goth VS stable-diffusion

Also really easy to implement as there are libraries that do all the heavy lifting for you (https://github.com/markbates/goth is a great starting place IMHO)

In the package 'markbates/goth' that provides a client implementation of OAuth 2.0, the authors have defined the function CompleteUserAuth at the package level like this:

You can use oauth2. Just take e.g. a look at the dex documentation dex. Dex is not a library but a standalone federated oidc provider. Highly recommended. For libraries take a look at goth.

For the kind of websites I prefer to build -- server side rendered with HTMX/Alpine for the extra niceness -- Rust I think could be a very good fit. The main downside for my personal projects is the ecosystem. E.g., a good standard way to handle CSRF tokens, standardised oauth2 implementations (like https://github.com/markbates/goth in Go), things like that. I found myself having to write a lot of code that just exists in the Go ecosystem. The main downside for a business is that it's going to make it harder to hire, since Rust genuinely requires more skill. Yes, developers will make mistakes in Go, as it's far too easy to do things like access shared memory in dangerous ways. But on the flip side, it's a lot easier for them to deliver a feature. In a choice between shipping a feature that is buggy in hard to detect ways, vs not being able to deliver at all because you can't get developers, I think it's better to ship.

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