Abstract
Recent unified multimodal large language models (MLLMs) have shown impressive capabilities, incorporating chain-of-thought (CoT) reasoning for enhanced text-to-image generation. However, existing approaches remain limited, either treating the model merely as a standalone generator or relying on abstract textual planning. To this end, we propose Draft-as-CoT (DraCo), a novel interleaved reasoning paradigm that fully leverages both textual and visual contents in CoT for better planning and verification. Our method first generates a low-resolution draft image as preview, providing more concrete and structural visual planning and guidance. Then, we employ the model's inherent understanding capability to verify potential semantic misalignments between the draft and input prompt, and performs refinement through selective corrections with super-resolution. In this way, our approach addresses two fundamental challenges: the coarse-grained nature of textual planning and the difficulty in generating rare attribute combinations. To support training, we curate DraCo-240K, aiming to enhance three atomic capabilities spanning general correction, instance manipulation, and layout reorganization. Supported by DraCo-CFG, a specialized classifier-free guidance (CFG) strategy for interleaved reasoning, DraCo achieves a tremendous increase on GenEval (+8%), Imagine-Bench (+0.91), and GenEval++ (+3%), significantly outperforming direct generation and other generation methods empowered by CoT.
Abstract (translated)
最近统一的多模态大型语言模型（MLLMs）展示了令人印象深刻的性能，通过链式思维（CoT）推理增强了文本到图像生成的能力。然而，现有的方法仍然有限，要么仅仅将模型视为独立的生成器，要么依赖于抽象的文字规划。为了解决这些问题，我们提出了Draft-as-CoT (DraCo)，这是一种新的交替推理范例，全面利用了链式思维中的文字和视觉内容以实现更好的计划与验证。 我们的方法首先生成低分辨率的草图图像作为预览，提供更具体且结构化的视觉规划和指导。然后，通过模型内在的理解能力来核实草图与输入提示之间的潜在语义不一致，并通过选择性修正并结合超分辨率技术进行细化处理。这样一来，我们的方法解决了两个根本性的挑战：文本规划的粗粒度性质以及生成罕见属性组合的难度。 为了支持训练，我们整理了DraCo-240K数据集，旨在增强三种基本能力——通用校正、实例操作和布局重组。通过专门用于交替推理的无分类器引导（CFG）策略DraCo-CFG的支持，DraCo在GenEval中提高了8%，在Imagine-Bench中提高了0.91，在GenEval++中提高了3%的成绩，明显优于直接生成和其他受链式思维增强的方法。
URL
https://arxiv.org/abs/2512.05112
