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Generated images have geometrical pattern #185
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What cfg are you using? Stylegan3-r is only for datasets with rotational symmetry. |
I am using stylegan3-r. Which cfg is better to use then? |
stylegan3-t seems to be the recommended config for general use. the other option is stylegan2, but that's just a fallback to the previous ADA methods btw, stylegan3-t uses less VRAM so you can probably increase your batch size from what it was on -R |
I'd recommend using So e.g., if you have around 2k images, then set |
Thank you for the explanation. I am using 10k images. But I don't see any --augpipe in train options in stylegan3 train.py |
It already is using |
Amazing work by NVLabs. I just have one curiosity. I was watching the generated results on pretrained network. I noted that most of the faces generated by stylegan3-t-ffhq-1024x1024.pkl had some geometric patterns mostly in the areas where different shades merges. What is the reason for this? |
As I understand it, it's due to the way stylegan3 does feature extraction. I noticed the same on my custom models trained with the same config, for the first 1000 kimg there were noticeable "deep dream"-esque artifacts that were not present on stylegan2. |
Okay. So @isademigod I guess this was also not discussed in the paper as well considering it is a major bug in the model? |
I am training stylegan3 on dermoscopic skin lesion dataset (HAM10000) in conditional mode with 7 classes . After almost 6k iterations I reached fid of 11. I assume 11 is a pretty good number to get. The issue is that when I generate samples there is a diamond or X shape patterns in the images that make the images unrealistic.
Here are some samples of the real dataset:
And here are some examples of generated samples:
Any advice on what id causing this or how can I overcome it?
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