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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.11061 |
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| _version_ | 1866916001740750848 |
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| author | Cai, Qi Chen, Jingwen Gao, Chengmin Gong, Zijian Li, Yehao Pan, Yingwei Peng, Yi Qiu, Zhaofan Yu, Kai Zhang, Yiheng Ai, Hao Bai, Siying Chen, Yang Chen, Zhihui Gao, Fengbin Guo, Ying Li, Dong Shen, Zhen Shi, Leilei Wang, Jing Wang, Siyu Wang, Yimeng Zheng, Rui Yao, Ting Mei, Tao |
| author_facet | Cai, Qi Chen, Jingwen Gao, Chengmin Gong, Zijian Li, Yehao Pan, Yingwei Peng, Yi Qiu, Zhaofan Yu, Kai Zhang, Yiheng Ai, Hao Bai, Siying Chen, Yang Chen, Zhihui Gao, Fengbin Guo, Ying Li, Dong Shen, Zhen Shi, Leilei Wang, Jing Wang, Siyu Wang, Yimeng Zheng, Rui Yao, Ting Mei, Tao |
| contents | The evolution of visual generative models has long been constrained by fragmented architectures relying on disjoint text encoders and external VAEs. In this report, we present HiDream-O1-Image, a natively unified generative foundation model via pixel-space Diffusion Transformer, that pioneers a paradigm shift from modular architectures to an end-to-end in-context visual generation engine. By mapping raw image pixels, text tokens, and task-specific conditions into a single shared token space, HiDream-O1-Image achieves a structural unification of multimodal inputs within an Unified Transformer (UiT) architecture. This native encoding paradigm eliminates the need for separate VAEs or disjoint pre-trained text encoders, allowing the model to treat diverse generation and editing tasks as a consistent in-context reasoning process. Extensive experiments show that HiDream-O1-Image excels across various generation tasks, including text-to-image generation, instruction-based editing, and subject-driven personalization. Notably, with only 8B parameters, HiDream-O1-Image (8B) achieves performance parity with or even surpasses established state-of-the-art models with significantly larger parameters (e.g., 27B Qwen-Image). Crucially, to validate the immense scalability of this paradigm, we successfully scale the architecture up to over 200B parameters. Experimental results demonstrate that this massive-scale version HiDream-O1-Image-Pro (200B+) unlocks unprecedented generative capabilities and superior performance, establishing new state-of-the-art benchmarks. Ultimately, HiDream-O1-Image highlights the immense potential of natively unified architectures and charts a highly scalable path toward next-generation multimodal AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_11061 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer Cai, Qi Chen, Jingwen Gao, Chengmin Gong, Zijian Li, Yehao Pan, Yingwei Peng, Yi Qiu, Zhaofan Yu, Kai Zhang, Yiheng Ai, Hao Bai, Siying Chen, Yang Chen, Zhihui Gao, Fengbin Guo, Ying Li, Dong Shen, Zhen Shi, Leilei Wang, Jing Wang, Siyu Wang, Yimeng Zheng, Rui Yao, Ting Mei, Tao Computer Vision and Pattern Recognition Multimedia The evolution of visual generative models has long been constrained by fragmented architectures relying on disjoint text encoders and external VAEs. In this report, we present HiDream-O1-Image, a natively unified generative foundation model via pixel-space Diffusion Transformer, that pioneers a paradigm shift from modular architectures to an end-to-end in-context visual generation engine. By mapping raw image pixels, text tokens, and task-specific conditions into a single shared token space, HiDream-O1-Image achieves a structural unification of multimodal inputs within an Unified Transformer (UiT) architecture. This native encoding paradigm eliminates the need for separate VAEs or disjoint pre-trained text encoders, allowing the model to treat diverse generation and editing tasks as a consistent in-context reasoning process. Extensive experiments show that HiDream-O1-Image excels across various generation tasks, including text-to-image generation, instruction-based editing, and subject-driven personalization. Notably, with only 8B parameters, HiDream-O1-Image (8B) achieves performance parity with or even surpasses established state-of-the-art models with significantly larger parameters (e.g., 27B Qwen-Image). Crucially, to validate the immense scalability of this paradigm, we successfully scale the architecture up to over 200B parameters. Experimental results demonstrate that this massive-scale version HiDream-O1-Image-Pro (200B+) unlocks unprecedented generative capabilities and superior performance, establishing new state-of-the-art benchmarks. Ultimately, HiDream-O1-Image highlights the immense potential of natively unified architectures and charts a highly scalable path toward next-generation multimodal AI. |
| title | HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2605.11061 |