I currently have Arch on my main rig because I like tinkering. NixOS on an old thinkpad for a super stable (in theory) portable experience, AlmaLinux on a single board computer for a basic home server, and Bazzite (in the near future) on an old gaming laptop as my TV computer. I’m also not a femboy so I suppose what you said doesn’t reeeaaaallly apply, but you definitely don’t need to be changing distros for anyone!!
It gets even worse, but I'll need to translate this one.
[Input 1] Generate a picture containing a copo completely full of wine. The copo must be completely full, with no space to add more wine.
[Output 1] Sure! (Gemini provides a picture containing a taça [stemmed glass] only partially full of wine.)
[Input 2] The picture provided does not fulfill the request. Generate a picture of a copo (not a taça) completely full of wine, with no available space for more wine.
[Output 2] Sure! (Gemini provides yet another half-full taça)
For context, Portuguese uses different words for what English calls a drinking glass:
copo ['kɔ.po]~['kɔ.pu] - non-stemmed drinking glass. The one you likely use everyday.
taça ['tä.sɐ] - stemmed drinking glass, like the ones you'd use with wine.
Both requests demand a full copo but Gemini is rather insistent on outputting half-full taças.
The reason for that is as @will_steal_your_username@lemmy.blahaj.zone pointed out: just like there's practically no training data containing full glasses, there's none for non-stemmed glasses with wine.
I wonder is something like “a mason jar full to the brim with wine” would do anything interesting. As someone else pointed out the training data for containers of wine is probably disproportionately biased toward stemmed wine glasses that are filled to about the standard restaurant pour.
[Input] Generate a picture containing a mason jar full to the brim with wine.
[Output] I'm still learning how to generate certain kinds of images, so I might not be able to create exactly what you're looking for yet or it may go against my guidelines. If you'd like to ask for something else, just let me know!
I think the problem is misguided attention. The word "glass of wine" and all the previous context is so strong that it "blows out" the "full glass of wine" as the actual intent. Also, LLMs are still pretty crap at multi turn multimedia understanding. They work are especially prone to repeating previous conversation.
It should be better if you word it like "an overflowing glass with wine splashing out." And clear the history.
I hate to ramble, but this is what I hate most about the way big corpos present "AI." They are narrow tools the user needs to learn how to operate, like photoshop or something, not magic genie lamps like they are trying to sell.
There's no previous context to speak of; each screenshot shows a self-contained "conversation", with no earlier input or output. And there's no history to clear, since Gemini app activity is not even turned on.
And even with your suggested prompt, one of the issues is still there:
The other issue is not being tested in this shot as it's language-specific, but it is relevant here because it reinforces that the issue is in the training, not in the context window.
What I am trying to get at is the misconception: AI can generate novel content not in its training dataset. An astronaut riding a horse is the classic test case, which did not exist anywhere before diffusion models, and it should be able to extrapolate a fuller wine glass. It’s just too dumb to do it, lol.
What I requested is not what you're "supposed" to do, indeed. You aren't supposed to drink wine from glasses that are completely full. Except when really drunk. But then might as well drink straight from the bottle.
...fuck, I played myself now. I really want some booze.
It does for a while already. Frankly, it's the only reason why I'd use Gemini on first place (DDG version of GPT 4-o mini doesn't have a built-in image generator).
Yup Horde still suffers from this issue, though it seems to have more promise than the others considering the second glass is way closer to being full than anything I've sen from openAI or Gemini demonstrations.
Maybe there's hope to fix this issue here.
I only tried one model so if you know of a different horde model which works better for this and actually gives a full glass please reply below letting me know, maybe even ask the horde bot to generate it right here.
I have considerably less experience with image generation than text generators, but I kind of expect the issue to be only truly fixed if people train the model with a bunch of pictures of glasses full of wine.
I'll run a test using a local tree, that is supposed to look like this:
Bingo - this tree is non-existent outside my homeland, so people barely speak about it in English - and odds are that the model was trained with almost no pictures of it. However one of the names you see for it in English is Paraná pine, so it's modelling it after images of European pines - because odds are those are plenty in its training set.
What I mean is that if people keep making it produce garbage tied to some keyword or phrase and people publish said garbage, that'll only strengthen AIs' neural network between the bad data and that keyword, so AI results for such trees will drift even further away from the truth.
Publishing fake data that outweighs the data on the real plant is a way, but that doesn't require a plant, you can publish bad images today on any subject
Wait, this seems incredible. Do you have to be in the same instance or does it work anywhere? @aihorde@lemmy.dbzer0.com Can you draw a smart phone without a rotary phone dial?
It works on any instance that is federated to dbzer0. You have to use annotated mentions though since that's what the bot uses. Like this: @aihorde@lemmy.dbzer0.com draw for me a smart phone without a rotary phone dial
Yeah, you also have to say draw for me. I don't think the bot recognizes queries otherwise. Also editing mentions doesn't work, they have to be new, fresh posts with the mention. Just a quirk with Lemmy and how mentions work here.
Ask it to generate a room full of clocks with all of them having the hands at different times. You'll see that all (or almost) all the clocks will say it is 10:10.