Deployment

A user opens the Taobao app, picks a model photo, and drops in six reference images: a coat, an inner shirt, pants, shoes, a hat and a bag. They tap a button. Less than seven seconds later, a fresh photo appears — same face, same background, every garment placed correctly with the coat unzipped, revealing the inner shirt. Multiply this by tens of millions of requests per service window, and you get a sense of what Tstars-Tryon 1.0 is solving. This is not the lab-clean VITON-HD setting where one t-shirt gets pasted onto a fashion model in a studio. This is virtual try-on at e-commerce scale, on real-world photos, with stacked outfits and accessories — and it is running today. ...

Imagine you want a model that can actually use your phone — tap, swipe, type, navigate apps, book a flight. The model exists. The benchmarks exist. So why, in 2026, can you still not pip install a GUI agent and have it do anything on your real device? The answer is almost never the model. It is the infrastructure around the model: the training environment, the evaluation harness, and the deployment stack, each of which is typically closed, fragmented, or both. ...