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✨ ViQ Weights ✨

Text-Aligned Visual Quantized Representations at Any Resolution

Xumin Yu^1,* Zuyan Liu^1,2,* Zhenyu Yang^1,2,* Yuhao Dong³
Shengsheng Qian⁴ Jiwen Lu² Han Hu¹ Yongming Rao^1,†

GitHub

This repository hosts the pretrained model weights for ViQ. For the inference / training / weight-conversion code, see the main repo: https://github.com/yuxumin/ViQ.

ViQ is trained in two stages, and this repository provides weights for both stages:

Folder	Stage	What it is
`anyres_vit/`	Stage 1	Text-aligned, any-resolution continuous SigLIP2 ViT encoders
`ViQ/`	Stage 2	Discrete ViQ tokenizers (multiple FSQ codebook sizes)

📦 `anyres_vit/` — Stage 1 (Any-Resolution ViT)

The text-aligned, any-resolution ViT encoders produced after Stage 1 pre-training. Two backbone sizes are released:

Size	Backbone	File
400M	SigLIP2-SO400M	`anyres_vit/so400m/siglip2_so400m_anyres_s4.pth`
1B	SigLIP2-g	`anyres_vit/giant1b/siglip2_g_anyres_s4.pth`

🔢 `ViQ/` — Stage 2 (Discrete Tokenizers)

The discretized ViQ tokenizers produced after Stage 2, released in several FSQ codebook sizes. Each converted_<size>/ folder contains the ViQ-inference-format weights:

Folder	Codebook size	FSQ levels
`ViQ/converted_2k/`	2304	`[8, 8, 4, 3, 3]`
`ViQ/converted_4k/`	4096	`[8, 8, 4, 4, 4]`
`ViQ/converted_8k/`	8192	`[8, 8, 8, 4, 4]`
`ViQ/converted_16k/`	15360	`[8, 8, 8, 6, 5]`
`ViQ/converted_64k/`	64000	`[8, 8, 8, 5, 5, 5]`

Each folder contains:

converted_<size>/
├── model_viq_fsq_<size>.pth   # ViQ encoder + Position-Aware FSQ head
├── embedder.pth               # discrete codes -> MLLM features
└── index_drawer.pth           # discrete codes -> VAE latent / reconstruction

🚀 Usage

Clone the code repo, then point inference at the downloaded weights:

python

from ViQ import load_viq

vq = load_viq('16k')
indices, sizes = vq.forward_indices(images)             # encode -> discrete codes
feats = vq.embedder(indices)                            # codes -> MLLM features
_, vae_latent, recon_np = vq.drawer(indices, sizes)     # codes -> reconstructed image

Download the weights with:

huggingface-cli download XuminYu/ViQ_weights --local-dir ViQ_weights

📚 Citation

bibtex

@article{yu2026viq,
  title   = {ViQ: Text-Aligned Visual Quantized Representations at Any Resolution},
  author  = {Yu, Xumin and Liu, Zuyan and Yang, Zhenyu and Dong, Yuhao and Qian, Shengsheng and Lu, Jiwen and Hu, Han and Rao, Yongming},
  journal = {arXiv preprint arXiv:xxxx.xxxxx},
  year    = {2026}
}

XuminYu/ViQ_weights