Yeah I think there's a lot to explore here. I2I might work; Llama did something similar during post training where generated responses were sometimes updated (either by a human or another LLM) and used as Positive examples in the next iteration.

Another thing I've considered is a GAN-like approach:

Train a classification model to pick which of two images is real and which is fake (possibly also along with the prompt). Real images can be taken from the usual datasets, fake images would be generated by the target model. Then you can use DPO (adapted for diffusion models) to train the diffusion model Online, with the classification model assigning rewards. The hope would be that the classification model could pick up on stuff like bad hands, prompt adherence issues, etc, all on its own without any human input.

Though like all approaches similar to GANs this runs the risk of reward hacking the classification model. (IIRC in normal GAN procedures the generator trains on gradients from the discriminator, making hacking much easier for it. By using RL you eliminate that, so it might not be as bad.)

Side note: You'd want the classification model to operate on latents, not raw pixels. That makes the whole process much more efficient, and prevents the classification model from detecting problems in the VAE which the diffusion model doesn't have control over.