onnxruntime

README.md

@@ -1 +1,149 @@
-python_version: 3.10
+---
+title: Virtual Try-On
+emoji: 👕
+colorFrom: blue
+colorTo: pink
+sdk: gradio
+app_file: app.py
+pinned: false
+python_version: 3.10
+---
+
+# 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>&ensp;
+  <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>&ensp;
+  <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>&ensp;
+  <a href=""><img src='https://img.shields.io/badge/arXiv-Coming%20Soon-b31b1b?style=flat&logo=arXiv&logoColor=b31b1b' alt='arXiv'></a>&ensp;
+  <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](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))
+