ComfyUI Extension: ComfyUI-WordEmbeddings

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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.

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    ComfyUI-WordEmbeddings

    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.

    Installation

    1. Clone or copy this folder into your ComfyUI custom_nodes/ directory, for example: 
      ComfyUI/custom_nodes/ComfyUI-WordEmbeddings
    2. Ensure dependencies are available (ComfyUI portable often already includes NumPy): 
      pip install gensim numpy
    3. Restart ComfyUI.

    The pretrained loader uses gensim.downloader. On first use it will download the selected model into your local gensim-data cache.

    Quick Start

    1. Load a model:
      • WordEmbeddings: Loader (pretrained dropdown), or
      • WordEmbeddings: Local Loader (point to a .kv / word2vec .bin / .txt file).
    2. Connect the we_model output into any of the analysis nodes below (Neighbors / Equation / Axis / Axis2D / Axis3D / Centrality).
    3. Run and read the numeric outputs and/or the human‑readable summary string.

    Nodes Overview

    1) Loaders

    WordEmbeddings: Loader

    • Inputs: model_name (dropdown of gensim.downloader models; default glove-wiki-gigaword-50)
    • Outputs: WE_MODEL
    • Purpose: Fetch a known pretrained embedding from the internet the first time, then cache.

    WordEmbeddings: Local Loader

    • 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.

    2) Exploration & Algebra

    WordEmbeddings: Explorer

    • Inputs: we_model, word, k
    • Outputs: words_csv, debug_csv
    • Purpose: Top‑k nearest neighbors for a single word.

    WordEmbeddings: Interpolator

    • 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.

    WordEmbeddings: Equation

    • 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.

    WordEmbeddings: Token Neighbors

    • Inputs: we_model, word, k
    • Outputs: words_csv, cosines_csv
    • Purpose: Same as Explorer but cosine scores separated for easy wiring.

    3) Axis Projections (Core)

    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,wife

    Inputs also accept phrases when token_can_be_phrase=true (we average the unit vectors of the words).

    WordEmbeddings: Token Axis (1D)

    • 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:
      • x is the projection of the token onto the axis direction.
      • cos_left/right are cosine similarities to each pole mean (how much the token “resonates” with each pole independently).
      • summary blends polarity (from x) and confidence (from cos_*) into a plain‑English sentence.

    WordEmbeddings: Token Axis 2D

    • 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.

    WordEmbeddings: Token Axis 3D

    • 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.

    4) Centrality

    WordEmbeddings: Token Centrality

    • 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.
      • norm is the raw vector length (some embeddings store frequency-ish info in norms; take with caution).

    Example Axes (copy/paste)

    • 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.

    Interpreting the Numbers (cheat sheet)

    • 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 (default 0.08), we call it about equal in the summary.

    Tips & Caveats

    • 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.

    Development

    • The code is intentionally dependency‑light (just gensim + numpy).
    • We cache the global mean direction per model in memory to avoid recomputation.
    • Axis internals:
      1. Compute unit‑mean vector per pole (averaged synonyms).
      2. Axis direction a = unit(left_mean - right_mean) (or single‑sided fallback).
      3. Projection x = token_unit · a and per‑pole cos_left/right = token_unit · pole_mean.
      4. Generate human summary from polarity (x) + confidence (cos_*) with thresholds.

    Acknowledgements

    • Built on top of Gensim: https://radimrehurek.com/gensim/
    • Inspired by classic “king − man + woman ≈ queen” style analogies and axis‑projection demos.