[D] Training denoising diffusion model when forward process is unknown but we have multiple examples for each step?

I hope the title at least kinda makes sense? More details below.

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The way I understand denoising diffusion models on a high level is we simulate the forward process with a known distribution like gaussian noise and then learn the reverse process. I am wondering if it is reasonable to try training a diffusion model when we have a dataset from which we can sample any step of the forward process, but the actual forward process itself is unknown. …

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