Add model card for NativeTok

README.md

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+---
+pipeline_tag: image-to-image
+---
+
+# NativeTok: Native Visual Tokenization for Improved Image Generation
+
+This repository contains the official weights for NativeTok, a framework that enforces causal dependencies during tokenization to improve generative modeling coherence and performance.
+
+[**Paper**](https://huggingface.co/papers/2601.22837) | [**GitHub**](https://github.com/wangbei1/Nativetok)
+
+## Introduction
+
+NativeTok consists of a Meta Image Transformer (MIT) for latent image modeling and a Mixture of Causal Expert Transformer (MoCET), where each lightweight expert block generates a single token conditioned on prior tokens and latent features. This approach addresses the mismatch between tokenization and generative modeling by embedding relational constraints within token sequences.
+
+## 📌 Model Checkpoints
+
+These weights are trained based on the MaskGIT architecture with OrderTok tokenization strategies.
+
+| File Name | Description | Resolution |
+| :--- | :--- | :--- |
+| **`maskgit128_ordertok.bin`** | MaskGIT | 256x256 |
+| **`Nativetok_128_300000_stage2.bin`** | Nativetok_128 checkpoint | 256x256 |
+
+## 🚀 Quick Start
+
+### Download Weights
+You can use `huggingface_hub` to download the weights directly:
+
+```python
+from huggingface_hub import hf_hub_download
+
+# Download the main weight
+checkpoint_path = hf_hub_download(
+    repo_id="wangbei1/Nativetok", 
+    filename="maskgit128_ordertok.bin"
+)
+
+print(f"Model downloaded to: {checkpoint_path}")
+```
+
+## Related Resources
+Base Framework (1D-Tokenizer): [1D-Tokenizer](https://yucornetto.github.io/projects/titok.html)
+
+## Citation
+
+```bibtex
+@article{wu2026nativetok,
+  title={NativeTok: Native Visual Tokenization for Improved Image Generation},
+  author={Bin Wu and Mengqi Huang and Weinan Jia and Zhendong Mao},
+  journal={arXiv preprint arXiv:2601.22837},
+  year={2026}
+}
+```