Exciting news in the world of AI music generation! Meet Stability AI Stable Audio, the latest addition to the text-to-music model scene, following in the footsteps of Google MusicLM, Meta's MusicGen and others .
Having actively fine-tuning their audio-to-audio dance diffusion model from its early stages, and being a part of their Discord community, it was evident that the next step for them would involve adding textual conditioning to their models.
While it's true that initial text-to-music models may face skepticism due to their limitations, it's important to remember that we are only scratching the surface of this technology.
What truly excites me about Stablity AI's approach to music generation is its use of diffusion algorithms similar to those employed in image generation models like Stable Diffusion. This opens the door to drawing inspiration from the more mature field of image generation, and considering how we can apply similar principles to music.
One promising direction, in my opinion, is taking cues from the ControlNet model, which has revolutionized image generation by allowing users to guide the process based on visual elements like edge detection, depth, etc. This shift towards a more domain-specific and non-textual approach holds immense potential for music generation, which is, in itself, a symbolic system with so much meaning that exceeds textual description. It could potentially enable us to control model outputs based on factors such as melodic contour, rhythmic patterns, and even sonic elements – aspects that cannot be adequately described through text alone.
Most importantly, these levels of control are critical steps toward making generative AI tools truly professional and artist-friendly, elevating them beyond their current status as mere playthings.
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