Image to True

The name is not a joke and it's not clickbait: Image to True receives an image and outputs true. It does nothing else. No resizing, no analysis, no pixel math - the entire implementation is a function that takes an image and returns True. And yet when the author announced the pack on r/StableDiffusion, this was the node a commenter singled out, calling it "cool, I could see that useful for switching workflow paths."

Here's why. ComfyUI is lazy about execution - a node doesn't run unless everything feeding it ran and produced output. So when you want a boolean signal that means "this branch actually executed and produced an image," this node is the cleanest way to get one. The value it outputs is always the same; the information is in that it ran at all.

How it works

The single required input is image. The node fires only when an upstream node hands it a real IMAGE tensor - if that branch failed, errored, or got bypassed, the node never executes. On execution it emits True from its single BOOLEAN output.

The trick for routing is that you chain it downstream of whatever you care about: an image that only exists when a certain path completes. Wire the BOOLEAN into a switch, a conditional, or the enable/disable input of another node, and you've turned "did this happen?" into a value the graph can act on.

A natural pairing inside this pack: use it to drive automation where an image appearing is the trigger - then BooleanIndexesToString or ConcatHistoryString on the receiving end turns that into a remembered signal.

The honest take

If you're on a recent ComfyUI, built-in switch/if nodes can already handle a lot of boolean routing, so this isn't a node you must have. But it's a different shape than those: it's a presence detector, and it's the kind of thing that's trivial to rebuild yourself and mildly annoying to rebuild for the fifth time. It costs you nothing to install, has zero dependencies beyond the pack, and when you're building elaborate automation with several "did the image come out?" gates, you'll be glad it's there.

The flip side: because the output is unconditional, it can't tell you anything about the image's content. It's a yes/no that's always yes when it runs. Don't wire it somewhere expecting it to judge quality - wire it somewhere expecting it to confirm completion.

Install

Manager → search "ComfyUI-northTools", or:

cd ComfyUI/custom_nodes
git clone https://github.com/northumber/ComfyUI-northTools

then restart. No models, no extra pip packages - requirements.txt is just pillow, numpy, torch, all already present.

Small pack, and this is the most "why does this exist" node in it. The answer is automation. If you never build trigger-on-completion workflows, skip it. If you do, it's the three-second fix for a problem you'd otherwise solve with a pile of duplicated logic.