ArXiv

IV-CoT: Implicit Visual Chain-of-Thought for Structure-Aware Text-to-Image Generation

2026-06-23 · 17:28 UTC ·Zixuan Li, Haokun Lin, Yicheng Xiao... ·1 min read
Authors
Zixuan Li, Haokun Lin, Yicheng Xiao...
Categories
cs.CV, cs.AI
arXiv
https://arxiv.org/abs/2606.24849v1
PDF
https://arxiv.org/pdf/2606.24849v1

Brief

IV-CoT is a latent visual reasoning framework for query-conditioned text-to-image generation that separates structural planning from appearance rendering via a structural-to-semantic query cascade. Training-only sketch supervision encourages structural queries to learn layout and relations; semantic queries then produce appearance conditioned on that plan. The model performs implicit Chain-of-Thought reasoning in one forward pass and outperforms prior baselines on GenEval and T2I-CompBench.

Why it matters

IV-CoT (Zixuan Li et al., published 2026-06-23) achieves superior results on GenEval and T2I-CompBench by decomposing text-to-image conditioning into a structural-to-semantic cascade.

Key details

  • The method uses training-only sketch supervision to force structural queries to capture object counts, spatial relations, and coarse layout from sketches, without requiring sketch extraction or any intermediate decoding at inference time.
  • IV-CoT implements implicit visual Chain-of-Thought reasoning in a single forward pass: structural queries form a latent visual plan and semantic queries render appearance conditioned on that plan.
Source evidence

Abstract

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.