Virtual try-on

FASHN VTON

A rare open try-on model with a commercial license: mask-free, accepts a photo of the item on a model or a flat lay. Weights are about 2 GB.

Developer
FASHN AI, Israel
First release
Jan 2026
Latest release
Feb 2026
Sizes
972M
License
Commercial use allowedApache 2.0
Running
On your own serverNeeds a GPU
Industries
Retail and marketplaces
Deploy on your server Hugging Face GitHub

What it does

  • Product cards on a model without a photo shoot
  • Fitting room on a store website
  • Catalog from flat-lay clothing photos

Where it is used

Online apparel retailMarketplacesFashion brands

Hardware requirements

LaptopLaptop or regular PC, up to 8 GB of VRAM — smaller versions
no versions
1 GPUOne GPU with 16–80 GB — mid-size versions
fits
ClusterServer with several GPUs — flagship versions
no versions

Versions

  1. FASHN VTON v1.5

How to run it

On your own serverWeights are downloaded from Hugging Face and served with vLLM (load and API) or llama.cpp (modest hardware). The model then runs in a closed network with no per-request fees.Hugging Face
How much hardware you needCalculate the VRAM for the model size, context length and number of concurrent requests.Open the hardware calculator

I can set this up end to end: pick the model size, deploy it on your server and connect it to your systems.

Frequently asked questions

Can FASHN VTON be used in a commercial project?

Yes. License: Apache 2.0. It allows commercial use, but it is still worth having a lawyer review the license before launch.

What hardware does FASHN VTON need?

At minimum: One GPU with 16–80 GB — mid-size versions. Without a GPU the model is not practical. You can calculate the exact VRAM for your model size and context in the hardware calculator.

Does FASHN VTON support Russian?

Language does not matter for this model: it does not work with text.

Where can I download FASHN VTON and what does it cost?

The FASHN VTON weights are open and free to download. You only pay for the hardware it runs on and for the setup. Source links are at the bottom of this page.

How I deploy it for clients

  1. SelectionI pick the model size for your task and hardware and test it on your examples.
  2. DeploymentI deploy it on your server or in a closed network and provide an API.
  3. Fine-tuningI fine-tune it on your data (LoRA) or connect a knowledge base — whichever is cheaper for the task.
  4. IntegrationI connect it to your CRM, ERP, bot, website or team chat and set up monitoring.

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Details

Source: huggingface.co/fashn-ai/fashn-vton-1.5. Data checked against the model card on 22 Sep 2026. Have a lawyer review the license before commercial launch.