Upload sd_token_similarity_calculator.ipynb
Browse files- sd_token_similarity_calculator.ipynb +34 -326
sd_token_similarity_calculator.ipynb
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# @title 🪐🖼️ -> 📝 Image to prompt : Add single token to existing prompt to match image\n",
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"from google.colab import files\n",
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"def
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"\n",
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"#Get image\n",
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"# You can use \"http://images.cocodataset.org/val2017/000000039769.jpg\" for testing\n",
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"url = \"
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"from PIL import Image\n",
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"import requests\n",
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"if url == \"\":\n",
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"else:\n",
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" image_A = Image.open(requests.get(url, stream=True).raw)\n",
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"\n",
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@@ -472,325 +495,10 @@
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"metadata": {
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"collapsed": true,
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"cellView": "form",
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"id": "fi0jRruI0-tu"
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"outputId": "6d7e8c39-a117-4b35-acfe-2a128c65aeb7",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"execution_count":
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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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"//---//\n",
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"\n",
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"These token pairings within the range ID = 12500 to ID = 13000 most closely match the text_encoding for the prompt \"the image\" : \n",
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"\n",
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"sits</w>yellow\n",
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"similiarity = 23.02 %\n",
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"--------\n",
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"neys</w>yellow\n",
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"similiarity = 19.74 %\n",
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"--------\n",
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"cody</w>yellow\n",
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"similiarity = 18.61 %\n",
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"--------\n",
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"wns</w>yellow\n",
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"similiarity = 18.43 %\n",
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"--------\n",
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"java</w>yellow\n",
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"similiarity = 18.15 %\n",
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"--------\n",
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"jj</w>yellow\n",
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"similiarity = 18.03 %\n",
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"--------\n",
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"eno</w>yellow\n",
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"similiarity = 17.87 %\n",
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"--------\n",
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"cled</w>yellow\n",
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"similiarity = 17.85 %\n",
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"--------\n",
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"nom</w>yellow\n",
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"similiarity = 17.75 %\n",
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"--------\n",
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"dads</w>yellow\n",
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"similiarity = 17.5 %\n",
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"--------\n",
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"mil</w>yellow\n",
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"similiarity = 17.47 %\n",
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"--------\n",
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"whom</w>yellow\n",
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"similiarity = 17.37 %\n",
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"--------\n",
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"itv</w>yellow\n",
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"similiarity = 17.34 %\n",
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"--------\n",
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"vibe</w>yellow\n",
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"similiarity = 17.2 %\n",
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"--------\n",
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"noir</w>yellow\n",
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"similiarity = 17.14 %\n",
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"--------\n",
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"yellowarel</w>\n",
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"similiarity = 17.1 %\n",
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"--------\n",
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"#âĢ¦</w>yellow\n",
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"similiarity = 17.04 %\n",
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"--------\n",
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"maya</w>yellow\n",
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"similiarity = 17.03 %\n",
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"--------\n",
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"yellowbam</w>\n",
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"similiarity = 17.01 %\n",
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"--------\n",
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"erts</w>yellow\n",
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"similiarity = 17.01 %\n",
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"--------\n",
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"xc</w>yellow\n",
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"similiarity = 16.98 %\n",
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"--------\n",
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"mob</w>yellow\n",
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"similiarity = 16.89 %\n",
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"--------\n",
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"dees</w>yellow\n",
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"similiarity = 16.87 %\n",
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"--------\n",
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"icc</w>yellow\n",
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"similiarity = 16.75 %\n",
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"--------\n",
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"aly</w>yellow\n",
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"similiarity = 16.63 %\n",
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"--------\n",
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"lis</w>yellow\n",
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"similiarity = 16.63 %\n",
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"--------\n",
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"yellowturf</w>\n",
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"similiarity = 16.62 %\n",
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"--------\n",
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"yellowbaba</w>\n",
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"similiarity = 16.58 %\n",
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"--------\n",
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":*</w>yellow\n",
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"similiarity = 16.42 %\n",
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"--------\n",
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"inho</w>yellow\n",
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"similiarity = 16.39 %\n",
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"--------\n",
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"yellowhes</w>\n",
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"similiarity = 16.37 %\n",
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"--------\n",
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"nity</w>yellow\n",
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"similiarity = 16.3 %\n",
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"--------\n",
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"lust</w>yellow\n",
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"similiarity = 16.3 %\n",
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"--------\n",
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"ikh</w>yellow\n",
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"similiarity = 16.26 %\n",
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"--------\n",
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"nyt</w>yellow\n",
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"similiarity = 16.24 %\n",
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"--------\n",
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"(+</w>yellow\n",
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"similiarity = 16.11 %\n",
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"--------\n",
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"foto</w>yellow\n",
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"similiarity = 16.11 %\n",
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"--------\n",
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"stl</w>yellow\n",
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"similiarity = 16.06 %\n",
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"--------\n",
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"mick</w>yellow\n",
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"similiarity = 16.06 %\n",
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"--------\n",
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"...@</w>yellow\n",
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"similiarity = 16.05 %\n",
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"--------\n",
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"ugh</w>yellow\n",
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"similiarity = 16.05 %\n",
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"--------\n",
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"gro</w>yellow\n",
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"similiarity = 16.01 %\n",
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"--------\n",
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"wski</w>yellow\n",
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"similiarity = 16.01 %\n",
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"--------\n",
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"ðŁĴ«</w>yellow\n",
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"similiarity = 15.74 %\n",
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"--------\n",
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"deen</w>yellow\n",
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"similiarity = 15.73 %\n",
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"--------\n",
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"assy</w>yellow\n",
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"similiarity = 15.72 %\n",
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"--------\n",
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"mtv</w>yellow\n",
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"similiarity = 15.72 %\n",
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"--------\n",
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"yellowðŁĺ»</w>\n",
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"similiarity = 15.72 %\n",
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"--------\n",
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"yellowfrm</w>\n",
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"similiarity = 15.65 %\n",
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"--------\n",
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"moss</w>yellow\n",
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"similiarity = 15.64 %\n",
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"--------\n",
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"bart</w>yellow\n",
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"similiarity = 15.61 %\n",
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"--------\n",
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"tw</w>yellow\n",
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"similiarity = 15.51 %\n",
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"--------\n",
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"yellowplug</w>\n",
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"similiarity = 15.46 %\n",
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"--------\n",
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"jen</w>yellow\n",
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"similiarity = 15.45 %\n",
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"--------\n",
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"pst</w>yellow\n",
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"similiarity = 15.43 %\n",
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"--------\n",
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| 656 |
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"omfg</w>yellow\n",
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| 657 |
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"similiarity = 15.43 %\n",
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"--------\n",
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| 659 |
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"dine</w>yellow\n",
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| 660 |
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"similiarity = 15.38 %\n",
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"--------\n",
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"vern</w>yellow\n",
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"similiarity = 15.33 %\n",
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"--------\n",
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"reno</w>yellow\n",
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"similiarity = 15.25 %\n",
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"--------\n",
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"yellow´</w>\n",
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"similiarity = 15.14 %\n",
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"--------\n",
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| 671 |
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"omic</w>yellow\n",
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| 672 |
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"similiarity = 15.14 %\n",
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"--------\n",
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"łï¸ı</w>yellow\n",
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| 675 |
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"similiarity = 15.11 %\n",
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"--------\n",
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"yellowgis</w>\n",
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"similiarity = 15.06 %\n",
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"--------\n",
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| 680 |
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"aunt</w>yellow\n",
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"similiarity = 15.0 %\n",
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"--------\n",
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"joan</w>yellow\n",
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"similiarity = 14.96 %\n",
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"--------\n",
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"anas</w>yellow\n",
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| 687 |
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"similiarity = 14.92 %\n",
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"--------\n",
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"ðŁĴĵ</w>yellow\n",
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| 690 |
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"similiarity = 14.9 %\n",
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"--------\n",
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"chad</w>yellow\n",
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"similiarity = 14.89 %\n",
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"--------\n",
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"yellowsake</w>\n",
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"similiarity = 14.88 %\n",
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"--------\n",
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"gues</w>yellow\n",
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"similiarity = 14.84 %\n",
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"--------\n",
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"gian</w>yellow\n",
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"similiarity = 14.84 %\n",
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"--------\n",
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"asi</w>yellow\n",
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"similiarity = 14.83 %\n",
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"--------\n",
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"yellowoven</w>\n",
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"similiarity = 14.82 %\n",
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"--------\n",
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"jury</w>yellow\n",
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"similiarity = 14.79 %\n",
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"--------\n",
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"blvd</w>yellow\n",
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"similiarity = 14.75 %\n",
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"--------\n",
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"omez</w>yellow\n",
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"similiarity = 14.72 %\n",
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"--------\n",
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"yellowyang</w>\n",
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"similiarity = 14.7 %\n",
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"--------\n",
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"gu</w>yellow\n",
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"similiarity = 14.48 %\n",
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"--------\n",
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"yellowova</w>\n",
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"similiarity = 14.45 %\n",
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"--------\n",
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"yellowinez</w>\n",
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"similiarity = 14.44 %\n",
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"--------\n",
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"pei</w>yellow\n",
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"similiarity = 14.44 %\n",
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"--------\n",
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"ãĢIJ</w>yellow\n",
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"similiarity = 14.43 %\n",
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"--------\n",
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"ãĢij</w>yellow\n",
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"similiarity = 14.43 %\n",
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"--------\n",
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"ðŁĮŀ</w>yellow\n",
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| 741 |
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"similiarity = 14.36 %\n",
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"--------\n",
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| 743 |
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"ðŁĺĿ</w>yellow\n",
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| 744 |
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"similiarity = 14.27 %\n",
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"--------\n",
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"troy</w>yellow\n",
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"similiarity = 14.16 %\n",
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"--------\n",
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"pale</w>yellow\n",
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| 750 |
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"similiarity = 14.14 %\n",
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"--------\n",
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"boi</w>yellow\n",
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| 753 |
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"similiarity = 14.11 %\n",
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"--------\n",
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| 755 |
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"nn</w>yellow\n",
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| 756 |
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"similiarity = 14.08 %\n",
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"--------\n",
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"âı°</w>yellow\n",
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| 759 |
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"similiarity = 14.01 %\n",
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| 760 |
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"--------\n",
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| 761 |
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"ooth</w>yellow\n",
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| 762 |
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"similiarity = 13.93 %\n",
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| 763 |
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"--------\n",
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| 764 |
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"pied</w>yellow\n",
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| 765 |
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"similiarity = 13.9 %\n",
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| 766 |
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"--------\n",
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| 767 |
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"bola</w>yellow\n",
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| 768 |
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"similiarity = 13.79 %\n",
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| 769 |
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"--------\n",
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| 770 |
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"âŀ¡</w>yellow\n",
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| 771 |
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"similiarity = 13.77 %\n",
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| 772 |
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"--------\n",
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| 773 |
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"rena</w>yellow\n",
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| 774 |
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"similiarity = 13.75 %\n",
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| 775 |
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"--------\n",
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| 776 |
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"dley</w>yellow\n",
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| 777 |
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"similiarity = 13.73 %\n",
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| 778 |
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"--------\n",
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| 779 |
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"evan</w>yellow\n",
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| 780 |
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"similiarity = 13.67 %\n",
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"--------\n",
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"pony</w>yellow\n",
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"similiarity = 13.63 %\n",
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"--------\n",
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"rene</w>yellow\n",
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"similiarity = 13.62 %\n",
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"--------\n",
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| 788 |
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"mock</w>yellow\n",
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| 789 |
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"similiarity = 13.57 %\n",
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"--------\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"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."
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],
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"metadata": {
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"id": "hyK423TQCRup"
|
| 319 |
+
}
|
| 320 |
+
},
|
| 321 |
{
|
| 322 |
"cell_type": "code",
|
| 323 |
"source": [
|
| 324 |
"# @title 🪐🖼️ -> 📝 Image to prompt : Add single token to existing prompt to match image\n",
|
| 325 |
"from google.colab import files\n",
|
| 326 |
+
"def upload_files():\n",
|
| 327 |
+
" from google.colab import files\n",
|
| 328 |
+
" uploaded = files.upload()\n",
|
| 329 |
+
" for k, v in uploaded.items():\n",
|
| 330 |
+
" open(k, 'wb').write(v)\n",
|
| 331 |
+
" return list(uploaded.keys())\n",
|
| 332 |
"#Get image\n",
|
| 333 |
"# You can use \"http://images.cocodataset.org/val2017/000000039769.jpg\" for testing\n",
|
| 334 |
+
"url = \"\" # @param {\"type\":\"string\",\"placeholder\":\"leave empty for local upload (scroll down to see it)\"}\n",
|
| 335 |
+
"\n",
|
| 336 |
+
"colab_image_path = \"\" # @param {\"type\":\"string\",\"placeholder\":\"(optional) Write colab image path to load from\"}\n",
|
| 337 |
"from PIL import Image\n",
|
| 338 |
"import requests\n",
|
| 339 |
+
"image_A = \"\"\n",
|
| 340 |
+
"\n",
|
| 341 |
+
"#----#\n",
|
| 342 |
"if url == \"\":\n",
|
| 343 |
+
" import cv2\n",
|
| 344 |
+
" from google.colab.patches import cv2_imshow\n",
|
| 345 |
+
" # Open the image.\n",
|
| 346 |
+
" if colab_image_path == \"\":\n",
|
| 347 |
+
" keys = upload_files()\n",
|
| 348 |
+
" for key in keys:\n",
|
| 349 |
+
" image_A = cv2.imread(\"/content/sd_tokens/\" + key)\n",
|
| 350 |
+
" colab_image_path = \"/content/sd_tokens/\" + key\n",
|
| 351 |
+
" else:\n",
|
| 352 |
+
" image_A = cv2.imread(colab_image_path)\n",
|
| 353 |
"else:\n",
|
| 354 |
" image_A = Image.open(requests.get(url, stream=True).raw)\n",
|
| 355 |
"\n",
|
|
|
|
| 495 |
"metadata": {
|
| 496 |
"collapsed": true,
|
| 497 |
"cellView": "form",
|
| 498 |
+
"id": "fi0jRruI0-tu"
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|
| 499 |
},
|
| 500 |
+
"execution_count": null,
|
| 501 |
+
"outputs": []
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|
| 502 |
},
|
| 503 |
{
|
| 504 |
"cell_type": "code",
|