Camie Tagger

WD14 is the default anime reverse-tagger and it's fine - but it only ever spits out general tags, character names and (optionally) a rating. The artist, the series, the meta tags: filtered straight out. Camie Tagger is the alternative that doesn't do that. It's a newer anime tag classifier, released March 2025 by Camais, trained on 70,527 Danbooru tags across seven categories including artist and copyright, which is exactly the stuff WD14 silently drops. If your captions keep missing who drew the thing, this is the node to try.

It ships as one of the three taggers in the booru-helper-mini pack, alongside WD14 and PixAI, and it runs the Camais03/camie-tagger-v2 ONNX model. The launch thread on r/StableDiffusion (the author's own post) made a decent splash - +104 - and the headline claim was an F1 of 61% on 70k tags versus ~47.7% for wd-eva02-large-tagger-v3. Take that with salt, it's a different tag count and different benchmark, but the direction is real: Camie is strong on character (73.9% F1) and copyright (78.9%) tags.

How it works

The node downloads camie-tagger-v2.onnx plus a metadata JSON from HuggingFace into models/camie/, and that metadata is the interesting bit - it carries the full tag-to-category map, so the node knows which of its 70k+ tags are characters, which are ratings, and which are general/artist/copyright/meta. Images get resized to 512px, padded, and normalized with ImageNet mean/std before inference (the code reads the model's img_size from the metadata, so it follows the repo). Output logits go through a sigmoid and get thresholded, with a separate character_threshold so character names stay strict.

The real difference from WD14 is in the output logic. WD14's node only selects general and character rows from its CSV. Camie emits everything that clears the threshold that isn't a rating - so artist tags, series tags and meta tags come through in the general bucket. That's the feature. The model itself is a two-stage cross-attention design: an initial classifier over EfficientNet V2-L features, then a refinement pass that models tag co-occurrence, which is why it's better at "this image with these characters usually has this artist."

The inputs that matter

• threshold / character_threshold - both default to 0.492, which is higher than WD14's 0.35. That's a deliberately balanced precision/recall point, so don't assume the defaults are wrong; lower them only if captions come out too sparse.
• repo_id - points at Camais03/camie-tagger-v2; it's editable, so you can point at a fork or an updated release without waiting for the pack to update.

There's no include_rating toggle here - unlike its sibling WD14 node, Camie never emits rating tags. batch_size, exclude_tags, separator, replace_underscore, trailing_comma and force_download behave exactly like the WD14 node in the same pack.

Output

A single tags output: a list of strings, one caption per image in the batch. Same wiring as any tagger - into a prompt/conditioning node for reverse-tagging, or into a caption saver for training data.

Installing it

Same story as the whole pack:

cd ComfyUI/custom_nodes
git clone https://github.com/adbrasi/booru-helper-mini
cd booru-helper-mini
pip install -r requirements.txt

Restart ComfyUI and the three nodes appear. Models download on first run, so the first execution is slower than everything after.

Where people get burned

The 0.492 defaults catch people off guard if they're used to WD14's 0.35 - you'll get shorter captions, on purpose. The model cache holds one session, so swapping back and forth with the WD14 node reloads each time. And remember this is a newer, less community-baked model than WD14: it's genuinely worth testing on your own images before you caption a whole dataset with it, because when it's wrong it's wrong with confidence. For a Pony or Illustrious training run where consistency beats cleverness, WD14 remains the safe default - Camie is the experiment you run when you want the artist and the series in the caption.