Infinity：面向高分辨率图像合成的可扩展按位自回归建模
Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis
AI总结：
本研究提出按位自回归建模方法Infinity，通过无限词汇表分词器等技术提升生成能力，在多项基准上优于SD3-Medium等扩散模型，生成速度更快，刷新自回归文生图模型纪录。
摘要：
我们提出了Infinity，这是一种按位视觉自回归建模方法，能够根据语言指令生成高分辨率的写实图像。Infinity在按位词元预测框架下重新定义了视觉自回归模型，搭配无限词汇表分词器与分类器以及按位自校正机制，显著提升了生成能力与细节表现。通过在理论上将分词器词汇量扩展至无穷大，同时扩展Transformer规模，我们的方法与普通VAR相比释放出了强大的扩展能力。Infinity刷新了自回归文本生成图像模型的纪录，性能优于SD3-Medium和SDXL等顶级扩散模型。值得注意的是，Infinity超越了SD3-Medium，将GenEval基准得分从0.62提升至0.73，ImageReward基准得分从0.87提升至0.96，胜率达到66%。在无需额外优化的情况下，Infinity可在0.8秒内生成高质量的1024x1024图像，速度是SD3-Medium的2.6倍，是目前最快的文本生成图像模型。我们将发布模型与代码，以推动对Infinity在视觉生成与统一分词器建模方向的进一步探索。
英文摘要：
We present Infinity, a Bitwise Visual AutoRegressive Modeling capable of generating high-resolution, photorealistic images following language instruction. Infinity redefines visual autoregressive model under a bitwise token prediction framework with an infinite-vocabulary tokenizer & classifier and bitwise self-correction mechanism, remarkably improving the generation capacity and details. By theoretically scaling the tokenizer vocabulary size to infinity and concurrently scaling the transformer size, our method significantly unleashes powerful scaling capabilities compared to vanilla VAR. 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 1024x1024 image in 0.8 seconds, making it 2.6x faster than SD3-Medium and establishing it as the fastest text-to-image model. Models and codes will be released to promote further exploration of Infinity for visual generation and unified tokenizer modeling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
