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Huvr Vitl16 Tintok32

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...

ArtOtherVersionRevision 4e69554BBewertung

Modellquelle

Art
Other
Dateigröße
1,32 GB
Version
vRevision 4e69554
Aktualisiert
10. Okt. 2026
Downloads
15
Quelle
Hugging Face

Quellenbeschreibung

Model Card for HUVR

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.

Details

We provide 4 models, trained using distillation from DINOv3 and pixel-wise reconstruction on DataComp and ImageNet-22k.

3 models are ViT-B, with the same standard embedding dimension size (768) but different TinTok dimensions: 8-dim, 16-dim, and 32-dim.

1 model is a ViT-L, with the standard embedding dimension (1024) and 32-dim TinToks.

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.

Getting Started

Please see our GitHub for more information: GitHub - tiktok/huvr: Hyper-networks for Unified Visual Representation (HUVR) use implicit neural re.

Citation

BibTeX

@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}
}

Quellen

1 Quelle
Hugging FacePrimärVersion 4e695544b6c7ff4c708f2c979f4c85413d30fdfb
huggingface.co/tiktok/huvr-vitl16-tintok32/tree/4e695544b6c7ff4c708f2c979f4c85413d30fdfb

Verifiziert 10. Okt.

Modellartefakte

1 Artefakt

model.safetensors

safetensors · 1,32 GB · SHA-256 64c5d1b884d9…a12e · Hugging Face

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Quellenauszüge

3 Auszüge

--- license: mit pipeline_tag: image-feature-extraction tags: - model_hub_mixin - pytorch_model_hub_mixin --- # Model Card for HUVR 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. ## Details We provide 4 models, trained using distillation from DINOv3 and pixel-wise reconstruction on DataComp and ImageNet-22k. 3 models are ViT-B, with the same standard embedding dimension size (768) but different TinTok dimensions: 8-dim, 16-dim, and 32-dim. 1 model is a ViT-L, with the standard embedding dimension (1024) and 32-dim TinToks. 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. ## Getting Started Please see our GitHub for more information: GitHub - tiktok/huvr: Hyper-networks for Unified Visual Representation (HUVR) use implicit neural re. ## Citation **BibTeX** ``` @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} } ```

Source context: 15 downloads · 4 likes · Pipeline image-feature-extraction · Repo tiktok/huvr-vitl16-tintok32

Source context: 19 downloads · 4 likes · Pipeline image-feature-extraction · Repo tiktok/huvr-vitl16-tintok32