  In recent years, with the realistic generation results and a wide range of
personalized applications, diffusion-based generative models gain huge
attention in both visual and audio generation areas. Compared to the
considerable advancements of text2image or text2audio generation, research in
audio2visual or visual2audio generation has been relatively slow. The recent
audio-visual generation methods usually resort to huge large language model or
composable diffusion models. Instead of designing another giant model for
audio-visual generation, in this paper we take a step back showing a simple and
lightweight generative transformer, which is not fully investigated in
multi-modal generation, can achieve excellent results on image2audio
generation. The transformer operates in the discrete audio and visual
Vector-Quantized GAN space, and is trained in the mask denoising manner. After
training, the classifier-free guidance could be deployed off-the-shelf
achieving better performance, without any extra training or modification. Since
the transformer model is modality symmetrical, it could also be directly
deployed for audio2image generation and co-generation. In the experiments, we
show that our simple method surpasses recent image2audio generation methods.
Generated audio samples can be found at
https://docs.google.com/presentation/d/1ZtC0SeblKkut4XJcRaDsSTuCRIXB3ypxmSi7HTY3IyQ/
