Word embedding utility nodes for ComfyUI. Load a pre-trained embedding model, explore neighbors, do analogies, and project any token/phrase onto 1D/2D/3D semantic axes with human‑readable summaries.
🔎 Built around classic static embeddings (e.g., GloVe, word2vec). Great for quick semantic experiments inside ComfyUI.
- Clone or copy this folder into your ComfyUI
custom_nodes/directory, for example:ComfyUI/custom_nodes/ComfyUI-WordEmbeddings
- Ensure dependencies are available (ComfyUI portable often already includes NumPy):
pip install gensim numpy
- Restart ComfyUI.
The pretrained loader uses
gensim.downloader. On first use it will download the selected model into your localgensim-datacache.
- Load a model:
- WordEmbeddings: Loader (pretrained dropdown), or
- WordEmbeddings: Local Loader (point to a
.kv/ word2vec.bin/.txtfile).
- Connect the
we_modeloutput into any of the analysis nodes below (Neighbors / Equation / Axis / Axis2D / Axis3D / Centrality). - Run and read the numeric outputs and/or the human‑readable summary string.
- Inputs:
model_name(dropdown ofgensim.downloadermodels; defaultglove-wiki-gigaword-50) - Outputs:
WE_MODEL - Purpose: Fetch a known pretrained embedding from the internet the first time, then cache.
- Inputs:
path(string to local embedding file)format(auto|kv|word2vec_bin|word2vec_text)label(string prefix for display)
- Outputs:
WE_MODEL - Purpose: Load your own embeddings from disk.
- Inputs:
we_model,word,k - Outputs:
words_csv,debug_csv - Purpose: Top‑k nearest neighbors for a single word.
- Inputs:
we_model,word_a,word_b,stops,topn,method (slerp|lerp) - Outputs:
words_csv,debug_txt - Purpose: Walk between two words on the unit sphere and list representative samples along the path.
- Inputs:
we_model,expression(e.g.,king - man + woman),k,topn_pool,normalize_terms,allow_inputs - Outputs:
words_csv,debug_txt - Purpose: Classic embedding arithmetic / analogies with lightweight parser.
- Inputs:
we_model,word,k - Outputs:
words_csv,cosines_csv - Purpose: Same as Explorer but cosine scores separated for easy wiring.
Axes are written
left|right. Each side may contain comma‑separated synonyms to define a pole mean. Example:
man,boy,he,him,his,father,brother,husband | woman,girl,she,her,hers,mother,sister,wifeInputs also accept phrases when
token_can_be_phrase=true(we average the unit vectors of the words).
- Inputs:
we_model,token,axis,token_can_be_phrase,lowercase,neutral_eps - Outputs:
x(FLOAT in [-1,1]; + means toward left pole)cos_left(FLOAT)cos_right(FLOAT)summary(STRING)
- What it means:
xis the projection of the token onto the axis direction.cos_left/rightare cosine similarities to each pole mean (how much the token “resonates” with each pole independently).summaryblends polarity (fromx) and confidence (fromcos_*) into a plain‑English sentence.
- Inputs:
we_model,token,axis1,axis2,token_can_be_phrase,lowercase,neutral_eps - Outputs:
x,y(FLOATs)summary(STRING)x_cos_left,x_cos_right,y_cos_left,y_cos_right(FLOATs)
- What it means: Two independent semantic meters. Useful for 2D plotting or UI overlays.
- Inputs:
we_model,token,axis1,axis2,axis3,token_can_be_phrase,lowercase,neutral_eps - Outputs:
x,y,z(FLOATs)summary(STRING)x_cos_left,x_cos_right,y_cos_left,y_cos_right,z_cos_left,z_cos_right(FLOATs)
- What it means: Three meters → place a word in a simple 3D semantic space (e.g., gender, temperature, royalty).
The 2D/3D nodes can ignore weak axes in their summaries (thresholded by
neutral_eps) so the text focuses on strong signals.
- Inputs:
we_model,word - Outputs:
centrality(FLOAT in [-1,1])norm(FLOAT)summary(STRING)
- What it means:
- Computes the cosine similarity between the token (unit‑length) and the corpus‑wide mean direction.
- Rough intuition: high positive = very generic / frequent‑ish semantic direction; negative = off‑center / unusual.
normis the raw vector length (some embeddings store frequency-ish info in norms; take with caution).
-
Gender
man,boy,he,him,his,father,brother,husband | woman,girl,she,her,hers,mother,sister,wife -
Temperature
hot,warm,heat,boiling | cold,cool,freezing,ice -
Royalty / Status
royal,king,queen,prince,noble | common,commoner,peasant,ordinary -
Formality
formal,proper,polite | casual,slang,informal
Feel free to tailor axes to your domain—adding 3–10 good synonyms per side usually stabilizes results.
- Cosine similarity (
cos_*): how strongly the token points toward a pole. Range[-1, 1].~0.7+→ strong affinity;~0.3–0.6→ moderate;<0.2→ weak.
- Projection (
x,y,z): signed position along the axis. Positive = more left, negative = more right. - Neutrality: if
|x| < neutral_eps(default0.08), we call it about equal in the summary.
- Static embeddings reflect biases of their training data. Be thoughtful when choosing axes (especially sensitive ones).
- Different models (e.g.,
glove-6B-300,word2vec-google-news-300) will give different absolute values and neighbors. - If a phrase is used, the node averages unit vectors of its words. This is a simple, fast heuristic.
norm(in Centrality) isn’t universally meaningful across models; use it comparatively within the same model only.- If a pole side has no in‑vocab words, the related axis (or cosine) will be reported as unavailable / neutral in summaries.
- The code is intentionally dependency‑light (just
gensim+numpy). - We cache the global mean direction per model in memory to avoid recomputation.
- Axis internals:
- Compute unit‑mean vector per pole (averaged synonyms).
- Axis direction
a = unit(left_mean - right_mean)(or single‑sided fallback). - Projection
x = token_unit · aand per‑polecos_left/right = token_unit · pole_mean. - Generate human summary from polarity (
x) + confidence (cos_*) with thresholds.
- Built on top of Gensim: https://radimrehurek.com/gensim/
- Inspired by classic “king − man + woman ≈ queen” style analogies and axis‑projection demos.