titleBoogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget
authorsGuoxuan Chen, Chufeng Xiao, Haoran Yang, Siyue Xie, Binxiao Huang, Ming Zhang, Cheuk Him Chau, Xinyu Fu, Yingzhao Lian, Tom S. Y. Li, Jintao Lin, Bowen Dong, Zian Qian, Yuhao Liu, Yuxuan Hu, Weikang Shi, Bin Zou, Bowen Zheng, Haoxuan Che, Chang Chen, Yuyang He, Heyang Sun, Tianyu Huang, Chong Hou Choi, Cheng Gong, Han Shi, Haoli Bai, Xihui Liu, Hongsheng Li, Qifeng Chen, Chao Huang, Rui Liu, Chenyang Lei
date2026-07-18
venuearXiv (preprint, not peer reviewed)
idarXiv:2607.13125
affiliationBoogu
upvotes125 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
abstractWe introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turbo variants. It delivers competitive performance in high-quality text-to-image generation, fast inference, instruction-based editing, and bilingual (Chinese-English) text rendering. Closed-source multimodal systems like Nano-Banana-Pro and GPT-Image-2 achieve strong performance through system-level integration rather than a single model, yet their internal practices remain largely undisclosed. In this work, we demonstrate that strengthening the understanding capability of the system, through a stronger multimodal encoder, agentic prompt rewriting, and related techniques, together with improvements in data quality, training pipelines, and agentic inference-time scaling, can substantially enhance generation and editing performance even under highly constrained compute budgets. Comprehensive evaluations show that Boogu-Image-0.1 consistently matches or surpasses other open-source models across standard benchmarks, and achieves results approaching leading closed-source systems. Notably, this is accomplished with only 208.62 million unique images. The base model's theoretical training cost is only approximately \$400K. We share practical discussions that we believe are valuable to the broader research community, and release weights, code, and recipes under Apache 2.0 to advance the open ecosystem for unified multimodal understanding and generation. Our code is available here: https://github.com/Boogu-Project/Boogu-Image.

cite

@misc{chen2026boogu,
  title={Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget},
  author={Guoxuan Chen and Chufeng Xiao and Haoran Yang and Siyue Xie and Binxiao Huang and Ming Zhang and Cheuk Him Chau and Xinyu Fu and Yingzhao Lian and Tom S. Y. Li and Jintao Lin and Bowen Dong and Zian Qian and Yuhao Liu and Yuxuan Hu and Weikang Shi and Bin Zou and Bowen Zheng and Haoxuan Che and Chang Chen and Yuyang He and Heyang Sun and Tianyu Huang and Chong Hou Choi and Cheng Gong and Han Shi and Haoli Bai and Xihui Liu and Hongsheng Li and Qifeng Chen and Chao Huang and Rui Liu and Chenyang Lei},
  year={2026},
  eprint={2607.13125},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2607.13125}
}