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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.

The queue can now be queried, entries can be deleted and prompts easily
queued to the front of the queue.

Just need to expose it in the UI next.

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.