Happy Horse 1.0 API Wrapper
https://github.com/anil-matcha/awesome-happy-horse-1.0-api-and-prompt- Category
- AI Tools
- Rank
- No. 1600Tools index
Previous survey · No. 1606 ·
- Pricing
- Open Source
- Type
- TOOL
- Builder
- Anil-matcha
- GitHub
- 8 stars
- Date
About
Python wrapper for Alibaba's Happy Horse 1.0 video model — generate native 1080p HD video with integrated audio from text or images.
What it does
This client submits remote video jobs through MuAPI, then polls for completion or accepts webhooks. It supports prompt-driven creation, start-frame animation, multi-image references, natural-language video editing, file uploads, multiple aspect ratios, and two resolution tiers. An MCP server exposes the same capabilities to compatible assistants.
Why it's ranked here
The feature surface is unusually broad for a small wrapper, especially reference-guided generation and instruction-based editing with audio controls. Input validation and polling reduce integration work. The decisive drawback is availability: the repository says API-key requests return 403 during closed beta, so the public interface can be wired up but not yet used successfully.
What's good
The client validates aspect ratio, duration, resolution, audio mode, and reference-image counts before submission. It supports explicit seeds for reference generation and editing, optional completion webhooks, local media uploads, and configurable polling with a timeout. Both Python and MCP interfaces cover the core workflow, while 720p offers a documented lower-cost option.
Tradeoffs
Closed-beta access makes the wrapper largely preparatory until the service becomes generally available. Network requests have no explicit timeout, and retries or backoff are absent. Image animation silently keeps only the first supplied image. Packaging claims Python 3.7 support, but the MCP server uses type syntax requiring newer Python versions. Generated media remains hosted by MuAPI.
How to use it well
It suits Python teams or assistant workflows preparing short-form video experiments around a managed remote service. Validate prompts and assets locally, submit asynchronously, prefer webhooks in production, and use polling for scripts or prototypes. Choose 720p while iterating, then move selected work to 1080p. It does not provide local model inference, media storage ownership, or a general video editor.
Technical notes+
happyhorse_api.py implements HappyHorseAPI with requests, dotenv key loading, endpoint selection, validation, upload support, result polling, and a blocking wait_for_completion; HTTP calls omit request timeouts and retry handling. mcp_server.py exposes six FastMCP tools and serializes results as indented JSON, but annotations such as list[str] | None conflict with the Python 3.7 floor declared in setup.py and pyproject.toml. Both packaging files declare the same two modules and dependencies from requirements.txt. .env.example documents MUAPI_API_KEY, while LICENSE supplies MIT terms.
Observed
- License
- MIT License
- Primary language
- Python
- Installation
- Published package installable with pip, with source installation through requirements.txt
- Interfaces
- Python library, MCP server, and remote HTTP API
- Platform support
- Package metadata declares operating-system independence
- Python compatibility metadata
- Requires Python 3.7 or newer
- Runtime dependencies
- requests, python-dotenv, and mcp with CLI extras
- Authentication
- MuAPI key supplied directly or through the MUAPI_API_KEY environment variable
Read from README.md, setup.py, pyproject.toml, requirements.txt, mcp_server.py, happyhorse_api.py, LICENSE, .env.example.
What it can do
Generate HD video from text description
Text prompt describing desired video content → 1080p HD video with integrated audio
Generate HD video from image
Image file → 1080p HD video with integrated audio
Convert text to native video format
Text description → Native format 1080p video file
Transform static images into video content
Static image → Dynamic video with audio track
Access Alibaba Happy Horse 1.0 model via Python
Python API calls with text or image parameters → Video generation results through API responses
Tags
Tech Stack
Comments (0)
No comments yet
Editorially curated, with community endorsements as a secondary signal. Corrections welcome.