Great idea! I can see what you intend this for, it's useful. You're right this will need more review, so I want to merge the 0.2 branch with some training improvements first.

(this has been updated to be compatible with the now merged 0.2 branch)

I am willing to make this compatible with 0.3, but am going to wait until there is some feedback indicating this is a desired feature in the main release.

Currently my focus is on making a new release that works reliably for all image types (with a variety of user options for models), but I've been thinking a lot about how to integrate your changes.

I'm wondering if having a plugin approach would help. So you could specify --plugin=smilevector and it would load smilevector.py and run the code there to load or pre-process images. This kind of customization would be very useful for me on another project, but I don't necessarily want all features in the base file (e.g. calling sort with a 1M dataset doesn't work ;-)

What do you think?

OK, without pulling my punches here are my thoughts:

• the hidden power of this library is that it can potentially learn to map between any two image datasets, thought it is streamlined for the case of super-resolution and (now) correcting jpeg artifacts. --seeds exposes this full power of the tool to power users.
• the built in options you've added are nice (eg: --train-jpeg, --train-blur), but I can easily replicate most of them the --seeds option and a three line imagemagick script. Also - they have hard-coded distribution and parameter assumptions built in that are not adjustable.
• sorting 1M strings is pretty trivial. I run --seeds routinely on 200k datasets.
• as written, --seeds is compatible with most other options you've included (--train-jpeg, train-noise, etc). It simply replaces the first step of using a resized version of the original as the basis of the seed.
• the plugin option is interesting, but I think using the filesystem is simpler. it also is much more efficient because the seeds themselves are implicitly cached on disk and reusable.

That said, I certainly appreciate that this is your library and I am admittedly somewhat of a fringe user at this point. So I certainly understand if you want to for now focus on the primary use cases, and I don't mind splicing in this functionality from time to time when I need it. Just let me know if / when you'd like to have a version for testing and I'm happy to update this pull request.

I added plugin support to [cb55929]. It requires Python 3.5. You create file in plugins/ and provide three functions to list the files, load the original image, and then the seed (load or modify original). Use the example using --train-plugin=simple and put files in data/*.jpg.

I think this will allow most of the custom things you need, while still allowing the seeds to be procedurally generated — since some transformations are best done at runtime (like noise/dropout).