Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -101,7 +101,8 @@
     {
       "cell_type": "markdown",
       "source": [
-        "Feel free to skip these cells if you do not plan on using them\n"
+        "**Feel free to skip these cells if you do not plan on using them**\n",
+        "\n"
       ],
       "metadata": {
         "id": "Xf9zoq-Za3wi"
@@ -221,7 +222,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "Save the reference prior to running the Interrogator"
+        "**Save the reference prior to running the Interrogator**"
       ],
       "metadata": {
         "id": "zeu6JcM-mk9z"
@@ -242,28 +243,20 @@
       ],
       "metadata": {
         "id": "lOQuTPfBMK82",
-        "outputId": "dfee8f93-f4bb-4bea-ba73-ca57ce96601c",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
+        "cellView": "form"
       },
-      "execution_count": 13,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "/content\n"
-          ]
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "code",
       "source": [
         "# @title ⚄ Run the CLIP interrogator on the saved reference\n",
         "LIST_SIZE = 1000  # @param {type:'number' , placeholder:'set how large the list should be'}\n",
-        "START_AT = 0  # @param {type:'number' , placeholder:'set how large the list should be'}\n",
+        "_START_AT = '0' # @param [\"0\", \"10000\", \"50000\"] {allow-input: true}\n",
+        "START_AT = 0\n",
+        "if _START_AT.isnumeric(): START_AT = int(_START_AT)\n",
+        "\n",
         "# @markdown -----\n",
         "# @markdown Select vocab\n",
         "general = False # @param {type:\"boolean\"}\n",
@@ -401,7 +394,7 @@
         "  #-------#\n",
         "  continue\n",
         "#---------#\n",
-        "print(f'\\nProcessed entire list of {total_items} items to find closest match.\\nSaved closest matching indices {START_AT} to {LIST_SIZE} as the dict \"similiar_prompts\" with {START_AT + LIST_SIZE} items.\\n')\n",
+        "print(f'\\nProcessed entire list of {total_items} items to find closest match.\\nSaved closest matching indices {START_AT} to {START_AT + LIST_SIZE} as the dict \"similiar_prompts\" with {LIST_SIZE} items.\\n')\n",
         "\n",
         "# Print results\n",
         "sorted , indices = torch.sort(similiar_sims , dim=0 , descending = True)\n",
@@ -428,12 +421,13 @@
         "    #-----#\n",
         "    prompt = (prompt + '}').replace('|}',  '} ')\n",
         "  #------#\n",
-        "  print(f'Similiar prompts: \\n\\n\\n {prompt} \\n\\n\\n//----//')\n",
+        "  print(f'Similiar prompts: \\n\\n\\n{prompt} \\n\\n\\n//----//')\n",
         "image\n",
         "#-----#\n"
       ],
       "metadata": {
-        "id": "kOYZ8Ajn-DD8"
+        "id": "kOYZ8Ajn-DD8",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []
@@ -467,7 +461,8 @@
         "save_file(_similiar_sims, 'similiar_sims.safetensors')\n"
       ],
       "metadata": {
-        "id": "m-N553nXz9Jd"
+        "id": "m-N553nXz9Jd",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []
@@ -497,6 +492,8 @@
         "similiar_sims = _similiar_sims['weights'].to(dot_dtype)\n",
         "\n",
         "# @title ⚄ Run the CLIP interrogator on the saved reference\n",
+        "\n",
+        "# @markdown Select which values within the saved list to print\n",
         "LIST_SIZE = 1000  # @param {type:'number' , placeholder:'set how large the list should be'}\n",
         "START_AT = 0  # @param {type:'number' , placeholder:'set how large the list should be'}\n",
         "\n",
@@ -526,7 +523,8 @@
         "#-----#\n"
       ],
       "metadata": {
-        "id": "XOMkIKc9-wZz"
+        "id": "XOMkIKc9-wZz",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []
@@ -589,7 +587,8 @@
         "    json.dump(_savefile, f)\n"
       ],
       "metadata": {
-        "id": "Q7vpNAXQilbf"
+        "id": "Q7vpNAXQilbf",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []
@@ -831,21 +830,6 @@
       },
       "execution_count": null,
       "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Check the average value for this set\n",
-        "sims = torch.matmul(vocab_encodings.dequantize(),average.t())\n",
-        "sorted , indices = torch.sort(sims,dim=0 , descending=True)\n",
-        "for index in range(10):\n",
-        "  print(prompts[f'{indices[index].item()}'])"
-      ],
-      "metadata": {
-        "id": "XNHz0hfhHRUu"
-      },
-      "execution_count": null,
-      "outputs": []
     }
   ]
 }