The Multi-Source Latent Diffusion Model (MSLDM) framework can be extended to accommodate a larger number of instrumental sources by implementing several key strategies. First, the architecture of the SourceVAE can be modified to support additional channels in the latent space. This involves increasing the latent dimension (C) and adjusting the downsampling factor (D) to ensure that the model can effectively capture the unique characteristics of each new instrumental source.
Second, the diffusion model can be adapted to handle a higher dimensional input by reshaping the input tensor to include the additional sources. This would require modifications to the 1D-Unet architecture to ensure that it can process the increased channel dimension without compromising performance.
Third, training data must be expanded to include a diverse set of instrumental sources, ensuring that the model learns to generate and harmonize a broader range of instruments. This could involve curating datasets that contain multi-track recordings of various instruments, thereby enriching the training process.
Finally, the inference pipeline should be designed to allow for flexible generation, enabling users to specify which sources to generate or mix, thus enhancing the model's usability in practical applications.