From an Applied research perspective, there’s still a lot of work done with GANs or reusing some of the concepts of Adversarial Learning (I believe some diffusion models actually use a type of adversarial loss during training). Although diffusion models showed to perform extremely well in various tasks, there’s still a lot of work to be done in order to make them usable in practical contexts: first of all, the hardware requirements to train them are extremely expensive (stable diffusion for instance used 256 GPUs to train their model), then these are also extremely large to be deployed for inference. These are all factors that in an applied context might make you use a GAN instead of a diffusion model (at least for now, you never know what people will find out in the next couple of months!)