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

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arxiv 2412.04431 v2 pith:DDTFBREX submitted 2024-12-05 cs.CV

Jian Han , Jinlai Liu , Yi Jiang , Bin Yan , Yuqi Zhang , Zehuan Yuan , Bingyue Peng , Xiaobing Liu This is my paper
classification cs.CV
keywords infinityautoregressivebitwisescalingmodelingmodelssd3-mediumtokenizer
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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