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jantic avatar jantic commented on June 19, 2024 1

The x_tfms are dynamic, so it shouldn't have anything to do with the tmp folder. If you can replicate it as a legit issue I'll certainly try to hunt it down.

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jantic avatar jantic commented on June 19, 2024

I've actually run into issues with Tensorboard doing weird caching of images in the browser. Have you tried moving the train_gen_images scrubber tool (the orange thing)?

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xyy-Iv avatar xyy-Iv commented on June 19, 2024

Yeah, of course.
I thought it would be some display errors, but in fact, it is not -- The train_gen_images trained so well that it must be some weird changes in input images.
Also refresh the webpage didn't get any better.

And maybe there is something wrong when the tmp images are first generated. After the first time it would be normal black and white images. I don't know where the bug is.

This is the second time I trained:

image

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jantic avatar jantic commented on June 19, 2024

That looks like it's working then.... in that second image you posted here.

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xyy-Iv avatar xyy-Iv commented on June 19, 2024

Yeah, I found that the tmp image files should be generated first and then go to the training process. Otherwise, the input would become colorful.

Don't know what the problem is.

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jantic avatar jantic commented on June 19, 2024

Going to close this for now.

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chakri-muthyala avatar chakri-muthyala commented on June 19, 2024

@xyy-Iv How many training images you've taken, I'm taking ~3k images of anime, is it enough?
@jantic Please suggest!

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jantic avatar jantic commented on June 19, 2024

@ChakriMuthyala It turns out that very soon there will be an update to DeOldify that should help you out quite a bit. The short of it is that you will be able to take the network having been pertained on Imagenet for colorization (which has over a million images), and then fine tune it with the anime images. I haven't tried this specifically yet but I'm almost certain it should work great. The fine tuning would first consist of pertaining with non-gan training generator only, then pertaining the critic to do binary classification on generated vs real images. Finally you do the (no)GAN training to top it off, which will be described in detail in the readme.

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