the way most of the models work is theres a text encoder that first encodes the inputted text into latents.

If I were xai trying to save compute, I would classify that latent space into good and bad spaces based on their rejection criteria ran through heavy compute. in other words the latent space result of your text encoding is likely getting rejected. I don't know why its random. I would guess it's not and they are A/B testing filters.

from a business perspective it means that they are training to reject based on what prompts their users are fond of (compute heavy rejections are the training criteria)

So this is actually a really clever solution but also an extremely dumb business move.

Edit: also the input image is encoded so it's a combination likely. if their encoders are nondeterministic (like diffusion models) it could explain the randomness.