| # 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 | |
| Note that this tool does not require any of the other files; just that you | |
| have the requisite python modules installed. (pip install -r requirements.txt) | |
| ### 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. | |