FASHN VTON v1.5: Efficient Maskless Virtual Try-On in Pixel Space

       

by 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.

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Local Installation

We recommend using a virtual environment:

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:

pip uninstall onnxruntime-gpu && pip install onnxruntime

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Model Weights

Download the required model weights (~2 GB total):

python scripts/download_weights.py --weights-dir ./weights

This downloads:

• model.safetensors — TryOnModel weights from HuggingFace
• 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.

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Usage

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

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 for additional options.

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Categories

Category	Description
tops	Upper body: t-shirts, blouses, jackets
bottoms	Lower body: pants, skirts, shorts
one-pieces	Full body: dresses, jumpsuits

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API

FASHN provides a suite of fashion AI APIs including virtual try-on, model generation, image-to-video, and more. See the docs to get started.

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Citation

If you use FASHN VTON v1.5 in your research, please cite:

@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}
}

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License

Apache-2.0. See LICENSE for details.

Third-party components:

• DWPose (Apache-2.0)
• YOLOX (Apache-2.0)
• fashn-human-parser (License)