InternVL-U: Democratizing Unified Multimodal Models for Understanding, Reasoning, Generation and Editing
This work addresses the challenge of democratizing unified multimodal capabilities for AI applications, though it appears incremental as it builds on existing MLLM and MMDiT-based methods.
The paper tackles the trade-offs in unified multimodal models by introducing InternVL-U, a lightweight 4B-parameter model that integrates understanding, reasoning, generation, and editing, achieving superior performance-efficiency balance and outperforming larger baseline models like BAGEL (14B) on various tasks.
Unified multimodal models (UMMs) that integrate understanding, reasoning, generation, and editing face inherent trade-offs between maintaining strong semantic comprehension and acquiring powerful generation capabilities. In this report, we present InternVL-U, a lightweight 4B-parameter UMM that democratizes these capabilities within a unified framework. Guided by the principles of unified contextual modeling and modality-specific modular design with decoupled visual representations, InternVL-U integrates a state-of-the-art Multimodal Large Language Model (MLLM) with a specialized MMDiT-based visual generation head. To further bridge the gap between aesthetic generation and high-level intelligence, we construct a comprehensive data synthesis pipeline targeting high-semantic-density tasks, such as text rendering and scientific reasoning, under a reasoning-centric paradigm that leverages Chain-of-Thought (CoT) to better align abstract user intent with fine-grained visual generation details. Extensive experiments demonstrate that InternVL-U achieves a superior performance - efficiency balance. Despite using only 4B parameters, it consistently outperforms unified baseline models with over 3x larger scales such as BAGEL (14B) on various generation and editing tasks, while retaining strong multimodal understanding and reasoning capabilities.