We present JoyAI-Image, a unified multimodal foundation model for visual understanding, text-to-image generation, and instruction-guided image editing. JoyAI-Image couples a spatially enhanced Multimodal Large Language Model (MLLM) with a Multimodal Diffusion Transformer (MMDiT), allowing perception and generation to interact through a shared multimodal interface. Around this architecture, we build a scalable training recipe that combines unified instruction tuning, long-text rendering supervision, spatially grounded data, and both general and spatial editing signals. This design gives the model broad multimodal capability while strengthening geometry-aware reasoning and controllable visual synthesis. Experiments across understanding, generation, long-text rendering, and editing benchmarks show that JoyAI-Image achieves state-of-the-art or highly competitive performance. More importantly, the bidirectional loop between enhanced understanding, controllable spatial editing, and novel-view-assisted reasoning enables the model to move beyond general visual competence toward stronger spatial intelligence. These results suggest a promising path for unified visual models in downstream applications such as vision-language-action systems and world models.

翻译：我们提出 JoyAI-Image，一个用于视觉理解、文本到图像生成以及指令引导图像编辑的统一多模态基础模型。JoyAI-Image 将空间增强的多模态大语言模型（MLLM）与多模态扩散 Transformer（MMDiT）耦合，使感知与生成能够通过共享的多模态接口进行交互。围绕这一架构，我们构建了一套可扩展的训练方案，该方案结合了统一指令微调、长文本渲染监督、空间基础数据以及通用和空间编辑信号。这种设计赋予了模型广泛的多模态能力，同时强化了几何感知推理和可控视觉合成。在理解、生成、长文本渲染和编辑基准上的实验表明，JoyAI-Image 取得了最先进或极具竞争力的性能。更重要的是，增强理解、可控空间编辑与新视角辅助推理之间的双向循环，使模型能够超越通用视觉能力，迈向更强的空间智能。这些结果表明，统一的视觉模型在视觉-语言-动作系统与世界模型等下游应用中具有广阔前景。