Image to image software isn’t going to return an image of you unless you create Lora, Lycoris or Textual Inversion of your face for it to work with. It doesn’t “know” what you look like. For things like this, it “looks” at the image based on shapes and colors alone and generates a face that fits those general dimensions. For AI, the word professional would simply mean the picture was taken in a studio.
She used Playground.ai which uses stable diffusion models. I’m not familiar with their interface, but definitely relies on a good prompt for the model to give you good results. Asking it to do something isn’t how diffusion models work. They weight keywords and infer based on those.
In addition to this, it’s all about the seed too. Let’s just say she used the prompt “professional looking” to an img2img. Based on the seed this could give her billions of different images.
Between the training data on the model and the seeds, there is simply little to no way to implicate biases from models in this fashion.
As always, check your model biases by having a blank positive prompt and a negative prompt for “low quality” then let the generations run for a long time.
Only then do you have a snippet of what the model by default trends towards. And the moment you add other tokens, that can go out the window.
Image to image software isn’t going to return an image of you unless you create Lora, Lycoris or Textual Inversion of your face for it to work with. It doesn’t “know” what you look like. For things like this, it “looks” at the image based on shapes and colors alone and generates a face that fits those general dimensions. For AI, the word professional would simply mean the picture was taken in a studio.
She used Playground.ai which uses stable diffusion models. I’m not familiar with their interface, but definitely relies on a good prompt for the model to give you good results. Asking it to do something isn’t how diffusion models work. They weight keywords and infer based on those.
In addition to this, it’s all about the seed too. Let’s just say she used the prompt “professional looking” to an img2img. Based on the seed this could give her billions of different images.
Between the training data on the model and the seeds, there is simply little to no way to implicate biases from models in this fashion.
As always, check your model biases by having a blank positive prompt and a negative prompt for “low quality” then let the generations run for a long time.
Only then do you have a snippet of what the model by default trends towards. And the moment you add other tokens, that can go out the window.