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JoyAI-Image, a unified multimodal foundation model, is introduced, coupling a spatially enhanced MLLM with a Multimodal Diffusion Transformer (MMDiT) to facilitate interaction between perception and generation. A scalable training recipe incorporating unified instruction tuning, long-text rendering supervision, spatially grounded data, and editing signals is used. The model achieves SOTA or competitive performance across understanding, generation, long-text rendering, and editing benchmarks, demonstrating enhanced geometry-aware reasoning and controllable visual synthesis.
Bidirectional interaction between enhanced understanding, controllable spatial editing, and novel-view-assisted reasoning enables a unified multimodal model to achieve spatial intelligence beyond general visual competence.
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.
