S1-Omni-Image：面向科学图像理解、生成与编辑的统一模型
S1-Omni-Image: A Unified Model for Scientific Image Understanding, Generation, and Editing
AI总结：
提出S1-Omni-Image，基于S1-VL-32B和“先思考后生成”范式，统一实现科学图像的理解、生成与编辑，在多个基准上达到领先性能。
摘要：
我们提出S1-Omni-Image，一个开放权重的统一多模态模型，用于科学图像的理解、生成和编辑。与通用图像生成模型不同，科学图像任务不仅需要高保真合成，还需要对科学语义、结构关系、领域知识和任务意图的稳健理解。为此，S1-Omni-Image建立在科学多模态推理主干S1-VL-32B之上，并在统一的“先思考后生成”范式下将其理解能力与图像生成模块耦合。给定用户指令，模型首先生成面向任务的推理轨迹、文本答案和任务特殊标记；然后将它们的隐藏状态注入生成模块，以条件化图像生成或编辑。S1-Omni-Image在统一框架中支持科学图像理解、生成和编辑。对于生成，它专注于科学插图和文本渲染，包括逻辑图、关系比较、数据图表和逼真的科学可视化。对于编辑，它将分割和其他领域特定的视觉任务视为原生图像编辑问题，支持多轮插图编辑、医学和地理图像分割、医学图像翻译以及科学图像超分辨率。我们构建了包含314K样本的训练数据集SciGenEdit，并发布了模型权重、推理代码和SciGenEdit-10K。实验表明，S1-Omni-Image在保持从S1-VL-32B继承的科学图像理解能力的同时，显著提升了科学图像生成和编辑性能。它在GenExam和TechImage-Bench上优于开源模型，在包括MSD、cigRockSEM、SynthRAD2025和IXI在内的四个编辑基准上达到最先进结果，并在科学图像理解评估中保持稳定性能。
英文摘要：
We present S1-Omni-Image, an open-weight unified multimodal model for scientific image understanding, generation, and editing. Unlike general-purpose image generation models, scientific image tasks require not only high-fidelity synthesis, but also robust understanding of scientific semantics, structural relations, domain knowledge, and task intent. To this end, S1-Omni-Image builds on the scientific multimodal reasoning backbone S1-VL-32B and couples its understanding capability with an image generation module under a unified think-before-generate paradigm. Given a user instruction, the model first produces a task-oriented reasoning trace, a textual answer, and a task special token; their hidden states are then injected into the generation module to condition image generation or editing. S1-Omni-Image supports scientific image understanding, generation, and editing in a unified framework. For generation, it focuses on scientific illustrations and text rendering, including logical diagrams, relational comparisons, data charts, and realistic scientific visualizations. For editing, it casts segmentation and other domain-specific vision tasks as native image editing problems, enabling multi-turn illustration editing, medical and geographic image segmentation, medical image translation, and scientific image super-resolution. We construct SciGenEdit, a 314K-sample training dataset, and release the model weights, inference code, and SciGenEdit-10K. Experiments show that S1-Omni-Image substantially improves scientific image generation and editing while preserving the scientific image understanding capability inherited from S1-VL-32B. It outperforms open-source models on GenExam and TechImage-Bench, achieves state-of-the-art results on four editing benchmarks including MSD, cigRockSEM, SynthRAD2025, and IXI, and maintains stable performance on scientific image understanding evaluations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
