Diffusion models, which generate data by iteratively denoising random noise, can be adapted to non-image domains like audio and text by rethinking how data is represented and processed. The core idea remains the same: train a model to reverse a gradual noising process. However, non-image data requires adjustments to handle their unique structures. For example, audio is a 1D time-series signal, and text is discrete and symbolic. Success hinges on designing appropriate noise schedules, neural network architectures, and data encoding methods that align with the data’s inherent properties.
For audio, diffusion models often operate on spectrograms (time-frequency representations) or raw waveforms. Spectrograms are 2D and can leverage architectures similar to image models, like U-Nets with convolutional layers. For raw audio, which is high-dimensional 1D data, models like DiffWave use dilated convolutions to capture long-range dependencies efficiently. In text, discrete tokens pose a challenge because standard diffusion requires continuous noise. Solutions include embedding text in a continuous space (e.g., using pretrained language models) before applying diffusion, as in Diffusion-LM, or using techniques like “absorbing diffusion” that mask tokens instead of adding Gaussian noise. For instance, absorbing diffusion randomly replaces tokens with a [MASK] symbol during training, and the model learns to predict the original tokens step by step.
Key challenges include computational efficiency and maintaining coherence in sequential data. Audio generation must preserve temporal consistency—a misaligned denoising step can introduce artifacts. Text generation must ensure grammatical structure and semantic meaning, which is harder when denoising discrete symbols. Developers might use transformer-based architectures for text to handle long-range dependencies or hybrid approaches that combine diffusion with autoregressive sampling. Practical applications include text-to-speech (denoising audio from text prompts) or document generation (iteratively refining masked text). The adaptability of diffusion lies in its framework—once the data representation and noise process are tailored, the same principles apply across domains.