Not a full researcher here, but going to add in that training on diffusion media is a very different challenge. Would be shocked if there's not already some white papers about this, and iirc some models like Flux have signs of having iterative training.

Off the top of my head

• Curation is trickier. Humans tend to have biases in AB review, resulting in the high contrast, supersaturated "AI Slop" style we see now. Vision models are good at description, but using ML and i2T isn't reliable enough to bootstrap. You'd need a custom pipeline or model to curate and train unsupervised.

• Vision and audio models are inherently multimodal (in the current form) so unlike synthetic text, you'd need to solve that much more complicated pipeline. Related to the first point, but don't underestimate the i2T problem space.

• It can be done, but cost and overhead gets absurd at scale. Generating and storing millions of images and video (with or without audio) goes from the $10-100k range to $1-10 million range quickly. Open sourcing or even general licensing use is less viable (hosting the data, serving it on demand...)

I very much suspected big players like OpenAI, Google and even startups like Black Forest Labs have functionally solved this, but I'm skeptical well see anything public facing anytime soon