[TeMPOraL]

Picking images from generated sets is a visual thing. Tweaking ControlNet might be too (IDK, I've never got a chance to use it - partly because of what I'm whining about here). However, writing prompts, fine-tuning models, assembling pipelines, renting GPUs, figuring out which software to use for what, where to get the weights, etc. - none of this is visual. It's pretty much programming and devops.

I can't see how covering this on YouTube, instead of (vs. in addition to) writing text + some screenshots and diagrams, makes any kind of sense.

[washadjeffmad]

This isn't for Stable Diffusion, but I wanted to provide a supplemental to my comment: https://kaiokendev.github.io/til

This is the level we're generally working at - first or second party to the authors of the research papers illustrating implementations of concepts, struggling with the Gradio interface, things going straight from commit to production.

It's way less frustrating to follow all of the authors in the citations of the projects you're interested in than wasting your attention sorting through blogspam, SEO, and YT trash just to find out they don't really understand anything, either.

[TeMPOraL]

Thank you. I was reluctant to chase after and track first-party research directly, or work directly derived from it, as my limited prior experience told me it's not the most efficient thing unless I want to go into that field of research myself. You're changing my mind about this; from now, I'll try sticking close to source.

[washadjeffmad]

There's a relatively thin layer between the papers and implementations, which is another way of saying this stuff is still for researchers and assumes a requisite level of background with them. It sounds like you'd benefit from seeking out the first party sources.

This is where video demonstrations come in handy. Since many concepts are novel, it's uncommon to find anyone who deeply understands them, but it's very easy to find people who have picked up on some tricks of the interfaces, which they're happy to click through. I think gradio/automatic1111 makes learning harder than it needs to be by hiding what it's doing behind its UI, while eg- comfyui has a higher initial learning curve but provides a more representational view of process and pipelines.