MANGO: Unleashing Image Generation Capability of Unified Multimodal Models

Unified multimodal models have demonstrated remarkableversatility in multimodal understanding and generation. However, theystruggle with image generation under complex compositional instructions,a setting frequently encountered in real-world applications. To addressthis, we present MANGO, a novel model that unleashes image generationcapability at both its performance floor and ceiling. (1) To raise theperformance floor, we introduce Functionality-Oriented Transformers,an architectural design that assigns each Transformer branch to a dedi-cated function, such as image generation or image understanding. Thisfunction-disentangled design alleviates cross-functional interference inher-ent in mainstream modality-oriented architectures, thereby unleashingthe model’s image generation capacity. (2) To elevate the performanceceiling, we propose Paint-CoT, a framework that enables image generationthrough a human-like artistic chain of thought, consisting of planning,acting, reflection, and correction. Leveraging the model’s multimodalunderstanding and reasoning capabilities, this reasoning-guided processfurther unleashes image generation capacity. Further, to overcome the dif-ficulty and high cost of collecting multi-step aligned chain-of-thought datafor end-to-end training, we develop a multi-task joint training paradigmthat decomposes the full reasoning-generation pipeline into manageablesubtasks. This strategy maximizes the utilization of existing or readilyconstructed supervision, enabling e!ective Paint-CoT-based image gener-ation without requiring fully aligned multi-step annotations. Extensiveexperiments demonstrate that MANGO consistently achieves superiorperformance across diverse image generation benchmarks, delivering sub-stantial improvements in complex image generation scenarios.