Apppublic

hugging-apps/sift-vton

sourceHugging Facecc-by-nc-sa-4.0updated 1mo agoView on Hugging Face

1likes

App README

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.