Update model card for InfinityCC: Spherical Leech Quantization

README.md

@@ -1,46 +1,62 @@
 ---
-license: mit
 language:
 - en
+license: mit
+pipeline_tag: text-to-image
 ---
-# Infinity ∞: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis
 
-<div align="center">
+# InfinityCC: Spherical Leech Quantization for Visual Tokenization and Generation
 
-[![demo platform](https://img.shields.io/badge/Play%20with%20Infinity%21-Infinity%20demo%20platform-lightblue)](https://opensource.bytedance.com/gmpt/t2i/invite)&nbsp;
-[![arXiv](https://img.shields.io/static/v1?label=Project%20Page&message=Github&color=blue&logo=github-pages)](https://foundationvision.github.io/infinity.project/)&nbsp;
-[![arXiv](https://img.shields.io/badge/arXiv%20paper-2412.04431-b31b1b.svg)](https://arxiv.org/abs/2412.04431)&nbsp;
-[![huggingface weights](https://img.shields.io/badge/%F0%9F%A4%97%20Weights-FoundationVision/Infinity-yellow)](https://huggingface.co/FoundationVision/infinity)&nbsp;
-[![code](https://img.shields.io/badge/%F0%9F%A4%96%20Code-FoundationVision/Infinity-green)](https://github.com/FoundationVision/Infinity)&nbsp;
+This repository hosts **InfinityCC**, a working example showcasing the power of [Non-Parametric Quantization (NPQ)](https://cs.stanford.edu/~yzz/npq/) for ImageNet-1k class-conditioned image generation.
 
-</div>
-<p align="center" style="font-size: larger;">
-  <a href="https://arxiv.org/abs/2412.04431">Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis</a>
-</p>
+The model is based on the paper: [**Spherical Leech Quantization for Visual Tokenization and Generation**](https://huggingface.co/papers/2512.14697)
+Yue Zhao, Hanwen Jiang, Zhenlin Xu, Chutong Yang, Ehsan Adeli, Philipp Krähenbühl.
 
+Project Page: [https://cs.stanford.edu/~yzz/npq/](https://cs.stanford.edu/~yzz/npq/)
+Code: [https://github.com/zhaoyue-zephyrus/InfinityCC](https://github.com/zhaoyue-zephyrus/InfinityCC)
 
+<img src="https://github.com/zhaoyue-zephyrus/InfinityCC/raw/main/assets/npq.png" width="640">
 
+## Introduction
+In this work, we explore Spherical Leech Quantization ($\Lambda_{24}$-SQ), a non-parametric quantization method rooted in lattice coding. This approach simplifies the training recipe and improves the reconstruction-compression tradeoff, thanks to its high symmetry and even distribution on the hypersphere. It has demonstrated better reconstruction quality than prior art in image tokenization and compression tasks, with improvements extending to state-of-the-art auto-regressive image generation frameworks. InfinityCC serves as a practical demonstration of this powerful quantization technique for visual generation.
 
+## Installation
 
+We use [uv](https://docs.astral.sh/uv/) to manage all dependencies.
+
+```bash
+uv sync
+source .venv/bin/activate
+```
 
+To evaluate ImageNet using the ADM evaluator, run the following command lines:
+```bash
+mkdir third_party/ && cd third_party/
+git clone https://${GIT_TOKEN}@github.com/openai/guided-diffusion.git
+cd guided-diffusion/evaluations
+wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/ref_batches/imagenet/256/VIRTUAL_imagenet256_labeled.npz
+```
 
-## 📖 Introduction
-We present Infinity, a Bitwise Visual AutoRegressive Modeling capable of generating high-resolution and photorealistic images. Infinity redefines visual autoregressive model under a bitwise token prediction framework with an infinite-vocabulary tokenizer & classifier and bitwise self-correction. Theoretically scaling the tokenizer vocabulary size to infinity and concurrently scaling the transformer size, our method significantly unleashes powerful scaling capabilities. Infinity sets a new record for autoregressive text-to-image models, outperforming top-tier diffusion models like SD3-Medium and SDXL. Notably, Infinity surpasses SD3-Medium by improving the GenEval benchmark score from 0.62 to 0.73 and the ImageReward benchmark score from 0.87 to 0.96, achieving a win rate of 66%. Without extra optimization, Infinity generates a high-quality 1024×1024 image in 0.8 seconds, making it 2.6× faster than SD3-Medium and establishing it as the fastest text-to-image model.
+## Results
 
-## 📌 Note
-This repo is used for hosting Infinity's checkpoints. For more details, please refer to [![code](https://img.shields.io/badge/%F0%9F%A4%96%20Code-FoundationVision/Infinity-green)](https://github.com/FoundationVision/Infinity)&nbsp;
+### InfinityCC Performance
+|   model    |  Resolution |  #layers | Tokenizer (HF weights🤗) | VAR Model (HF weights🤗)  |   FID   |
+|:----------:|:-----:|:--------:|:---------:|:-----------------------------------------------------------------------------------:|:----:|
+| InfinityCC |     256     |    12    | [bitvae_l24_xl](https://huggingface.co/zhaoyue-zephyrus/InfinityCC_L24SQ/tree/main/tokenization/infinity_l24_stage1_xl)   | [infinitycc_12layer_weights](https://huggingface.co/zhaoyue-zephyrus/InfinityCC_L24SQ/tree/main/generation/infinitycc_12layer_256x256_l24_xl_ep50_cce_zloss_improved_schedule_dion) | 6.66 |
+| InfinityCC |     256     |    24    | [bitvae_l24_xl_vf](https://huggingface.co/zhaoyue-zephyrus/InfinityCC_L24SQ/tree/main/tokenization/infinity_l24_stage1_xl_vf)   | [infinitycc_24layer_weights](https://huggingface.co/zhaoyue-zephyrus/InfinityCC_L24SQ/tree/main/generation/infinitycc_24layer_256x256_l24_xl_vf_ep350_cce_zloss_improved_schedule_dion_unsharedaln) | 2.21 |
+| InfinityCC-2B |   256    |    32    | [TBD]()   | [TBD]() | 1.80 |
 
-## 📖 Citation
+## Citation
 If our work assists your research, feel free to give us a star ⭐ or cite us using:
 
-```
-@misc{han2024infinityscalingbitwiseautoregressive,
-    title={Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis}, 
-    author={Jian Han and Jinlai Liu and Yi Jiang and Bin Yan and Yuqi Zhang and Zehuan Yuan and Bingyue Peng and Xiaobing Liu},
-    year={2024},
-    eprint={2412.04431},
-    archivePrefix={arXiv},
-    primaryClass={cs.CV},
-    url={https://arxiv.org/abs/2412.04431}, 
+```bibtex
+@article{zhao2025spherical,
+  title={Spherical Leech Quantization for Visual Tokenization and Generation},
+  author={Zhao, Yue and Jiang, Hanwen and Xu, Zhenlin and Yang, Chutong and Adeli, Ehsan and Krähenbühl, Philipp},
+  journal={arXiv preprint arXiv:2512.14697},
+  year={2025}
 }
-```
+```
+
+## License
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.