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"https://localhost:8080/" }, - "outputId": "87487b44-9037-4adc-b978-b107be7854d1" + "outputId": "5f10f1c3-9147-42f7-d30b-bdae861e7792" }, "execution_count": 1, "outputs": [ @@ -136,13 +5612,13 @@ "text": [ "/content\n", "Cloning into 'text-to-image-prompts'...\n", - "remote: Enumerating objects: 1095, done.\u001b[K\n", - "remote: Counting objects: 100% (1092/1092), done.\u001b[K\n", - "remote: Compressing objects: 100% (1059/1059), done.\u001b[K\n", - "remote: Total 1095 (delta 147), reused 0 (delta 0), pack-reused 3 (from 1)\u001b[K\n", - "Receiving objects: 100% (1095/1095), 3.63 MiB | 5.75 MiB/s, done.\n", - "Resolving deltas: 100% (147/147), done.\n", - "Filtering content: 100% (275/275), 795.84 MiB | 45.49 MiB/s, done.\n" + "remote: Enumerating objects: 1204, done.\u001b[K\n", + "remote: Counting objects: 100% (1201/1201), done.\u001b[K\n", + "remote: Compressing objects: 100% (1163/1163), done.\u001b[K\n", + "remote: Total 1204 (delta 153), reused 0 (delta 0), pack-reused 3 (from 1)\u001b[K\n", + "Receiving objects: 100% (1204/1204), 4.24 MiB | 5.47 MiB/s, done.\n", + "Resolving deltas: 100% (153/153), done.\n", + "Filtering content: 100% (317/317), 915.83 MiB | 39.45 MiB/s, done.\n" ] } ] @@ -161,14 +5637,15 @@ "\n", "first_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔹\"}\n", "last_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n", - "full_names = True # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n", - "\n", + "full_names = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n", "celebs = False # @param {\"type\":\"boolean\",\"placeholder\":\"🆔👨\"}\n", - "\n", "#These are borked\n", "celebs_young = False # param {\"type\":\"boolean\",\"placeholder\":\"🔸\"}\n", "#-------#\n", "\n", + "danbooru_tags = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎀\"}\n", + "danbooru_ai_tags = True # @param {\"type\":\"boolean\",\"placeholder\":\"🎀\"}\n", + "\n", "#-----#\n", "female_fullnames = False # @param {\"type\":\"boolean\",\"placeholder\":\"😃\"}\n", "debug = False\n", @@ -179,6 +5656,16 @@ "nA = 0\n", "#--------#\n", "\n", + "if danbooru_tags:\n", + " url = '/content/text-to-image-prompts/danbooru'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "#--------#\n", + "\n", + "if danbooru_ai_tags:\n", + " url = '/content/text-to-image-prompts/danbooru/ai_tags'\n", + " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", + "#--------#\n", + "\n", "if first_names:\n", " url = '/content/text-to-image-prompts/names/firstnames'\n", " prompts , text_encodings, nA = append_from_url(prompts , text_encodings, nA , url , '')\n", @@ -257,68 +5744,140 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "03b73537-6081-452e-d1fa-8ff6de9f0bf0" + "outputId": "d352dce4-a351-44b6-8da5-47379cda95e8" }, - "execution_count": 9, + "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "reading names_fullnames_text_👱_♀️female_fullnames-4-2.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-8-2.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-6-1.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-9-1.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-5-1.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-7-1.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-7-2.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-2-1.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-1-1.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-4-1.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-6-2.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-3-2.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-1-2.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-9-2.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-2-2.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-3-1.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-8-1.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n", - "reading names_fullnames_text_👱_♀️female_fullnames-5-2.json....\n", - "/content/text-to-image-prompts/names/fullnames/text\n", - "/content/text-to-image-prompts/names/fullnames/text_encodings\n" + "reading danbooru_tags-16.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-9.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-7.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-10.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-5.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-14.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-3.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-8.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-4.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-2.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-15.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-13.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-12.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-6.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-20.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-11.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-21.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-17.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-18.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-1.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_tags-19.json....\n", + "/content/text-to-image-prompts/danbooru/text\n", + "/content/text-to-image-prompts/danbooru/text_encodings\n", + "reading danbooru_ai_tags-20.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-5.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-18.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-21.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-19.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-14.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-12.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-16.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-17.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-15.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-4.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-11.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-10.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-3.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-9.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-7.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-13.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-2.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-6.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-1.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n", + "reading danbooru_ai_tags-8.json....\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text\n", + "/content/text-to-image-prompts/danbooru/ai_tags/text_encodings\n" ] } ] @@ -352,16 +5911,224 @@ "sorted , indices = torch.sort(sims,dim=0 , descending=True)" ], "metadata": { - "id": "xc-PbIYF428y" + "id": "xc-PbIYF428y", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 273, + "referenced_widgets": [ + "00c526713c124c70a4b8e104aaf0cb32", + "1f8a17aa41be4ef8beca12ed04ecced3", + "61c5d23e8a864d509f8f0a3bdd6c4847", + "b8c74d2d307d4f43ada285b12e1dd217", + "5d5dd400b66e47f0896f4f0d037aeff1", + "91799ef9fc2d4c20a20d1a7f39b7edc9", + "300bbd7fc7a74bd389d957880869ee87", + "2b4b4b2bd8804c22bb8bdbea844bfe5e", + "8fdca668d8b5434f926571571600d478", + "d49abc346b5b4024a1f35c0f3574f068", + "b73a47e04d694bc8b544343592fac55b", + "dd682b63a2f14b7abe2f2574606e78de", + "865720304d72487a874e54995a62afda", + "ee6e710e9b4546229dbb1a81242ca4e1", + "061a8a456c014a9fbd97889af25334e0", + "3765f1182a2d4c3ba92594427a3a963a", + "256f819639b14a6ab3a7031bbd2ee5aa", + "08c6872de90c4a538079cdaddfa8f5ea", + "fa192573c1f3465f9b4b5335d40010eb", + "eb72e8d6939e42709fdde85bbf514a39", + "ff0b00bc75914b6ca67ac7a7c0149dfc", + "b68b5d8ac5e44550ac56e2c856c837b3", + "0e8858226d5c4040be3a3b37c96903a5", + "ac23bf88a6b04b2b9273f7be8ed19f00", + "ba104b9d0389402bbd4fe398317f176b", + "6bec4fa7f4444cfb8dd60259469aba62", + "115d73b7fa854bcb80d5041309eca5d9", + "b14b891246d54f1bafaf123d441d1ce9", + "20fd1482398e4ce9826775c665c59873", + "e9741bc939994f7ba0214b68e2891438", + "2d0e63904a0b4cc791377bfd3502709c", + "9ea0dbaf640845ad834793ff5bbe8bfe", + "8aeaa749ebcf4e7ba80d1474ca74126b", + "b078df1b04074deb9c5dedbb7e189d5f", + "5cd54df89b2547d49927a43320e53827", + "4dfe8949c2594eeabfb719c4648f4b62", + "ff7c1c1158954695b4aa2d0d522c86f0", + "b8c420955f2a4850960699eae83d101a", + "8a043cc505674b15bf14cc8dffa7c4f0", + "16e1f51aaecb4aa99bf3922a593dbbef", + "bf4dbc18d9054f2e93ba2ef7bb69824f", + "0a4f4324bf4b48e38fbd38693f300b0c", + "26db8694abdb4e578a6003c820ca315c", + "53bd03cb8ba0496b946984a507d0c420", + "0f656d9c27d1486680e0f54db6d682ea", + "e3ef034b507340998c6344e0de6fc66f", + "251a945f4c1e4ab49013fda1a0aa628f", + "717ced80c6ae42f99ed0299fa09af615", + "f2f4711fb5614aff957e5373f9567bed", + "8b0a6405e0184b72a61dedff4cda8d90", + "0e264b936f5048778c59d1a251250c27", + "7669b2ab624345839c8019d6dd68e0a7", + "b46a6c77a3854c49b067b2376829952d", + "d9cfad6742bd47dfa9ad6f57ed414c1a", + "91f740d9eaa74c90b0fe83b7b186249d", + "0fac4bbdda554b78a5f98b251e86b59d", + "f870dadfd0304a0f98ce2ea33ec55a62", + "bb44e239fb2f4b5b926b831e799ae4c2", + "21a87ed7967c49aab8d74e6a52a2d73d", + "756bde5e0af541f8b81c218084fff55c", + "054bc8a63a824e48b9eed1c29c1b9220", + "a1e5dbdb41bf4c2089cf66cc2459914a", + "07a6f0e03a1d4376bfbac886666a2b4c", + "725901c29d7344aeb78d64d6022d7e92", + "9d6feff769004a00a75321385f63a280", + "8bd12711e9fe47f1853118d0477705f6", + "c55cd43893c14126a1462d059c1ff633", + "a3c5e1431869405c8e2f8e8b92013960", + "8ef37cd504e0401a96be609318349cb1", + "332a3b9d31d44d1085c666f1e8a3439f", + "5279a8d61c684be99abf1cfccf08a121", + "020dcee49a714344977d4448c923c7eb", + "3f90926df74745449ddd3da07026f91b", + "855b0339245a4910a3da92a4c6d87e51", + "66f068cfa54141ccaa54fb3eec961e07", + "6cd6237c1a1646bb9f5f7908fb0f3b71", + "fc6427311546471ca805d38cd205f216", + "19ce4be8d2824aa985203ae3d6bb619d", + "d661189d20ef4111b62a28b956024b21", + "6e67945c02e949409743bc9864639450", + "e2522bff95f747118cfcdeebe9b74d1e", + "4593dfed32ba4dde8a51a5547266a34f", + "7071d681c13b43edb906fbcab118ff4c", + "e867e0a7d453498090bf7243b6fa87f2", + "bf5ce31ffbcf429a85b17887d3c51367", + "84f88dcb3ff64b07ba7ab9785f33e86c", + "84083b47cb2945ab9d0728e0f63f1388", + "2e6b3972cdee44878fe7aa129094e233" + ] + }, + "outputId": "fbf03ac2-4e25-4b9e-a9a9-9adb737f1f4f" }, - "execution_count": 10, - "outputs": [] + "execution_count": 4, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/905 [00:00Related tags', r.text)\n", + " for x in matches:\n", + " prompts[f'{index}'] = x.replace('\\\">Related tags','').replace('%5D=','')\n", + " index = index + 1\n", + "\n", + "#-------#\n", + "with open('danbooru_tags.json', 'w') as f:\n", + " json.dump(prompts, f)" + ], + "metadata": { + "cellView": "form", + "id": "l8t-4GmsviJt" + }, + "execution_count": 6, "outputs": [] }, { @@ -1093,209 +7649,56 @@ "\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/\n", - "\n", - "https://arxiv.org/pdf/2303.03032" - ], - "metadata": { - "id": "njeJx_nSSA8H" - } - }, - { - "cell_type": "code", - "source": [ - "# @title ⚡ Get similiar tokens (not updated yet)\n", - "import torch\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", - "token_name = \"banana \" # @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", - "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", - "# @markdown Limit char size of included token\n", - "\n", - "min_char_size = 0 # param {type:\"slider\", min:0, max: 50, step:1}\n", - "char_range = 50 # param {type:\"slider\", min:0, max: 50, step:1}\n", - "\n", - "tokenizer_output = tokenizer(text = prompt)\n", - "input_ids = tokenizer_output['input_ids']\n", - "id_A = input_ids[1]\n", - "A = torch.tensor(token[id_A])\n", - "A = A/A.norm(p=2, dim=-1, keepdim=True)\n", - "#-----#\n", - "tokenizer_output = tokenizer(text = mix_with)\n", - "input_ids = tokenizer_output['input_ids']\n", - "id_C = input_ids[1]\n", - "C = torch.tensor(token[id_C])\n", - "C = C/C.norm(p=2, dim=-1, keepdim=True)\n", - "#-----#\n", - "sim_AC = torch.dot(A,C)\n", - "#-----#\n", - "print(input_ids)\n", - "#-----#\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(A.shape)\n", - " R = R/R.norm(p=2, dim=-1, keepdim=True)\n", - " A = R\n", - " name_A = 'random_A'\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(A.shape)\n", - " R = R/R.norm(p=2, dim=-1, keepdim=True)\n", - " C = R\n", - " name_C = 'random_C'\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", - "name_A = \"A of random type\"\n", - "if (id_A>-1):\n", - " name_A = vocab(id_A)\n", + "You can verify the results using this online tokenizer: https://sd-tokenizer.rocker.boo/\n", "\n", - "name_C = \"token C of random type\"\n", - "if (id_C>-1):\n", - " name_C = vocab(id_C)\n", + "
\n", + "\n", + "\n", + "\n", + "
\n", "\n", - "print(f\"The similarity between A '{name_A}' and C '{name_C}' is {round(sim_AC.item()*100,2)} %\")\n", + "# What is that gibberish tokens that show up?\n", "\n", - "if (mix_method == \"None\"):\n", - " print(\"No operation\")\n", + "The gibberish tokens like \"ðŁĺħ\\\" are actually emojis!\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", + "Try writing some emojis in this online tokenizer to see the results: https://sd-tokenizer.rocker.boo/\n", "\n", - "if (mix_method == \"Subtract\"):\n", - " tmp = w*A - (1-w)*C\n", - " tmp = tmp/tmp.norm(p=2, dim=-1, keepdim=True)\n", - " A = tmp\n", - " #//---//\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", + "It is a bit borked as it can't process capital letters properly.\n", "\n", - "#OPTIONAL : Add/subtract + normalize above result with another token. Leave field empty to get a random value tensor\n", + "Also note that this is not reversible.\n", "\n", - "dots = torch.zeros(NUM_TOKENS)\n", - "for index in range(NUM_TOKENS):\n", - " id_B = index\n", - " B = torch.tensor(token[id_B])\n", - " B = B/B.norm(p=2, dim=-1, keepdim=True)\n", - " sim_AB = torch.dot(A,B)\n", - " dots[index] = sim_AB\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", - "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", + "SD 1.5 models actually have training for Emojis.\n", "\n", - "#Produce a list id IDs that are most similiar to the prompt ID at positiion 1 based on above result\n", + "But you have to set CLIP skip to 1 for this to work is intended.\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", + "For example, this is the result from \"photo of a 🧔🏻‍♂️\"\n", "\n", "\n", - "if (print_Divider):\n", - " print('//---//')\n", + "
\n", + "\n", + "
\n", "\n", - "print('')\n", - "print('Here is the result : ')\n", - "print('')\n", + "A tutorial on stuff you can do with the vocab.list concluded.\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", + "Anyways, have fun with the notebook.\n", "\n", - "#Print the sorted list from above result\n", + "There might be some updates in the future with features not mentioned here.\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", "\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", + "https://codeandlife.com/2023/01/26/mastering-the-huggingface-clip-model-how-to-extract-embeddings-and-calculate-similarity-for-text-and-images/\n", "\n", - "# Save results as .db file\n", - "import shelve\n", - "VOCAB_FILENAME = 'tokens_most_similiar_to_' + name_A.replace('','').strip()\n", - "d = shelve.open(VOCAB_FILENAME)\n", - "#NUM TOKENS == 49407\n", - "for index in range(NUM_TOKENS):\n", - " #print(d[f'{index}']) #<-----Use this to read values from the .db file\n", - " d[f'{index}']= vocab(indices[index].item()) #<---- write values to .db file\n", - "#----#\n", - "d.close() #close the file\n", - "# See this link for additional stuff to do with shelve: https://docs.python.org/3/library/shelve.html" + "https://arxiv.org/pdf/2303.03032" ], "metadata": { - "id": "iWeFnT1gAx6A" - }, - "execution_count": null, - "outputs": [] + "id": "njeJx_nSSA8H" + } }, { "cell_type": "code", @@ -1374,7 +7777,8 @@ "\n" ], "metadata": { - "id": "JR0wl2ecj6RJ" + "id": "JR0wl2ecj6RJ", + "cellView": "form" }, "execution_count": null, "outputs": [] @@ -1730,6 +8134,160 @@ }, "execution_count": null, "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# @title ⚡ Get similiar tokens (not updated yet)\n", + "import torch\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", + "token_name = \"banana \" # @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", + "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", + "# @markdown Limit char size of included token\n", + "\n", + "min_char_size = 0 # param {type:\"slider\", min:0, max: 50, step:1}\n", + "char_range = 50 # param {type:\"slider\", min:0, max: 50, step:1}\n", + "\n", + "tokenizer_output = tokenizer(text = prompt)\n", + "input_ids = tokenizer_output['input_ids']\n", + "id_A = input_ids[1]\n", + "A = torch.tensor(token[id_A])\n", + "A = A/A.norm(p=2, dim=-1, keepdim=True)\n", + "#-----#\n", + "tokenizer_output = tokenizer(text = mix_with)\n", + "input_ids = tokenizer_output['input_ids']\n", + "id_C = input_ids[1]\n", + "C = torch.tensor(token[id_C])\n", + "C = C/C.norm(p=2, dim=-1, keepdim=True)\n", + "#-----#\n", + "sim_AC = torch.dot(A,C)\n", + "#-----#\n", + "print(input_ids)\n", + "#-----#\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(A.shape)\n", + " R = R/R.norm(p=2, dim=-1, keepdim=True)\n", + " A = R\n", + " name_A = 'random_A'\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(A.shape)\n", + " R = R/R.norm(p=2, dim=-1, keepdim=True)\n", + " 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", + "print(f\"The similarity between A '{name_A}' and C '{name_C}' is {round(sim_AC.item()*100,2)} %\")\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 = tmp/tmp.norm(p=2, dim=-1, keepdim=True)\n", + " A = tmp\n", + " #//---//\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 = torch.tensor(token[id_B])\n", + " B = B/B.norm(p=2, dim=-1, keepdim=True)\n", + " sim_AB = torch.dot(A,B)\n", + " dots[index] = sim_AB\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\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", + "# Save results as .db file\n", + "import shelve\n", + "VOCAB_FILENAME = 'tokens_most_similiar_to_' + name_A.replace('','').strip()\n", + "d = shelve.open(VOCAB_FILENAME)\n", + "#NUM TOKENS == 49407\n", + "for index in range(NUM_TOKENS):\n", + " #print(d[f'{index}']) #<-----Use this to read values from the .db file\n", + " d[f'{index}']= vocab(indices[index].item()) #<---- write values to .db file\n", + "#----#\n", + "d.close() #close the file\n", + "# See this link for additional stuff to do with shelve: https://docs.python.org/3/library/shelve.html" + ], + "metadata": { + "id": "iWeFnT1gAx6A", + "cellView": "form" + }, + "execution_count": null, + "outputs": [] } ] } \ No newline at end of file