Draw What You Hear: High-Fidelity Image Generation and Manipulation via SoundAdapter

Currently, the text-to-image (T2I) generation has established itself as a cornerstone within the realm of AI-generated content (AIGC), due its remarkable success to the availability of extensive datasets comprising paired text-vision samples. Nevertheless, the absence of audio-visual pairs hinders the growth of audio-to-image (A2I). Although prior approaches have pioneered the A2I task, the tight entanglement between initial audio and image encoders imposes the challenge of gathering audio-visual samples, resulting in degraded performance and limited sound flexibility. Therefore, this article proposes a novel SoundAdapter to draw what you hear. Specifically, the SoundAdapter's structure is meticulously designed around transformer blocks, which are critical for capturing overarching patterns and dependencies within the data. In addition, it integrates a sophisticated multigranularity approach coupled with a hybrid supervisory signal, ensuring both fine-grained semantic alignment and seamless optimization across various levels of representation. Extensive tests demonstrate that the SoundAdapter excels in training, setting new benchmarks in zero-shot audio classification, as well as in creating and modifying images across a variety of datasets. The implementation code and several demos supporting this study are openly accessible at https://github.com/CV-MM-Lab/SoundAdapter, facilitating reproducibility and further research.