--- license: mit language: - en --- # Infinity ∞: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis
Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis
## 📖 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. ## 📌 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) ## 📖 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}, } ```