41–50 of 83 posts
Fun, but it’s no longer a special memory. There’s so little tying the source and result.
My immediate issue, as the father of a mixed son who has just witnessed him spend hours trying to represent his own appearance accurately for an art task at school: This turns black and mixed people into white-presenting caricatures of themselves. E.g. compare [1] and [2] where the hairstyles of the black people are turned into distinctly "white" hairstyles. My son's hair is closer in texture to that of the man on th…
I was not at all surprised when it turned Black people into White people.
This to me is one of the clearest examples I've seen of what people talk about when they talk about biases in training data. Clearly the training data of "coloring books" is heavily biased towards "stereotypical 1950s white people" and turns everyone into them.
(And you can add [1] and [2] to your list of comparison images.)
1. https://portraitart.app/static/gallery/father1_original_1024...
2. https://portraitart.app/static/gallery/father1_coloring_page...
IMO your FAQ needs an entry for: when you store uploaded photos, what will they be used for. Will my uploaded photos be used as training data for your AI models for example?
Good suggestion. We do not used the data for training. We will clarify that in FAQ.
> #1 Photo to Art App According to whom? There’s just that sentence, surrounded by laurel leaves, above five stars. Zero indication of where that award/rating came from. Did you make it up?
Earlier quoted context omitted.
I had to look for multiple examples myself before I was sure I wasn't being oversensitive. As I said, I'm sure there's no ill intent, and in a way I'm not surprised either, because as my son himself found out, if you're going to draw line art - especially with clear, thick lines like this, then accurately representing black or mixed hair without it turning into a caricature that'd be even worse is a skill. And one th…
Thanks for calling this out. I think the dev cycle for production AI Systems will involve a lot of bug squashing on this kind of thing. These models handle a huge variety of inputs; by definition much more than you could hand code. It’s hard to even identify systematic failures. Once a failure mode is known - like here - how do you fix it? The foundational problem is minorities are a minority of the training set. Goo…
There absolutely are good examples, yes, and I think you're right that the failure of capturing it is largely down to volume in the extant datasets, and particularly in datasets that provide a direct match for the type of line-art colouring in style, which is in itself fairly dated.
I agree with all of your recommendations. I think it's definitely a problem that will be solved. The main thing is for people to get used to looking for it.
My immediate issue, as the father of a mixed son who has just witnessed him spend hours trying to represent his own appearance accurately for an art task at school: This turns black and mixed people into white-presenting caricatures of themselves. E.g. compare [1] and [2] where the hairstyles of the black people are turned into distinctly "white" hairstyles. My son's hair is closer in texture to that of the man on th…
This was absolutely my first reaction as well. I scrolled through the first few photos of White families where it seemed to turn even them into 1950s caricatures of "small town White Americans" and thought "hmmm, how's it going to treat Black people?" I was not at all surprised when it turned Black people into White people. This to me is one of the clearest examples I've seen of what people talk about when they talk…
Earlier quoted context omitted.
Use a personal domain with a default address that filters to a mailbox then use a different email address for every site you use. This plus random passwords from a password manager contains the privacy leakage, blast radius of credential loss, and keeps your mail spam free.
Or, you know, just don’t normalize indiscriminate email collection as a filter for using a service.
For example, I used a random selfie of me on the beach and big rocks in the water, wearing dark sunglasses and mouth closed.
Watercolor: Added eyes to my glasses (!). The rocks and water turned green, and it looked like I was standing in a park. Coloring Page: Ferns and trees added that weren't there before. Both: Gave me a teethy smile when my lips were closed.
The examples on the home page must have been extremely handpicked because I'm not seeing anything to that same fidelity.
I really think this should have GenAI in the title on the webpage or on here, because it's taking the source photo and making best guesses while creating details out of whole cloth. This isn't just "another photo filter", it's instead taking the picture and running with it with sometimes pretty poor results. For example, I used a random selfie of me on the beach and big rocks in the water, wearing dark sunglasses and…
