📊 Range Int

InitialBRangeInt is the range() builtin from Python, packaged as a node. You give it a start, a stop, and a step, and it hands you back a list of integers. That sounds trivial, and it mostly is - but in a workflow language that has no native loops or lists, a range generator is genuinely load-bearing. It's the kind of node that looks pointless until the moment you need it and there's nothing else.

The three inputs (all required, all simple)

• start - first value (default 0).
• stop - where the sequence stops, exclusive (default 10). This trips up everyone once: range(0, 10) gives you 0 through 9, not 10.
• step - the increment (default 1).

The output is values: the full list, e.g. start=0, stop=10, step=2 → [0, 2, 4, 6, 8].

One quirk worth knowing before you hit it: the schema calls the output INT, but what comes out is a Python list of ints, not a single scalar. Treat it as a list/sequence output and wire it into things that consume sequences - most commonly into the InitialB For Loop machinery or a list-aware node - not into something expecting one number.

Why you'd reach for it

The honest use cases in a real workflow:

• Seeding a loop. Feed values into a For Loop Start or a per-item processing node and iterate over a sequence of seeds, strengths, or indices without hand-typing each one.
• Generating a batch of parameter values. A step of 10 from 0 to 100 gives you 11 evenly spaced numbers to sweep a strength or a scale over.
• Index math for lists. Combined with the pack's Math Int, it's the raw material for any "for each item N" pattern.

The gotchas

• stop is exclusive. If you want a range that includes 10, set stop to 11.
• You can't count down. step has a minimum of 1, and range() with a positive step over a start > stop yields nothing. Need a descending sequence? Generate ascending and flip it, or do the math with a Math Int node.
• No early output. The node produces the whole list at once; it's not lazy. For a start/stop/step combo that would produce tens of thousands of values, think about whether you actually want that list materialized.

Install

Pack: InitialB Util. ComfyUI Manager → search "InitialB Util" → Install, or:

cd ComfyUI/custom_nodes
git clone https://github.com/benjiyaya/Comfyui_InitialB_Util
cd Comfyui_InitialB_Util
pip install -r requirements.txt

Restart after. No model downloads - requirements are just torch/numpy/scipy/Pillow, all shipped with ComfyUI. (The README's clone URL is a yourusername placeholder; use the real repo above or Manager.) It sits in the InitialB/utility menu with the other data-type and math nodes.