StyleBlend: Enhancing Style‐Specific Content Creation in Text‐to‐Image Diffusion Models
Synthesizing visually impressive images that seamlessly align both text prompts and specific artistic styles remains a significant challenge in Text‐to‐Image (T2I) diffusion models. This paper introduces StyleBlend, a method designed to learn and apply style representations from a limited set of ref...
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| Veröffentlicht in: | Computer graphics forum Jg. 44; H. 2 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Oxford
Blackwell Publishing Ltd
01.05.2025
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| Schlagworte: | |
| ISSN: | 0167-7055, 1467-8659 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Synthesizing visually impressive images that seamlessly align both text prompts and specific artistic styles remains a significant challenge in Text‐to‐Image (T2I) diffusion models. This paper introduces StyleBlend, a method designed to learn and apply style representations from a limited set of reference images, enabling content synthesis of both text‐aligned and stylistically coherent. Our approach uniquely decomposes style into two components, composition and texture, each learned through different strategies. We then leverage two synthesis branches, each focusing on a corresponding style component, to facilitate effective style blending through shared features without affecting content generation. StyleBlend addresses the common issues of text misalignment and weak style representation that previous methods have struggled with. Extensive qualitative and quantitative comparisons demonstrate the superiority of our approach. |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0167-7055 1467-8659 |
| DOI: | 10.1111/cgf.70034 |