stylegan2-ada VS LiminalGan

If you smudge just a few locations I doubt it would fool a simple discriminator. You could also train a discriminator that is robust to post-processing by using augmentations. This was popular with StyleGAN models: https://github.com/NVlabs/stylegan2-ada

https://github.com/NVlabs/stylegan2-ada + clip guided diffusion

StyleGAN2-ADA is a neural network good at learning styles from images, you can give it a dataset and 'learn' its style into a file (a trained model). In this example, I load a model and given a random seed, generate a random texture which is applied to the object's material.

You have to modify the code, it's line 60 in https://github.com/NVlabs/stylegan2-ada/blob/main/generate.py

no i dont.. but as for training - i just use default tf stylegan2-ada repo ( https://github.com/NVlabs/stylegan2-ada )

You should check stylegan-2 ada, it works on colab and can be trained less than 12 hours tensorflow implementation

Center crop / crop the images to be square and filter them so they are a consistent resolution, to do this i used this code here: https://github.com/limgan/LiminalGan/blob/main/center_crop_images.py. The usage is make_dataset(in_dir, out_dir, resolution)