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Huvr Vitb16 Tintok8

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...

TipoOtherVersiónRevision 11c48a6Bcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
477 MB
Versión
vRevision 11c48a6
Actualizado
10 oct 2026
Descargas
14
Fuente
Hugging Face

Descripción de la fuente

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}
}

Fuentes

1 fuente
Hugging FacePrincipalVersión 11c48a60cfb0c417dc558963d98c0c16849fae45
huggingface.co/tiktok/huvr-vitb16-tintok8/tree/11c48a60cfb0c417dc558963d98c0c16849fae45

Verificado 10 oct

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 477 MB · SHA-256 80d5af0d2d9b…02d9 · Hugging Face

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Extractos de fuentes

3 extractos

--- 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: 14 downloads · 1 likes · Pipeline image-feature-extraction · Repo tiktok/huvr-vitb16-tintok8

Source context: 20 downloads · 1 likes · Pipeline image-feature-extraction · Repo tiktok/huvr-vitb16-tintok8