Hierarchical Vision-Language Alignment for Text-to-Image Generation via Diffusion Models
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913632728645632 |
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| author | Johnson, Emily Wilson, Noah |
| author_facet | Johnson, Emily Wilson, Noah |
| contents | Text-to-image generation has witnessed significant advancements with the integration of Large Vision-Language Models (LVLMs), yet challenges remain in aligning complex textual descriptions with high-quality, visually coherent images. This paper introduces the Vision-Language Aligned Diffusion (VLAD) model, a generative framework that addresses these challenges through a dual-stream strategy combining semantic alignment and hierarchical diffusion. VLAD utilizes a Contextual Composition Module (CCM) to decompose textual prompts into global and local representations, ensuring precise alignment with visual features. Furthermore, it incorporates a multi-stage diffusion process with hierarchical guidance to generate high-fidelity images. Experiments conducted on MARIO-Eval and INNOVATOR-Eval benchmarks demonstrate that VLAD significantly outperforms state-of-the-art methods in terms of image quality, semantic alignment, and text rendering accuracy. Human evaluations further validate the superior performance of VLAD, making it a promising approach for text-to-image generation in complex scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00917 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Vision-Language Alignment for Text-to-Image Generation via Diffusion Models Johnson, Emily Wilson, Noah Computer Vision and Pattern Recognition Text-to-image generation has witnessed significant advancements with the integration of Large Vision-Language Models (LVLMs), yet challenges remain in aligning complex textual descriptions with high-quality, visually coherent images. This paper introduces the Vision-Language Aligned Diffusion (VLAD) model, a generative framework that addresses these challenges through a dual-stream strategy combining semantic alignment and hierarchical diffusion. VLAD utilizes a Contextual Composition Module (CCM) to decompose textual prompts into global and local representations, ensuring precise alignment with visual features. Furthermore, it incorporates a multi-stage diffusion process with hierarchical guidance to generate high-fidelity images. Experiments conducted on MARIO-Eval and INNOVATOR-Eval benchmarks demonstrate that VLAD significantly outperforms state-of-the-art methods in terms of image quality, semantic alignment, and text rendering accuracy. Human evaluations further validate the superior performance of VLAD, making it a promising approach for text-to-image generation in complex scenarios. |
| title | Hierarchical Vision-Language Alignment for Text-to-Image Generation via Diffusion Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.00917 |