The rapid evolution of multimodal foundation model has demonstrated significant progresses in vision-language understanding and generation, e.g., our previous work SEED-LLaMA. However, there remains a gap between its capability and the real-world applicability, primarily due to the model's limited capacity to effectively respond to various user instructions and interact with diverse visual data. In this work, we focus on bridging this gap through integrating two enhanced features: (1) comprehending images of arbitrary sizes and ratios, and (2) enabling multi-granularity image generation. We present a unified and versatile foundation model, namely, SEED-X, which is able to model multi-granularity visual semantics for comprehension and generation tasks. Besides the competitive results on public benchmarks, SEED-X demonstrates its effectiveness in handling real-world applications across various domains after instruction tuning. We hope that our work will inspire future research into what can be achieved by versatile multimodal foundation models in real-world applications. The models, codes, and datasets will be released in https://github.com/AILab-CVC/SEED-X.

模态基础模型在视觉语言理解和生成方面取得了显著的进展，但是存在应用能力和真实世界适应性之间的差距。本文提出了一种统一和多功能的基础模型SEED-X，通过整合两个增强特性来弥合这一差距：（1）理解任意大小和比例的图像，（2）实现多粒度图像生成。SEED-X在公共基准测试中取得了竞争性结果，并在指导调节后展示了在各个领域处理真实世界应用的有效性。我们希望我们的工作能够启发未来研究，探索多功能模态基础模型在真实世界应用中的潜力。模型、代码和数据集将在此URL中发布。

SEED-X：统一多粒度理解与生成的多模态模型