Diffusion models can be trained just fine on the same datasets as GANs. They also do not take longer to train as you generally just sample one "time step" (noise level) per training step. What does take longer is inference, as GANs need a single generator execution while diffusion models require multiple.
Diffusion models also do not inherently need more resources than GANs. It's basically the same: GANs have a generator and a discriminator, while diffusion models often follow a "encoder-decoder"-style U-net architecture. You can train small diffusion models on MNIST or whatever, you can train gigantic GANs (look up GigaGAN), this is not inherent to the type of model.
That is, again, not an advantage of GANs per se, also you will have a hard time finding anything remotely comparable to Stable Diffusion that is based on GANs (unless I missed some big release).
The reasons you listed are actually not true.