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LICENSE

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README.md

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+<p align="center">
+ <img src="assets/hunyuan_logo.png" width="400"/> <br>
+</p>
+
+<div align="center">
+
+# ✨ ViQ Weights ✨
+### Text-Aligned Visual Quantized Representations at Any Resolution
+
+<p align="center">
+    <a href="https://yuxumin.github.io/">Xumin Yu</a><sup>1,*</sup>&emsp;
+    Zuyan Liu<sup>1,2,*</sup>&emsp;
+    Zhenyu Yang<sup>1,2,*</sup>&emsp;
+    Yuhao Dong<sup>3</sup>
+    <br>
+    Shengsheng Qian<sup>4</sup>&emsp;
+    Jiwen Lu<sup>2</sup>&emsp;
+    <a href="https://ancientmooner.github.io/">Han Hu</a><sup>1</sup>&emsp;
+    <a href="https://raoyongming.github.io/">Yongming Rao</a><sup>1,†</sup>
+</p>
+
+[![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-ViQ__weights-ffc107?color=ffc107&logoColor=white)](https://huggingface.co/XuminYu/ViQ_weights)
+&nbsp;&nbsp;
+[![GitHub](https://img.shields.io/badge/GitHub-ViQ-181717?logo=github)](https://github.com/yuxumin/ViQ)
+
+</div>
+
+---
+
+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/`](anyres_vit) | **Stage 1** | Text-aligned, any-resolution **continuous** SigLIP2 ViT encoders |
+| [`ViQ/`](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](https://github.com/yuxumin/ViQ), 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:
+
+```bash
+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}
+}
+```

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