I would assume that someone who is capable of programming a GAN and go through all the steps of Parameter Tuning at some Point should realize that the randomness shouldnt do anything.

Yeah i actually agree with your rant. However there is a small Chance they acted in good faith and did Not see that the randomness in the GAN wont do anything.

Yeah thats true. My comment relates to unconditional diffusion Models a la Song and not stable Diffusion. The Argument might be adapted for conditional Generation.

Note that this argument really isnt about Diffusion or generative models but about optimization. I know my fair Share of generative modelling, but this Idea is a lot more general and might have been popped up somewhere else in optimization/inverse Problems?

I thought it wasnt about the latent Code but the Training Set?

Lets think about a dataset consisting of only one image x and that the optimization process is known and deterministic.

Then given the weights of the diffusion model, and the optimization procedure P(theta_0,t, x) which maps the initial weights theta_0 to theta_t after t steps trained on image x, this problem would be:

Find x of |Theta_t - P(theta_0,t,x) | = 0 for all times t.

I would IMAGINE (i am not sure) that for enough times t, we get a unique solution x.

This argument should even hold for datasets consisting of more images.

All of my papers

Yep

Okay, yeah makes sense. I am currently working in the Context of FNOs. What is the way youd do it there?