  In this paper we present the first steps towards the creation of a tool which
enables artists to create music visualizations using pre-trained, generative,
machine learning models. First, we investigate the application of network
bending, the process of applying transforms within the layers of a generative
network, to image generation diffusion models by utilizing a range of
point-wise, tensor-wise, and morphological operators. We identify a number of
visual effects that result from various operators, including some that are not
easily recreated with standard image editing tools. We find that this process
allows for continuous, fine-grain control of image generation which can be
helpful for creative applications. Next, we generate music-reactive videos
using Stable Diffusion by passing audio features as parameters to network
bending operators. Finally, we comment on certain transforms which radically
shift the image and the possibilities of learning more about the latent space
of Stable Diffusion based on these transforms.
