---
title: ViT-Up Feature Upsampler
emoji: 🔼
colorFrom: blue
colorTo: gray
sdk: gradio
sdk_version: "5.50.0"
app_file: app.py
short_description: DINOv3 feature upsampling with ViT-Up
python_version: "3.12"
startup_duration_timeout: "15m"
---

# ViT-Up: Faithful Feature Upsampling for Vision Transformers

This Space demonstrates **ViT-Up**, an implicit feature upsampler for Vision
Transformers that predicts backbone-aligned features at arbitrary continuous
image coordinates.

## How it works

1. **Input**: An image is padded to square, resized to 448×448, and normalised
   with ImageNet statistics.
2. **Backbone**: A DINOv3-S+ ViT backbone (loaded from the non-gated
   `timm/vit_small_plus_patch16_dinov3.lvd1689m` mirror) extracts multi-layer
   hidden states. LoRA adapters from the ViT-Up checkpoint are applied.
3. **Upsampling**: ViT-Up queries features at a dense grid of user-selected
   resolution (e.g. 112×112), producing high-resolution feature maps aligned
   with the backbone.
4. **Visualization**: The 3 principal components of the upsampled features are
   projected to RGB via PCA, showing the semantic structure learned by ViT-Up.

## Model

- **Paper**: [ViT-Up: Faithful Feature Upsampling for Vision Transformers](https://huggingface.co/papers/2606.14024)
- **Weights**: [Krispin/vit-up](https://huggingface.co/Krispin/vit-up)
- **Code**: [GitHub](https://github.com/krispinwandel/vit-up)
- **License**: CC-BY-NC-SA-4.0