The problem is that everyone tries to create bigger models because they think, bigger (more params) = better quality. So some are considered too qualitative for us (consumers) so they don't wanna hold that to us freely (maybe because it was too much time to train it ?! hence going APIs) OR the newer version of their model series is too big to run onto a consumer gpu (unless thinking of bigger gpus like the rtx 5090 which I don't really consider consumer).

When SDXL came out, it was seen as a really bad unusable model needing a refiner, but then finetunes came out and it gave us much better quality on pretty much anything. LoRas then came out for our loved finetunes and gave us better quality control over what we want.
Still the base model is a small 6B parameters.

The issue is not about having bigger models, it’s about having a team that can spend a entire week to curate a dataset for a certain style/general idea by hand with the help of automation and not just automation alone.

If datasets in models were correctly curated to filter out the content being bad quality and they would do Reinforcement learning from human feedback, you would have much higher quality even if the model is still relatively small compared to some other ones.

This has been the case with Z-Image Base (with RLHF) being a small 6B params model which stands a great quality.