NeuralBooru

If you've ever wanted to describe a scene in plain English and have it come out as proper Danbooru tags for an Illustrious or NoobAI checkpoint, this is the node that finally makes it click. You type "a vampire girl with fangs in a dark classroom at night," and out the other end comes 1girl, vampire, fangs, dark, classroom, night, standing - real tags your anime model was actually trained on, not tag-shaped prose that only sort of works.

The key word is real. Most tag generators are specialized fine-tunes like TIPO or DanTagGen that bake the vocabulary into their weights. NeuralBooru goes the other way: it's a model-agnostic adapter. A general LLM proposes tags, and a whitelist of ~140,000 actual Danbooru tags decides what survives. The LLM proposes, the whitelist disposes.

How it works

NeuralBooru is a prompt converter, not a model. It calls any OpenAI-compatible server you already run - LM Studio, Ollama, llama.cpp, vLLM - asking a chat model to turn your user_prompt into comma-separated tags. The bundled system_prompt (editable) does the steering: lowercase, no quality tags, real Danbooru style, 1girl/1boy for people. The model defaults to qwen/qwen3-1.7b - a good call: small enough to run off the GPU and it understands booru tagging.

Then the fun part. Every candidate tag goes through a validation pipeline against the bundled vocabulary (data/danbooru.csv, ~140k real tags):

1. Exact match
2. Alias remap (blonde → blonde hair)
3. Word-form fix (smirking → smirk)
4. Sub-phrase recovery (black crop top yields crop top)
5. Optional fuzzy remap for near-misses (off by default)
6. Drop whatever's left

Dropped tags land on dropped_tags and get reported in the console, so you always see what was filtered. Survivors are reordered Danbooru-style - people counts, character, copyright, artist, general, meta - because booru-trained models respond to that order, then wrapped in a quality-tag template.

That validation layer isn't there by accident. The author posted the node to r/comfyui and got the most useful critique imaginable: "I don't want danbooru-style prompts, I want danbooru tags... dark atmosphere is not a tag." A 1.7B general model can't guarantee a tag exists - it can only make things look tag-shaped. The whitelist closes exactly that gap.

The inputs that matter

• user_prompt - your plain-English description. The one thing you actually type.
• prompt_template / template_preset - the wrapper around your tags, with {prompt} as the placeholder. One-click presets for Illustrious, Pony, Animagine XL, and NovaAnimeXL. Don't reinvent the wrapper for your model family; just pick it.
• lm_studio_url - server address. http://localhost:1234 for LM Studio, http://localhost:11434 for Ollama (auto-detected).
• validate_tags and strict_tags - both on by default; this is what makes the output real tags.
• seed - change it to re-roll a fresh tag variation (there's a randomize control on the widget).
• on_error - set to use_input_text if you'd rather get an image from your raw description than have the workflow die on a dead server.

The prompt output feeds straight into a CLIP Text Encode (Positive) node - a single STRING, nothing exotic. tags is the validated tags without the template, handy for display or metadata. Wire dropped_tags to a PreviewAny node to see what got filtered.

Installation

It installs like any custom node, and refreshingly there's nothing to pip-install afterward - the whole pack is pure Python stdlib. Zero dependencies is a shipped feature.

Via ComfyUI Manager: search NeuralBooru and install. Or manually:

cd ComfyUI/custom_nodes
git clone https://github.com/ChrisJohnson89/ComfyUI-NeuralBooru

Restart ComfyUI and the node appears under the NeuralBooru category. The only "model file" you need is the LLM itself: load Qwen3-1.7B in LM Studio and enable its local server on port 1234. Text gen doesn't touch your image VRAM, so a Mac or a second machine works fine.

Common issues

• Connection refused / dead server - the node fails loudly with a clear message, which is honestly the right behavior. If you don't want a hard stop, on_error=use_input_text falls back to your description.
• "It's dropping tags!" - that's the design, the precision tradeoff. Someone in the release thread reported long prompts lose tags under strict validation. If it's too aggressive, lower the temperature, enable fuzzy_cutoff around 0.9, or turn strict_tags off.
• Brand-new tags missing - the vocabulary is a snapshot from the tagcomplete project, so very recent Danbooru tags won't resolve until a refresh script ships.
• Ollama acting different - the node probes /api/version and switches to Ollama's native API automatically; just point lm_studio_url at the right port.

If you want to peek at the raw LLM output before the filter, or run validation without an LLM at all, that's exactly what the two split nodes in the same pack - NeuralBooru LLM and NeuralBooru Validator - are for.