Diffusion models were initially developed for image data, but their underlying principles can be adapted to non-image data such as audio and text. At a high level, diffusion models work by gradually corrupting data with noise and then training a model to reverse this process, effectively learning how to generate data from noise. This basic concept can be applied to various types of data by adjusting how the noise is added and how the data is represented.
For audio data, diffusion models can operate similarly to how they process images. The audio signal can be represented in the time domain or transformed into a spectrogram, which is essentially a visual representation of sound frequencies over time. Noise can be added to these representations, and the model can be trained to reconstruct the original audio signal from the noisy version. For instance, a diffusion model could learn to generate new music or human speech by sampling from a noise distribution and progressively transforming it into a clear audio signal.
In the case of text, diffusion models can handle sequences of words or characters rather than pixel values. Noise can be introduced by randomly modifying words or shuffling sentence structures during the training phase. The model then learns to predict the correct sequence from the corrupted version. This allows for applications in text generation, such as writing stories or generating dialogues. An example is using diffusion models to create realistic conversations between characters in a video game, allowing for dynamic storytelling based on different player choices. By adapting the noise addition and representation for the specific characteristics of audio and text, diffusion models can effectively generate high-quality non-image data.