DOGMA AuditBatch v1.0.11

Same job as v1.0.10, twice the room

If you read the v1.0.10 article: this node does the same thing in the same graph position - take a bundle of SAM candidate masks, render numbered audit sheets, emit the instruction text for each - but it packs two candidates per sheet instead of four, at roughly 768px per panel rather than 512. The author's own 1.0.11 notes state the change bluntly: "two candidates per larger audit sheet instead of four compressed candidates."

That's not a cosmetic change. A mask audit is a fine-grained judgement - is that cyan region the body of the object or a wall behind it? - and four candidates squeezed into a 2×2 grid means each one gets a quarter of the pixels. Two candidates in a vertical strip is a lot more readable for a 7B-class vision model, at the cost of twice as many VLM calls on the same mask set. If your VLM keeps failing obviously-good masks, try this before you blame the model.

What's on the sheet

Per candidate: original crop with margin, white mask, cyan overlay, selected pixels on gray. One banner per tile so the model can answer by index.

The prompt is where v1.0.11 diverges from v1.0.10, and it's noticeably stricter:

• PASS only selections whose substantial regions actually belong to the named category.
• Partial objects and real openings are explicitly allowed (an arch keeps its opening; an occluded person stays unfilled).
• FAIL for substantial selection of unrelated objects, sky, architecture, ground or background - and FAIL an absent target.
• The line that shows what was actually breaking: "In a people mask, any substantial selected building or sky is a FAIL even if real people are also selected."

That last sentence is the difference between "this mask contains a person" and "this mask is a person mask". If your search pass has a habit of grabbing a facade along with the pedestrians, v1.0.11 is the version that will tell you so.

Inputs and outputs

• image - source image. candidates - a DOGMA_CANDIDATES bundle from a DOGMA SAM search node.
• panel_size - 256–768, step 64, default 512. Unlike v1.0.10 this one is not clamped down; 768 gives you larger tiles inside the same sheet geometry.

Outputs, both lists, index-aligned:

• instance_sheets (IMAGE, list) - one sheet per pair of candidates.
• audit_instructions (STRING, list) - the prompt for each sheet.

Feed them into a list-mapped VLM (the pack's workflows use ModernVLM) and send the returned text list into DOGMAReviewBatchV111, which expects exactly ceil(n/2) lines and raises DOGMA: audit batch count mismatch. otherwise. Do not interpose anything that changes list length.

Zero candidates gives you the usual sentinel - a 1×64×64 black image and No candidates. - rather than an exception, which is what you want inside a phase you might have intentionally left empty.

One behavioural change worth knowing

DOGMAReviewBatchV111 defaults audit_mode to reject_explicit_fail, not report_only like v1.0.10. Both modes keep malformed or unparseable answers as REVIEW (they are never silently treated as PASS - the 1.0.11 changelog calls that out). The difference is that an explicit numbered FAIL now deletes that candidate from the mask set. On a first run against a new scene I'd set the reviewer back to report_only, read the report, and only then let it reject.

Install

Manager → search DOGMA Nodes (publisher axior), or:

cd ComfyUI/custom_nodes
git clone https://github.com/axior/ComfyUI-DOGMA-Nodes
# restart ComfyUI

requirements.txt declares scipy>=1.10 and that's the whole Python story - no model downloads, no core patches. The heavier dependencies are workflow-level: SAM 3 for the masks (ComfyUI's own comfy_extras SAM 3 node, which the KB notes is Triton-dependent and awkward on Windows) and whatever VLM you point the audit at, chosen for being widely used and open rather than freshly posted, because an LLM/VLM node is exactly the shape of node where unexpected network calls don't look suspicious.

Finally, calibrate your expectations about "officialness". DOGMA Nodes is one person's restoration pipeline published as nodes, it has no measurable presence in the r/comfyui discussion corpus, and the changelogs are unusually honest that the audit path has been validated on CPU with mocked model calls - not on real GPU segmentation. The prompts here are better engineering than most of what's on the hub. The quality of your VLM's verdicts is still yours to verify.