And while being easier to train, they give better results.

Diffusion models are also so much more versatile in their application because of their iterative process.

You can do inpainting or img-to-img for example by just conditioning the noise in different ways. You would have to retrain the whole GAN to achieve that.

Would want to see citations per capita instead of just articles. Better metric for actual scientific influence.

EDIT: Stumbled upon this! Paints a very different picture for France and Germany.

No definitely - GANs can still fail and they are much less stable than Diffusion models. But GANs have still enjoyed a huge popularity despite that and research has found ways to mitigate it.

I just think it's not the main reason why diffusion models are gaining traction. If it was we probably would have seen a lot more of Variational Autoencoders. My work is not at BigGAN or DALLE2 scale though so might indeed miss some scaling aspect of this. :)

I wouldn't say it is primarily because it is more stable though, it just gives better results and the properties of diffusion easily leads into other applications like in/outpainting and multimodality.

GANs are quite stable these days. Tricks like feature matching loss, spectral normalization, gradient clipping, TTUR etc makes modal collapse quite rare.

You're correct that it is quite slower at the moment though. The diffusion process needs to iterate per pass and thus takes longer both to train and to infer.