---
title: Virtual Try-On
emoji: 👕
colorFrom: blue
colorTo: pink
sdk: gradio
app_file: app.py
pinned: false
python_version: 3.10.20
---
# FASHN VTON v1.5: Efficient Maskless Virtual Try-On in Pixel Space
by [FASHN AI](https://fashn.ai)
Virtual try-on model that generates photorealistic images directly in pixel space without requiring segmentation masks.
This repo contains minimal inference code to run virtual try-on with the FASHN VTON v1.5 model weights. Given a person image and a garment image, the model generates a photorealistic image of the person wearing the garment. Supports both model photos and flat-lay product shots as garment inputs.
---
## Local Installation
We recommend using a virtual environment:
```bash
git clone https://github.com/fashn-AI/fashn-vton-1.5.git
cd fashn-vton-1.5
python -m venv .venv && source .venv/bin/activate
pip install -e .
```
**Note:** Installation includes `onnxruntime-gpu` for GPU-accelerated pose detection. Ensure CUDA is properly configured on your system. For CPU-only environments, replace with the CPU version:
```bash
pip uninstall onnxruntime-gpu && pip install onnxruntime
```
---
## Model Weights
Download the required model weights (~2 GB total):
```bash
python scripts/download_weights.py --weights-dir ./weights
```
This downloads:
- `model.safetensors` — TryOnModel weights from [HuggingFace](https://huggingface.co/fashn-ai/fashn-vton-1.5)
- `dwpose/` — DWPose ONNX models for pose detection
**Note:** The human parser weights (~244 MB) are automatically downloaded on first use to the HuggingFace cache folder. Set `HF_HOME` to customize the location.
---
## Usage
```python
from fashn_vton import TryOnPipeline
from PIL import Image
# Initialize pipeline (automatically uses GPU if available)
pipeline = TryOnPipeline(weights_dir="./weights")
# Load images
person = Image.open("examples/data/model.webp").convert("RGB")
garment = Image.open("examples/data/garment.webp").convert("RGB")
# Run inference
result = pipeline(
person_image=person,
garment_image=garment,
category="tops", # "tops" | "bottoms" | "one-pieces"
)
# Save output
result.images[0].save("output.png")
```
### CLI
```bash
python examples/basic_inference.py \
--weights-dir ./weights \
--person-image examples/data/model.webp \
--garment-image examples/data/garment.webp \
--category tops
```
**Note:** The pipeline automatically uses GPU if available. The try-on model weights are stored in bfloat16 and will run in bf16 precision on Ampere+ GPUs (RTX 30xx/40xx, A100, H100). On older hardware or CPU, weights are converted to float32.
See [`examples/basic_inference.py`](examples/basic_inference.py) for additional options.
---
## Categories
| Category | Description |
|----------|-------------|
| `tops` | Upper body: t-shirts, blouses, jackets |
| `bottoms` | Lower body: pants, skirts, shorts |
| `one-pieces` | Full body: dresses, jumpsuits |
---
## API
FASHN provides a suite of [fashion AI APIs](https://fashn.ai/products/api) including virtual try-on, model generation, image-to-video, and more. See the [docs](https://docs.fashn.ai/) to get started.
---
## Citation
If you use FASHN VTON v1.5 in your research, please cite:
```bibtex
@article{bochman2026fashnvton,
title={FASHN VTON v1.5: Efficient Maskless Virtual Try-On in Pixel Space},
author={Bochman, Dan and Bochman, Aya},
journal={arXiv preprint},
year={2026},
note={Paper coming soon}
}
```
---
## License
Apache-2.0. See [LICENSE](LICENSE) for details.
**Third-party components:**
- [DWPose](https://github.com/IDEA-Research/DWPose) (Apache-2.0)
- [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) (Apache-2.0)
- [fashn-human-parser](https://github.com/fashn-AI/fashn-human-parser) ([License](https://github.com/fashn-AI/fashn-human-parser?tab=readme-ov-file#license))
