IV-CoT：面向結構感知文本到圖像生成的隱式視覺思維鏈

摘要

统一多模态大语言模型（MLLMs）在文本到图像生成质量上已取得显著进展，但在处理需保留对象数量、空间关系、属性绑定和大致布局等结构信息的结构感知提示时仍存在困难。我们将其归因于部分设计中，结构规划与外观渲染被纠缠在单一条件流中。为解决此问题，我们提出隐式视觉思维链（IV-CoT）——一种面向查询条件图像生成的潜在视觉推理框架。IV-CoT将视觉条件查询分解为结构到语义的级联过程：结构查询首先形成潜在视觉规划，语义查询再基于该规划渲染外观。为引导结构查询，我们引入仅训练阶段的草图监督，鼓励其从草图中捕获结构信息，且推理时无需提取草图或中间解码。IV-CoT通过单次前向传播执行隐式思维链推理，在GenEval和T2I-CompBench上取得了更优结果。可视化与分析表明，学习到的结构查询与语义查询在结构感知生成中扮演互补角色。

English

Unified multi-modal large language models (MLLMs) have achieved strong text-to-image generation quality, but still struggle with structure-aware prompt following, where object counts, spatial relations, attribute bindings, and coarse layouts must be preserved. We attribute this limitation in part to the entanglement of structural planning and appearance rendering within a single conditioning stream. To address this issue, we propose Implicit Visual Chain-of-Thought (IV-CoT), a latent visual reasoning framework for query-conditioned image generation. IV-CoT decomposes the visual conditioning queries into a structural-to-semantic cascade, where structural queries first form a latent visual plan and semantic queries then render appearance conditioned on this plan. To guide the structural queries, we introduce training-only sketch supervision, which encourages them to capture structure from sketches without requiring sketch extraction or intermediate decoding at inference time. IV-CoT performs implicit CoT reasoning in a single forward pass and achieves superior results on GenEval and T2I-CompBench. Visualizations and analyses demonstrate that the learned structural and semantic queries play complementary roles in structure-aware generation.