Martin Heinze | marts~

Tag: neural networks

  • Black Latents | Latent Diffusion

    Black Latents | Latent Diffusion is a gradio application that allows you to spawn audio items from Black Latents, a RAVE V2 VAE trained on the Black Plastics series using RAVE-Latent Diffusion models.

    A demo version is accessible on Huggingface. The full application can be retrieved from GitHub to use in local inference.

    Latent Diffusion with RAVE

    The RAVE architecture makes timbre transfer on audio input possible, but you can also generate audio by using its decoder layer as a neural audio synthesizer, e.g. in Latent Jamming.

    Another approach to use RAVE to spawn new audio information has been provided by Moisés Horta Valenzuela (aka 𝔥𝔢𝔵𝔬𝔯𝔠𝔦𝔰𝔪𝔬𝔰) with his RAVE-Latent Diffusion model.

    Latent diffusion models in general are quite efficient since they operate on the highly compressed representations of the original data. The key idea of RAVE-Latent Diffusion is to replicate structural coherency of audio information by encoding (longer) audio sequences into their latent representations using a RAVE encoder and then train a denoising diffusion model on these embeddings. The trained model is able to unconditionally generate new and similar sequences of the same length which can be decoded back into the audio domain using the RAVE model’s decoder.

    The original package by 𝔥𝔢𝔵𝔬𝔯𝔠𝔦𝔰𝔪𝔬𝔰 supports a latent embedding length down to a window size of 2048, which translates to about 95 seconds of audio at 44.1 KHz, suitable for compositional level information.

    In my fork RAVE-Latent Diffusion (Flex’ed), I extended the code to support a minimum of 256, which equals about 12 seconds at 44.1 KHz, and implemented a few other improvements and additional training options.

    Black Latents: turning Black Plastics into a RAVE model

    The motivation to train Black Latents was to extract dominant characteristics from my Black Plastics series, a compilation of 7 EPs with a total of 28 audio tracks of genres Experimental Techno, Breakbeats and Drum & Bass, I released between 2012-2020.

    I trained the model using the RAVE V2 architecture with a higher capacity of 128 and submitted it to the RAVE model challenge 2025 hosted by IRCAM, where it was publicly voted into first place. The model is available on the Forum IRCAM website.

    Using Black Latents | Latent Diffusion to spawn audio

    For Black Latents | Latent Diffusion, I trained diffusion models in 7 different configurations and context window lengths using once again the audio material from the Black Plastics series as base data set together with the Black Latents VAE.

    The application itself is a simple gradio interface to the generate script of RAVE-Latent Diffusion (Flex’ed). In the UI, you can choose from the different diffusion models, define seeds and set additional parameters like temperature or latent normalization before generating audio items through the Black Latents model decoder.

    Depending on the diffusion model and parameter selection, the resulting output varies from stumbling rhythmic micro structures to items with resemblances of their base training data’s macro scale considerations.

    Other examples

    I published earlier experiments with RAVE-Latent Diffusion and a different set of RAVE models in the form of two albums:

    MARTSMÆN – RLDG_0da02c80cb [datamarts/2KOMMA4]: Bandcamp, Nina

    MARTSM^N – RLDG_835770db1c [datamarts/2KOMMA3]: Bandcamp, Nina

  • Neural network bending in Pure Data

    The practice of bending systems, that is: modifying or disrupting their intended functions, has been a recurring aspect of artistic practice across different cultural contexts. More recently, the bending of neural networks has become a point of interest for researchers and practitioners, driven partly by the desire to expand the models’ generative capabilities through alterations to their underlying structures for processing and reproducing information.

    “One common criticism of using deep generative models in an artistic and creative context, is that they can only reproduce samples that fit the distribution of samples in the training set. However, by introducing deterministic controlled filters into the computation graph during inference, these models can be used to produce a large array of novel results.”

    Broad et. al. “Network Bending: Expressive Manipulation of Generative Models in Multiple Domains” https://www.mdpi.com/1421002

    A few months back I came across Błażej Kotowski’s fork of nn~. It adds a new functionality to the nn~ object that exposes neural net layers along with their weights and biases for compatible model architectures (e.g. RAVE, vschaos2, MSPrior or AFTER). It also allows you to modify weights and biases and push them back into the respective layer. That means we can hack into these models and do some network bending experimentation in real time now, purposefully altering, partly disrupting the capabilities of the model both in terms of processing and creating audio information.

    Bender abstraction for Pure Data

    Inspired by Błażej’s video, i’ve created an abstraction in Pure Data that can modify the neural net’s data in various ways, such as off-setting, randomizing or inverting values. That component is called Bender and is available on Github.

    Since the changes can have a dramatic effect on the sound, I’ve added a method that lets you control the percentage of data points affected when applying adjustments. This makes the results much less extreme, allowing you to bend your neural network in a more subtle way.

    You can select the desired percentage by moving the slider to a position between 0 and 100%. The number of data points is then calculated and evenly distributed within the selected layer. Any adjustments made using the sliders next to the array will only affect these specific data points, not the entire array.

    Limitations

    The number of data points per layer can range from a few thousand to millions, depending on the model’s architecture and training setup. This can impact the real-time performance of network bending, especially based on your workstation’s configuration. I haven’t found a practical solution for this issue yet, but it might be addressed in the future.