HCMA: Hierarchical Cross-model Alignment for Grounded Text-to-Image Generation

• URL: http://arxiv.org/abs/2505.06512v3
• Date: Thu, 15 May 2025 01:04:26 GMT
• Title: HCMA: Hierarchical Cross-model Alignment for Grounded Text-to-Image Generation
• Authors: Hang Wang, Zhi-Qi Cheng, Chenhao Lin, Chao Shen, Lei Zhang,
• Abstract summary: We propose a Hierarchical Cross-Modal Alignment (HCMA) framework for grounded text-to-image generation.<n>HCMA integrates two alignment modules into each diffusion sampling step.<n>Experiments on the MS-COCO 2014 validation set show that HCMA surpasses state-of-the-art baselines.
• Score: 27.770224730465237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-image synthesis has progressed to the point where models can generate visually compelling images from natural language prompts. Yet, existing methods often fail to reconcile high-level semantic fidelity with explicit spatial control, particularly in scenes involving multiple objects, nuanced relations, or complex layouts. To bridge this gap, we propose a Hierarchical Cross-Modal Alignment (HCMA) framework for grounded text-to-image generation. HCMA integrates two alignment modules into each diffusion sampling step: a global module that continuously aligns latent representations with textual descriptions to ensure scene-level coherence, and a local module that employs bounding-box layouts to anchor objects at specified locations, enabling fine-grained spatial control. Extensive experiments on the MS-COCO 2014 validation set show that HCMA surpasses state-of-the-art baselines, achieving a 0.69 improvement in Frechet Inception Distance (FID) and a 0.0295 gain in CLIP Score. These results demonstrate HCMA's effectiveness in faithfully capturing intricate textual semantics while adhering to user-defined spatial constraints, offering a robust solution for semantically grounded image generation. Our code is available at https://github.com/hwang-cs-ime/HCMA.

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