From 80fe9aa268e350245ecc0daf9bddb2b0c3c028ff Mon Sep 17 00:00:00 2001 From: Claude Date: Sun, 16 Aug 2026 20:04:11 +0000 Subject: [PATCH] docs: mention the existing "Include detections" option as a debugging aid Pre-existing SD.Next feature, unrelated to this patch, that overlays each detection with its class name and confidence score on a second output image. Worth calling out here since it pairs directly with [CLASS=name] tagging as a visual way to confirm labels without reading logs. --- DETAILER_CLASS_PROMPTS.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/DETAILER_CLASS_PROMPTS.md b/DETAILER_CLASS_PROMPTS.md index c928d0b74..d9bd911d7 100644 --- a/DETAILER_CLASS_PROMPTS.md +++ b/DETAILER_CLASS_PROMPTS.md @@ -75,6 +75,12 @@ This had to be aggregate rather than per-model: chaining two detailer models wit **It cannot tell a typo apart from a legitimate miss.** A correctly-spelled `[CLASS=face]` on an image where the face model simply found nothing this run (occluded, low confidence, out of frame) produces the exact same warning text as a real misspelling — the check only knows "declared tag X never matched a detection this generation," not *why*. To tell them apart, cross-reference the tag against the model's real vocabulary in the one-time `Load: type=Detailer name='...' ... classes=[...]` line printed when that model first loads; if your tag is in that list verbatim, it's not a typo, the class just wasn't found this time. That said, the warning is still useful either way it fires: it's a reliable signal that **"the `[CLASS=face]` prompt was not applied to anything this generation,"** regardless of the underlying cause — worth knowing on its own, independent of diagnosing why. +### Visual debugging: "Include detections" + +SD.Next's Detailer already ships a checkbox for this, unrelated to this patch — **"Include detections"** (`detailer_include_detections` / `detailer_save`), in the same Detailer panel as the model list and prompt fields. Enable it and every generation produces a second output image, annotated with a semi-transparent overlay on each detected region plus a text label showing its index, class name, and confidence score (e.g. `1 face 0.87`). + +Paired with `[CLASS=name]` tagging, this turns the log-reading exercise above into something you can just look at: enable it once, generate, and see directly which region got tagged as which class — no cross-referencing warnings or the `Load:` line required. It's the fastest way to confirm your tags line up with what the model is actually calling things, especially the first time you wire up a new multi-class model. + ## Implementation Three files touched, all in `modules/detailer/`: