  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.
