Imagine you've laid down a killer guitar riff, but your drumming skills are... let's just say "under construction." A new AI technique called Subtractive Training could be your virtual bandmate. Researchers at Stanford University have developed a method to seamlessly insert missing instrument stems into existing music tracks. This isn't just about filling in the blanks; it's about intelligently crafting accompaniments that fit the existing music like a glove. They achieve this by using latent diffusion models, a type of AI known for generating realistic images and audio. The real magic comes from their novel training approach. They use AI to remove stems from complete mixes, creating a 'before' and 'after' dataset. Then, they train the model to predict the missing stem based on the remaining music and text prompts like "Add aggressive rock drums." The results are impressive, generating convincing drum tracks that match the style and rhythm of the original music. The team has also applied this method to other instruments and even MIDI data, making it useful for different musical applications. This research has the potential to transform music production, empowering musicians with AI tools to bring their musical visions to life. However, there are still some challenges, like occasional difficulties in generating drum tracks for certain genres like EDM. The researchers are actively working on improving the model's capabilities and addressing these issues, pointing to a future where AI can not only create music from scratch but also enhance and remix our own musical ideas.