StyleMamba : State Space Model for Efficient Text-driven Image Style Transfer

arXiv:2405.05027v1 Announce Type: new
Abstract: 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 …

abstract arxiv computing computing resources cs.ai cs.cv framework image images integrity prompts resources space speed state state space model style style transfer text training transfer type visual while