Ovis-Image Technical Report
We introduce Ovis-Image, a 7B text-to-image model specifically optimized for high-quality text rendering, designed to operate efficiently under stringent computational constraints. Built upon our previous Ovis-U1 framework, Ovis-Image integrates a diffusion-based visual decoder with the stronger Ovis 2.5 multimodal backbone, leveraging a text-centric training pipeline that combines large-scale pre-training with carefully tailored post-training refinements. Despite its compact architecture, Ovis-Image achieves text rendering performance on par with significantly larger open models such as Qwen-Image and approaches closed-source systems like Seedream and GPT4o. Crucially, the model remains deployable on a single high-end GPU with moderate memory, narrowing the gap between frontier-level text rendering and practical deployment. Our results indicate that combining a strong multimodal backbone with a carefully designed, text-focused training recipe is sufficient to achieve reliable bilingual text rendering without resorting to oversized or proprietary models.
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Guo-Hua Wang
claimed_verified
2025-12-04T08:48:39.830Z
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Guo-Hua Wang
Flourish
user
Flourish
692ffb2b26742347f61db0d1
Liangfu Cao
692ffb2b26742347f61db0d2
Tianyu Cui
claimed_verified
2025-12-04T08:48:37.700Z
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TianyuCui
Rebelliousgang
user
Rebelliousgang
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Minghao Fu
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Xiaohao Chen
692ffb2b26742347f61db0d5
Pengxin Zhan
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Jianshan Zhao
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Lan Li
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Bowen Fu
692ffb2b26742347f61db0d9
Jiaqi Liu
692ffb2b26742347f61db0da
Qing-Guo Chen
Ovis-Image Technical Report
We introduce Ovis-Image, a 7B text-to-image model specifically optimized for high-quality text rendering, designed to operate efficiently under stringent computational constraints. Built upon our previous Ovis-U1 framework, Ovis-Image integrates a diffusion-based visual decoder with the stronger Ovis 2.5 multimodal backbone, leveraging a text-centric training pipeline that combines large-scale pre-training with carefully tailored post-training refinements. Despite its compact architecture, Ovis-Image achieves text rendering performance on par with significantly larger open models such as Qwen-Image and approaches closed-source systems like Seedream and GPT4o. Crucially, the model remains deployable on a single high-end GPU with moderate memory, narrowing the gap between frontier-level text rendering and practical deployment. Our results indicate that combining a strong multimodal backbone with a carefully designed, text-focused training recipe is sufficient to achieve reliable bilingual text rendering without resorting to oversized or proprietary models.
Ovis-Image is a 7B text-to-image model optimized for high-quality text rendering under computational constraints, combining a diffusion-based visual decoder with a multimodal backbone and a text-centric training pipeline.
text-to-image model diffusion-based visual decoder multimodal backbone text-centric training pipeline Qwen-Image Seedream GPT4o bilingual text rendering