For RLDG_0da02c80cb, I used a denoising diffusion model trained on a RAVE neural net based on my earlier discography (excluding remixes and collaborations with other artists) and an augmented version of the original dataset itself to create outputs based on different configurations of seed, diffusion steps and temperature.

RAVE-Latent Diffusion by Moisés Horta Valenzuela / 𝔥𝔢𝔵𝔬𝔯𝔠𝔦𝔰𝔪𝔬𝔰 is a denoising diffusion model designed to generate new RAVE latent codes with a large context window, faster than realtime, while maintaining music structural coherency. https://github.com/moiseshorta/RAVE-Latent-Diffusion

RAVE is a variational autoencoder for fast and high-quality neural audio synthesis created by Antoine Caillon and Philippe Esling at IRCAM. https://github.com/acids-ircam/RAVE