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A newer version of the Gradio SDK is available: 6.26.0
title: SIFT-VTON
emoji: 👕
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 6.24.0
app_file: app.py
short_description: Virtual try-on with SIFT correspondence supervision
python_version: '3.10'
startup_duration_timeout: 1h
license: cc-by-nc-sa-4.0
SIFT-VTON
Interactive demo of SIFT-VTON: Geometric Correspondence Supervision on Cross-Attention for Virtual Try-On (ICPR 2026).
- Paper: https://huggingface.co/papers/2605.01296
- Model: https://huggingface.co/takesuke/SIFT-VTON
- Code: https://github.com/takesukeDS/SIFT-VTON
The sampling path mirrors the authors' inference_hf.py (PLMS, 50 steps, cfg 1.5,
--start_from_noised_agn, --repaint) at the training resolution of 512×384. Because the model
consumes VITON-HD-style inputs (agnostic person image + agnostic mask + DensePose), the Space runs
that preprocessing chain on the fly — SCHP human parsing, OpenPose body keypoints and
DensePose (densepose_rcnn_R_50_FPN_s1x) — so arbitrary photographs can be used.
Credits
The preprocessing code (vendored detectron2 / densepose, human parsing, OpenPose, the VITON-HD
agnostic-mask builder) and the preprocessing checkpoints come from
IDM-VTON (CC BY-NC-SA 4.0). The example person and
garment images are VITON-HD test-set items redistributed from the same Space under CC BY-NC-SA 4.0.
The SIFT-VTON weights are licensed CC BY-NC-SA 4.0 — this demo is for non-commercial research use.