UniRect-CoT: Unleashing the Generative Potential of Unified Multimodal Models Without Training

This article introduces the UniRect-CoT framework, which activates the inherent understanding capabilities of unified multimodal models through a "think-and-draw" paradigm, significantly improving generation quality without additional training. The framework leverages the model's own strong understanding capabilities to guide and correct the generation process, with advantages such as zero training cost, plug-and-play functionality, and strong generality.

Unified Multimodal Models (UMMs) aim to integrate visual understanding and generative capabilities, but they generally suffer from an imbalance where understanding capabilities far exceed generative capabilities. The model's rich internal knowledge performs excellently in understanding tasks but fails to be fully activated and utilized in the generation process.

Inspired by humans' self-correction process of "thinking while drawing", researchers propose the training-free UniRect-CoT framework. Its technical mechanisms include: 1. Aligning intermediate results with target instructions; 2. Generating self-supervised signals to correct generation; 3. Sustaining a self-reflection loop during the generation process. The framework treats diffusion denoising as an inherent visual reasoning process and uses the model's own understanding capabilities to guide generation.

Extensive experiments show that UniRect-CoT can be easily integrated into existing UMMs, significantly improving generation quality across various complex tasks. Its advantages include: zero training cost (no additional data or computing resources required), plug-and-play (directly applicable to existing models), and strong generality (suitable for multiple complex generation tasks).

UniRect-CoT reveals a key fact: the potential of many models is already encoded internally, but they lack appropriate activation mechanisms. This finding not only applies to multimodal models but also provides new ideas for exploring the capabilities of other types of models.

Future exploration directions include: designing more efficient self-reflection mechanisms and extending this method to more modalities and tasks.