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.
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:
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
Citation
@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}
}