Comfy Divide

Comfy Divide is the least famous node in this pack and the one doing the heaviest lifting. Everything else in Wild Divide rolls prompts; this node takes a list of conditionings - one per image region - and actually makes each region listen to its own prompt. It's the regional-prompting engine, the thing the community has collectively called fiddly and sometimes brilliant since the "attention couple" days, and this is a compact implementation of it.

What it does

You feed it the base model, a negative conditioning, and positives - a list of positive conditionings, which is exactly what Wildcard Encode hands you when you split a prompt on [SEP]. The first entry is treated as the base prompt for the whole image; every entry after that gets a rectangle of the canvas. orientation picks horizontal (left-to-right strips) or vertical (top-to-bottom), and width/height set the canvas the masks are built against. By default the regions are equal parts - three positives after the base means three equal strips. overall, on by default, is what keeps that first conditioning applied across the entire image while the rest only steer their own strip.

The mechanism is worth understanding because it explains both the magic and the fickleness. The node builds a solid mask for each region, applies each conditioning to its mask via standard masked-ConditioningSetMask logic, and concatenates them into one combined conditioning. Then it patches the model's cross-attention - an attention-couple patch - so that at every attention block, each image region's query tokens attend mostly to their own region's text instead of the whole prompt. The model output is that patched clone; you route it into your sampler in place of the original. positive and negative are the combined conditionings.

The catch

Regional prompting via attention coupling is not a precision instrument. The community consensus after years of threads is that it's good for steering composition - "sky on top, city on bottom" - and unreliable for hard separation: prompts bleed across region borders, masks don't always align with where you think they are, and the results change from checkpoint to checkpoint. There's also a persistent confusion in the wild between regional prompting (this technique, attention-level) and regional sampling (the detect-crop-resample approach Impact Pack made famous), and they behave very differently. If your mental model is "two airtight boxes," reset it to "two overlapping suggestions with a hard border." It works; it's just honest about being soft.

Install

Same as the rest of the pack: ComfyUI Manager → search "Wild Divide," or

cd ComfyUI/custom_nodes
git clone https://github.com/Julian-adv/WildDivide

then restart ComfyUI. No model downloads, no extra dependencies beyond what's already installed. One practical warning: as of mid-2026 the upstream repo returns 404 - the author's GitHub now only shows an unrelated Rust project - so if the clone fails, that's why, and you'll need a mirror. This whole pack is small and barely spoken of, so don't anchor a long-lived workflow to it without a copy of the source on disk. But if you want regional prompting without adopting a whole separate conditioning suite, Comfy Divide is the leanest way I've seen to get there.