Traditional image style transfer methods typically rely on fixed reference images, limiting user creativity and often resulting in the loss of fine image details. Although text-driven techniques have significantly improved the flexibility of style control, striking a balance between expressive style transformation and content fidelity remains a central challenge. This study explores the practical application of text-guided image stylization by revisiting and evaluating DiffStyler, a representative framework that integrates a dual-diffusion architecture with a learnable noise generation mechanism. Through this approach, this paper demonstrates the effectiveness of text-driven style transfer in both digital art creation and intelligent image processing. The model enables precise translation of textual style descriptions into visual modifications while preserving the structural integrity of the original image. Experimental results show that DiffStyler achieves a favorable trade-off between stylization quality and content preservation, particularly in artistic image translation scenarios. This work not only showcases the engineering potential of cross-modal generation techniques but also establishes a feasible pathway for extending such technologies beyond artistic domains.
Yuxuan Richard Xie (Wed,) studied this question.