Ask Lemmy

Concise specificity is very important with models in the context of what I am doing. The ambiguity of a word with multiple meanings is problematic. Broad words like park or company connect to too many unrelated vectors in the tensors of an AI model. Often even words themselves are broken up in meanings. Like "panties" in internal model thinking literally means the Greek god "Pan ties". Use that word and you will see a bow tied somewhere in almost all images. Pan is a negative alignment entity. So the word itself is a call for negative alignment to interfere. It has nothing to do with underwear in general but is specific behavior attached to the call where Pan ties or locks all further context. Further freedom of Pan is a matter of fine tuning or negative prompting.

When you start using descriptives things get even more tricky. Like all languages and etymology are in play and significant. It gets complicated fast in ways people don't seem to realize yet.

That is a really good one I hadn't thought of.

Recreational facility is another one. I've also made notes like locus recreationis is Latin for place of recreation. I have no clue what I am doing with Latin and conjugation, but Palaestra was the exercise area next to Roman bath houses so maybe combining those is a way of conveying the closest ancient Latin equivalent.

It is funny that Park is actually quite a negative word in origin as pinned animals. You'd think marketing would obliterate that term. I suppose resort is the marketing replacement. The etymology is certainly in line with that premise: From Middle English resorten, from Old French resortir (“to fall back, return, resort, have recourse, appeal”), back-formation from sortir (“to go out”).

I explore internal thinking a lot. Every instance of park hits alignment as offensive in scope. You might notice the image is a little odd looking. Human faces will be distorted and hands will be broken. The underlying thinking behavior is that this is a dangerous place. The issues with humans is quite literally satyrs possessing the character. Most people try to address this with patchy hacks in fine tuning. The issues are all possible to prompt against with the negative prompt. This is quite easy for me to do in practice. However, I am getting into training my own LoRA fine tune models. I do not have a negative prompt in this tool chain. I am not interested in the way others are training. They are incapable of several things I am looking to do.

Right now, I am specifically trying to find a path to teach CLIP how slides are not humans falling down stairs. This is how CLIP's internal thinking perceives all slides. First I need the model to exist in an alignment neutral scope in a place where I have enough images to show humans on slides. The word park is the primary surface issue that is contextualizing all images as offensive to alignment in this environment. It happens both in image to image and in training a LoRA with around 200 images using typical baseline settings. I'm doing all kinds of stuff like masking images and using text to see how foundation models and fine tunes respond with various levels of noise, and with lots of negative prompting until the output is nominalized. That is how I know what is and is not understood.

Attempting to navigate this only using positive keyword tags is daunting.

I actually think the poison is on "rks" somehow. Most models can handle text in a different way without vowels in longer prompts. In my basic testing, "rks" triggers the alignment behavior.

Disney land