This paper presents the first steps toward the creation of a tool which enables artists to create music visualizations using pretrained, generative, machine learning models. First, the authors 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. A number of visual effects that result from various operators, including some that are not easily recreated with standard image editing tools, are identified. The authors find that this process allows for continuous, fine-grain control of image generation, which can be helpful for creative applications. Next, music-reactive videos are generated using Stable Diffusion by passing audio features as parameters to network bending operators. Finally, the authors comment on certain transforms that radically shift the image and the possibilities of learning more about the latent space of Stable Diffusion based on these transforms. This paper is an extended version of the paper “Network Bending of Diffusion Models,” which appeared in the 27th International Conference on Digital Audio Effects.
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