AudioToken: Adaptation of Text-Conditioned Diffusion Models for
  Audio-to-Image Generation
Guy Yariv Itai Gat Lior Wolf Yossi Adi Idan Schwartz
In recent years, image generation has shown a great leap in performance,
where diffusion models play a central role. Although generating high-quality
images, such models are mainly conditioned on textual descriptions. This begs
the question: "how can we adopt such models to be conditioned on other
modalities?". In this paper, we propose a novel method utilizing latent
diffusion models trained for text-to-image-generation to generate images
conditioned on audio recordings. Using a pre-trained audio encoding model, the
proposed method encodes audio into a new token, which can be considered as an
adaptation layer between the audio and text representations. Such a modeling
paradigm requires a small number of trainable parameters, making the proposed
approach appealing for lightweight optimization. Results suggest the proposed
method is superior to the evaluated baseline methods, considering objective and
subjective metrics. Code and samples are available at:
https://pages.cs.huji.ac.il/adiyoss-lab/AudioToken.