Ovis-U1 Technical Report
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.
686347cd588cea0da970c87a
Guo-Hua Wang
claimed_verified
2025-07-01T10:13:51.516Z
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Guo-Hua Wang
user
Flourish
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Shanshan Zhao
claimed_verified
2025-08-19T10:50:48.355Z
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zss
user
Suikong
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Xinjie Zhang
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Liangfu Cao
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Pengxin Zhan
686347cd588cea0da970c87f
Lunhao Duan
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Shiyin Lu
claimed_verified
2025-08-19T10:50:52.924Z
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Shiyin Lu
user
runninglsy
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Minghao Fu
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Xiaohao Chen
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Jianshan Zhao
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Yang Li
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Qing-Guo Chen
claimed_verified
2025-08-19T10:50:50.588Z
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/avatars/8964cbbc64578f839a5a2773dcd565dd.svg
Qing-Guo Chen
user
cqgwin
Ovis-U1 Technical Report
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.
Ovis-U1, a 3-billion-parameter unified model, integrates multimodal understanding, text-to-image generation, and image editing using a diffusion-based visual decoder and bidirectional token refiner, achieving state-of-the-art performance across various benchmarks.
diffusion-based visual decoder bidirectional token refiner unified training multimodal understanding text-to-image generation image editing OpenCompass Multi-modal Academic Benchmark DPG-Bench GenEval ImgEdit-Bench GEdit-Bench-EN