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README.md

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-Image Tokenizers Needs Post-Training
+---
+pipeline_tag: image-feature-extraction
+---
+
+# Image Tokenizer Needs Post-Training
+
+This repository contains the official implementation and checkpoints for the paper [Image Tokenizer Needs Post-Training](https://huggingface.co/papers/2509.12474).
+
+Project page: https://qiuk2.github.io/works/RobusTok/index.html
+Code: https://github.com/qiuk2/RobusTok
+
+<div align="center">
+  <img src="https://github.com/qiuk2/RobusTok/raw/main/assets/teaser.png" alt="Teaser" width="95%">
+</div>
+
+---
+
+## TL;DR
+
+We present RobusTok, a new image tokenizer with a two-stage training scheme:
+
+Main training → constructs a robust latent space.
+
+Post-training → aligns the generator’s latent distribution with its image space.
+
+## Key highlights of Post-Training
+
+- 🚀 **Better generative quality**: gFID 1.60 → 1.36.
+- 🔑 **Generalizability**: applicable to both autoregressive & diffusion models.
+- ⚡ **Efficiency**: strong results with only ~400M generative models.
+
+---
+
+## Model Zoo
+| Generator \ Tokenizer | RobusTok w/o. P.T([weights](https://huggingface.co/qiuk6/RobusTok/resolve/main/main-train.pt?download=true)) | RobusTok w/. P.T ([weights](https://huggingface.co/qiuk6/RobusTok/resolve/main/post-train.pt?download=true)) |
+|---|---:|---:|
+| Base ([weights](https://huggingface.co/qiuk6/RobusTok/resolve/main/rar_b.bin?download=true)) | gFID = 1.83 | gFID = 1.60 |
+| Large ([weights](https://huggingface.co/qiuk6/RobusTok/resolve/main/rar_l.bin?download=true)) | gFID = 1.60 | gFID = 1.36 |
+
+---
+
+## Usage
+For detailed installation, training, and inference instructions, please refer to the [GitHub repository](https://github.com/qiuk2/RobusTok).
+
+---
+
+## Visualization
+
+<div align="center">
+  <img src="https://github.com/qiuk2/RobusTok/raw/main/assets/ft-diff.png" alt="vis" width="95%">
+  <p>
+    visualization of 256&times;256 image generation before (top) and after (bottom) post-training. Three improvements are observed: (a) OOD mitigation, (b) Color fidelity, (c) detail refinement.
+  </p>
+</div>
+
+---
+
+## Citation
+
+If our work assists your research, feel free to give us a star ⭐ or cite us using
+
+```bibtex
+@misc{qiu2025imagetokenizerneedsposttraining,
+      title={Image Tokenizer Needs Post-Training}, 
+      author={Kai Qiu and Xiang Li and Hao Chen and Jason Kuen and Xiaohao Xu and Jiuxiang Gu and Yinyi Luo and Bhiksha Raj and Zhe Lin and Marios Savvides},
+      year={2025},
+      eprint={2509.12474},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2509.12474}, 
+}
+```