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

TL;DR

CoCo introduces a code-based reasoning framework for text-to-image generation, enabling explicit scene structure planning and refinement, leading to significant quality improvements over previous methods.

Contribution

This work presents CoCo, a novel code-as-CoT approach that uses executable code for explicit scene layout planning and refinement in text-to-image generation.

Findings

Achieves up to 68.83% improvement over direct generation methods.

Outperforms other CoT-empowered generation approaches.

Demonstrates the effectiveness of code-driven reasoning for structured image synthesis.

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, which is then executed in a sandboxed environment to render a deterministic draft image. The model subsequently 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% over direct generation, while also outperforming other generation methods empowered by CoT. These results demonstrate that executable code is an effective and reliable reasoning paradigm for precise, controllable, and structured text-to-image generation. The code is available at: https://github.com/micky-li-hd/CoCo