🌀 BV Regional Native Conditioning

You've authored a BV Regional Prompt document with regions, prompts, and geometry. Now something has to turn that into conditioning a KSampler actually eats. BV Regional Native Conditioning is the compiler that does it with zero model patches - it translates your document into standard ComfyUI masked conditioning, and because it doesn't touch the model's internals, it works with any model family. SDXL checkpoints, Anima, Pony, Illustrious, you name it.

That portability is the whole pitch. The fancier attention backends in this pack are model-specific - SDXL Attention only routes SDXL, the Z-Image backend only Z-Image. Native Conditioning is the one that never rejects your model, which makes it the default first thing to try and the baseline you compare the attention backends against. The README's own A/B tests use Native Conditioning as the "untuned baseline" side of the comparison, and it's honest about the results: it gives you masks, not model-internal attention isolation.

How it works

It takes regional (your BV document) and a clip, and compiles each region's prompt into conditioning that's masked to that region's geometry. The single control that matters for a beginner is region_strength_multiplier (default 1.0) - it scales how much influence the regional prompts have relative to global and background. Turn it up when regions feel timid, down when region prompts are fighting the global prompt.

Mechanically this is the classic masked-conditioning approach: each branch is encoded with the region mask attached, so the sampler applies that prompt's influence where the mask is set. Overlapping regions blend their influence in the overlap. The important mental model from the pack's docs: this is guidance, not a boundary. A mask says "push this prompt here," and the model is free to push back. Untuned masked conditioning tends to bleed - that's why the README compares it to the attention backends, which route inside the model instead of just masking the outside.

The outputs

positive and negative (both CONDITIONING) go straight into a standard KSampler's positive/negative inputs. That's it - two outputs, dead simple wiring.

The compile can also mix branch strategies - blending every region's masked influence together, or keeping background and regions as exclusive branches - but the core contract is the same: regional document in, sampler-ready conditioning out. Don't get lost in composition theory on your first run; the multiplier is the lever you'll actually use day to day.

Install

Search BV Node Pack in ComfyUI Manager, or:

cd ComfyUI/custom_nodes
git clone https://github.com/BlackVortexAI/bv_nodepack.git

Restart, hard-refresh with Ctrl + F5. No extra dependencies - this node is pure ComfyUI core conditioning.

When to reach for it vs. the attention nodes

Reach for Native when: you're on a model family with no BV attention backend (anything non-SDXL/non-Anima/non-Z-Image/non-Klein/non-Krea), you want maximum portability in a shared workflow, or you want a clean baseline before trying the fancy stuff. Reach for an attention backend when you've done that and the masks aren't holding - that's exactly the ladder the pack's docs describe. One trap to avoid: don't crank region_strength_multiplier to 10 and expect hard segmentation. You'll get artifacts and mush. Regional conditioning earns its keep through clean geometry and clear prompts, not brute force.