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There only needs to be one example in the folder.

LATENT is now a dict that can contain properties.

Put them in models/clip

It's pretty much the same as the LatentUpscale node for now but for images
in pixel space.

The models are modified in place before being used and unpatched after.
I think this is better than monkeypatching since it might make it easier
to use faster non pytorch unet inference in the future.

This can be used to "paste" one latent image on top of the other.

This node exposes more sampling options and makes it possible for example
to sample the first few steps on the latent image, do some operations on it
 and then do the rest of the sampling steps. This can be achieved using the
start_at_step and end_at_step options.

to apply conditioning/prompts only to a specific area of the image.

Add ConditioningCombine node.
so that multiple conditioning/prompts can be applied to the image at the
same time

Resize nodes automatically if they are too small because of added properties.

LoadImage.IS_CHANGED returns the hash of the image so it will execute again
if the image changed on the disk.