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#qwen#image#model#models#text#https#porn#images#com#more

Discussion (154 Comments)Read Original on HackerNews
I find it easy to envision a world, maybe 50 years from now, in which the very concept of "truth in advertising" is viewed as a lost, idyllic fantasy. Something people are nostalgic for, but feel powerless to regain.
From what I hear that's already the case right now with much more consequential transactions, like renting real estate in NYC. Square footages that are blatant lies, etc.
I find it easy now!
Regardless, I'd rather see real clothing on a real person when it comes to my purchasing decisions. I buy a lot of vintage clothes online and I've noticed a dramatic uptick in AI images of models wearing the clothes. I've never once bought from those sellers because it feels disingenuous. Sometimes they have fake runways which is actual false advertising because it makes the item appear more expensive than it really is. I've also noticed that the AI models' body types are always thin even if the item is a L or XL. Needless to say, the AI isn't showing me what an XL looks like on a small model; it's showing what a small model would look like if the item fit perfectly.
[1] https://www.primermagazine.com/wp-content/uploads/2011/02/St...
Here's a recent example: https://www.etsy.com/ca/listing/4509158065/corner-wall-shelf...
Ironically, ChatGPT is decently good at ferreting these out. Like I sent it a screenshot of that listing and it not only helped me find where the original item was for sale, but also pointed out how the dimensioned diagram shows it as being just 49" tall, whereas the "in real life" image looks like it's at least six feet, based on it coming up over the top of the picture frame.
I ended up engaging a local woodworker to make me a piece like it instead. Obviously way, way more expensive, but it will be real solid walnut finished to match my dining table.
Sure, it’s idealized, but some people benefit from seeing color / neckline / etc on themselves as a visual reference.
Me, I’m a text-learner so I don’t get it at all. But I know people who get value.
In the end the one thing that is completely honest is the portion of the picture that is the item you are re-selling. But somehow to me the entire thing feels disingenuous.
The result was always someone extremely good looking
There’s going to be an entirely new class of mental disorders that will emerge from people being deluded by AI
This is something that can be fixed over time. And if this forces clothing manufacturers to stick more to their advertised "specs" (width/length), then it's a win for us.
Results are mixed, expensive, but it really feels you're few months off the next improvement to really nail it. It's already good enough.
Wonder what Qwen image will provide over nano banana.
Run this in console to see all the tags:
(i.e. the porn references)
It apparently adds common search terms that contain words like "qwen". This evidently includes possibly mistyped searches for "gwen" or "ben" in a NSFW context.
Maybe someone knows more about how such SEO tools work, and where they pull the data from.
Am i gregnant?
https://youtu.be/EShUeudtaFg
(See kids, it is possible to fight memes/racism with memes! And well... yeah, this really is racist.)
https://knowyourmeme.com/memes/bobs-and-vegana
https://pastes.io/uenL6X9K
It also seems to have an obsession with this celebrity, based on how many times ctrl-f for "stefani" turns up a result.
https://en.wikipedia.org/wiki/Gwen_Stefani
[whynotboth.gif]
Do you want to know more?
[ ] Yes [x] no
erm... what?
https://www.nytimes.com/2019/06/07/us/hate-groups-porn-consp...
porn is as American as apple pie.
So not at all then as Apple Pie is a traditional English desert. :P
https://en.wikipedia.org/wiki/Apple_pie
Apart from that, I made no judgement about porn in general, just about porn tags in Chinese backed AI websites.
https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/i...
https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/i...
https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/i...
https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen-Image/i...
source: work at a photograph start, even training on raw images things get tinted, it is an uphill battle
What training mechanism or model architecture provides the glue to go from human text to images?
Don't you need to have millions of really descriptively labelled images?
There are ML models that do the reverse and output image to text, which assist quite a lot.
The better the text represents the unique thing in the photo, the better the model understands what that text means.
Slightly longer answer for older text to image models you teach them how to encode images and text into the same latent space. Then you simply do a conversion, take a text input, put it into latent space and then extract the image that latent space represents.
It's a shame they didn't share that prompt - it would make that demo more convincing.
I am seeing third legs and glowing eyes. It's a Microsoft Lens level of quality and that one was pulled.
Edit: just to test myself I asked Nano Banana 2 to generate “an undergraduate infographic poster about how atoms work” - and the result was something right out of a middle-school science textbook and very Bohr…
Impressive.
Btw, what is currently the best model to run locally on a 16GB Vram? Is it Z-Image Turbo?
Any suggestions for the best open, non-opinionated model?
But: not open-source/open-weights, and no indication that weights/source will be released either.
As for the image model, wow...
And yet the Korean text is not accurate... [1]
[1] E.g. "드레스 컬렉션 dress collection" has vowels ㅔ mixed with ㅐ, "초웜한" should be "초월한 exceeding", "신키한" should be "실키한 silky", "디자언되다" should be "디자인되다 have been designed", "로얼" should be "로열 royal", and so on.
When I want to emphasize something, I tend to repeat it
Try asking it for a plot of Polish GDP growth over the past 20 years. It's slop.
> Especially text rendering
That's true though. I still got some completely fried letters in headings.
They can't.
It included the table verbatim and even managed to hallucinate a reasonable heading for it, but then the graph doesn't even manage to align the data points with the time axis, leading to an unfortunate collision in the middle.
I guess you should use a traditional graphing library for your presentation slides for now.