# tokenspace directory

This directory contains utilities for the purpose of browsing the
"token space" of CLIP ViT-L/14

Primary tools are:

*  "generate-distances.py": allows command-line browsing of words and their neighbours
*  "graph-embeddings.py": plots graph of full values of two embeddings


## generate-distances.py

Loads the generated embeddings, reads in a word, calculates "distance" to every
embedding, and then shows the closest "neighbours".

To run this requires the files "embeddings.safetensors" and "dictionary"

You will need to rename or copy appropriate files for this as mentioned below

## graph-embeddings.py

Run the script. It will ask you for two text strings. 
Once you enter both, it will plot the graph and display it for you

### embeddings.safetensors

You can either copy one of the provided files, or generate your own.
See generate-embeddings.py for that. 

Note that you muist always use the "dictionary" file that matchnes your embeddings file

### dictionary

Make sure to always use the dictionary file that matches your embeddings file.

The "dictionary.fullword" file is pulled from fullword.json, which is distilled from "full words"
present in the ViT-L/14 CLIP model's provided token dictionary, called "vocab.json".
Thus there are only around 30,000 words in it

If you want to use the provided "embeddings.safetensors.huge" file, you will want to use the matching
"dictionary.huge" file, which has over 300,000 words

This huge file comes from the linux "wamerican-huge" package, which delivers it under
/usr/share/dict/american-english-huge

There also exists a "american-insane" package


## generate-embeddings.py

Generates the "embeddings.safetensor" file, based on the "dictionary" file present.
Takes a few minutes to run, depending on size of the dictionary

The shape of the embeddings tensor, is
 [number-of-words][768]

Note that yes, it is possible to directly pull a tensor from the CLIP model,
using keyname of  text_model.embeddings.token_embedding.weight

This will NOT GIVE YOU THE RIGHT DISTANCES!
Hence why we are calculating and then storing the embedding weights actually
generated by the CLIP process


## fullword.json

This file contains a collection of "one word, one CLIP token id" pairings.
The file was taken from vocab.json, which is part of multiple SD models in huggingface.co

The file was optimized for what people are actually going to type as words.
First all the non-(/w) entries were stripped out.
Then all the garbage punctuation and foreign characters were stripped out.
Finally, the actual (/w) was stripped out, for ease of use.