Diffusion Models have recently gained popularity in the field of image generation, with widely used products such as Stable Diffusion employing this approach and yielding impressive results. While GANs are also recognized for their efficiency, in what scenarios do I need to choose GANs over Diffusion Models and do GANs have any advantages compared to Diffusion Models in image generation?

Here are a few reasons I can think of:

• Diffusion Models take more time and larger datasets to train.
• To train a Diffusion Model project, one must have substantial computational resources (a lot of GPUs), compared to GANs.
• The codebases of some popular Diffusion Models projects are not open source.

I don’t know if these are correct. As for the mathematical aspect, I’m not an expert in that area.