stable-diffusion-docker VS data-efficient-gans

Honestly though, you’re being a bit paranoid about it. Running your own local install would be much easier, and if you use .safetensors then from my understanding you’re pretty safe. You could also run it using the Docker container for a bit more security, though imo it’s not worth the headache.

You could try a dockerized implementation of it like this one https://github.com/fboulnois/stable-diffusion-docker

https://github.com/pieroit/stable-diffusion-jupyterlab-docker/ https://github.com/fboulnois/stable-diffusion-docker

Code for https://arxiv.org/abs/2006.10738 found: https://github.com/mit-han-lab/data-efficient-gans

I would like to make a tiny project with GANs using some high quality pictures of a single individual. I am planning to get around 500 of these and then x-flip them, however I am not sure what model I should consider for the training. I have used StyleGan2 ADA for another project which ended quite well, but I had around 14k pictures, here now the training size is much smaller and I was therefore thinking about using DiffAugment which has seemingly promising results with just 100 images.

I used a modified version of this repo, and there's also the official NVIDIA implementation, though neither have official notebooks. You can Google 'StyleGAN2 ADA Colab' and find a few starting points that way, but wait a few hours and I can clean up my notebook and post it here!

Paper: https://arxiv.org/abs/2006.10738

There are some repos which I've looked at to add deepspeed to such as DiffAugment-stylegan2-pytorch, lucidrains/stylegan2-pytorch and eps696/stylegan2 (which is in tensorflow so it would need to be translated to pytorch as deepspeed only works with pytorch right now).

Here is the paper using GANs with few data points https://arxiv.org/abs/2006.10738

Hey, for image gans, you can use smooth data aumentation https://github.com/mit-han-lab/data-efficient-gans in case you have a reasonable sized dataset.