Upload fusion_t2i_CLIP_interrogator.ipynb
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Google Colab Notebooks/fusion_t2i_CLIP_interrogator.ipynb
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "UEYEdzjgOEOE"
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "efa821e0-8c2d-4861-9d99-f08cacb2e85f"
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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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"/content\n",
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"/content\n",
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"Cloning into 'text-to-image-prompts'...\n",
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"remote: Enumerating objects: 4504, done.\u001b[K\n",
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"remote: Counting objects: 100% (4504/4504), done.\u001b[K\n",
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"remote: Compressing objects: 100% (4029/4029), done.\u001b[K\n",
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"remote: Total 4504 (delta 659), reused 2366 (delta 418), pack-reused 0 (from 0)\u001b[K\n",
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"Receiving objects: 100% (4504/4504), 32.32 MiB | 3.25 MiB/s, done.\n",
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"Resolving deltas: 100% (659/659), done.\n",
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"Updating files: 100% (3062/3062), done.\n",
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"Filtering content: 100% (768/768), 2.21 GiB | 48.63 MiB/s, done.\n"
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]
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}
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],
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"source": [
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"# @title ✳️ Load/initialize values\n",
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"#Imports\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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"if
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" url = '/content/text-to-image-prompts/artby'\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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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "UEYEdzjgOEOE"
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},
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"outputs": [],
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"source": [
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"# @title ✳️ Load/initialize values\n",
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"#Imports\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 artby_prompts:\n",
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" url = '/content/text-to-image-prompts/artby'\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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