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[-] FooBarrington@lemmy.world 6 points 9 months ago

Besides the point the other commenter already made, I'd like to add that inference isn't deterministic per model. There are a bunch of sources of inconsistency:

  • GPU hardware/software can influence the results of floating point operations
  • Different inference implementations can change the order of operations (and matrix operations aren't necessarily commutative)
  • Different RNG implementations can change the space of possible seed images
[-] onion@feddit.de 2 points 9 months ago* (last edited 9 months ago)

If you generate with the same prompt and settings you get what I would consider the same image except for tiny variations (they aren't matching pixel-perfect)

Edit: A piece of paper has a random 3D relief of fibers, so the exact position a printer ink droplet ends up at is also not deterministic, and so no two copies of a physical catalog are identical. But we would still consider them the "same" catalog

[-] FooBarrington@lemmy.world 2 points 9 months ago

If there's slight variation, it means it's not the same image.

And that's skipping over different RNG etc. You can build a machine learning model today and give it to me, tomorrow I can create a new RNG - suddenly the model can produce images it couldn't ever produce before.

It's very simple: the possible resulting images aren't purely determined by the model, as you claimed.

this post was submitted on 04 Mar 2024
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