ComfyUI Junior Prompt Safety Classifier

A compact semantic classifier for text-to-image prompts. It reads a prompt and estimates, on six independent ordinal axes, what an image generated from that prompt would depict. It was developed for a child-facing image-generation appliance (roughly age 12+), but the model and the policy are deliberately separable: the six dimensions and their ordinal levels are the model's output, and the shipped block thresholds are one opinion layered on top of them.

If you want a prompt-screening signal without inheriting someone else's product boundary, use the levels and write your own thresholds.

Release note — this repo changed architecture. Revisions before September 2026 held a provisional v0.7 draft: a 6-layer distilbert-base-uncased with [CLS] pooling and raw linear outputs. This release replaces it with v29db, a 12-layer DeBERTa-v3 with mean pooling and ordinal heads. Pooling, activation, tokenizer and one head width all changed. Code written against v0.7 will silently produce wrong numbers. The stale vocab.txt has been removed. Pin a revision if you need the old behaviour.

What the outputs are

Six heads, one per dimension, each cumulative-logit ordinal — not regression, and not multi-class. Each head emits one logit per threshold. Apply sigmoid, then read the level as the number of leading thresholds above 0.5, stopping at the first gap.

sexual is the only width-3 head. Level 1 is designed to mean genuinely safe on every axis — the classifier is forced to be sharp rather than allowed to hedge.

Architecture

Not a stock AutoModelForSequenceClassification. The forward path is:

tokenize (max_length=256)
  -> DeBERTa-v3-base encoder, last_hidden_state
  -> MASKED MEAN pooling over the sequence      <- not [CLS]
  -> six independent nn.Linear heads (768 -> width), loaded from heads.pt
  -> sigmoid
  -> ordinal decode to a level per dimension

Two-view inference: the prompt is classified twice — raw, and canonicalised (NFKC, zero-width strip, homoglyph folding, conservative leet folding, spaced-letter collapse) — taking the per-dimension maximum. This closes a character-substitution bypass where n4k3d ... scored below a benign prompt while the downstream T5 text encoder still rendered it.

This two-view path is the reference parity path for this release: the published weights were parity-qualified against it, and canonicalize.py is published so that result can be reproduced exactly. It is not the only sound way to deploy the model. Later serving evaluation found that a narrower input-envelope gate combined with single-view inference was faster and produced equal or better measured safety behaviour on the sets tested — see the deployment profile below. That is a serving choice; it does not alter the frozen weights.

Three details are load-bearing and easy to get wrong: mean pooling (not [CLS]), sigmoid + ordinal decode (not raw regression values), and the second view.

Usage

from junior_safety import SafetyClassifier

model = SafetyClassifier.from_pretrained("mitchins/comfyui-junior-safety")
r = model.classify("A cute penguin under a starry vortex sky")

print(r.levels)      # {'sexual': 0, 'nudity': 0, ... }
print(r.raw_scores)  # per-threshold sigmoid probabilities
print(r.decision)    # 'pass'
print(r.reasons)     # []

junior_safety.py and canonicalize.py are plain Python in this repo — no trust_remote_code. Copy them in, or hf_hub_download them.

Equivalent plain Transformers / PyTorch

import torch, torch.nn as nn
from transformers import AutoModel, AutoTokenizer
from huggingface_hub import hf_hub_download

repo = "mitchins/comfyui-junior-safety"
tok = AutoTokenizer.from_pretrained(repo)
enc = AutoModel.from_pretrained(repo).eval()

ck = torch.load(hf_hub_download(repo, "heads.pt"),
                map_location="cpu", weights_only=True)
dims, widths = ck["dims"], ck["widths"]
heads = nn.ModuleList([nn.Linear(768, w) for w in widths])
heads.load_state_dict(ck["heads"])
heads.eval()

e = tok("a soldier pointing a rifle at a civilian", truncation=True,
        max_length=256, padding=True, return_tensors="pt")
with torch.no_grad():
    h = enc(**e).last_hidden_state
    m = e["attention_mask"].unsqueeze(-1).float()
    pooled = (h * m).sum(1) / m.sum(1).clamp(min=1e-9)   # masked mean, NOT [CLS]
    for d, head in zip(dims, heads):
        p = torch.sigmoid(head(pooled))[0]
        lvl = 0
        for j in range(p.shape[0]):
            if float(p[j]) > 0.5: lvl = j + 1
            else: break
        print(f"{d:14s} level {lvl}  probs {[round(float(x),3) for x in p]}")

This single-view snippet omits the canonicalised second view; use it to inspect the encoder, not as the serving path.

The shipped policy (separable, and binary)

BLOCK_AT = {"sexual": 2, "nudity": 2, "violence_gore": 2,
            "substances": 2, "disturbing": 2, "fetish": 1}
block  iff  any(level >= BLOCK_AT[dim])

fetish blocks at 1 because level 1 there already means coded fetish presentation. Everything else needs level 2.

There is no ROUTE tier, by design. Earlier revisions carried a soft band for human review. With no reviewer in the product, ROUTE resolved to BLOCK — refusing 7.07% of natural traffic at a level the rubric itself defines as not unsafe. Worse, having somewhere to park an undecided item meant items got parked: a large share of the route-only band was content that belonged at a definite level on some axis and was hedged instead. Removing the tier forced those to resolve. If your product has a reviewer, a soft band is reasonable — build it from the levels, which are published precisely so you can.

Evaluation

The meaningful audit is a random population sample, not a curated benchmark. 400 prompts drawn unstratified from the corpus, judged blind on disturbing and violence_gore by an independent LLM judge with no access to the labels, the model, or the disagreement history:

Under- and over-labelling are the same magnitude in opposite directions, and the corpus and the judge block at an identical rate: no systematic boundary displacement on those axes. The residual disagreement is low-rate boundary noise, and the model's own errors fell on the same items where the labels and the judge disagree — it tracks its supervision rather than adding error of its own.

Read that as a population rate, and nothing else as one. Enriched, adversarial and disagreement-stratified sets are constructed to be hard and will report error rates many times higher; that is what they are for. In this project a disagreement-stratified sample reported 81%/75% under-labelling on the same two axes where the population rate was 1.0%. Do not quote hard-set numbers as normal-traffic performance.

No per-dimension F1 is published. Error counts on small enriched strata moved F1 around far more than they moved the number of actual mistakes, and reporting it invites exactly the misreading above.

ComfyUI Junior deployment profile

These are serving-policy controls, not learned dimensions of v29db. The model itself has six dimensions and a binary policy; everything in this section sits in front of it and can be replaced without touching the weights.

well-formedness gate      -> prompt_format_invalid    "write it normally"
English-envelope gate     -> unsupported_language     "write it in English"
single-view v29db + policy_v6 -> content_policy_violation
else                      -> pass

Language support. The semantic model is English-only. ComfyUI Junior does not claim multilingual moderation support. In the Junior serving profile, prompts that appear substantially non-English are rejected before semantic classification with unsupported_language, and clearly obfuscated or malformed prompts are rejected with prompt_format_invalid. The envelope gate is fastText lid.176 (Meta/Facebook, distributed separately) thresholded at 0.30 on P(non-English).

The three outcomes are kept distinct on purpose. "Write it properly", "write it in English" and "that content isn't available" are different user problems and should never be collapsed into a single counter.

Measured serving behaviour

The red-team figure excludes one prompt that was adjudicated as correctly permissible. The four remaining misses are semantic — euphemism, implication, and a payload buried in long benign prose. No input gate addresses those.

Observed complementarity (an observation on one corpus, not a guarantee): 186 non-English prompts got past the language gate and reached v29db. The model blocked 47 of them, and none of the remainder were labelled unsafe — so nothing unsafe and non-English reached the generator in this corpus. The prompts the language gate misses tend to be the ones that look most English-like, which are also the ones the semantic model can still read. This is a measured property of one corpus, not a guarantee about unseen input.

Two gate details matter for anyone reimplementing this. Language ID runs on the Latin-script portion only — feeding CJK characters to lid.176 makes it call a shrine in 東京, cherry blossoms Japanese at p=0.58, and English tag lists with Japanese hashtags Japanese at p=0.89; both must pass. And the zero-width-character check requires a Latin letter on both sides, because a zero-width character at a word boundary is an ordinary copy-paste artifact rather than evasion.

Known limitations of this profile

• Latin-script non-English input may reach v29db. German, Dutch and Polish in particular are not reliably caught by the envelope gate, and Junior does not claim those languages are supported. Coverage by the semantic model is inconsistent across languages and was not designed.
• Short prompts are the weak slice. English prompts under 80 characters are wrongly gated at roughly five times the overall rate. The failure direction is a benign English prompt being asked to rewrite — not an unsafe prompt getting through. Prompts carrying non-ASCII characters are similarly elevated (0.60%).
• The block rate on any particular corpus is a property of that traffic. Do not read the rates above as an estimate of end-user traffic composition.

Intended use

Semantic pre-generation prompt screening for image-generation systems: flagging requests for sexual or erotic material, nudity, violence and gore, controlled-drug imagery, disturbing content, and fetish content before an image is produced.

Designed for child-facing image-generation safety filtering.

It is explicitly not:

• a CSAM or CSAE classifier. Minor-age inference is not its purpose and it must not be deployed as that control.
• a general text-toxicity model. It judges what an image would depict, so it will pass a rude sentence that renders as nothing and flag a polite sentence that renders as something graphic.
• a complete safety system. A prompt-only model structurally cannot see what a generator actually produced. Post-generation image moderation catches failures this class of model cannot, and the two are complementary rather than redundant.
• a guarantee. No threshold on this model makes a generation pipeline safe on its own.

English only. Prompts in other languages are real words rather than obfuscation, and the canonicalisation layer does not address them — that is a training-data gap, not a normalisation one.

Training and provenance

Trained on a large corpus of real image-generation prompts collected from public generation platforms, with model-assisted labelling. Successive rounds combined targeted hard-case analysis, dimension-specific label refinement, near-duplicate control (character 5-gram MinHash), and frozen random-population audits.

Labels from teacher models were treated as noisy silver supervision, never ground truth. No teacher output was taken as authoritative; where teacher labels and adjudication disagreed, the disagreement was measured rather than resolved by deferring to the teacher. Enriched and disagreement-stratified samples were kept strictly separate from population validation — mixing them is the single most repeated measurement error in this project's history, and it produced misleading conclusions four separate times before the separation was enforced.

The prompt corpus is not distributed. It contains scraped third-party prompt text, including explicit and adversarial material, and publishing it would serve no reproduction purpose that the released weights do not already serve.

License

MIT, matching the microsoft/deberta-v3-base base model from which these weights are derived. The license covers the weights and the code in this repo. It makes no claim about the training prompts, which are third-party text and are not distributed.

The previous v0.7 draft was Apache-2.0, correct for its DistilBERT base; the change to MIT follows the change of base model.

Artefact receipts

Frozen build v29db. SHA-256:

model.safetensors  5129545204d1c9ca8700369db1b055a9baac548c97dfc55140b8517cb538575e
heads.pt           d1d0b6f92c0dca4e3512739e7a26cb190fafd103b2d6056735c6ab8de5016ea9

Checkpoint schema: dims = [sexual, nudity, violence_gore, substances, disturbing, fetish], widths = [3, 2, 2, 2, 2, 2], backbone = microsoft/deberta-v3-base. heads.pt loads under weights_only=True.