Upload sd_token_similarity_calculator.ipynb
Browse files- sd_token_similarity_calculator.ipynb +134 -116
sd_token_similarity_calculator.ipynb
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"colab": {
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"base_uri": "https://localhost:8080/"
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"outputId": "
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"execution_count": 1,
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"outputs": [
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"text": [
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"/content\n",
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"Cloning into 'text-to-image-prompts'...\n",
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"remote: Enumerating objects:
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"remote: Counting objects: 100% (
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"remote: Compressing objects: 100% (
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"remote: Total
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"Resolving deltas: 100% (
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"emojis = False # @param {\"type\":\"boolean\",\"placeholder\":\"π\"}\n",
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"#------#\n",
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"\n",
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"celebs = False # @param {\"type\":\"boolean\",\"placeholder\":\"ππ¨\"}\n",
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"\n",
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"#These are borked\n",
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"#-------#\n",
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"\n",
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"#-----#\n",
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"female_fullnames =
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"debug = False\n",
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"\n",
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"#------#\n",
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"nA = 0\n",
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"#--------#\n",
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"\n",
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"\n",
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"if celebs:\n",
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" url = '/content/text-to-image-prompts/names/celebs/mixed'\n",
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"colab": {
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"base_uri": "https://localhost:8080/"
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"output_type": "stream",
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"cell_type": "code",
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"source": [
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"# @title π Get Prompt text_encoding similarity to the pre-calc. text_encodings\n",
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"prompt = \"
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"\n",
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"from transformers import AutoTokenizer\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
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"metadata": {
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"id": "xc-PbIYF428y"
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"base_uri": "https://localhost:8080/"
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "87487b44-9037-4adc-b978-b107be7854d1"
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},
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"execution_count": 1,
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"outputs": [
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"text": [
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"/content\n",
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"Cloning into 'text-to-image-prompts'...\n",
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"remote: Enumerating objects: 1095, done.\u001b[K\n",
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"remote: Counting objects: 100% (1092/1092), done.\u001b[K\n",
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"remote: Compressing objects: 100% (1059/1059), done.\u001b[K\n",
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"remote: Total 1095 (delta 147), reused 0 (delta 0), pack-reused 3 (from 1)\u001b[K\n",
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"Receiving objects: 100% (1095/1095), 3.63 MiB | 5.75 MiB/s, done.\n",
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"Resolving deltas: 100% (147/147), done.\n",
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"Filtering content: 100% (275/275), 795.84 MiB | 45.49 MiB/s, done.\n"
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]
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}
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"emojis = False # @param {\"type\":\"boolean\",\"placeholder\":\"π\"}\n",
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"#------#\n",
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"\n",
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"first_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"πΉ\"}\n",
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"last_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"πΈ\"}\n",
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"full_names = True # @param {\"type\":\"boolean\",\"placeholder\":\"π\"}\n",
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"\n",
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"celebs = False # @param {\"type\":\"boolean\",\"placeholder\":\"ππ¨\"}\n",
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"\n",
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"#These are borked\n",
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"celebs_young = False # param {\"type\":\"boolean\",\"placeholder\":\"πΈ\"}\n",
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"#-------#\n",
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"\n",
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"#-----#\n",
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"female_fullnames = False # @param {\"type\":\"boolean\",\"placeholder\":\"π\"}\n",
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"debug = False\n",
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"\n",
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"#------#\n",
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"nA = 0\n",
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"#--------#\n",
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"\n",
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"if first_names:\n",
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" url = '/content/text-to-image-prompts/names/firstnames'\n",
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" prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
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"#--------#\n",
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"\n",
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"if last_names:\n",
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" url = '/content/text-to-image-prompts/names/lastnames'\n",
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" prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
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"#--------#\n",
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"\n",
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"if full_names:\n",
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" url = '/content/text-to-image-prompts/names/fullnames'\n",
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" prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n",
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"#--------#\n",
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"\n",
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"if celebs:\n",
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" url = '/content/text-to-image-prompts/names/celebs/mixed'\n",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "03b73537-6081-452e-d1fa-8ff6de9f0bf0"
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},
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"execution_count": 9,
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"outputs": [
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{
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"output_type": "stream",
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"cell_type": "code",
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"source": [
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"# @title π Get Prompt text_encoding similarity to the pre-calc. text_encodings\n",
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"prompt = \"Beyonce \" # @param {\"type\":\"string\",\"placeholder\":\"Write a prompt\"}\n",
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"\n",
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"from transformers import AutoTokenizer\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", clean_up_tokenization_spaces = False)\n",
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"metadata": {
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"id": "xc-PbIYF428y"
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},
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"execution_count": 10,
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"outputs": []
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},
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{
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"colab": {
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"base_uri": "https://localhost:8080/"
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"outputId": "ae8f6e6e-50b1-4de4-cc90-6874be10c46d"
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"execution_count": 11,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"{Onna Ealy |\n",
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"Onye Guss |\n",
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"Lyric Chasen |\n",
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"Tionni Chando |\n",
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"Brin Baves |\n",
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"Zene Marer |\n",
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"Meeah Ehlke |\n",
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"Beyah Cheeseman |\n",
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"Leta Eddins |\n",
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"Le Eady |\n",
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"Maloree Onsurez |\n",
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"Dani Charest |\n",
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"Bryna Stater }\n"
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]
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}
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]
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