4 ms·

Don’t understand the remove from face example. Without other pictures showing the persons face, it’s just using some stereotypical image, no?

vessenes 1y ago
Mm, depends on the underlying model and where it is in the pipeline; identity models are pretty sophisticated at interpolating faces from partial geometry.
Scaevolus 1y ago
The slideshow appears to be glitched on that first example. The input image has a snowflake covering most of her face.
- whywhywhywhy 1y ago
  That's the point, it can remove it.
sharkjacobs 1y ago
There's no "truth" it's uncovering, no real face, these are all just generated images, yes.
- amazingamazing 1y ago
  I get that but usually you would have two inputs, the reference “true”, and the target that it to be manipulated.
  - nine_k 1y ago
    Not necessarily. "As you may see, this is a Chinese lady. You have seen a number of Chinese ladies in your training set. Imagine the face of this lady so that it won't contradict the fragment visible on the image with the snowflake". (Damn, it's a pseudocode prompt.)
    - amazingamazing 1y ago
      yes, so a stereotypical image. my point is best illustrated if you look at all of the photos of the woman.
      - throwaway314155 1y ago
        Even if you provide another image (which you totally can btw) the model is still generalizing predictions enough that you can say it's just making a strong guess about what is concealed. I guess my main point is "this is where you draw the line? at a mostly accurate reconstruction of a partial of someone's face?" this was science fiction a few years ago. Training the model to accept two images (which it can, just not for explicit purposes of reconstructing (although it learns that too )) seems like a very task-specific, downstream way to handle this issue. This field is now about robust, general ways to emerge intelligent behavior not task specific models.
        amazingamazing 1y ago
        is it mostly accurate though? how would you know? suppose you had an asian woman whose face is entirely covered with snow. sure you could tell AI to remove the snow and some face will be revealed, but who is to say it's accurate? that's why traditionally you have a reference input.
        Gracana 1y ago
        What's the traditional workflow? I haven't seen that done before, but it's something I'd like to try. Could supply the "wrong" reference too, to get something specific.
        throwaway314155 1y ago
        > sure you could tell AI to remove the snow and some face will be revealed, but who is to say it's accurate? that's why traditionally you have a reference input. As I stated a few times, the model HAS SUPPORT FOR MULTIPLE IMAGES. The article here doesn't try your very specific reference-image-benchmark but that doesn't mean you can't do it yourself - and it also doesn't imply there's anything wrong with the article or BFL - they're merely presenting a common usecase - not defining how the model should be used.
jorgemf 1y ago
I think they are doing that because using real images the model changes the face. So that problem is removed if the initial image doesn't show the face
ilaksh 1y ago
Look more closely at the example. Clearly there is an opportunity for inference with objects that only partially obscure.
pkkkzip 1y ago
They chosen Asian traits that Western beauty standards fetishize that in Asia wouldn't be taken serious at all. I notice American text2image models tend to generate less attractive and more darker skinned humans where as Chinese text2image generate attractive and more light skinned humans. I think this is another area where Chinese AI models shine.
- throwaway314155 1y ago
  > notice American text2image models tend to generate less attractive and more darker skinned humans where as Chinese text2image generate attractive and more light skinned humans This seems entirely subjective to me.
- viraptor 1y ago
  > They chosen Asian traits that Western beauty standards fetishize that in Asia wouldn't be taken serious at all. > where as Chinese text2image generate attractive and more light skinned humans. Are you saying they have chosen Asian traits that Asian beauty standards fetishize that in the West wouldn't be taken seriously at all? ;) There is no ground truth here that would be more correct one way or the other.
- turnsout 1y ago
  Wow, that is some straight-up overt racism. You should be ashamed.
  - fc417fc802 1y ago
    It reads as racist if you parse it as (skin tone and attractiveness) but if you instead parse it as (skin tone) and (attractiveness), ie as two entirely unrelated characteristics of the output, then it reads as nothing more than a claim about relative differences in behavior between models. Of course, given the sensitivity of the topic it is arguably somewhat inappropriate to make such observations without sufficient effort to clarify the precise meaning.
    - pkkkzip 1y ago

vessenes 1y ago

Mm, depends on the underlying model and where it is in the pipeline; identity models are pretty sophisticated at interpolating faces from partial geometry.

Scaevolus 1y ago

The slideshow appears to be glitched on that first example. The input image has a snowflake covering most of her face.

whywhywhywhy 1y ago
That's the point, it can remove it.

sharkjacobs 1y ago

There's no "truth" it's uncovering, no real face, these are all just generated images, yes.

amazingamazing 1y ago
I get that but usually you would have two inputs, the reference “true”, and the target that it to be manipulated.
- nine_k 1y ago
  Not necessarily. "As you may see, this is a Chinese lady. You have seen a number of Chinese ladies in your training set. Imagine the face of this lady so that it won't contradict the fragment visible on the image with the snowflake". (Damn, it's a pseudocode prompt.)
  - amazingamazing 1y ago
    yes, so a stereotypical image. my point is best illustrated if you look at all of the photos of the woman.
    - throwaway314155 1y ago
      Even if you provide another image (which you totally can btw) the model is still generalizing predictions enough that you can say it's just making a strong guess about what is concealed. I guess my main point is "this is where you draw the line? at a mostly accurate reconstruction of a partial of someone's face?" this was science fiction a few years ago. Training the model to accept two images (which it can, just not for explicit purposes of reconstructing (although it learns that too )) seems like a very task-specific, downstream way to handle this issue. This field is now about robust, general ways to emerge intelligent behavior not task specific models.
      - amazingamazing 1y ago
        is it mostly accurate though? how would you know? suppose you had an asian woman whose face is entirely covered with snow. sure you could tell AI to remove the snow and some face will be revealed, but who is to say it's accurate? that's why traditionally you have a reference input.
        Gracana 1y ago
        What's the traditional workflow? I haven't seen that done before, but it's something I'd like to try. Could supply the "wrong" reference too, to get something specific.
        throwaway314155 1y ago
        > sure you could tell AI to remove the snow and some face will be revealed, but who is to say it's accurate? that's why traditionally you have a reference input. As I stated a few times, the model HAS SUPPORT FOR MULTIPLE IMAGES. The article here doesn't try your very specific reference-image-benchmark but that doesn't mean you can't do it yourself - and it also doesn't imply there's anything wrong with the article or BFL - they're merely presenting a common usecase - not defining how the model should be used.

jorgemf 1y ago

I think they are doing that because using real images the model changes the face. So that problem is removed if the initial image doesn't show the face

ilaksh 1y ago

Look more closely at the example. Clearly there is an opportunity for inference with objects that only partially obscure.

pkkkzip 1y ago

They chosen Asian traits that Western beauty standards fetishize that in Asia wouldn't be taken serious at all. I notice American text2image models tend to generate less attractive and more darker skinned humans where as Chinese text2image generate attractive and more light skinned humans. I think this is another area where Chinese AI models shine.

throwaway314155 1y ago
> notice American text2image models tend to generate less attractive and more darker skinned humans where as Chinese text2image generate attractive and more light skinned humans This seems entirely subjective to me.
viraptor 1y ago
> They chosen Asian traits that Western beauty standards fetishize that in Asia wouldn't be taken serious at all. > where as Chinese text2image generate attractive and more light skinned humans. Are you saying they have chosen Asian traits that Asian beauty standards fetishize that in the West wouldn't be taken seriously at all? ;) There is no ground truth here that would be more correct one way or the other.
turnsout 1y ago
Wow, that is some straight-up overt racism. You should be ashamed.
- fc417fc802 1y ago
  It reads as racist if you parse it as (skin tone and attractiveness) but if you instead parse it as (skin tone) and (attractiveness), ie as two entirely unrelated characteristics of the output, then it reads as nothing more than a claim about relative differences in behavior between models. Of course, given the sensitivity of the topic it is arguably somewhat inappropriate to make such observations without sufficient effort to clarify the precise meaning.
  - pkkkzip 1y ago