Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models
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2025
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| author | Ma, Xu Sun, Peize Ma, Haoyu Tang, Hao Ma, Chih-Yao Wang, Jialiang Li, Kunpeng Dai, Xiaoliang Shi, Yujun Ju, Xuan Hu, Yushi Sanakoyeu, Artsiom Juefei-Xu, Felix Hou, Ji Tian, Junjiao Xu, Tao Hou, Tingbo Liu, Yen-Cheng He, Zecheng He, Zijian Feiszli, Matt Zhang, Peizhao Vajda, Peter Tsai, Sam Fu, Yun |
| author_facet | Ma, Xu Sun, Peize Ma, Haoyu Tang, Hao Ma, Chih-Yao Wang, Jialiang Li, Kunpeng Dai, Xiaoliang Shi, Yujun Ju, Xuan Hu, Yushi Sanakoyeu, Artsiom Juefei-Xu, Felix Hou, Ji Tian, Junjiao Xu, Tao Hou, Tingbo Liu, Yen-Cheng He, Zecheng He, Zijian Feiszli, Matt Zhang, Peizhao Vajda, Peter Tsai, Sam Fu, Yun |
| contents | Autoregressive (AR) models, long dominant in language generation, are increasingly applied to image synthesis but are often considered less competitive than Diffusion-based models. A primary limitation is the substantial number of image tokens required for AR models, which constrains both training and inference efficiency, as well as image resolution. To address this, we present Token-Shuffle, a novel yet simple method that reduces the number of image tokens in Transformer. Our key insight is the dimensional redundancy of visual vocabularies in Multimodal Large Language Models (MLLMs), where low-dimensional visual codes from visual encoder are directly mapped to high-dimensional language vocabularies. Leveraging this, we consider two key operations: token-shuffle, which merges spatially local tokens along channel dimension to decrease the input token number, and token-unshuffle, which untangles the inferred tokens after Transformer blocks to restore the spatial arrangement for output. Jointly training with textual prompts, our strategy requires no additional pretrained text-encoder and enables MLLMs to support extremely high-resolution image synthesis in a unified next-token prediction way while maintaining efficient training and inference. For the first time, we push the boundary of AR text-to-image generation to a resolution of 2048x2048 with gratifying generation performance. In GenAI-benchmark, our 2.7B model achieves 0.77 overall score on hard prompts, outperforming AR models LlamaGen by 0.18 and diffusion models LDM by 0.15. Exhaustive large-scale human evaluations also demonstrate our prominent image generation ability in terms of text-alignment, visual flaw, and visual appearance. We hope that Token-Shuffle can serve as a foundational design for efficient high-resolution image generation within MLLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17789 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Ma, Xu Sun, Peize Ma, Haoyu Tang, Hao Ma, Chih-Yao Wang, Jialiang Li, Kunpeng Dai, Xiaoliang Shi, Yujun Ju, Xuan Hu, Yushi Sanakoyeu, Artsiom Juefei-Xu, Felix Hou, Ji Tian, Junjiao Xu, Tao Hou, Tingbo Liu, Yen-Cheng He, Zecheng He, Zijian Feiszli, Matt Zhang, Peizhao Vajda, Peter Tsai, Sam Fu, Yun Computer Vision and Pattern Recognition Autoregressive (AR) models, long dominant in language generation, are increasingly applied to image synthesis but are often considered less competitive than Diffusion-based models. A primary limitation is the substantial number of image tokens required for AR models, which constrains both training and inference efficiency, as well as image resolution. To address this, we present Token-Shuffle, a novel yet simple method that reduces the number of image tokens in Transformer. Our key insight is the dimensional redundancy of visual vocabularies in Multimodal Large Language Models (MLLMs), where low-dimensional visual codes from visual encoder are directly mapped to high-dimensional language vocabularies. Leveraging this, we consider two key operations: token-shuffle, which merges spatially local tokens along channel dimension to decrease the input token number, and token-unshuffle, which untangles the inferred tokens after Transformer blocks to restore the spatial arrangement for output. Jointly training with textual prompts, our strategy requires no additional pretrained text-encoder and enables MLLMs to support extremely high-resolution image synthesis in a unified next-token prediction way while maintaining efficient training and inference. For the first time, we push the boundary of AR text-to-image generation to a resolution of 2048x2048 with gratifying generation performance. In GenAI-benchmark, our 2.7B model achieves 0.77 overall score on hard prompts, outperforming AR models LlamaGen by 0.18 and diffusion models LDM by 0.15. Exhaustive large-scale human evaluations also demonstrate our prominent image generation ability in terms of text-alignment, visual flaw, and visual appearance. We hope that Token-Shuffle can serve as a foundational design for efficient high-resolution image generation within MLLMs. |
| title | Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.17789 |