A Training-Free Approach for Music Style Transfer with Latent Diffusion Models

Sooyoung Kim, Joonwoo Kwon, Heehwan Wang, Shinjae Yoo, Yuewei Lin, Jiook Cha

Abstract: Music style transfer, while offering exciting possibilities for personalized music generation, often requires extensive training or detailed textual descriptions. This paper introduces a novel training-free approach leveraging pre-trained Latent Diffusion Models (LDMs). By manipulating the self-attention features of the LDM, we effectively transfer the style of reference music onto content music without additional training. Our method achieves superior style transfer and melody preservation compared to existing methods. This work opens new creative avenues for personalized music generation.