Progressive Prompt Detailing for Improved Alignment in Text-to-Image Generative Models
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918039754113024 |
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| author | Saichandran, Ketan Suhaas Thomas, Xavier Kaushik, Prakhar Ghadiyaram, Deepti |
| author_facet | Saichandran, Ketan Suhaas Thomas, Xavier Kaushik, Prakhar Ghadiyaram, Deepti |
| contents | Text-to-image generative models often struggle with long prompts detailing complex scenes, diverse objects with distinct visual characteristics and spatial relationships. In this work, we propose SCoPE (Scheduled interpolation of Coarse-to-fine Prompt Embeddings), a training-free method to improve text-to-image alignment by progressively refining the input prompt in a coarse-to-fine-grained manner. Given a detailed input prompt, we first decompose it into multiple sub-prompts which evolve from describing broad scene layout to highly intricate details. During inference, we interpolate between these sub-prompts and thus progressively introduce finer-grained details into the generated image. Our training-free plug-and-play approach significantly enhances prompt alignment, achieves an average improvement of more than +8 in Visual Question Answering (VQA) scores over the Stable Diffusion baselines on 83% of the prompts from the GenAI-Bench dataset. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_17794 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Progressive Prompt Detailing for Improved Alignment in Text-to-Image Generative Models Saichandran, Ketan Suhaas Thomas, Xavier Kaushik, Prakhar Ghadiyaram, Deepti Computer Vision and Pattern Recognition Artificial Intelligence Text-to-image generative models often struggle with long prompts detailing complex scenes, diverse objects with distinct visual characteristics and spatial relationships. In this work, we propose SCoPE (Scheduled interpolation of Coarse-to-fine Prompt Embeddings), a training-free method to improve text-to-image alignment by progressively refining the input prompt in a coarse-to-fine-grained manner. Given a detailed input prompt, we first decompose it into multiple sub-prompts which evolve from describing broad scene layout to highly intricate details. During inference, we interpolate between these sub-prompts and thus progressively introduce finer-grained details into the generated image. Our training-free plug-and-play approach significantly enhances prompt alignment, achieves an average improvement of more than +8 in Visual Question Answering (VQA) scores over the Stable Diffusion baselines on 83% of the prompts from the GenAI-Bench dataset. |
| title | Progressive Prompt Detailing for Improved Alignment in Text-to-Image Generative Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2503.17794 |