If it's something you think is useful for others, sure that'd probably be the best place. Otherwise feel free to dm me!
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Replying to @citnaj @CpnTaters
I’ve been having trouble getting the repeatable GAN process to run the second time. Still working on investigating. But I’ll follow later this week.
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Replying to @braddwyer @CpnTaters
I carried over a habit from fastai notebooks which may not necessarily be great for this case which is that you have to run some cells carefully before doing the repeat. Namely- you'll need to make sure data_gen is initialized under 64px again. That part is dumb and I know it.
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Replying to @citnaj @CpnTaters
If I recall correctly it was that when I switched that part from 0 to 1 it wasn’t finding the 0 checkpoint it had tried to save.
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Replying to @braddwyer @CpnTaters
Ohh that’s right. So what you need to do is look for the set of weights that work of all the checkpoints that are saved. Select it, and rename in to what you need here. This is the pain in the ass part of NoGAN at this point. Larger batch size helps a lot in stabilization.
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Replying to @citnaj @CpnTaters
I'm not sure I understand what you mean. How do I check which ones work? I've got a list of weights like this from run 0 that I'm not sure what the _#### at the end represents:pic.twitter.com/xtfZO3MM7o
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Replying to @citnaj @CpnTaters
Ah I see, that's what this is referring to? "Find the checkpoint just before where glitches start to be introduced." I thought that was referring to the same thing as "repeating the cycle below a few times (about 5-8?)" -- misunderstood! I may have to start over
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Replying to @braddwyer @CpnTaters
Yes on the checkpoint question. So basically you’re finding the good checkpoint, then doing a new round of NoGAN critic pretraining on newly generated images, that then trains the generator again in GAN setting, etc
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Replying to @citnaj @CpnTaters
Do you have any examples of the "glitches" I should be looking for? Is it like the splotchy colors in the bottom prediction here? (I realize my preprocessing on that one ate the snowman so there's not going to be any way for it to do well on that example)pic.twitter.com/2usRT0io8H
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It’s going to be harder to evaluate here because of the use case. With normal images, it’s things like orange skin and lips looking like they have herpes (no joke). Here you might just have to compare before/afters until you see the inflection point of bad quality
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