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.
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.