Infinity / README.md

[update] add model card

b3f9627 verified almost 2 years ago

2.9 kB

---
license: mit
language:
- en
---
# Infinity ∞: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

<div align="center">

[![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;

</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>







## 📖 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)&nbsp;

## 📖 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}, 
}
```

	---
	license: mit
	language:
	- en
	---
	# Infinity ∞: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

	<div align="center">

	[![demo platform](https://img.shields.io/badge/Play%20with%20Infinity%21-Infinity%20demo%20platform-lightblue)](https://opensource.bytedance.com/gmpt/t2i/invite)
	[![arXiv](https://img.shields.io/static/v1?label=Project%20Page&message=Github&color=blue&logo=github-pages)](https://foundationvision.github.io/infinity.project/)
	[![arXiv](https://img.shields.io/badge/arXiv%20paper-2412.04431-b31b1b.svg)](https://arxiv.org/abs/2412.04431)
	[![huggingface weights](https://img.shields.io/badge/%F0%9F%A4%97%20Weights-FoundationVision/Infinity-yellow)](https://huggingface.co/FoundationVision/infinity)
	[![code](https://img.shields.io/badge/%F0%9F%A4%96%20Code-FoundationVision/Infinity-green)](https://github.com/FoundationVision/Infinity)

	</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>







	## 📖 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},
	}
	```