Text-to-Image Alignment in Denoising-Based Models through Step Selection
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912344268865536 |
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| author | Grimal, Paul Borgne, Hervé Le Ferret, Olivier |
| author_facet | Grimal, Paul Borgne, Hervé Le Ferret, Olivier |
| contents | Visual generative AI models often encounter challenges related to text-image alignment and reasoning limitations. This paper presents a novel method for selectively enhancing the signal at critical denoising steps, optimizing image generation based on input semantics. Our approach addresses the shortcomings of early-stage signal modifications, demonstrating that adjustments made at later stages yield superior results. We conduct extensive experiments to validate the effectiveness of our method in producing semantically aligned images on Diffusion and Flow Matching model, achieving state-of-the-art performance. Our results highlight the importance of a judicious choice of sampling stage to improve performance and overall image alignment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17525 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Text-to-Image Alignment in Denoising-Based Models through Step Selection Grimal, Paul Borgne, Hervé Le Ferret, Olivier Computer Vision and Pattern Recognition Visual generative AI models often encounter challenges related to text-image alignment and reasoning limitations. This paper presents a novel method for selectively enhancing the signal at critical denoising steps, optimizing image generation based on input semantics. Our approach addresses the shortcomings of early-stage signal modifications, demonstrating that adjustments made at later stages yield superior results. We conduct extensive experiments to validate the effectiveness of our method in producing semantically aligned images on Diffusion and Flow Matching model, achieving state-of-the-art performance. Our results highlight the importance of a judicious choice of sampling stage to improve performance and overall image alignment. |
| title | Text-to-Image Alignment in Denoising-Based Models through Step Selection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.17525 |