Apparently people who specialize in AI/ML have a very hard time trying to replicate the desired results when training models with ‘poisoned’ data. Is that true?
I’ve only heard that running images through a VAE just once seems to break the Nightshade effect, but no one’s really published anything yet.
You can finetune models on known bad and incoherent images to help it to output better images if the trained embedding is used in the negative prompt. So there’s a chance that making a lot of purposefully bad data could actually make models better by helping the model recognize bad output and avoid it.
So there’s a chance that making a lot of purposefully bad data could actually make models better by helping the model recognize bad output and avoid it.
Apparently people who specialize in AI/ML have a very hard time trying to replicate the desired results when training models with ‘poisoned’ data. Is that true?
I’ve only heard that running images through a VAE just once seems to break the Nightshade effect, but no one’s really published anything yet.
You can finetune models on known bad and incoherent images to help it to output better images if the trained embedding is used in the negative prompt. So there’s a chance that making a lot of purposefully bad data could actually make models better by helping the model recognize bad output and avoid it.
This would be truly ironic
Until they come with some preprocessing step, or some better feature extractors etc. This is an arms race like there are many of