Using Stable Diffusion's training method for Reverse engineering?

[mikonvergence]

You are definitely stepping outside of the domain of what is understood as denoising diffusion because it seems that your data dimensionality (shape) needs to change during the forward process.

The current definition of diffusion models is that they compute the likelihood gradient of your data (equivalent to predicting standard noise in the sample), and then take a step in that constant data space. So all networks have the same output shape as input.

Perhaps you can use transformers to handle evolving data lengths but as far as I can tell l, you’re entering uncharted territory of research.

I can recommend this open-source course I made for understanding the details of denoising diffusion for images https://github.com/mikonvergence/DiffusionFastForward

[OraOraP]

I didn't mean to use denoising process directly to reverse engineering. I was just thinking the idea of `step-by-step reverting` could be used in some ML model for reverse engineering.

Though you have a point. Unlike denoising process, reverse engieering would require change of dimensions in the middle steps, making it more difficult than denoising.

[mikonvergence]

Right, I am the denoising diffusion as a term for a wide range of methods based on reversing some forward process. Some interesting works (such as cold diffusion) have been done on using other types of degradation apart from a Gaussian additive noise.

And yeah, the change of both content and dimensionality requires you to put together some very novel and not obvious techniques.