Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

Jian Han, Jinlai Liu, Yi Jiang, Bin Yan, Yuqi Zhang, Zehuan Yuan, Bingyue Peng, Xiaobing Liu; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 15733-15744

Abstract

We present Infinity, a Bitwise Visual AutoRegressive Modeling capable of generating high-resolution, photorealistic images following language instruction. Infinity refactors visual autoregressive model under a bitwise token prediction framework with an infinite-vocabulary classifier and bitwise self-correction mechanism. By theoretically expanding the tokenizer vocabulary size to infinity in Transformer, our method significantly unleashes powerful scaling capabilities to infinity compared to vanilla VAR. Extensive experiments indicate Infinity outperforms AutoRegressive Text-to-Image models by large margins, matches or surpasses leading diffusion models. Without extra optimization, Infinity generates a 1024x1024 image in 0.8s, 2.6xfaster than SD3-Medium, making it the fastest Text-to-Image model. Models and codes will be released to promote the further exploration of Infinity for visual generation.

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Han_2025_CVPR, author = {Han, Jian and Liu, Jinlai and Jiang, Yi and Yan, Bin and Zhang, Yuqi and Yuan, Zehuan and Peng, Bingyue and Liu, Xiaobing}, title = {Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)}, month = {June}, year = {2025}, pages = {15733-15744} }