Upload sd_token_similarity_calculator.ipynb
Browse files
Google Colab Notebooks/sd_token_similarity_calculator.ipynb
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@@ -271,7 +271,7 @@
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{
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"cell_type": "code",
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"source": [
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"# @title
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"neg_prompt = \"\" # @param {\"type\":\"string\",\"placeholder\":\"Write something to avoid\"}\n",
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"\n",
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"neg_strength = 1 # @param {type:\"slider\", min:0, max:5, step:0.01}\n",
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@@ -377,8 +377,6 @@
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"processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\" , clean_up_tokenization_spaces = True)\n",
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"model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
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"\n",
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"if image_NEG != \"\":\n",
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"\n",
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"# Get text features for user input\n",
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"inputs = tokenizer(text = prompt, padding=True, return_tensors=\"pt\")\n",
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"text_features_A = model.get_text_features(**inputs)\n",
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@@ -519,13 +517,14 @@
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"\n",
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"if(not compact_Output):\n",
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" if(print_Descriptions):\n",
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" print(f'The {start_at_index}-{start_at_index + RANGE} most similiar items to prompt : \\n\\n '
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" print(f'The {start_at_index}-{start_at_index + RANGE} similarity % for items : \\n\\n' + __sims)\n",
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" print('')\n",
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" else:\n",
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" print(__prompts)\n",
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"else:\n",
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" print(__prompts)\n",
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"#-------#\n",
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"\n",
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"\n",
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@@ -704,6 +703,81 @@
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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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{
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"cell_type": "code",
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"source": [
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"# @title 📝🚫 Penalize similarity to Prompt text_encoding (optional)\n",
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"neg_prompt = \"\" # @param {\"type\":\"string\",\"placeholder\":\"Write something to avoid\"}\n",
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"\n",
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"neg_strength = 1 # @param {type:\"slider\", min:0, max:5, step:0.01}\n",
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"processor = CLIPProcessor.from_pretrained(\"openai/clip-vit-large-patch14\" , clean_up_tokenization_spaces = True)\n",
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"model = CLIPModel.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
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"\n",
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"# Get text features for user input\n",
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"inputs = tokenizer(text = prompt, padding=True, return_tensors=\"pt\")\n",
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"text_features_A = model.get_text_features(**inputs)\n",
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"\n",
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"if(not compact_Output):\n",
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" if(print_Descriptions):\n",
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" print(f'The {start_at_index}-{start_at_index + RANGE} most similiar items to prompt : \\n\\n ')\n",
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" for i in range(N) : print(__prompts)\n",
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" print(f'The {start_at_index}-{start_at_index + RANGE} similarity % for items : \\n\\n' + __sims)\n",
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" print('')\n",
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" else:\n",
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" for i in range(N) : print(__prompts)\n",
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"else:\n",
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" for i in range(N) : print(__prompts)\n",
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"#-------#\n",
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"\n",
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"\n",
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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 ⚙️🖼️ Print the results (Advanced)\n",
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"list_size = 1000 # @param {type:'number'}\n",
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"start_at_index = 0 # @param {type:'number'}\n",
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"print_Similarity = True # @param {type:\"boolean\"}\n",
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"print_Prompts = True # @param {type:\"boolean\"}\n",
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"print_Prefix = True # @param {type:\"boolean\"}\n",
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"print_Descriptions = True # @param {type:\"boolean\"}\n",
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"compact_Output = True # @param {type:\"boolean\"}\n",
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"newline_Separator = True # @param {type:\"boolean\"}\n",
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"\n",
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"\n",
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"import random\n",
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"# @markdown -----------\n",
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"# @markdown Mix with...\n",
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"list_size2 = 1000 # @param {type:'number'}\n",
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"start_at_index2 = 10000 # @param {type:'number'}\n",
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"rate_percent = 50 # @param {type:\"slider\", min:0, max:100, step:1}\n",
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"\n",
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"# @markdown -----------\n",
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"# @markdown Repeat output N times\n",
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"\n",
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"N = 6 # @param {type:\"slider\", min:0, max:10, step:1}\n",
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"\n",
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"# title Show the 100 most similiar suffix and prefix text-encodings to the text encoding\n",
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"RANGE = list_size\n",
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"separator = '|'\n",
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"if newline_Separator : separator = separator + '\\n'\n",
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"\n",
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"_prompts = '{'\n",
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"_sims = '{'\n",
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"for _index in range(start_at_index + RANGE):\n",
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" if _index < start_at_index : continue\n",
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" index = indices[_index]\n",
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"\n",
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" prompt = prompts[f'{index}']\n",
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" if rate_percent >= random.randint(0,100) : prompt = prompts[f'{random.randint(start_at_index2 , start_at_index2 + list_size2)}']\n",
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"\n",
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" #Remove duplicates\n",
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" if _prompts.find(prompt + separator)<=-1:\n",
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" _sims = _sims + f'{round(100*sims[index].item(), 2)} %' + separator\n",
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" #-------#\n",
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" _prompts = _prompts.replace(prompt + separator,'')\n",
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" _prompts = _prompts + prompt + separator\n",
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" #------#\n",
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"#------#\n",
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"__prompts = (_prompts + '}').replace(separator + '}', '}')\n",
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"__sims = (_sims + '}').replace(separator + '}', '}')\n",
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"#------#\n",
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"\n",
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"if(not print_Prompts): __prompts = ''\n",
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"if(not print_Similarity): __sims = ''\n",
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"\n",
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"if(not compact_Output):\n",
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" if(print_Descriptions):\n",
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" print(f'The {start_at_index}-{start_at_index + RANGE} most similiar items to prompt : \\n\\n ')\n",
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" for i in range(N) : print(__prompts)\n",
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" print(f'The {start_at_index}-{start_at_index + RANGE} similarity % for items : \\n\\n' + __sims)\n",
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" print('')\n",
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" else:\n",
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" for i in range(N) : print(__prompts)\n",
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"else:\n",
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" for i in range(N) : print(__prompts)\n",
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"#-------#\n",
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"\n",
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"\n"
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],
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"metadata": {
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"id": "6FEmV02tArrh"
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},
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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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