Free Lunch for Unified Multimodal Models: Enhancing Generation via Reflective Rectification with Inherent Understanding

TL;DR

This paper introduces UniRect-CoT, a training-free framework that leverages the inherent understanding of unified multimodal models to improve their generation quality through reflective rectification inspired by human reasoning.

Contribution

It proposes a novel, training-free rectification method that activates internal knowledge during generation, significantly enhancing UMMs' performance on complex tasks.

Findings

UniRect-CoT improves generation quality across diverse tasks.

The method aligns intermediate results with target instructions for self-supervision.

It can be integrated into existing UMMs without additional training.

Abstract

Unified Multimodal Models (UMMs) aim to integrate visual understanding and generation within a single structure. However, these models exhibit a notable capability mismatch, where their understanding capability significantly outperforms their generation. This mismatch indicates that the model's rich internal knowledge, while effective for understanding tasks, remains underactivated during generation. To address this, we draw inspiration from the human ``Thinking-While-Drawing'' paradigm, where humans continuously reflect to activate their knowledge and rectify intermediate results. In this paper, we propose UniRect-CoT, a training-free unified rectification chain-of-thought framework. Our approach unlocks the ``free lunch'' hidden in the UMM's powerful inherent understanding to continuously reflect, activating its internal knowledge and rectifying intermediate results during generation.We regard the diffusion denoising process in UMMs as an intrinsic visual reasoning process and align the intermediate results with the target instruction understood by the model, serving as a self-supervisory signal to rectify UMM generation.Extensive experiments demonstrate that UniRect-CoT can be easily integrated into existing UMMs, significantly enhancing generation quality across diverse complex tasks.