We're sleeping on a amazing model right in front of our eyes. With diffusion models you can properly ban phrases. You can decensor and deslopiffy the model output without touching a single weight or doing a single backwards pass.
IIRC you do need to retrain the model, just closer to the level of finetuning rather than e.g. pretraining from scratch. Which also means that the model will perform differently with a different capability profile.
At a high level: It doesn't benefit from batching to reduce memory bandwidth in the same way that a "standard" autoregressive model does, which ends up meaning that it's great at reducing latency for individual/low-concurrency users and delivers better outcomes on certain tasks but much more expensive per token at scale.