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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"This Notebook is a Stable-diffusion tool which allows you to find similiar tokens from the SD 1.5 vocab.json that you can use for text-to-image generation."
],
"metadata": {
"id": "L7JTcbOdBPfh"
}
},
{
"cell_type": "code",
"source": [
"# Load the tokens into the colab\n",
"!git clone https://huggingface.co/datasets/codeShare/sd_tokens\n",
"import torch\n",
"from torch import linalg as LA\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"%cd /content/sd_tokens\n",
"token = torch.load('sd15_tensors.pt', map_location=device, weights_only=True)\n",
"#-----#\n",
"\n",
"#Import the vocab.json\n",
"import json\n",
"import pandas as pd\n",
"with open('vocab.json', 'r') as f:\n",
" data = json.load(f)\n",
"\n",
"_df = pd.DataFrame({'count': data})['count']\n",
"\n",
"vocab = {\n",
" value: key for key, value in _df.items()\n",
"}\n",
"#-----#\n",
"\n",
"# Define functions/constants\n",
"NUM_TOKENS = 49407\n",
"\n",
"def absolute_value(x):\n",
" return max(x, -x)\n",
"\n",
"def similarity(id_A , id_B):\n",
" #Tensors\n",
" A = token[id_A]\n",
" B = token[id_B]\n",
" #Tensor vector length (2nd order, i.e (a^2 + b^2 + ....)^(1/2)\n",
" _A = LA.vector_norm(A, ord=2)\n",
" _B = LA.vector_norm(B, ord=2)\n",
" #----#\n",
" result = torch.dot(A,B)/(_A*_B)\n",
" similarity_pcnt = absolute_value(result.item()*100)\n",
" similarity_pcnt_aprox = round(similarity_pcnt, 3)\n",
" result = f'{similarity_pcnt_aprox} %'\n",
" return result\n",
"#----#\n",
"\n",
"mix_with = \"\"\n",
"mix_method = \"None\""
],
"metadata": {
"id": "Ch9puvwKH1s3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#print(vocab[8922]) #the vocab item for ID 8922\n",
"#print(token[8922].shape) #dimension of the token"
],
"metadata": {
"id": "S_Yh9gH_OUA1"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Get the IDs from a prompt text.\n",
"\n",
"The prompt will be enclosed with the <|start-of-text|> and <|end-of-text|> tokens. Leave the field empty to get a random value tensor"
],
"metadata": {
"id": "f1-jS7YJApiO"
}
},
{
"cell_type": "code",
"source": [
"from transformers import AutoTokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
"\n",
"prompt= \"banana\" # @param {type:'string'}\n",
"\n",
"tokenizer_output = tokenizer(text = prompt)\n",
"input_ids = tokenizer_output['input_ids']\n",
"print(input_ids)\n",
"id_A = input_ids[1]\n",
"A = token[id_A]\n",
"_A = LA.vector_norm(A, ord=2)\n",
"\n",
"#if no imput exists we just randomize the entire thing\n",
"if (prompt == \"\"):\n",
" id_A = -1\n",
" print(\"Tokenized prompt tensor A is a random valued tensor with no ID\")\n",
" R = torch.rand(768)\n",
" _R = LA.vector_norm(R, ord=2)\n",
" A = R*(_A/_R)\n",
"\n",
"#Save a copy of the tensor A\n",
"id_P = input_ids[1]\n",
"P = token[id_A]\n",
"_P = LA.vector_norm(A, ord=2)"
],
"metadata": {
"id": "RPdkYzT2_X85"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"OPTIONAL : Add/subtract + normalize above result with another token. Leave field empty to get a random value tensor"
],
"metadata": {
"id": "JKnz0aLFVGXc"
}
},
{
"cell_type": "code",
"source": [
"mix_with = \"\" # @param {type:'string'}\n",
"mix_method = \"None\" # @param [\"None\" , \"Average\", \"Subtract\"] {allow-input: true}\n",
"w = 0.5 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
"\n",
"#prevent re-iterating A by reading from stored copy\n",
"id_A = id_P\n",
"A = P\n",
"_A = _P\n",
"#----#\n",
"\n",
"tokenizer_output = tokenizer(text = mix_with)\n",
"input_ids = tokenizer_output['input_ids']\n",
"id_C = input_ids[1]\n",
"C = token[id_C]\n",
"_C = LA.vector_norm(C, ord=2)\n",
"\n",
"#if no imput exists we just randomize the entire thing\n",
"if (mix_with == \"\"):\n",
" id_C = -1\n",
" print(\"Tokenized prompt 'mix_with' tensor C is a random valued tensor with no ID\")\n",
" R = torch.rand(768)\n",
" _R = LA.vector_norm(R, ord=2)\n",
" C = R*(_C/_R)\n",
"\n",
"if (mix_method == \"None\"):\n",
" print(\"No operation\")\n",
"\n",
"if (mix_method == \"Average\"):\n",
" A = w*A + (1-w)*C\n",
" _A = LA.vector_norm(A, ord=2)\n",
" print(\"Tokenized prompt tensor A has been recalculated as A = w*A + (1-w)*C , where C is the tokenized prompt 'mix_with' tensor C\")\n",
"\n",
"if (mix_method == \"Subtract\"):\n",
" tmp = (A/_A) - (C/_C)\n",
" _tmp = LA.vector_norm(tmp, ord=2)\n",
" A = tmp*((w*_A + (1-w)*_C)/_tmp)\n",
" _A = LA.vector_norm(A, ord=2)\n",
" print(\"Tokenized prompt tensor A has been recalculated as A = (w*_A + (1-w)*_C) * norm(w*A - (1-w)*C) , where C is the tokenized prompt 'mix_with' tensor C\")\n",
"\n",
"\n"
],
"metadata": {
"id": "oXbNSRSKPgRr"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Produce a list id IDs that are most similiar to the prompt ID at positiion 1 based on above result"
],
"metadata": {
"id": "3uBSZ1vWVCew"
}
},
{
"cell_type": "code",
"source": [
"\n",
"dots = torch.zeros(NUM_TOKENS)\n",
"for index in range(NUM_TOKENS):\n",
" id_B = index\n",
" B = token[id_B]\n",
" _B = LA.vector_norm(B, ord=2)\n",
" result = torch.dot(A,B)/(_A*_B)\n",
" result = absolute_value(result.item())\n",
" dots[index] = result\n",
"\n",
"name_A = \"A of random type\"\n",
"if (id_A>-1):\n",
" name_A = vocab[id_A]\n",
"\n",
"name_C = \"token C of random type\"\n",
"if (id_C>-1):\n",
" name_C = vocab[id_C]\n",
"\n",
"\n",
"sorted, indices = torch.sort(dots,dim=0 , descending=True)\n",
"#----#\n",
"if (mix_method == \"Average\"):\n",
" print(f'Calculated all cosine-similarities between the average of token {name_A} and {name_C} with Id_A = {id_A} and mixed Id_C = {id_C} as a 1x{sorted.shape[0]} tensor')\n",
"if (mix_method == \"Subtract\"):\n",
" print(f'Calculated all cosine-similarities between the subtract of token {name_A} and {name_C} with Id_A = {id_A} and mixed Id_C = {id_C} as a 1x{sorted.shape[0]} tensor')\n",
"if (mix_method == \"None\"):\n",
" print(f'Calculated all cosine-similarities between the token {name_A} with Id_A = {id_A} with the the rest of the {NUM_TOKENS} tokens as a 1x{sorted.shape[0]} tensor')"
],
"metadata": {
"id": "juxsvco9B0iV"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Print the sorted list from above result"
],
"metadata": {
"id": "y-Ig3glrVQC3"
}
},
{
"cell_type": "code",
"source": [
"list_size = 100 # @param {type:'number'}\n",
"\n",
"print_ID = False # @param {type:\"boolean\"}\n",
"print_Similarity = True # @param {type:\"boolean\"}\n",
"print_Name = True # @param {type:\"boolean\"}\n",
"print_Divider = True # @param {type:\"boolean\"}\n",
"\n",
"for index in range(list_size):\n",
" id = indices[index].item()\n",
" if (print_Name):\n",
" print(f'{vocab[id]}') # vocab item\n",
" if (print_ID):\n",
" print(f'ID = {id}') # IDs\n",
" if (print_Similarity):\n",
" print(f'similiarity = {round(sorted[index].item()*100,2)} %') # % value\n",
" if (print_Divider):\n",
" print('--------')"
],
"metadata": {
"id": "YIEmLAzbHeuo",
"collapsed": true
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Find the most similiar Tokens for given input"
],
"metadata": {
"id": "qqZ5DvfLBJnw"
}
},
{
"cell_type": "markdown",
"source": [
"Valid ID ranges for id_for_token_A / id_for_token_B are between 0 and 49407"
],
"metadata": {
"id": "kX72bAuhOtlT"
}
},
{
"cell_type": "code",
"source": [
"id_for_token_A = 4567 # @param {type:'number'}\n",
"id_for_token_B = 4343 # @param {type:'number'}\n",
"\n",
"similarity_str = 'The similarity between tokens A and B is ' + similarity(id_for_token_A , id_for_token_B)\n",
"\n",
"print(similarity_str)"
],
"metadata": {
"id": "MwmOdC9cNZty"
},
"execution_count": null,
"outputs": []
}
]
} |