Ovis-U1 Technical Report

Guo-Hua Wang; Shanshan Zhao; Xinjie Zhang; Liangfu Cao; Pengxin Zhan; Lunhao Duan; Shiyin Lu; Minghao Fu; Xiaohao Chen; Jianshan Zhao; Yang Li; Qing-Guo Chen

arXiv · 2506.23044

Ovis-U1 Technical Report

Guo-Hua Wang·Shanshan Zhao·Xinjie Zhang·Liangfu Cao·Pengxin Zhan·Lunhao Duan·Shiyin Lu·Minghao Fu·Xiaohao Chen·Jianshan Zhao·Yang Li·Qing-Guo Chen

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Abstract

In this report, we introduce Ovis-U1, a 3-billion-parameter unified model that integrates multimodal understanding, text-to-image generation, and image editing capabilities. Building on the foundation of the Ovis series, Ovis-U1 incorporates a diffusion-based visual decoder paired with a bidirectional token refiner, enabling image generation tasks comparable to leading models like GPT-4o. Unlike some previous models that use a frozen MLLM for generation tasks, Ovis-U1 utilizes a new unified training approach starting from a language model. Compared to training solely on understanding or generation tasks, unified training yields better performance, demonstrating the enhancement achieved by integrating these two tasks. Ovis-U1 achieves a score of 69.6 on the OpenCompass Multi-modal Academic Benchmark, surpassing recent state-of-the-art models such as Ristretto-3B and SAIL-VL-1.5-2B. In text-to-image generation, it excels with scores of 83.72 and 0.89 on the DPG-Bench and GenEval benchmarks, respectively. For image editing, it achieves 4.00 and 6.42 on the ImgEdit-Bench and GEdit-Bench-EN, respectively. As the initial version of the Ovis unified model series, Ovis-U1 pushes the boundaries of multimodal understanding, generation, and editing.

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Guo-Hua Wang, Shanshan Zhao, Xinjie Zhang, Liangfu Cao, Pengxin Zhan, Lunhao Duan, Shiyin Lu, Minghao Fu, Xiaohao Chen, Jianshan Zhao, Yang Li, Qing-Guo Chen. 2025-07-01. Ovis-U1 Technical Report. https://arxiv.org/abs/2506.23044

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