CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation

Haodong Li; Chunmei Qing; Huanyu Zhang; Dongzhi Jiang; Yihang Zou; Hongbo Peng; Dingming Li; Yuhong Dai; ZePeng Lin; Juanxi Tian; Yi Zhou; Siqi Dai; Jingwei Wu; Pheng-Ann Heng

arXiv · 2603.08652

CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation

Haodong Li·Chunmei Qing·Huanyu Zhang·Dongzhi Jiang·Yihang Zou·Hongbo Peng·Dingming Li·Yuhong Dai·ZePeng Lin·Juanxi Tian·Yi Zhou·Siqi Dai·Jingwei Wu·Pheng-Ann Heng

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Abstract

Recent advancements in Unified Multimodal Models (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the integration of Chain-of-Thought (CoT) reasoning. However, existing CoT-based T2I methods largely rely on abstract natural-language planning, which lacks the precision required for complex spatial layouts, structured visual elements, and dense textual content. In this work, we propose CoCo (Code-as-CoT), a code-driven reasoning framework that represents the reasoning process as executable code, enabling explicit and verifiable intermediate planning for image generation. Given a text prompt, CoCo first generates executable code that specifies the structural layout of the scene. The code is then executed in a sandboxed environment to render a deterministic draft image. Subsequently, the model refines this draft through fine-grained image editing to produce the final high-fidelity result. To support this training paradigm, we construct CoCo-10K, a curated dataset containing structured draft-final image pairs designed to teach both structured draft construction and corrective visual refinement. Empirical evaluations on StructT2IBench, OneIG-Bench, and LongText-Bench show that CoCo achieves improvements of 68.83%, 54.8%, and 41.23%, respectively, over direct generation, while also outperforming other CoT-enhanced generation methods. These results demonstrate that executable code is an effective and reliable reasoning paradigm for precise, controllable, and structured text-to-image generation.

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Haodong Li, Chunmei Qing, Huanyu Zhang, Dongzhi Jiang, Yihang Zou, Hongbo Peng, Dingming Li, Yuhong Dai, ZePeng Lin, Juanxi Tian, Yi Zhou, Siqi Dai, Jingwei Wu, Pheng-Ann Heng. 2026-03-09. CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation. https://arxiv.org/abs/2603.08652

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