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"/content\n", + "Cloning into 'text-to-image-prompts'...\n", + "remote: Enumerating objects: 18622, done.\u001b[K\n", + "remote: Counting objects: 100% (3/3), done.\u001b[K\n", + "remote: Compressing objects: 100% (3/3), done.\u001b[K\n", + "remote: Total 18622 (delta 1), reused 0 (delta 0), pack-reused 18619 (from 1)\u001b[K\n", + "Receiving objects: 100% (18622/18622), 190.46 MiB | 8.02 MiB/s, done.\n", + "Resolving deltas: 100% (2369/2369), done.\n", + "Updating files: 100% (8539/8539), done.\n", + "Filtering content: 100% (3503/3503), 10.27 GiB | 46.93 MiB/s, done.\n" + ] + } + ] }, { "cell_type": "code", @@ -116,10 +1852,24 @@ " print(vocab['8922']) #ID for banana is 8922" ], "metadata": { - "id": "EDCd1IGEqj3-" + "id": "EDCd1IGEqj3-", + "outputId": "7e73d97b-eaee-4686-d0a7-1c5bbff96162", + "colab": { + "base_uri": "https://localhost:8080/" + } }, - "execution_count": null, - "outputs": [] + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "reading vocab.json....\n", + "/content/text-to-image-prompts/vocab/text\n", + "/content/text-to-image-prompts/vocab/token_vectors\n" + ] + } + ] }, { "cell_type": "code", @@ -131,7 +1881,7 @@ "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", - "token_name = \"bare\" # @param {type:'string',\"placeholder\":\"leave empty for random value token\"}\n", + "token_name = \"visual\" # @param {type:'string',\"placeholder\":\"leave empty for random value token\"}\n", "\n", "prompt = token_name\n", "# @markdown (optional) Mix the token with something else\n", @@ -270,10 +2020,470 @@ "# See this link for additional stuff to do with shelve: https://docs.python.org/3/library/shelve.html" ], "metadata": { - "id": "ZwGqg9R5s1QS" + "id": "ZwGqg9R5s1QS", + "outputId": "7899dd81-440f-4401-d205-f52f5a2da7b8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "700de9123d6743bd8cfd38b2472b6a77", + "f32d3ac414d84dae8181a9de8dc58dc9", + "05ce5f19bcb2456a91aac192a0b7bddb", + "bdb7dfb9e0ae4bc7bb838d645d243b4a", + "26c9faf9770248a8bae7f49870014dda", + "fcce776a2aed47fc89e507dfbee0f825", + "47dee4d380f94855819f77c908da461d", + "8fc1ab9ac8b2424f9610b8c37f16c27a", + "37d8b4b560d7467b8765a13d307bde60", + "c5386323e3354561be7394b242020759", + "03a8799d0c00459f8f0eaa7f4eea17cb", + "370aa09913bc4ecd86d3c728f7178c13", + "5595c063c66a4d05b3336cacd20ce80f", + "707b8b4b6ca04e5b9e21a0baff59d550", + "602d0688585449539c53b290b892e5dd", + "f4123196f5454d7e9929aeb22ee46d7f", + "7f13e11fcebc491aaed844296ad80b4b", + "00b5b8ac2124480c998d55b01cc1eba9", + "961a7ac497c74091ad176cf88f7751fc", + "5c2b7467d4bb4b3aae97a4e55d7cd2ab", + "ecd447c08c18457190ebfc8b8b893bee", + "1e8afcb0a94646609b9026f1e665125a", + "d6b23aaa2bc44eda8d98b7240f9e52d2", + "57d83a1e1f9246949ed93794457ec790", + "0bca2db2c1c94670b906ae9d7dc91277", + "92a0d332f3db4feca034eb46eba30059", + "660083229b4648eea1fb34d7013b6bd6", + "02eed4862c35402189d6602da0aadd66", + "a3fc76725200482db0fe024499664f12", + "50dafe65120245b18c40367a1b4b4eaf", + "52064524aba34437a534e0c01c9f625e", + "fa599a118d7a4f989c50715cba9b76d2", + "c110929e7c6e4d0888f6884880badfcd", + "dc9ce716b97c48289bea7896d5747e92", + "a0a5408cc1d042c7ad56a67941a4c000", + "a4486983a51d49afa4a4ea3eec6311f0", + "9fe57aae7d6347f4a043acff2c97fa10", + "f7faf855b1414b089ae0a0429a7d2dae", + "7c651de2340c4be18cbc41154dc01045", + "205bda7206984dafa6a35e3bd310d35d", + "6652a081684d4f13a61bcc2067698cba", + "ed37d698176d4b91889a1627a1b3b5d9", + "e54b9aed9b9e42dfa5a5caf91471136e", + "696e6aba710f42b4b1c8d79ec9fd263d", + "1128c744b3694b31a3f8e323f971685d", + "bb70227e6e8b40c8a96b91bc4c479837", + "dd694122e4d8485381a683a8136d625e", + "04b082701b6a4b5d92459425141da697", + "63420bca392b477797aa9e422aa9fc20", + "99f3f056980944d3aed55212e6ac73a1", + "73389602be0a4944acff041ba7d8f200", + "52b9ea1168c944919fba9cee8c3571a8", + "2978d050850145c3a6e59c53941f258e", + "6c0de1e97a52417bbae2f354c846d9db", + "58d9ae79f827490d8cb6339749746331" + ] + } }, - "execution_count": null, - "outputs": [] + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/905 [00:00:23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " A = torch.tensor(tokens[f'{id_A}'])\n", + ":29: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " C = torch.tensor(tokens[f'{id_C}'])\n", + ":85: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " B = torch.tensor(tokens[f'{id_B}'])\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[49406, 49407]\n", + "Tokenized prompt 'mix_with' tensor C is a random valued tensor with no ID\n", + "The similarity between A 'visual' and C 'token C of random type' is -2.89 %\n", + "No operation\n", + "Calculated all cosine-similarities between the token visual with Id_A = 7195 with the the rest of the 49407 tokens as a 1x49407 tensor\n", + "//---//\n", + "\n", + "Here is the result : \n", + "\n", + "visual\n", + "similiarity = 100.0 %\n", + "--------\n", + "visual\n", + "similiarity = 48.21 %\n", + "--------\n", + "visuals\n", + "similiarity = 38.77 %\n", + "--------\n", + "visually\n", + "similiarity = 29.68 %\n", + "--------\n", + "visu\n", + "similiarity = 27.89 %\n", + "--------\n", + "visualization\n", + "similiarity = 21.76 %\n", + "--------\n", + "graphical\n", + "similiarity = 21.66 %\n", + "--------\n", + "multimedia\n", + "similiarity = 21.13 %\n", + "--------\n", + "pictorial\n", + "similiarity = 20.29 %\n", + "--------\n", + "video\n", + "similiarity = 20.2 %\n", + "--------\n", + "image\n", + "similiarity = 19.46 %\n", + "--------\n", + "creative\n", + "similiarity = 19.2 %\n", + "--------\n", + "visualize\n", + "similiarity = 19.04 %\n", + "--------\n", + "graphic\n", + "similiarity = 18.41 %\n", + "--------\n", + "spatial\n", + "similiarity = 18.4 %\n", + "--------\n", + "aesthetic\n", + "similiarity = 17.92 %\n", + "--------\n", + "cinematic\n", + "similiarity = 17.38 %\n", + "--------\n", + "photo\n", + "similiarity = 17.29 %\n", + "--------\n", + "artistic\n", + "similiarity = 17.14 %\n", + "--------\n", + "physical\n", + "similiarity = 17.12 %\n", + "--------\n", + "sensory\n", + "similiarity = 17.07 %\n", + "--------\n", + "elegant\n", + "similiarity = 17.0 %\n", + "--------\n", + "photographic\n", + "similiarity = 16.98 %\n", + "--------\n", + "infographics\n", + "similiarity = 16.94 %\n", + "--------\n", + "technical\n", + "similiarity = 16.87 %\n", + "--------\n", + "dataviz\n", + "similiarity = 16.86 %\n", + "--------\n", + "cosmetic\n", + "similiarity = 16.81 %\n", + "--------\n", + "portray\n", + "similiarity = 16.78 %\n", + "--------\n", + "vision\n", + "similiarity = 16.68 %\n", + "--------\n", + "image\n", + "similiarity = 16.63 %\n", + "--------\n", + "resource\n", + "similiarity = 16.42 %\n", + "--------\n", + "imaging\n", + "similiarity = 16.19 %\n", + "--------\n", + "documentation\n", + "similiarity = 16.15 %\n", + "--------\n", + "witness\n", + "similiarity = 16.13 %\n", + "--------\n", + "impactful\n", + "similiarity = 16.11 %\n", + "--------\n", + "optical\n", + "similiarity = 16.11 %\n", + "--------\n", + "imagery\n", + "similiarity = 15.97 %\n", + "--------\n", + "neurological\n", + "similiarity = 15.86 %\n", + "--------\n", + "aesthetics\n", + "similiarity = 15.72 %\n", + "--------\n", + "light\n", + "similiarity = 15.72 %\n", + "--------\n", + "aerial\n", + "similiarity = 15.49 %\n", + "--------\n", + "vocal\n", + "similiarity = 15.37 %\n", + "--------\n", + "infographic\n", + "similiarity = 15.16 %\n", + "--------\n", + "video\n", + "similiarity = 15.07 %\n", + "--------\n", + "decorative\n", + "similiarity = 15.07 %\n", + "--------\n", + "viz\n", + "similiarity = 14.95 %\n", + "--------\n", + "visible\n", + "similiarity = 14.91 %\n", + "--------\n", + "erotic\n", + "similiarity = 14.82 %\n", + "--------\n", + "graphicdesign\n", + "similiarity = 14.8 %\n", + "--------\n", + "graphic\n", + "similiarity = 14.77 %\n", + "--------\n", + "symbolic\n", + "similiarity = 14.67 %\n", + "--------\n", + "virtual\n", + "similiarity = 14.62 %\n", + "--------\n", + "plastic\n", + "similiarity = 14.54 %\n", + "--------\n", + "sight\n", + "similiarity = 14.54 %\n", + "--------\n", + "viewing\n", + "similiarity = 14.48 %\n", + "--------\n", + "clinical\n", + "similiarity = 14.44 %\n", + "--------\n", + "graphics\n", + "similiarity = 14.4 %\n", + "--------\n", + "thetic\n", + "similiarity = 14.39 %\n", + "--------\n", + "subtle\n", + "similiarity = 14.35 %\n", + "--------\n", + "basic\n", + "similiarity = 14.32 %\n", + "--------\n", + "retrospective\n", + "similiarity = 14.3 %\n", + "--------\n", + "suspected\n", + "similiarity = 14.21 %\n", + "--------\n", + "vivid\n", + "similiarity = 14.18 %\n", + "--------\n", + "eye\n", + "similiarity = 14.14 %\n", + "--------\n", + "riverside\n", + "similiarity = 14.14 %\n", + "--------\n", + "representation\n", + "similiarity = 14.12 %\n", + "--------\n", + "visionary\n", + "similiarity = 14.11 %\n", + "--------\n", + "illa\n", + "similiarity = 14.05 %\n", + "--------\n", + "tural\n", + "similiarity = 14.03 %\n", + "--------\n", + "consumer\n", + "similiarity = 14.0 %\n", + "--------\n", + "chart\n", + "similiarity = 13.98 %\n", + "--------\n", + "architectural\n", + "similiarity = 13.92 %\n", + "--------\n", + "webdesign\n", + "similiarity = 13.89 %\n", + "--------\n", + "artsy\n", + "similiarity = 13.88 %\n", + "--------\n", + "conceptu\n", + "similiarity = 13.82 %\n", + "--------\n", + "photograph\n", + "similiarity = 13.82 %\n", + "--------\n", + "illustrating\n", + "similiarity = 13.81 %\n", + "--------\n", + "displaying\n", + "similiarity = 13.8 %\n", + "--------\n", + "psychotic\n", + "similiarity = 13.71 %\n", + "--------\n", + "distinctive\n", + "similiarity = 13.71 %\n", + "--------\n", + "signaling\n", + "similiarity = 13.67 %\n", + "--------\n", + "mutual\n", + "similiarity = 13.61 %\n", + "--------\n", + "demographic\n", + "similiarity = 13.59 %\n", + "--------\n", + "distinct\n", + "similiarity = 13.57 %\n", + "--------\n", + "symboli\n", + "similiarity = 13.54 %\n", + "--------\n", + "capturing\n", + "similiarity = 13.43 %\n", + "--------\n", + "spiritual\n", + "similiarity = 13.43 %\n", + "--------\n", + "looking\n", + "similiarity = 13.41 %\n", + "--------\n", + "cultural\n", + "similiarity = 13.37 %\n", + "--------\n", + "ential\n", + "similiarity = 13.36 %\n", + "--------\n", + "vision\n", + "similiarity = 13.32 %\n", + "--------\n", + "journal\n", + "similiarity = 13.31 %\n", + "--------\n", + "rett\n", + "similiarity = 13.31 %\n", + "--------\n", + "girly\n", + "similiarity = 13.3 %\n", + "--------\n", + "ical\n", + "similiarity = 13.22 %\n", + "--------\n", + "sight\n", + "similiarity = 13.22 %\n", + "--------\n", + "demonstr\n", + "similiarity = 13.21 %\n", + "--------\n", + "quantitative\n", + "similiarity = 13.16 %\n", + "--------\n", + "communications\n", + "similiarity = 13.14 %\n", + "--------\n", + "poster\n", + "similiarity = 13.11 %\n", + "--------\n" + ] + } + ] }, { "cell_type": "markdown", @@ -382,26 +2592,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "AyhYBlP2pYyI", - "outputId": "9e2fc730-23ee-4b05-9957-6fb2db82f2cf" + "id": "AyhYBlP2pYyI" }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content\n", - "/content\n", - "/content/text-to-image-prompts/vocab/raw\n", - "/content/text-to-image-prompts/vocab/raw\n", - "/content/output/vocab/token_vectors\n", - "/content/output/vocab/text\n" - ] - } - ], + "outputs": [], "source": [ "# @title Process the raw vocab into json + .safetensor pair\n", "\n", @@ -488,29 +2681,10 @@ "cell_type": "code", "source": [], "metadata": { - "id": "W_Ig4ZGH18hX", - "outputId": "5f2c0a6e-9b6e-4135-d7de-900673f34e1c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } + "id": "W_Ig4ZGH18hX" }, "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'banana'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 4 - } - ] + "outputs": [] }, { "cell_type": "code", @@ -532,3112 +2706,10 @@ "!zip -r {zip_dest} {root_output_folder}" ], "metadata": { - "id": "9uIDf9IUpzh2", - "outputId": "db00f858-400e-4c17-f757-71a9301be22d", - "colab": { - "base_uri": "https://localhost:8080/" - } + "id": "9uIDf9IUpzh2" }, - "execution_count": 26, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content\n", - "/content\n", - " adding: content/output/ (stored 0%)\n", - " adding: content/output/rolls/ (stored 0%)\n", - " adding: content/output/rolls/naked/ (stored 0%)\n", - " adding: content/output/rolls/naked/text/ (stored 0%)\n", - " adding: content/output/rolls/naked/token_vectors/ (stored 0%)\n", - " adding: content/output/rolls/naked/token_vectors/ID19847-R739.safetensors (deflated 6%)\n", - " adding: content/output/rolls/naked/token_vectors/ID19847-R148.safetensors (deflated 6%)\n", - " adding: content/output/rolls/naked/token_vectors/ID19847-R122.safetensors (deflated 6%)\n", - " adding: content/output/rolls/naked/token_vectors/ID19847-R234.safetensors (deflated 6%)\n", 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