{ "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. Try this Free online SD 1.5 generator with the results: https://perchance.org/fusion-ai-image-generator\n", "\n", "Scroll to the bottom of the notebook to see the guide for how this works." ], "metadata": { "id": "L7JTcbOdBPfh" } }, { "cell_type": "code", "source": [ "# @title ✳️ Load/initialize values\n", "# 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", "\n", "def token_similarity(A, B):\n", "\n", " #Vector length#\n", " _A = LA.vector_norm(A, ord=2)\n", " _B = LA.vector_norm(B, ord=2)\n", "\n", " #----#\n", " result = torch.dot(A,B)/(_A*_B)\n", " #similarity_pcnt = absolute_value(result.item()*100)\n", " similarity_pcnt = result.item()*100\n", " similarity_pcnt_aprox = round(similarity_pcnt, 3)\n", " result = f'{similarity_pcnt_aprox} %'\n", " return result\n", "\n", "\n", "def similarity(id_A , id_B):\n", " #Tensors\n", " A = token[id_A]\n", " B = token[id_B]\n", " return token_similarity(A, B)\n", "#----#\n", "\n", "#print(vocab[8922]) #the vocab item for ID 8922\n", "#print(token[8922].shape) #dimension of the token\n", "\n", "mix_with = \"\"\n", "mix_method = \"None\"\n", "\n", "#-------------#\n", "# UNUSED\n", "\n", "# Get the 10 lowest values from a tensor as a string\n", "def get_valleys (A):\n", " sorted, indices = torch.sort(A,dim=0 , descending=False)\n", " result = \"{\"\n", " for index in range(10):\n", " id = indices[index].item()\n", " result = result + f\"{id}\"\n", " if(index<9):\n", " result = result + \",\"\n", " result = result + \"}\"\n", " return result\n", "\n", "# Get the 10 highest values from a tensor as a string\n", "def get_peaks (A):\n", " sorted, indices = torch.sort(A,dim=0 , descending=True)\n", " result = \"{\"\n", " for index in range(10):\n", " id = indices[index].item()\n", " result = result + f\"{id}\"\n", " if(index<9):\n", " result = result + \",\"\n", " result = result + \"}\"\n", " return result" ], "metadata": { "id": "Ch9puvwKH1s3", "collapsed": true, "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title ⚡ Get similiar tokens\n", "from transformers import AutoTokenizer\n", "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n", "\n", "# @markdown Write name of token to match against\n", "prompt= \"banana\" # @param {type:'string',\"placeholder\":\"leave empty for random value token\"}\n", "\n", "tokenizer_output = tokenizer(text = prompt)\n", "input_ids = tokenizer_output['input_ids']\n", "print(input_ids)\n", "\n", "\n", "#The prompt will be enclosed with the <|start-of-text|> and <|end-of-text|> tokens, which is why output will be [49406, ... , 49407].\n", "\n", "#You can leave the 'prompt' field empty to get a random value tensor. Since the tensor is random value, it will not correspond to any tensor in the vocab.json list , and this it will have no ID.\n", "\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", " name_A = 'random_A'\n", "\n", "# @markdown (optional) Mix the token with something else\n", "mix_with = \"\" # @param {\"type\":\"string\",\"placeholder\":\"leave empty for random value token\"}\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", "# @markdown Limit char size of included token\n", "min_char_size = 3 # @param {type:\"slider\", min:0, max: 50, step:1}\n", "char_range = 5 # @param {type:\"slider\", min:0, max: 50, step:1}\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", " name_C = 'random_C'\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", "# Peaks feature\n", "#peaks_A = get_valleys(A)\n", "#peaks_C = get_valleys(C)\n", "#print(f\"The elementwise top 10 highest values for A is at indices {peaks_A}\")\n", "#print(\"-------\")\n", "#print(f\"The elementwise top 10 highest values for C is at indices {peaks_C}\")\n", "#print(\"-------\")\n", "#//------//\n", "\n", "print(f\"The similarity between A '{name_A}' and C '{name_C}' is {token_similarity(A, C)}\")\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(f\"Tokenized prompt tensor A '{name_A}' token has been recalculated as A = w*A + (1-w)*C , where C is '{name_C}' token , for w = {w} \")\n", "\n", "if (mix_method == \"Subtract\"):\n", " tmp = w*A - (1-w)*C\n", " _tmp = LA.vector_norm(tmp, ord=2)\n", " A = (_A/_tmp)*tmp\n", " #//---//\n", " _A = LA.vector_norm(A, ord=2)\n", " print(f\"Tokenized prompt tensor A '{name_A}' token has been recalculated as A = _A*norm(w*A - (1-w)*C) , where C is '{name_C}' token , for w = {w} \")\n", "\n", "#OPTIONAL : Add/subtract + normalize above result with another token. Leave field empty to get a random value tensor\n", "\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", " result = result.item()\n", " dots[index] = result\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')\n", "\n", "#Produce a list id IDs that are most similiar to the prompt ID at positiion 1 based on above result\n", "\n", "# @markdown Set print options\n", "list_size = 100 # @param {type:'number'}\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", "\n", "if (print_Divider):\n", " print('//---//')\n", "\n", "print('')\n", "print('Here is the result : ')\n", "print('')\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)} %')\n", " if (print_Divider):\n", " print('--------')\n", "\n", "#Print the sorted list from above result" ], "metadata": { "id": "iWeFnT1gAx6A", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Below image interrogator appends CLIP tokens to either end of the 'must_contain' text , and seeks to maximize similarity with the image encoding.\n", "\n", "It takes a long while to check all the tokens (too long!) so this cell only samples a range of the 49K available tokens.\n", "\n", "You can run this cell, then paste the result into the 'must_contain' box , and then run the cell again.\n", "\n" ], "metadata": { "id": "IUCuV9RtQpBn" } }, { "cell_type": "code", "source": [ "# @title 🪐🖼️ -> 📝 Slow Recursive Token Image interrogator\n", "\n", "# @markdown # What do you want to to mimic?\n", "use = '🖼️image_encoding from image' # @param ['📝text_encoding from prompt', '🖼️image_encoding from image']\n", "# @markdown --------------------------\n", "use_token_padding = True # param {type:\"boolean\"} <---- Enabled by default\n", "prompt = \"photo of a banana\" # @param {\"type\":\"string\",\"placeholder\":\"Write a prompt\"}\n", "\n", "prompt_A = prompt\n", "\n", "from google.colab import files\n", "def upload_files():\n", " from google.colab import files\n", " uploaded = files.upload()\n", " for k, v in uploaded.items():\n", " open(k, 'wb').write(v)\n", " return list(uploaded.keys())\n", "#Get image\n", "# You can use \"http://images.cocodataset.org/val2017/000000039769.jpg\" for testing\n", "image_url = \"http://images.cocodataset.org/val2017/000000039769.jpg\" # @param {\"type\":\"string\",\"placeholder\":\"leave empty for local upload (scroll down to see it)\"}\n", "\n", "\n", "colab_image_path = \"\" # @param {\"type\":\"string\",\"placeholder\": \"eval. as '/content/sd_tokens/' + **your input**\"}\n", "\n", "# @markdown --------------------------\n", "from PIL import Image\n", "import requests\n", "image_A = \"\"\n", "\n", "#----#\n", "\n", "if(use == '🖼️image_encoding from image'):\n", " if image_url == \"\":\n", " import cv2\n", " from google.colab.patches import cv2_imshow\n", " # Open the image.\n", " if colab_image_path == \"\":\n", " keys = upload_files()\n", " for key in keys:\n", " image_A = cv2.imread(\"/content/sd_tokens/\" + key)\n", " colab_image_path = \"/content/sd_tokens/\" + key\n", " else:\n", " image_A = cv2.imread(\"/content/sd_tokens/\" + colab_image_path)\n", " else:\n", " image_A = Image.open(requests.get(image_url, stream=True).raw)\n", "#------#\n", "\n", "from transformers import AutoTokenizer\n", "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n", "from transformers import CLIPProcessor, CLIPModel\n", "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\" , clean_up_tokenization_spaces = True)\n", "model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n", "\n", "\n", "if(use == '🖼️image_encoding from image'):\n", " # Get image features\n", " inputs = processor(images=image_A, return_tensors=\"pt\")\n", " image_features = model.get_image_features(**inputs)\n", " image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)\n", " name_A = \"the image\"\n", "#-----#\n", "\n", "\n", "if(use == '📝text_encoding from prompt'):\n", " # Get text features\n", " inputs = tokenizer(text = prompt, padding=True, return_tensors=\"pt\")\n", " text_features_A = model.get_text_features(**inputs)\n", " name_A = prompt\n", "#-----#\n", "\n", "\n", "# @markdown # The output...\n", "must_start_with = \"\" # @param {\"type\":\"string\",\"placeholder\":\"write a text\"}\n", "must_contain = \"banana \" # @param {\"type\":\"string\",\"placeholder\":\"write a text\"}\n", "must_end_with = \"\" # @param {\"type\":\"string\",\"placeholder\":\"write a text\"}\n", "token_B = must_contain\n", "\n", "# @markdown -----\n", "\n", "# @markdown # Use a range of tokens from the vocab.json (slow method)\n", "start_search_at_ID = 27700 # @param {type:\"slider\", min:0, max: 49407, step:100}\n", "search_range = 100 # @param {type:\"slider\", min:100, max: 2000, step:0}\n", "restrictions = 'None' # @param [\"None\", \"Suffix only\", \"Prefix only\"]\n", "\n", "#markdown Limit char size of included token <----- Disabled\n", "min_char_size = 0 #param {type:\"slider\", min:0, max: 20, step:1}\n", "char_range = 50 #param {type:\"slider\", min:0, max: 20, step:1}\n", "\n", "\n", "# markdown # ...or paste prompt items\n", "# markdown Format must be {item1|item2|...}. You can aquire prompt items using the Randomizer in the fusion gen: https://perchance.org/fusion-ai-image-generator\n", "_enable = False # param {\"type\":\"boolean\"}\n", "prompt_items = \"\" # param {\"type\":\"string\",\"placeholder\":\"{item1|item2|...}\"}\n", "#-----#\n", "name_B = must_contain\n", "#-----#\n", "\n", "START = start_search_at_ID\n", "RANGE = min(search_range , 49407 - start_search_at_ID)\n", "\n", "dots = torch.zeros(RANGE)\n", "is_BC = torch.zeros(RANGE)\n", "\n", "import re\n", "\n", "for index in range(RANGE):\n", " id_C = START + index\n", " C = token[id_C]\n", " _C = LA.vector_norm(C, ord=2)\n", " name_C = vocab[id_C]\n", " is_Prefix = 0\n", "\n", "\n", " #Skip if non-AZ characters are found\n", " if re.search(\"\\W/g\" , name_C.replace('', '')):\n", " continue\n", "\n", "\n", " # Decide if we should process prefix/suffix tokens\n", " if name_C.find('')<=-1:\n", " is_Prefix = 1\n", " if restrictions != \"Prefix only\":\n", " continue\n", " else:\n", " if restrictions == \"Prefix only\":\n", " continue\n", " #-----#\n", "\n", " # Decide if char-size is within range\n", " if len(name_C) < min_char_size:\n", " continue\n", " if len(name_C) > min_char_size + char_range:\n", " continue\n", " #-----#\n", " name_CB = must_start_with + name_C + name_B + must_end_with\n", " if is_Prefix>0:\n", " name_CB = must_start_with + ' ' + name_C.strip() + '-' + name_B.strip() + ' ' + must_end_with\n", " #-----#\n", "\n", " if(use == '🖼️image_encoding from image'):\n", " ids_CB = processor.tokenizer(text=name_CB, padding=use_token_padding, return_tensors=\"pt\")\n", " text_features = model.get_text_features(**ids_CB)\n", " text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\n", " logit_scale = model.logit_scale.exp()\n", " torch.matmul(text_features, image_features.t()) * logit_scale\n", " sim_CB = torch.nn.functional.cosine_similarity(text_features, image_features) * logit_scale\n", " #-----#\n", "\n", " if(use == '📝text_encoding from prompt'):\n", " ids_CB = processor.tokenizer(text=name_CB, padding=use_token_padding, return_tensors=\"pt\")\n", " text_features = model.get_text_features(**ids_CB)\n", " text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\n", " sim_CB = torch.nn.functional.cosine_similarity(text_features, text_features_A)\n", " #-----#\n", "\n", "\n", "\n", " #-----#\n", " if restrictions == \"Prefix only\":\n", " result = sim_CB\n", " result = result.item()\n", " dots[index] = result\n", " continue\n", " #-----#\n", "\n", " if(use == '🖼️image_encoding from image'):\n", " name_BC = must_start_with + name_B + name_C + must_end_with\n", " ids_BC = processor.tokenizer(text=name_BC, padding=use_token_padding, return_tensors=\"pt\")\n", " text_features = model.get_text_features(**ids_BC)\n", " text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\n", " logit_scale = model.logit_scale.exp()\n", " torch.matmul(text_features, image_features.t()) * logit_scale\n", " sim_BC = torch.nn.functional.cosine_similarity(text_features, image_features) * logit_scale\n", " #-----#\n", "\n", " if(use == '📝text_encoding from prompt'):\n", " name_BC = must_start_with + name_B + name_C + must_end_with\n", " ids_BC = processor.tokenizer(text=name_BC, padding=use_token_padding, return_tensors=\"pt\")\n", " text_features = model.get_text_features(**ids_BC)\n", " text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\n", " sim_BC = torch.nn.functional.cosine_similarity(text_features, text_features_A)\n", " #-----#\n", "\n", " result = sim_CB\n", " if(sim_BC > sim_CB):\n", " is_BC[index] = 1\n", " result = sim_BC\n", "\n", " #result = absolute_value(result.item())\n", " result = result.item()\n", " dots[index] = result\n", "#----#\n", "\n", "sorted, indices = torch.sort(dots,dim=0 , descending=True)\n", "\n", "\n", "# @markdown ----------\n", "# @markdown # Print options\n", "list_size = 100 # @param {type:'number'}\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", "\n", "if (print_Divider):\n", " print('//---//')\n", "\n", "print('')\n", "print(f'These token pairings within the range ID = {START} to ID = {START + RANGE} most closely match the text_encoding for {prompt_A} : ')\n", "print('')\n", "#----#\n", "aheads = \"{\"\n", "trails = \"{\"\n", "tmp = \"\"\n", "#----#\n", "max_sim_ahead = 0\n", "max_sim_trail = 0\n", "sim = 0\n", "max_name_ahead = ''\n", "max_name_trail = ''\n", "#----#\n", "for index in range(min(list_size,RANGE)):\n", " id = START + indices[index].item()\n", " name = vocab[id]\n", " #-----#\n", " if (name.find('')<=-1):\n", " name = name + '-'\n", " else:\n", " name = name.replace('', ' ')\n", " if(is_BC[index]>0):\n", " trails = trails + name + \"|\"\n", " else:\n", " aheads = aheads + name + \"|\"\n", " #----#\n", " sim = sorted[index].item()\n", "\n", " if(is_BC[index]>0):\n", " if sim>max_sim_ahead:\n", " max_sim_ahead = sim\n", " max_name_ahead = name\n", " else:\n", " if sim>max_sim_trail:\n", " max_sim_trail = sim\n", " max_name_trail = name\n", "\n", "#------#\n", "trails = (trails + \"&&&&\").replace(\"|&&&&\", \"}\").replace(\"\", \" \").replace(\"{&&&&\", \"\")\n", "aheads = (aheads + \"&&&&\").replace(\"|&&&&\", \"}\").replace(\"\", \" \").replace(\"{&&&&\", \"\")\n", "max_sim_ahead=max_sim_ahead\n", "max_sim_ahead=max_sim_trail\n", "#-----#\n", "print(f\"place these items ahead of prompt : {aheads}\")\n", "print(\"\")\n", "print(f\"place these items behind the prompt : {trails}\")\n", "print(\"\")\n", "print(f\"max_similarity = {max_sim_ahead} % when using '{max_name_ahead + must_contain}' \")\n", "print(\"\")\n", "print(f\"max_similarity = {max_sim_trail} % when using '{must_contain + max_name_trail}' \")\n", "#-----#\n", "#STEP 2\n", "import random\n", "\n", "names = {}\n", "\n", "NUM_PERMUTATIONS = 4 # 0 1 2 3\n", "dots = torch.zeros(NUM_PERMUTATIONS)\n", "for index in range(NUM_PERMUTATIONS):\n", " name = must_start_with\n", " if index == 0 : name = name + must_contain\n", " if index == 1 : name = name + max_name_ahead + must_contain\n", " if index == 2 : name = name + must_contain + max_name_trail\n", " if index == 3 : name = name + max_name_ahead + must_contain + max_name_trail\n", " name = name + must_end_with\n", " #----#\n", " ids = processor.tokenizer(text=name, padding=use_token_padding, return_tensors=\"pt\")\n", "\n", " if(use == '🖼️image_encoding from image'):\n", " text_features = model.get_text_features(**ids)\n", " text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\n", " logit_scale = model.logit_scale.exp()\n", " torch.matmul(text_features, image_features.t()) * logit_scale\n", " sim = torch.nn.functional.cosine_similarity(text_features, image_features) * logit_scale\n", " #-----#\n", "\n", " if(use == '📝text_encoding from prompt'):\n", " text_features = model.get_text_features(**ids)\n", " text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\n", " sim = torch.nn.functional.cosine_similarity(text_features, text_features_A)\n", " #-----#\n", "\n", "\n", " dots[index] = sim\n", " names[index] = name\n", "\n", "\n", "#------#\n", "\n", "sorted, indices = torch.sort(dots,dim=0 , descending=True)\n", "\n", "for index in range(NUM_PERMUTATIONS):\n", " print(names[indices[index].item()])\n", " print(f'similiarity = {round(sorted[index].item(),2)} %')\n", " print('------')\n", "\n", "\n", "\n" ], "metadata": { "collapsed": true, "id": "fi0jRruI0-tu" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title 💫 Compare Text encodings\n", "\n", "prompt_A = \"banana\" # @param {\"type\":\"string\",\"placeholder\":\"Write a prompt\"}\n", "prompt_B = \"\" # @param {\"type\":\"string\",\"placeholder\":\"Write a prompt\"}\n", "use_token_padding = True # @param {type:\"boolean\"}\n", "\n", "from transformers import CLIPProcessor, CLIPModel\n", "\n", "processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\" , clean_up_tokenization_spaces = True)\n", "\n", "model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n", "\n", "ids_A = processor.tokenizer(text=prompt_A, padding=use_token_padding, return_tensors=\"pt\")\n", "text_encoding_A = model.get_text_features(**ids_A)\n", "\n", "\n", "ids_B = processor.tokenizer(text=prompt_B, padding=use_token_padding, return_tensors=\"pt\")\n", "text_encoding_B = model.get_text_features(**ids_B)\n", "\n", "similarity_str = 'The similarity between the text_encoding for A:\"' + prompt_A + '\" and B: \"' + prompt_B +'\" is ' + token_similarity(text_encoding_A[0] , text_encoding_B[0])\n", "\n", "\n", "print(similarity_str)\n", "#outputs = model(**inputs)\n", "#logits_per_image = outputs.logits_per_image # this is the image-text similarity score\n", "#probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities" ], "metadata": { "id": "QQOjh5BvnG8M", "collapsed": true, "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "You can write an url or upload a file locally from your device to use as reference. The image will by saved in the 'sd_tokens' folder. Note that the 'sd_tokens' folder will be deleted upon exiting this runtime." ], "metadata": { "id": "hyK423TQCRup" } }, { "cell_type": "markdown", "source": [ "# ↓ Sub modules (use these to build your own projects) ↓" ], "metadata": { "id": "_d8WtPgtAymM" } }, { "cell_type": "code", "source": [ "# @title 📝 -> 🆔 Tokenize prompt into IDs\n", "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", "\n", "\n", "#The prompt will be enclosed with the <|start-of-text|> and <|end-of-text|> tokens, which is why output will be [49406, ... , 49407].\n", "\n", "#You can leave the 'prompt' field empty to get a random value tensor. Since the tensor is random value, it will not correspond to any tensor in the vocab.json list , and this it will have no ID." ], "metadata": { "id": "RPdkYzT2_X85", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title 🆔->🥢 Take the ID at index 1 from above result and get its corresponding tensor value\n", "\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 = id_A\n", "P = A\n", "_P = LA.vector_norm(A, ord=2)\n" ], "metadata": { "id": "YqdiF8DIz9Wu", "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# @title 🥢 -> 🥢🔀 Take the ID at index 1 from above result and modify it (optional)\n", "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", "#------#\n", "#If set to TRUE , this will use the output of this cell , tensor A, as the input of this cell the 2nd time we run it. Use this feature to mix many tokens into A\n", "re_iterate_tensor_A = True # @param {\"type\":\"boolean\"}\n", "if (re_iterate_tensor_A == False) :\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", "#OPTIONAL : Add/subtract + normalize above result with another token. Leave field empty to get a random value tensor" ], "metadata": { "id": "oXbNSRSKPgRr", "collapsed": true, "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "# @title 🥢->🧾🥢 Find Similiar Tokens to ID at index 1 from above result\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", " result = 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')\n", "\n", "#Produce a list id IDs that are most similiar to the prompt ID at positiion 1 based on above result" ], "metadata": { "id": "juxsvco9B0iV", "collapsed": true, "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [], "metadata": { "id": "cYYu5C5C6MHH" } }, { "cell_type": "code", "source": [ "# @title 🥢🧾 -> 🖨️ Print Result from the 'Similiar Tokens' list from above result\n", "list_size = 100 # @param {type:'number'}\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('--------')\n", "\n", "#Print the sorted list from above result" ], "metadata": { "id": "YIEmLAzbHeuo", "collapsed": true, "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "# @title 🆔 Get similarity % of two token IDs\n", "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)\n", "\n", "#Valid ID ranges for id_for_token_A / id_for_token_B are between 0 and 49407" ], "metadata": { "id": "MwmOdC9cNZty", "collapsed": true, "cellView": "form" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "\n", "\n", "# How does this notebook work?\n", "\n", "Similiar vectors = similiar output in the SD 1.5 / SDXL / FLUX model\n", "\n", "CLIP converts the prompt text to vectors (“tensors”) , with float32 values usually ranging from -1 to 1.\n", "\n", "Dimensions are \\[ 1x768 ] tensors for SD 1.5 , and a \\[ 1x768 , 1x1024 ] tensor for SDXL and FLUX.\n", "\n", "The SD models and FLUX converts these vectors to an image.\n", "\n", "This notebook takes an input string , tokenizes it and matches the first token against the 49407 token vectors in the vocab.json : [https://huggingface.co/black-forest-labs/FLUX.1-dev/tree/main/tokenizer](https://www.google.com/url?q=https%3A%2F%2Fhuggingface.co%2Fblack-forest-labs%2FFLUX.1-dev%2Ftree%2Fmain%2Ftokenizer)\n", "\n", "It finds the “most similiar tokens” in the list. Similarity is the theta angle between the token vectors.\n", "\n", "
\n", "\n", "
\n", "\n", "The angle is calculated using cosine similarity , where 1 = 100% similarity (parallell vectors) , and 0 = 0% similarity (perpendicular vectors).\n", "\n", "Negative similarity is also possible.\n", "\n", "# How can I use it?\n", "\n", "If you are bored of prompting “girl” and want something similiar you can run this notebook and use the “chick” token at 21.88% similarity , for example\n", "\n", "You can also run a mixed search , like “cute+girl”/2 , where for example “kpop” has a 16.71% similarity\n", "\n", "There are some strange tokens further down the list you go. Example: tokens similiar to the token \"pewdiepie\" (yes this is an actual token that exists in CLIP)\n", "\n", "
\n", "\n", "
\n", "\n", "Each of these correspond to a unique 1x768 token vector.\n", "\n", "The higher the ID value , the less often the token appeared in the CLIP training data.\n", "\n", "To reiterate; this is the CLIP model training data , not the SD-model training data.\n", "\n", "So for certain models , tokens with high ID can give very consistent results , if the SD model is trained to handle them.\n", "\n", "Example of this can be anime models , where japanese artist names can affect the output greatly. \n", "\n", "Tokens with high ID will often give the \"fun\" output when used in very short prompts.\n", "\n", "# What about token vector length?\n", "\n", "If you are wondering about token magnitude,\n", "Prompt weights like (banana:1.2) will scale the magnitude of the corresponding 1x768 tensor(s) by 1.2 . So thats how prompt token magnitude works.\n", "\n", "Source: [https://huggingface.co/docs/diffusers/main/en/using-diffusers/weighted\\_prompts](https://www.google.com/url?q=https%3A%2F%2Fhuggingface.co%2Fdocs%2Fdiffusers%2Fmain%2Fen%2Fusing-diffusers%2Fweighted_prompts)\\*\n", "\n", "So TLDR; vector direction = “what to generate” , vector magnitude = “prompt weights”\n", "\n", "# How prompting works (technical summary)\n", "\n", " 1. There is no correct way to prompt.\n", "\n", "2. Stable diffusion reads your prompt left to right, one token at a time, finding association _from_ the previous token _to_ the current token _and to_ the image generated thus far (Cross Attention Rule)\n", "\n", "3. Stable Diffusion is an optimization problem that seeks to maximize similarity to prompt and minimize similarity to negatives (Optimization Rule)\n", "\n", "Reference material (covers entire SD , so not good source material really, but the info is there) : https://youtu.be/sFztPP9qPRc?si=ge2Ty7wnpPGmB0gi\n", "\n", "# The SD pipeline\n", "\n", "For every step (20 in total by default) for SD1.5 :\n", "\n", "1. Prompt text => (tokenizer)\n", "2. => Nx768 token vectors =>(CLIP model) =>\n", "3. 1x768 encoding => ( the SD model / Unet ) =>\n", "4. => _Desired_ image per Rule 3 => ( sampler)\n", "5. => Paint a section of the image => (image)\n", "\n", "# Disclaimer /Trivia\n", "\n", "This notebook should be seen as a \"dictionary search tool\" for the vocab.json , which is the same for SD1.5 , SDXL and FLUX. Feel free to verify this by checking the 'tokenizer' folder under each model.\n", "\n", "vocab.json in the FLUX model , for example (1 of 2 copies) : https://huggingface.co/black-forest-labs/FLUX.1-dev/tree/main/tokenizer\n", "\n", "I'm using Clip-vit-large-patch14 , which is used in SD 1.5 , and is one among the two tokenizers for SDXL and FLUX : https://huggingface.co/openai/clip-vit-large-patch14/blob/main/README.md\n", "\n", "This set of tokens has dimension 1x768. \n", "\n", "SDXL and FLUX uses an additional set of tokens of dimension 1x1024.\n", "\n", "These are not included in this notebook. Feel free to include them yourselves (I would appreciate that).\n", "\n", "To do so, you will have to download a FLUX and/or SDXL model\n", "\n", ", and copy the 49407x1024 tensor list that is stored within the model and then save it as a .pt file.\n", "\n", "//---//\n", "\n", "I am aware it is actually the 1x768 text_encoding being processed into an image for the SD models + FLUX.\n", "\n", "As such , I've included text_encoding comparison at the bottom of the Notebook.\n", "\n", "I am also aware thar SDXL and FLUX uses additional encodings , which are not included in this notebook.\n", "\n", "* Clip-vit-bigG for SDXL: https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/blob/main/README.md\n", "\n", "* And the T5 text encoder for FLUX. I have 0% understanding of FLUX T5 text_encoder.\n", "\n", "//---//\n", "\n", "If you want them , feel free to include them yourself and share the results (cuz I probably won't) :)!\n", "\n", "That being said , being an encoding , I reckon the CLIP Nx768 => 1x768 should be \"linear\" (or whatever one might call it)\n", "\n", "So exchange a few tokens in the Nx768 for something similiar , and the resulting 1x768 ought to be kinda similar to 1x768 we had earlier. Hopefully.\n", "\n", "I feel its important to mention this , in case some wonder why the token-token similarity don't match the text-encoding to text-encoding similarity.\n", "\n", "# Note regarding CLIP text encoding vs. token\n", "\n", "*To make this disclaimer clear; Token-to-token similarity is not the same as text_encoding similarity.*\n", "\n", "I have to say this , since it will otherwise get (even more) confusing , as both the individual tokens , and the text_encoding have dimensions 1x768.\n", "\n", "They are separate things. Separate results. etc.\n", "\n", "As such , you will not get anything useful if you start comparing similarity between a token , and a text-encoding. So don't do that :)!\n", "\n", "# What about the CLIP image encoding?\n", "\n", "The CLIP model can also do an image_encoding of an image, where the output will be a 1x768 tensor. These _can_ be compared with the text_encoding.\n", "\n", "Comparing CLIP image_encoding with the CLIP text_encoding for a bunch of random prompts until you find the \"highest similarity\" , is a method used in the CLIP interrogator : https://huggingface.co/spaces/pharmapsychotic/CLIP-Interrogator\n", "\n", "List of random prompts for CLIP interrogator can be found here, for reference : https://github.com/pharmapsychotic/clip-interrogator/tree/main/clip_interrogator/data\n", "\n", "The CLIP image_encoding is not included in this Notebook.\n", "\n", "If you spot errors / ideas for improvememts; feel free to fix the code in your own notebook and post the results.\n", "\n", "I'd appreciate that over people saying \"your math is wrong you n00b!\" with no constructive feedback.\n", "\n", "//---//\n", "\n", "Regarding output\n", "\n", "# What are the symbols?\n", "\n", "The whitespace symbol indicate if the tokenized item ends with whitespace ( the suffix \"banana\" => \"banana \" ) or not (the prefix \"post\" in \"post-apocalyptic \")\n", "\n", "For ease of reference , I call them prefix-tokens and suffix-tokens.\n", "\n", "Sidenote:\n", "\n", "Prefix tokens have the unique property in that they \"mutate\" suffix tokens\n", "\n", "Example: \"photo of a #prefix#-banana\"\n", "\n", "where #prefix# is a randomly selected prefix-token from the vocab.json\n", "\n", "The hyphen \"-\" exists to guarantee the tokenized text splits into the written #prefix# and #suffix# token respectively. The \"-\" hypen symbol can be replaced by any other special character of your choosing.\n", "\n", " Capital letters work too , e.g \"photo of a #prefix#Abanana\" since the capital letters A-Z are only listed once in the entire vocab.json.\n", "\n", "You can also choose to omit any separator and just rawdog it with the prompt \"photo of a #prefix#banana\" , however know that this may , on occasion , be tokenized as completely different tokens of lower ID:s.\n", "\n", "Curiously , common NSFW terms found online have in the CLIP model have been purposefully fragmented into separate #prefix# and #suffix# counterparts in the vocab.json. Likely for PR-reasons.\n", "\n", "You can verify the results using this online tokenizer: https://sd-tokenizer.rocker.boo/\n", "\n", "
\n", "\n", "\n", "\n", "
\n", "\n", "# What is that gibberish tokens that show up?\n", "\n", "The gibberish tokens like \"ðŁĺħ\\\" are actually emojis!\n", "\n", "Try writing some emojis in this online tokenizer to see the results: https://sd-tokenizer.rocker.boo/\n", "\n", "It is a bit borked as it can't process capital letters properly.\n", "\n", "Also note that this is not reversible.\n", "\n", "If tokenization \"😅\" => ðŁĺħ\n", "\n", "Then you can't prompt \"ðŁĺħ\" and expect to get the same result as the tokenized original emoji , \"😅\".\n", "\n", "SD 1.5 models actually have training for Emojis.\n", "\n", "But you have to set CLIP skip to 1 for this to work is intended.\n", "\n", "For example, this is the result from \"photo of a 🧔🏻‍♂️\"\n", "\n", "\n", "
\n", "\n", "
\n", "\n", "A tutorial on stuff you can do with the vocab.list concluded.\n", "\n", "Anyways, have fun with the notebook.\n", "\n", "There might be some updates in the future with features not mentioned here.\n", "\n", "//---//\n", "\n", "https://codeandlife.com/2023/01/26/mastering-the-huggingface-clip-model-how-to-extract-embeddings-and-calculate-similarity-for-text-and-images/" ], "metadata": { "id": "njeJx_nSSA8H" } } ] }