| --- |
| license: mit |
| pipeline_tag: image-feature-extraction |
| tags: |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
| --- |
| |
| # Model Card for HUVR |
|
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| Implicit neural representation Hyper-networks for Unified Visual Representation (HUVR) aim to unify visual modeling on two axes: embedding dimension, and task family. The embeddings generated by the models include both standard embeddings (e.g. 768 for ViT-B) as well as compressed embeddings (as small as 8-dim), which we call Tiny Tokens or TinToks. Trained with image reconstruction and distillation objectives, the embeddings support tasks including generation, classification, segmentation, reconstruction, and more. |
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| ## Details |
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| We provide 4 models, trained using distillation from DINOv3 and pixel-wise reconstruction on DataComp and ImageNet-22k. |
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| 3 models are ViT-B, with the same standard embedding dimension size (768) but different TinTok dimensions: 8-dim, 16-dim, and 32-dim. |
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| 1 model is a ViT-L, with the standard embedding dimension (1024) and 32-dim TinToks. |
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| The models are pre-trained at 256x256 resolution, and fine-tuned at mixed resolution (256 and 512), and with RoPE embeddings the models can support inference at a range of resolutions. The models take images as inputs, and process them as 16x16 patches, yielding a single global (cls) token and many patch tokens. With a 480x480 input image, the model would yield 901 tokens: 1 class token + 900 (30x30) patch tokens. |
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| ## Getting Started |
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| Please see our GitHub for more information: GitHub - tiktok/huvr: Hyper-networks for Unified Visual Representation (HUVR) use implicit neural re. |
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| ## Citation |
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| **BibTeX** |
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|
| ``` |
| @article{gwilliam2026HUVR, |
| title={Accelerate High-Quality Diffusion Models with Inner Loop Feedback}, |
| author={Gwilliam, Matthew and Wang, Xiao and Hu, Xuefeng and Yang, Zhenheng}, |
| journal={arXiv preprint arXiv:2601.14256}, |
| year={2026} |
| } |
| ``` |
