Saving Intermediate Noising Images

A request for information/documentation rather than an 'issue', but I've been trying to track and document the diffusion process in image2image.py in mlx-examples from start to finish, and I can easily save intermediate denoising images to show the emergence of new models, but where can I insert intermediate save-image code to do the same for the noising trajectory? I've tried for several days without success. Sorry to be dense.

The denoising of a single image with --strength = [0.1, 0.2, ..., 1.0] is illuminating. Here's a (greatly-reduced-resolution) triangulated image that illustrates the whole thing as we emerge from the latent space. What I'd like to be able to do is trace the 'descent' into the latent space as well from the original image. Of course nothing here is new or surprising, but I find it interesting to see how the variation from the original evolves as the strength increases. (These intermediate steps are saved every 20 iterations. What is more or less exactly the original image - also generated by mlx-examples - is, of course, the one at the bottom.)

Start by reading image2image.py in mlx-examples and comparing the existing intermediate denoising-image saves with the noising trajectory. Trace where the original image enters the process and document the appropriate insertion point for intermediate saves. Done means the location is explained clearly and the resulting noising images can be verified.