ConvNeXt VS stable-diffusion

Excited to share a python package that I've released (working on it for a while now): git: https://github.com/sashank-tirumala/yaml_config_override pypi: https://pypi.org/project/yaml-config-override/ The idea is simple, often you need to write hundred lines of 'argparse' code for deep learning and machine learning projects (example). To avoid that we create config files (yaml) but then there are times when you just want to overwrite the config values with 'argparse'. This package automates that process. It automatically adds command line arguments from config file definitions and then also overwrites config file arguments with your command line arguments. So for example:

Found relevant code at https://github.com/facebookresearch/ConvNeXt + all code implementations here

Have you considered using monitor services such as Wandb? https://github.com/facebookresearch/ConvNeXt provides the code for distributed training, which you can refer to. It is a big tricky to make it work when using distributed training, but obviously it is better than regularly checking progress.

The ConvNeXt code is available on the project’s GitHub. The paper A ConvNet for the 2020s is on arXiv.

The ConvNeXt code is available on the project’s GitHub. The paper A ConvNet for the 2020s is on arXiv.

Code: https://github.com/facebookresearch/ConvNeXt

Which is a 1-click install of Stable Diffusion with an alternative web interface. You can choose a different approach but this one is pretty simple and I am new to this stuff.

an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.

It sounds like you just want to set up Stable Diffusion to run locally. I don't think your computer's specs will be able to do it. You need a graphics card with a decent amount of VRAM. Stable diffusion is in Python as is almost every AI open source project I've seen. If you can get your hands on a system with an Nvidia RTX card with as much VRAM as possible, you're in business. I have an RTX 3060 with 12 gigs of VRAM and I can run stable diffusion and a whole variety of open source LLMs as well as other projects like face swap, Roop, tortoise TTS, sadtalker, etc...

Use specific GPU on multi GPU systems · Issue #87 · CompVis/stable-diffusion · GitHub

I dealt with this in one of my posts. At least SD 1.1 till 1.5 are all trained on a batch size of 2048. The version pretty much everyone uses (1.5) is first pretrained at a resolution of 256x256 for 237K steps on laion2B-en, at the end of those training steps it will have seen roughly 500M images in laion2B-en. After that it is pre-trained for 194K steps on laion-high-resolution at a resolution of 512x512, which is a subset of 170M images from laion5B. Finally it is trained for 1.110K steps on LAION aesthetic v2 5+. This is easily verified by taking a glance at the model card of SD 1.5. Though that one doesn't specify for part of the training exactly which aesthetic set was used for part of the training, for that you have to look at the CompVis github repo. Thus at the end of it all both the most recent images and the majority of images will have come from LAION aesthetic v2 5+ (seeing every image approx 4 times). Realistically a lot of the weights obtained from pretraining on 2B will have been lost, and only provided a good starting point for the weights.

stable diffusion · CompVis/stable-diffusion@2ff270f · GitHub

Here for example --> https://github.com/CompVis/stable-diffusion