deglazer

program to remove glaze / nightshade / glaze2 from images

requirements

pip install -r requirements.txt

usage

python run.py -h

python run.py clean image.png

python run.py clean_folder path/to/images --newfolder path/to/cleaned/images

methods

• vaeloop

+ most consistent
~ passes image through VAE, which removes imperfections (e.g. glaze)
~ resizes images to a mult of 8
- needs a gpu

• glaze1

~ classical glaze remover (has it even worked at all?)

• glaze2

+ both vaeloop and glaze1 combined
~ passes image through VAE, which removes imperfections (e.g. glaze)
~ resizes images to a mult of 8
- needs a gpu

effectiveness

considering that glaze/nightshade doesnt work at all, this is extremely effective (100% captioning accuracy after deglazing)

training effectiveness decreases after deglazing since glaze actually helps training (the glaze/shade acts as noise offset)

faq

what does glaze actually do?

basically nothing; it adds adversarial noise which supposedly makes the model think a dog is a cat

the issue is SD doesnt really care about that since we pass it through a VAE, but it does confuse the CLIP (see: nightshade)

vaeloop will fix the 'glaze' and make it CLIP taggable (even though most people dont use CLIP)

why did you make this

glaze n co think that they've made a miracle cure for ai training on other art; they have not

is it possible to actually make images untrainable

not without making it terrible to watch/private

pick 2 of 3 things:

• untrainable
• good to watch
• publicly viewable