# OpenTryOn

> Open-source AI toolkit for fashion tech and virtual try-on, with a CLI, MCP server, web studio and Python APIs over a unified model registry.

OpenTryOn is an open-source AI toolkit from TryOn Labs for fashion technology and virtual try-on. It puts virtual try-on, image and video generation and editing, multimodal understanding, background removal, preprocessing and datasets behind a single model registry. The latest release listed is v0.0.8, published on PyPI and GitHub.

## What It Is

OpenTryOn is a developer toolkit that wraps many third-party and local fashion-AI models behind one consistent interface. Users supply inputs such as a person image and a garment image, pick a model, and get generated try-on images, edited images, videos, text descriptions, body measurements, embeddings or decisions. It also includes garment and pose preprocessing, fashion datasets (Fashion-MNIST, VITON-HD, Subjects200K), Gradio demos and TryOnDiffusion research code.

## Four Ways to Run It

The same registry is exposed through four surfaces:
- A CLI of the form `opentryon <service> --model <model>`, with a `--dry-run` flag to verify wiring without calling an API.
- A FastMCP server that exposes every registry model as a tool for Claude, Cursor and other MCP clients.
- TryOn Studio, a Next.js playground with agent chat, a connect screen and capability screens for image, VTON, understand, video and background removal, talking to the MCP server over HTTP.
- Python adapters under `tryon.api` plus a single `invoke_model()` entry point.

## Model and Service Coverage

Services include virtual try-on (`vton`), text-to-image (`generate`), `edit`, `understand`, `video-generate`, `audio-to-video`, `generate-3d`, `bg-remove`, `tts`, `decide`, `classify`, `embed` and `measure`. Try-on adapters listed include FLUX VTO, Google Vertex Virtual Try-On, FASHN, Photoroom, Segmind and Kling AI, along with local-weight models such as Leffa and CatVTON. Local and GPU models need the optional `opentryon[local]` install.

## Setup Path

Install from PyPI with `pip install opentryon` or clone the repository and install into a conda environment, then copy `env.template` to `.env` and add the API keys for the providers you use. The docs list Python 3.10 or higher as a prerequisite and recommend a CUDA-capable GPU for best performance with local models.

## Licensing

The project is released under Creative Commons BY-NC 4.0, which permits non-commercial use with attribution. The license file gives contact@tryonlabs.ai for commercial licensing inquiries.

## Features
- Virtual try-on across many provider and local models
- Unified registry-driven CLI with --dry-run
- FastMCP server exposing every registry model as a tool
- TryOn Studio web playground with agent chat
- Image generation and editing
- Video generation, lip-sync and image-to-3D
- Multimodal image and video understanding
- Background removal with BEN2
- Body measurement from photo or questionnaire
- Embeddings and calibrated decision services
- Garment and pose preprocessing
- Fashion datasets and TryOnDiffusion research code
- OpenAPI/Swagger and Postman snapshots
- Gradio demos and Jupyter notebooks

## Integrations
Claude, Cursor, Google Vertex AI, FASHN, Photoroom, Segmind, Kling AI, FLUX, OpenAI GPT Image, Veo, Luma, Runway, ElevenLabs, Hugging Face

## Platforms
LINUX, WEB, API, DEVELOPER_SDK, CLI

## Pricing
Free

## Version
0.0.8

## Links
- Website: https://tryonlabs.github.io/opentryon/
- Documentation: https://tryonlabs.github.io/opentryon/
- Repository: https://github.com/tryonlabs/opentryon
- EveryDev.ai: https://www.everydev.ai/tools/opentryon