Music Style Transfer with Time-Varying Inversion of Diffusion Models

With the development of diffusion models, text-guided image style transfer has demonstrated high-quality controllable synthesis results. However, the utilization of text for diverse music style transfer poses significant challenges, primarily due to the limited availability of matched audio-text datasets. Music, being an abstract and complex art form, exhibits variations and intricacies even within the same genre, thereby making accurate textual descriptions challenging. This paper presents a music style transfer approach that effectively captures musical attributes using minimal data. We introduce a novel time-varying textual inversion module to precisely capture mel-spectrogram features at different levels. During inference, we propose a bias-reduced stylization technique to obtain stable results. Experimental results demonstrate that our method can transfer the style of specific instruments, as well as incorporate natural sounds to compose melodies. Samples and source code are available at https://lsfhuihuiff.github.io/MusicTI/.

Dance-to-Music Generation with Encoder-based Textual Inversion of Diffusion Models

The harmonious integration of music with dance movements is pivotal in vividly conveying the artistic essence of dance. This alignment also significantly elevates the immersive quality of gaming experiences and animation productions. While there has been remarkable advancement in creating high-fidelity music from textual descriptions, current methodologies mainly concentrate on modulating overarching characteristics such as genre and emotional tone. They often overlook the nuanced management of temporal rhythm, which is indispensable in crafting music for dance, since it intricately aligns the musical beats with the dancers' movements. Recognizing this gap, we propose an encoder-based textual inversion technique for augmenting text-to-music models with visual control, facilitating personalized music generation. Specifically, we develop dual-path rhythm-genre inversion to effectively integrate the rhythm and genre of a dance motion sequence into the textual space of a text-to-music model. Contrary to the classical textual inversion method, which directly updates text embeddings to reconstruct a single target object, our approach utilizes separate rhythm and genre encoders to obtain text embeddings for two pseudo-words, adapting to the varying rhythms and genres. To achieve a more accurate evaluation, we propose improved evaluation metrics for rhythm alignment. We demonstrate that our approach outperforms state-of-the-art methods across multiple evaluation metrics. Furthermore, our method seamlessly adapts to in-the-wild data and effectively integrates with the inherent text-guided generation capability of the pre-trained model. Samples are available at \url{https://youtu.be/D7XDwtH1YwE}.