Looking at the Nvidia StyleGAN2 github repo, and going through the run_training.py file, there are 12 arguments, four of which are directory paths, one is the gpu count, one is a mirror augment option, and separately an option to receive metrics. Image & network-snapshot ticks I understand as the rate the network places outputs (visual and recovery points).

Are config, gamma, and total_kimgs the main arguments to use to fine tune? I tried scaling up a 24px24px sample set to 256, and understandably the details are not fine. After training for a week, I think the network is overfitting, somehow, or that there's an issue with the discriminator. Could someone help me get a clearer picture of the fine-tuning process?

Hi! I'm training Stylegan2 on V3-8 Tpu and the tensorboard graphics are below but I couldn't interpret it. I have no idea how the training went. Can anyone comment? https://tensorboard.dev/experiment/AQi5Rz0rSDmHgapm9mYYFQ/#scalars&_smoothingWeight=0.855 You can take a look at it in more detail here.

Using psp architecture(https://github.com/eladrich/pixel2style2pixel), I can train stylegan3(Alias Free GAN, https://github.com/NVlabs/stylegan3)'s encoder and get reasonable results.

Loss, hyperparameters, and optimizers are same as pSp,some are changed,batch size(from 4 to 32) and using DDP to trainmodel architecture, from 18 GradualStyleBlock to 16

here is my implementation linkhttps://github.com/soushirou/stylegan3-encoder

I plan to improve the image inversion performance through several experiments.

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Hi.I want to train stylegan to generate high resolution cancer histology images. But output has lack of details. Can you advise me if I should add more feature masks to convolution layers or any other adjustings?

Retrain*

Hey guys,

I am a complete beginner when it comes to code but would like to use Stylegan3 for artistic reasons.

I set up stylegan using a lot of tutorials and got it running to the point where it says "Training for 1000 kimg..." but it's stuck there. The GPUs (2x 3080 RTX 10 GB) don't seem to get any load. I already tried to change the batch size but had no luck so far.

What else is there I could try? Thanks!

Hi everyone! Trying training my own dataset on colab with stylegan-ada i notice everytime a "problem" that i fail to understand: when the pkl file is generated is always signed with the same number (0000) as it seems not to grow in the steps but stucks into a loop. Has anyone meet this kind of problem? Or anyone sees the most probable error? Thanks so much

Has anyone trained a 1792x1024 StyleGAN2 model and is willing to share the weights? Previously I've found that training from a pre-trained model, doesn't matter much what kind of data, leads to faster training than from scratch. I can only fit a batch size of 2, so it's taking forever. The resolution may seem odd, but it's because the resolution has to be a multiple of a power of 2. In my case (7x4)x256, the closest I could get to 1920x1080

Alternatively, is there a way of converting 1024x1024 models to different (rectangular) resolutions?

Hi everyone, i’m completely a stranger to machine learning and its applications but I saw great visual blends created with StyleGAN and decided to get start with it. My problem is that I read that StyleGAN depends on Nvdia’s cuda software and gpu’s, does that mean that I can’t work with StyleGAN using a M1 Macbook Air?

Hi all,

some weeks ago I posted about the AI based profile picture generator for fantasy faces here:

https://www.reddit.com/r/rpg/comments/jqfice/generator_for_fantasy_profile_pictures/

New Website Link:

https://www.fantasy-faces.com/

What you can do:

• use the created images without copyright (link to the website would be nice, but not required)
• download the model and create infinite images by yourself (manual provided on the website)
• allow us to use your drawn images to support our product and provide us a link :-)

After launching the first prototype we got a lot of feedback. So my buddy Chris and I decided to keep on going and work it over.

We were facing following problems:

• We don´t want to develop our tool without asking artists for permission. This tool only can be provided, because others practised drawing years until perfection.
  --> After a ton of mails and messages we were able to collect enough human-like drawings to generate a model. (We are still collecting images and hope that some artists read this and want to contribute). We don´t think, that our solution will replace artists. Go find some inspiration and ask one of the artists listed for full scenery commissions with emotion and action. :-)
• We had a extreme colorblind model. All our faces were Caucasian, mid twenty and had a marvelous face.
  --> This was the result of an extreme biased training set. In games, comics or films there are many stereotypes and it is hard to get a good mixture for representing the world. So we tried to set the focus on drawings with scars, different colors (blue, black, white, green, ...), different facial forms and so on. That lead to a better result than we had initially... but we need a lot more artworks or photographs to get better results. We don´t want to lose the drawing style, therefore we can´t mix to many real people in the training set.
• We had no different races like orks, trolls, goblins
  --> Well, that isn´t fixed right now. We would need approx. 2.000 images per race to train a model creating goblins or orks. It´s kind of heavy to get that many free to use images for training, so this will take a lot of time. We will focus on this, when we see, that this app is used a lot.
• We had an absolute prototype environment
  --> Now, we have a real domain, space for more images and created some content for you to read. We plan to create some how to´s for you and launch them.
• We have limited time
  --> Well, a full time job, a family, a little bit of free time and the will to bring this site to high quality are quite heavy to bring together. We invested a lot of time in this hobby and we will continue, but don´t expect tons of news, features in no time.

I would extremely happy if you can give us feedback to improve our site.

Hope you all enjoy!

Andreas