Multi-Prompt Style Interpolation for Fine-Grained Artistic Control

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 $\mathbf{\text{StyleMamba}}$ 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 $\mathit{\text{Multi-Prompt}}\mathit{\text{Embedding}}\mathit{\text{Mixer}}$ combined with $\mathit{\text{Adaptive}}\mathit{\text{Blending}}\mathit{\text{Weights}}$ 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.