For me it’s the missing strong AD engine. And a lot of people in science request differentiable forward models these days.
Yes, Zygote works and there is also Enzyme, ForwardDiff, etc. But combining those is not easy.
As a user, it’s much easier just using PyTorch or JAX. Everything is differentiable and works.
Performance is one thing but quite often my Julia code is not differentiable at all or causes some obscure errors in combination with CUDA. I don’t like the general usage of Python + PyTorch but for prototyping I feel it is more convenient.
So happy to see progress happening in Diffractor.jl