maybe for people who play video games all day, this is the most real life use case

It's a joke, "Boston Consulting Group", but also "Bi-modal Conditional Generation"

Perhaps they meant this: https://twitter.com/BryanCa87413332/status/1601010538364162056?t=_0Se-AdTPQKliEMD1RR8Tw&s=19

Any software that processes audio uses a digital representation of audio. Only when designing circuits would you ever be dealing with analog signals, for example a guitar amp.

You may be confusing analog/digital with the concept of time-domain (waveforms) vs frequency domain (spectrograms).

OP must have built their own neuromorphic chip

I think the most straightforward way to solve this is to generate a dataset of text->image with the diffusion model, and then learn the inverse function with a new model. But you'd need a gigantic dataset for this to work.

Diffusion models have quite diverse outputs, even given the same prompt. Maybe what your asking for is, given an image, and a random seed, design a prompt that replicates the image as close as possible?

In that case, you can imagine each image->text inference as an optimization problem, and use a deep-dream style loss to optimize for the best prompt. It may be helpful to first use this method to select best latent encoding of the text, and then figure out how to learn the inverse function for the text embedding