Text effect transfer (TET) aims to preserve the content of character images while rendering their style into various forms, including colors, outlines, shadows, textures, and glyphs. However, manually designing a complete font library is a labor-intensive task, making few-shot text effect transfer an increasingly important research focus. Existing methods often suffer from poor generalization, as their models are limited to a small range of text effects. Some approaches attempt to address this issue, but due to the scarcity of reference-style images, they tend to overfit or lack fine details, leading to failures when handling unseen text effects. To overcome these challenges, we propose a novel fine-tuning strategy that integrates Local Perceptual Fusion and Discrimination to enhance few-shot text effect transfer. Specifically, our fine-tuning strategy allows the model to adapt its parameters based on a small set of reference images from previously unseen styles, enabling the generation of realistic text effects. Additionally, we introduce a structure-level fusion mechanism in the style encoder to improve detail fidelity. To mitigate overfitting, we design a global discriminator and a local discriminator: the global discriminator assesses the overall realism of the generated styles, while the local discriminator performs fine-grained evaluation based on localized observations, ensuring both global consistency and fine-detail preservation. Experimental results demonstrate that our approach achieves advanced performance in few-shot text effect transfer, generating high-quality and highly faithful text effects.