1. https://twitter.com/emostaque/status/1595731407095140352?s=4...
1. https://twitter.com/emostaque/status/1595731407095140352?s=4...
You can see nobody likes this new model in any of the stable diffusion communities. It's a big flop and for a good reason. The reason it was so successful in the first place was because you could combine artist names to get the model to the outcome you want.
I'll again remind anyone who thinks they might want to use this to download a working version of SD now. They might break their own libraries in the future, and getting SD1.4 could be a real hassle in a year or so. Getting the right .ckpt file, which can have pickled python malware, is not so trivial, and this will get worse in time.
It's going to diverge into castrated official model that intentionally breaks the older models and older models from unofficial shady sources that might contain malware.
I suspect for similar liability issues as SD 2.0, that they will not strt embedding sub-2.0 weights into consumer electronics.
Social media/"AI ethics" pressure groups will eventually come from these organizations (see Meta's recent debacle with Galactica). Being an unknown org without these pressures was a big reason Stable Diffusion got so popular in the first place.
https://github.com/AUTOMATIC1111/stable-diffusion-webui
https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki...
It does look like artist names have a significantly reduced effect.
All these models are pretty good as that community is strong on art, styles, art skill, and tagging, causing the models to be a serious test case for what's possible. The model with artist names was indeed capable of invoking their styles (for instance, an artist with exceptional anatomy rendering had it translate into the AI version). The more-trained model without the artist names was much more intelligent. It was simply more capable of quality output, so long as your intention wasn't 'remind me of this artist'.
I think that's likely to be true in the general case, too. This tech is destined for artist/writer/creator enhancement, so it needs to get smarter at divining INTENT, not just blindly generating 'knock-offs' with little guidance.
What you want is better tagging in the dataset, and more personalized. If I have a particular notion of an 'angry sky', this tech should be able to deliver that unfailingly, in any context I like. Greg Rutkowski not required or invoked :)
StableDiffusion 1.0 used CLIP released by OpenAI. 2.0 uses a CLIP retrained from scratch by Stability.
We don’t know OpenAI’s dataset so don’t know what was in it or how to recreate it. Nothing was “removed”.
I suspect that people will find keywords that would improve the aesthetics further again, or that fine-tuning will also take place.