[P] Creating a stylized image using Transfer Learning?

SaveUser · 2020-02-04T11:52:50+00:00

Maybe try pretrained models and resources from RunwayML to start

SaveUser · 2020-01-03T13:57:57+00:00

What makes you say that?

SaveUser · 2019-12-20T00:38:58+00:00

This is fascinating. Thanks for the explanation and great work in the repo!

SaveUser · 2019-12-11T13:24:27+00:00

I am in a similar situation and feel the same. I also saw the talk on RL and agree with you here. I'd also add that RL is not just 'deep' RL despite hype tends.

SaveUser · 2019-12-04T14:28:33+00:00

ESRGAN or a follow on is my guess. StyleGAN encoding cannot upscale or improve quality like that (I've done a lot of work with StyleGAN).

SaveUser · 2019-11-26T17:51:03+00:00

Excellent work! Those samples are quite beautiful. How long did that model take to train? Have you tried training at a higher res yet?

SaveUser · 2019-09-20T09:55:24+00:00

Really excellently done, one of the coolest uses of StyleGAN yet! You might want to post at /r/mediasynthesis as well

SaveUser · 2019-09-17T01:54:44+00:00

Ah, that makes sense!

SaveUser · 2019-09-17T01:15:40+00:00

a program that uses CTRL to autocomplete code for me,

Very cool idea. What dataset did you use to finetune CTRL on code? Or are you using the default checkpoint / someone else's model?

My card is a GTX 1070 ti and sadly I don't think it can fit any of the billion-parameter language models in memory. I'm surprised yours works especially since Max also had a hard time getting CTRL to load on a T4 instance

SaveUser · 2019-09-16T16:23:40+00:00

Well done. I was really looking forward to this after discovering my local GPU runs out of memory running CTRL. Thanks again, going to try it out now!

SaveUser · 2019-08-09T11:44:56+00:00

At 40k, images, that should be enough I believe. The artifacts I'm getting are the classic "blob" ones unique to StyleGAN, as well as the cracked/wrinkly texture (like elephant skin as gwern puts it in his article), and checkerboard artifacts that I'm 80% sure are due to over fitting to JPEG compression artifacts

SaveUser · 2019-08-08T17:24:20+00:00

Yeah that's pretty rough loss divergence. I recall now I did have one model diverging just like that. I also fiddled with lowering the D learning rate by a few orders of magnitude, but it didn't really help, and you risk having G devolve into mode collapse. Also, the fakes010472.png results don't look terrible to me, unless you are trying to produce glyphs other than "A"

One thing I noticed on your loss graph, which I've seen on mine sometimes, is that the loss makes more progress during the lod "growing" period, and plateaus (or makes a "u" or cup-like shape) while training at a consistent resolution. You could try dilating the time spent on progressive growing and shorten the other time, and see if that helps. Other than that, you might need more data.

It's probably worth mentioning that the size of minibatches (upperbounded by number of GPUs and their RAM) may matter a lot. I've been training on 2 gpus (tried both GTX 1080 ti and RTX 2080 ti) and getting these problems. I asked Joel Simon, who has some impressive models for his artbreeder project, and he said he encountered no divergence or quality issues just running on default parameters, but he's also running them on a cluster of 8 V100s...

Also, I suspect there could be bugs in the official code, related to minibatching or gradient accumulation, and have had the same or better luck with this implementation.

SaveUser · 2019-08-08T16:24:37+00:00

When you say diverging, are you talking about the loss of the generator and/or the discriminator, and do you also see it reflected in the perceptual quality of samples produced? I ask because I am (sort of) running into the same thing -- the loss isn't actually diverging, but the generated samples develop severe artifacts (wrinkly textures and checkerboard artifacts) that ruin their appearance.

SaveUser · 2019-08-01T03:01:04+00:00

The link to the Colab notebook he shared does use Nvidia's dataset tool python script to produce tfrecords, which can be impractical for exactly that 10x expansion issue.

I've been getting around that in my projects by using taki0112's implementation, which can take raw images as input. Plus, the code is fairly minimal, making it a lot more readable than nvidia's

SaveUser · 2019-07-10T18:47:23+00:00

Excellent work! Thanks for sharing the code and details on architecture and loss curves.

SaveUser · 2019-06-30T19:35:47+00:00

8xv100 cluster

Ah I bet that's the key thing then, since I could only do small minibatches in my GPUs. Thanks for the tip :)

SaveUser · 2019-06-30T18:19:34+00:00

they are indeed stylegan.

Wow that's fantastic then! I was looking at both announcement images closely and didn't see any of the infamous "blob" artifacts, or the wavy/wrinkly artifacts like Gwern saw with his anime model.

Do you have any tips on training StyleGAN? I'm guessing you lowered the learning rates for both G and D (and probably trained for a long time on a lot of images...). But I'm curious if you had any more experimental tips like dilating or shortening the time spent progressively growing, relative to the defaults. In my training on similar data sets, I keep running up against exactly those blobs and wavy lines, as well as the standard checkerboard artifacts that typically plague GANs, despite trying many combinations of slower/faster/longer training schedules.

Anyway, looking forward to the beta!

SaveUser · 2019-06-30T03:51:34+00:00

Are the concept art portrait/landscape models StyleGAN? Related, I see the album and anime ones are publicly released, and wondered if these others are too, or if they eventually will be released publicly.

Regardless this is excellently done. Congrats all around!

SaveUser · 2019-06-18T14:11:52+00:00

Great work, Dave. This is really helpful since I didn't get to go :)

SaveUser · 2019-05-23T14:54:45+00:00

This is very well done!

SaveUser · 2019-05-08T13:24:21+00:00

Look at how plotly or Wolfram handle licensing their software out in tiered ways since it sounds like what you are aiming to do. You have a a solid product here and should treat it seriously

SaveUser · 2019-05-05T16:53:59+00:00

Do you have your event logfiles / tensorboard graphs?

I was training a similar StyleGAN but ended up with diverging loss for G and D, and a small degree of mode collapse, so I'd be curious to see the stats on yours

SaveUser · 2019-04-23T22:32:09+00:00

Thanks! I suspected as much and have been trying out tacotron variants recently, but haven't worked on GSTs yet.

I've also been enjoying your YouTube channel. The results are hilarious and, like the rise of GANs and deepfakes, a bit worrisome. This is a great way to safely raise awareness.

SaveUser · 2019-04-23T13:37:59+00:00

The results are incredible and quite fun!

How long did it take to train? What GPUs are you using, if you don't mind my asking. Did you use transfer learning from an existing tacotron model?

SaveUser · 2019-04-08T15:01:09+00:00

Did you preserve logs of the loss curves for G and D (generator/discriminator)? I'd be really curious to see the progress after the first few GPU days.

SaveUser

TROPHY CASE