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

• URL: http://arxiv.org/abs/2405.05027v1
• Date: Wed, 8 May 2024 12:57:53 GMT
• Title: StyleMamba : State Space Model for Efficient Text-driven Image Style Transfer
• Authors: Zijia Wang, Zhi-Song Liu,
• Abstract summary: StyleMamba is an efficient image style transfer framework that translates text prompts into corresponding visual styles. Existing text-guided stylization requires hundreds of training iterations and takes a lot of computing resources.
• Score: 9.010012117838725
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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