Multi-Prompt Style Interpolation for Fine-Grained Artistic Control
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909711262023680 |
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| author | Chen, Lei Li, Hao Zhang, Yuxin Li, Chao Wen, Kai |
| author_facet | Chen, Lei Li, Hao Zhang, Yuxin Li, Chao Wen, Kai |
| contents | Text-driven image style transfer has seen remarkable progress with methods leveraging cross-modal embeddings for fast, high-quality stylization. However, most existing pipelines assume a \emph{single} textual style prompt, limiting the range of artistic control and expressiveness. In this paper, we propose a novel \emph{multi-prompt style interpolation} framework that extends the recently introduced \textbf{StyleMamba} approach. Our method supports blending or interpolating among multiple textual prompts (eg, ``cubism,'' ``impressionism,'' and ``cartoon''), allowing the creation of nuanced or hybrid artistic styles within a \emph{single} image. We introduce a \textit{Multi-Prompt Embedding Mixer} combined with \textit{Adaptive Blending Weights} to enable fine-grained control over the spatial and semantic influence of each style. Further, we propose a \emph{Hierarchical Masked Directional Loss} to refine region-specific style consistency. Experiments and user studies confirm our approach outperforms single-prompt baselines and naive linear combinations of styles, achieving superior style fidelity, text-image alignment, and artistic flexibility, all while maintaining the computational efficiency offered by the state-space formulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16133 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Prompt Style Interpolation for Fine-Grained Artistic Control Chen, Lei Li, Hao Zhang, Yuxin Li, Chao Wen, Kai Graphics Text-driven image style transfer has seen remarkable progress with methods leveraging cross-modal embeddings for fast, high-quality stylization. However, most existing pipelines assume a \emph{single} textual style prompt, limiting the range of artistic control and expressiveness. In this paper, we propose a novel \emph{multi-prompt style interpolation} framework that extends the recently introduced \textbf{StyleMamba} approach. Our method supports blending or interpolating among multiple textual prompts (eg, ``cubism,'' ``impressionism,'' and ``cartoon''), allowing the creation of nuanced or hybrid artistic styles within a \emph{single} image. We introduce a \textit{Multi-Prompt Embedding Mixer} combined with \textit{Adaptive Blending Weights} to enable fine-grained control over the spatial and semantic influence of each style. Further, we propose a \emph{Hierarchical Masked Directional Loss} to refine region-specific style consistency. Experiments and user studies confirm our approach outperforms single-prompt baselines and naive linear combinations of styles, achieving superior style fidelity, text-image alignment, and artistic flexibility, all while maintaining the computational efficiency offered by the state-space formulation. |
| title | Multi-Prompt Style Interpolation for Fine-Grained Artistic Control |
| topic | Graphics |
| url | https://arxiv.org/abs/2503.16133 |