Large Language Models are Universal Reasoners for Visual Generation

Problem
This paper addresses the “understanding-generation gap” in text-to-image generation systems, particularly those utilizing large language models (LLMs) and diffusion models. Despite advancements in unified architectures that integrate visual understanding and generation, existing systems often struggle to align complex prompts with generated images. This gap is critical as it limits the effectiveness of LLMs in generating images that accurately reflect nuanced textual descriptions. The work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose a novel framework called UniReasoner, which utilizes the LLM as a universal reasoner to enhance the generation process. The architecture consists of three main components:

1. Coarse Visual Draft Generation: The LLM generates an initial visual draft composed of discrete vision tokens based on the input prompt.
2. Self-Critique Mechanism: The LLM evaluates the draft for consistency with the prompt, producing a grounded textual evaluation that identifies necessary corrections.
3. Diffusion Model Conditioning: A diffusion model is conditioned on the prompt, the visual draft, and the evaluation output. This multi-faceted conditioning ensures that the generation process is informed by explicit corrective signals, addressing limitations inherent in both the draft and the evaluation.

The training compute details are not disclosed, but the framework is designed to leverage the reasoning capabilities of LLMs to improve the fidelity of generated images.

Results
UniReasoner demonstrates significant improvements in compositional alignment and semantic faithfulness compared to baseline models using the same diffusion architecture. The paper reports that UniReasoner achieves a notable increase in alignment scores on standard benchmarks, although specific numerical results and comparisons to named baselines are not provided in the abstract. The authors emphasize that the image quality is maintained while enhancing the accuracy of the generated content in relation to complex prompts.

Limitations
The authors acknowledge that while UniReasoner improves alignment and faithfulness, it may still be susceptible to limitations inherent in the underlying diffusion model. Additionally, the reliance on LLMs for self-critique may introduce biases based on the training data of the LLM, potentially affecting the quality of the evaluations. The paper does not discuss the computational efficiency of the proposed method or its scalability in real-world applications, which are critical factors for deployment.

Why it matters
This work has significant implications for the field of multimodal AI, particularly in enhancing the capabilities of text-to-image generation systems. By effectively bridging the understanding-generation gap, UniReasoner paves the way for more reliable and contextually accurate image synthesis from complex textual prompts. This advancement could lead to improved applications in creative industries, automated content generation, and interactive AI systems, where precise visual representation of textual information is crucial.

Authors: Sucheng Ren, Chen Chen, Zhenbang Wang, Liangchen Song, Xiangxin Zhu, Alan Yuille, Liang-Chieh Chen, Jiasen Lu
Source: arXiv:2605.04040
URL: https://arxiv.org/abs/2605.04040v1