We present StyleMamba, an efficient image style transfer framework that translates text prompts into corresponding visual styles while preserving the content integrity of the original images. Existing text-guided stylization requires hundreds of training iterations and takes a lot of computing resources. To speed up the process, we propose a conditional State Space Model for Efficient Text-driven Image Style Transfer, dubbed StyleMamba, that sequentially aligns the image features to the target text prompts. To enhance the local and global style consistency between text and image, we propose masked and second-order directional losses to optimize the stylization direction to significantly reduce the training iterations by 5 times and the inference time by 3 times. Extensive experiments and qualitative evaluation confirm the robust and superior stylization performance of our methods compared to the existing baselines.

StyleMamba是一种有效的图像风格转换框架，通过将文本提示转化为相应的视觉风格来保持原始图像的内容完整性。它通过引入条件状态空间模型来加快处理速度，并且通过使用遮罩和二阶方向损失来优化风格化方向，从而大幅减少训练迭代次数和推理时间。与现有基线方法相比，广泛的实验和定性评估验证了我们方法的鲁棒性和优越的风格化性能。

StyleMamba：高效的文本驱动图像风格转换的状态空间模型