Inner workings

A Simple Scenario

Say you want to generate 12 images and you hit the generate button on the master instance:

1. If there is no workers.json file, it will benchmark every machine(worker) and save that information to workers.json
2. Assume we have 3 workers, with each worker measured to run at ~20ipm. Images will be split equally among them.
3. The master instance will begin generating its portion of the images(4) like it would if you had set the batch_size slider to 4 normally
4. Once the 4 images are done and the image viewer appears, the extension will start adding all of the images received from the remote machines to the gallery.
5. Profit?

A Not So Simple Scenario

That was the simple case though, step 2 gets much more complicated if the machines' compute speeds and/or memory sizes are much different. For example, a setup which utilizes 3 distinct workers that operate at 5, 15, and 20 ipm each would have the following job assignment if the master instance requests 12 images:

After job optimization, job layout is the following:
worker 'master' - 8 images
worker 'laptop' - 12 images
worker 'argon' - 3 images

Reasoning

• 'laptop' is the fastest real-time worker at 20 ipm so it (initially) gets dealt an equal share of 4 images
• both of the other workers are considered 'complementary' workers because they cannot keep up with 'laptop' enough*
• both 'complementary' workers will then calculate how much, in addition, they can make in the time that 'laptop' will take to make the main 12.