* RandomLora & RandomModel
* Walks the `models/checkpoints` and `models/loras` folder for `.safetensors` files and uses Regex patterns to filter them down, then randomly select one to apply.
* If the `pattern` is changed, filtering and randomization will be triggered next time the node is executed.
* `every` parameter allows the selected model to loop for a certain number of times before next randomization.
* `pause` holds off randomization and keep using the same model for generation indefinitely.
* `skip` will randomly select another model ignoring the `every` parameter but the looping will continue to count up.
* `RESULT` output will show in text format the current number of loops, which model is currently selected, which model has ran and how many times, and lists all the models that hasn't ran. Use `Preview as Text` or `Show Text` node to access.
* Models are pooled - loaded to RAM from HDD/SSDs, this causes RAM to be filled to the brim, however, Python seem to be able to automatically release memory for new ones
* If previously loaded models are selected again, it is already in the RAM, so no need to access SSD or HDD for it again.
* This saves the tiny bit of load time when using SSD, but could potentially save a lot of loading time for HDD.
* Eventually this could be used for cycling through multiple designated models to compare their differences.
The way equalize_clahe() function's parameters being auto generated based on image size was resulting in errors.
Issue seems to be some kind of dimensional mismatch which was required to be the same by the kornia function.
Reverting back to a working method.
Plus some minor fixes.
Brent Liu