available.

Prerequisites

1
NVIDIA GPU with ≥48GB VRAM
H100 recommended, A100 supported. Consumer GPUs (RTX 4090 with 24GB) are insufficient for the full model.
2
Python 3.10+
Required for the inference code and dependencies.
3
CUDA 12.0+ and PyTorch 2.x
For GPU acceleration and model inference.
4
~30GB disk space
For model weights (base + distilled + super-resolution).

Installation

# Clone the repository
git clone https://github.com/happy-horse/happyhorse-1.git
cd happyhorse-1

# Install dependencies
pip install -r requirements.txt

# Download model weights
bash download_weights.sh

Your First Generation

Option 1: Command Line

python demo_generate.py \
  --prompt "a robot dancing on the moon" \
  --duration 5

Option 2: Python API

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")

What to Expect

Setting	Generation Time	Output
Default (256p)	~2s	Fast preview, lower resolution
With super-res (540p)	~8s	Good balance of speed and quality
Full quality (1080p)	~38s	Cinema-grade output

Tips for Best Results

• Start with single-character portrait prompts — this is where Happy Horse excels
• Keep prompts descriptive but concise — include subject, action, setting, and style
• Use the 256p mode for rapid iteration, then upscale your best results
• For lip-sync, specify the language parameter matching your prompt language
• Avoid complex multi-character scenes until the community develops optimization techniques