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FASHN VTON v1.5: Efficient Maskless Virtual Try-On in Pixel Space
| Date | Stars |
|---|---|
| 2026-07-24 | 265 |
| 2026-07-25 | 267 |
| 2026-07-28 | 272 |
| 2026-07-30 | 272 |
| 2026-08-06 | 272 |
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# FASHN VTON v1.5: Efficient Maskless Virtual Try-On in Pixel Space
<div align="center">
<a href="https://fashn.ai/research/vton-1-5"><img src='https://img.shields.io/badge/Project-Page-1A1A1A?style=flat' alt='Project Page'></a> 
<a href='https://huggingface.co/fashn-ai/fashn-vton-1.5'><img src='https://img.shields.io/badge/Hugging%20Face-Model-FFD21E?style=flat&logo=HuggingFace&logoColor=FFD21E' alt='Hugging Face Model'></a> 
<a href="https://huggingface.co/spaces/fashn-ai/fashn-vton-1.5"><img src='https://img.shields.io/badge/Hugging%20Face-Spaces-FFD21E?style=flat&logo=HuggingFace&logoColor=FFD21E' alt='Hugging Face Spaces'></a> 
<a href=""><img src='https://img.shields.io/badge/arXiv-Coming%20Soon-b31b1b?style=flat&logo=arXiv&logoColor=b31b1b' alt='arXiv'></a> 
<a href="LICENSE"><img src='https://img.shields.io/badge/License-Apache--2.0-gray?style=flat' alt='License'></a>
</div>
by [FASHN AI](https://fashn.ai)
Virtual try-on model that generates photorealistic images directly in pixel space without requiring segmentation masks.
<p align="center">
<img src="https://static.fashn.ai/repositories/fashn-vton-v15/results/hero_collage.webp" alt="FASHN VTON v1.5 examples" width="900">
</p>
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](Excerpt of 4,743 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c27f9990a00ecb60, topic:deep-learning, topic:pytorch
matched fp:c27f9990a00ecb60, topic:computer-vision, topic:pose-estimation
matched fp:c27f9990a00ecb60, topic:diffusion-models, topic:image-generation