Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -2935,7 +2935,7 @@
           ]
         }
       },
-      "execution_count": 1,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -3120,8 +3120,8 @@
         "\n",
         "%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n",
         "references = torch.load('reference_text_and_image_encodings.pt'  , weights_only=False)\n",
-        "reference = torch.add(reference, C * references[index][0].dequantize())\n",
-        "reference = torch.add(reference, (1-C) * references[index][1].dequantize())\n",
+        "reference = torch.add(reference, prompt_strength * C * references[index][0].dequantize())\n",
+        "reference = torch.add(reference, prompt_strength * (1-C) * references[index][1].dequantize())\n",
         "references = ''\n",
         "# @markdown -----------\n",
         "# @markdown 📝➕ Enhance similarity to prompt(s)\n",
@@ -3147,7 +3147,8 @@
         "# @markdown -----------\n",
         "# @markdown ⏩ Skip item(s) containing the word(s)\n",
         "SKIP = 'futa ' # @param {type:'string' , placeholder:'item1 , item2 , ...'}\n",
-        "def isBlacklisted(txt):\n",
+        "\n",
+        "def isBlacklisted(txt, blacklist):\n",
         "  if txt.strip().isnumeric(): return True\n",
         "  if blacklist.strip() == '': return False\n",
         "  for item in list(blacklist.split(',')):\n",
@@ -3155,6 +3156,7 @@
         "    if txt.find(item.strip())> -1 : return True\n",
         "  #------#\n",
         "  return False\n",
+        "\n",
         "# @markdown -----------\n",
         "# @markdown 🔍 How similar should the results be?\n",
         "list_size = 1000 # @param {type:'number'}\n",
@@ -3198,7 +3200,9 @@
         "#---#\n",
         "  output = '{'\n",
         "  for _index in range(list_size):\n",
-        "    output = output + prompts[f'{indices[min(_index+start_at_index,NUM_VOCAB_ITEMS-1)].item()}'] + '|'\n",
+        "    tmp = prompts[f'{indices[min(_index+start_at_index,NUM_VOCAB_ITEMS-1)].item()}']\n",
+        "    if isBlacklisted(tmp , SKIP): continue\n",
+        "    output = output + tmp + '|'\n",
         "  #---------#\n",
         "  output = (output + '}').replace('|}' , '} ')\n",
         "  for iter in range(N):\n",
@@ -3536,93 +3540,12 @@
       "execution_count": null,
       "outputs": []
     },
-    {
-      "cell_type": "code",
-      "source": [
-        "ref"
-      ],
-      "metadata": {
-        "id": "J-IUkhBXe_a2",
-        "outputId": "a4e1c5b2-9d10-4113-ccf8-43fd25b32749",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
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-              "       dtype=torch.uint8)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 3
-        }
-      ]
-    },
     {
       "cell_type": "code",
       "source": [
         "\n",
         "# @title \t⚄ New code (work in progress)\n",
-        "_ref = 'And I can still taste your skin' # @param {type:'string' , placeholder:'type a single prompt to match'}\n",
+        "_ref = '' # @param {type:'string' , placeholder:'type a single prompt to match'}\n",
         "LIST_SIZE = 1000  # @param {type:'number' , placeholder:'set how large the list should be'}\n",
         "\n",
         "SCALE = 0.0043\n",
@@ -3651,8 +3574,6 @@
         "  return 1\n",
         "#----------#\n",
         "\n",
-        "\n",
-        "\n",
         "inputs = tokenizer(text = _ref.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
         "ref = model.get_text_features(**inputs)[0]\n",
         "\n",
@@ -3663,13 +3584,12 @@
         "\n",
         "vocab =  load_file(url)\n",
         "\n",
-        "\n",
         "#get_most_similiar_items_to(ref , url , LIST_SIZE)"
       ],
       "metadata": {
         "id": "PGyLzCmYqCPg"
       },
-      "execution_count": 14,
+      "execution_count": null,
       "outputs": []
     }
   ]