This sort of thing would be really nifty, except that it relies on the model using encrypted message text whenever it needs "random numbers".

The thing is that I'm under the impression that people who actually use diffusion models to produce images value reproducibility, which they achieve by using pseudorandom generators with relatively short seeds, and remembering the seeds.

Not only that, but the software tends to try to embed prompts and the like in the created image files, and I suspect it puts the seeds, model identities, and anything else that matters in there, too, with the idea that the generated image contains everything you'd need to reproduce it. If it does leave leaves anything out, there's a good chance somebody is going change it to include that thing.

It's not that they actually want to reproduce it exactly, but that that stuff gives a baseline they can tweak to create refined versions.

That's incompatible with the steganography. If you have all the alleged inputs, you can detect an image bearing steganographic data by trying to reproduce it. And if it's normal to include all of the inputs, then images without them are going to look suspicious.

It doesn't seem useless. Even so, it seems as though it'd be a lot more useful if the people writing image generation software could be convinced to play along by using truly unpredictable cryptographic RNGs. But that would mean giving up reproducibility that I think they value.