Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -177,7 +177,8 @@
         "print(f'Using settings SCALE = {SCALE} and ZERO_POINT = {ZERO_POINT} for visualizing the text_encoding')"
       ],
       "metadata": {
-        "id": "YDu8XlehhWID"
+        "id": "YDu8XlehhWID",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []
@@ -185,13 +186,135 @@
     {
       "cell_type": "markdown",
       "source": [
-        "**Feel free to skip these cells if you do not plan on using them**\n",
+        "**Paste a prompt in the cell below to create an encoding**\n",
         "\n"
       ],
       "metadata": {
         "id": "Xf9zoq-Za3wi"
       }
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# @markdown 📝 Write a text prompt (this will overwrite any savefile already stored)\n",
+        "NEW_ENCODING = '' # @param {type:'string' ,placeholder:'write a prompt'}\n",
+        "enable = True # @param {type:\"boolean\"}\n",
+        "# @markdown -----\n",
+        "# @markdown 📝 Enhance/Penalize Similarity and skip items containing word(s)\n",
+        "POS = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
+        "NEG = ''# @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
+        "# @markdown -----\n",
+        "# @markdown logarithmic prompt strength x for value 10^(x-1)\n",
+        "_POS = 0 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "_NEG = 0  # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "# @markdown -----\n",
+        "# @markdown Check similiarity for this encoding against any written prompt(s)\n",
+        "# @title ⚄ Evaluate saved  reference similarity to select items (optional)\n",
+        "EVAL = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
+        "\n",
+        "show_local_reference = True # @param {type:\"boolean\"}\n",
+        "show_encoding = True # @param {type:\"boolean\"}\n",
+        "\n",
+        "try:\n",
+        "  %cd /content/\n",
+        "  _ref = load_file('reference.safetensors' )\n",
+        "  ref = _ref['weights'].to(dot_dtype)\n",
+        "except:\n",
+        "  ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
+        "  _ref = {}\n",
+        "  _ref['weights'] = ref\n",
+        "  %cd /content/\n",
+        "  save_file(_ref, 'reference.safetensors')\n",
+        "#-----#\n",
+        "\n",
+        "if NEW_ENCODING.strip() != ''\n",
+        "  item = NEW_ENCODING.strip()\n",
+        "  inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
+        "  ref = model.get_text_features(**inputs)[0]\n",
+        "  ref= ref/ref.norm(p=2 , dim=-1 , keepdim=True)\n",
+        "#------#\n",
+        "\n",
+        "try: ref\n",
+        "except: ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
+        "\n",
+        "if EVAL.strip() != '':\n",
+        "  print(\"Saved Reference:\\n\")\n",
+        "  for item in EVAL.split(','):\n",
+        "    if item.strip()=='':continue\n",
+        "    inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
+        "    test = model.get_text_features(**inputs)[0]\n",
+        "    test = test/test.norm(p=2 , dim = -1 , keepdim = True)\n",
+        "    ref= ref/ref.norm(p=2 , dim=-1 , keepdim=True)\n",
+        "    eval = torch.dot(ref , test)\n",
+        "    print(f'{item.strip()} : {round(eval.item()*100, 2)}%')\n",
+        "  #-----#\n",
+        "  if(show_local_reference):\n",
+        "    print(\"\\n---------\\nLocal Reference with enchancements added :\\n\")\n",
+        "\n",
+        "    for _item in POS.split(','):\n",
+        "      item = _item.strip()\n",
+        "      if item == '':continue\n",
+        "      inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
+        "      ref = ref + math.pow(10,_POS-1) * model.get_text_features(**inputs)[0]\n",
+        "    #-------#\n",
+        "\n",
+        "    for _item in NEG.split(','):\n",
+        "      item = _item.strip()\n",
+        "      if item == '':continue\n",
+        "      inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
+        "      ref = ref + math.pow(10,_NEG-1) * model.get_text_features(**inputs)[0]\n",
+        "    #-------#\n",
+        "\n",
+        "    ref= ref/ref.norm(p=2 , dim=-1 , keepdim=True)\n",
+        "    for item in EVAL.split(','):\n",
+        "      if item.strip()=='':continue\n",
+        "      inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
+        "      test = model.get_text_features(**inputs)[0]\n",
+        "      test = test/test.norm(p=2 , dim = -1 , keepdim = True)\n",
+        "      eval = torch.dot(ref , test)\n",
+        "      print(f'{item.strip()} : {round(eval.item()*100, 2)}%')\n",
+        "    #-----#\n",
+        "\n",
+        "  if show_encoding:\n",
+        "    # create figure\n",
+        "    fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
+        "    fig.patch.set_facecolor((56/255,56/255,56/255))\n",
+        "    rows = 1\n",
+        "    columns = 3\n",
+        "    fig.add_subplot(rows, columns, 1)\n",
+        "    plt.imshow( visualize(ref))\n",
+        "    plt.axis('off')\n",
+        "    plt.title( \"Encoding (local variable)\",  color='white',  fontsize=round(20*image_size))\n",
+        "    if num_plots>1:\n",
+        "      fig.add_subplot(rows, columns, 2)\n",
+        "      plt.imshow( visualize( _ref['weights'].to(dot_dtype)))\n",
+        "      plt.axis('off')\n",
+        "      plt.title(\"Encoding (saved file)\",  color='white',  fontsize=round(20*image_size))\n",
+        "\n",
+        "      fig.add_subplot(rows, columns, 3)\n",
+        "      plt.imshow( visualize(ref - _ref['weights'].to(dot_dtype)))\n",
+        "      plt.axis('off')\n",
+        "      plt.title(\"Changes\",  color='white',  fontsize=round(20*image_size))\n",
+        "    #------#\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "Oxi6nOyrUTAe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Use a pre-encoded image+prompt pair as reference (optional)**"
+      ],
+      "metadata": {
+        "id": "f9_AcquM7AYZ"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
@@ -206,7 +329,7 @@
         "try:prompt\n",
         "except: prompt = ''\n",
         "\n",
-        "# @markdown 🖼️+📝 Choose a pre-encoded reference (optional)\n",
+        "# @markdown 🖼️+📝 Choose a pre-encoded reference (note: some results are NSFW!)\n",
         "index = 596 # @param {type:\"slider\", min:0, max:1666, step:1}\n",
         "PROMPT_INDEX = index\n",
         "prompt = target_prompts[f'{PROMPT_INDEX}']\n",
@@ -268,6 +391,15 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Use an image as a reference via URL (optional)**"
+      ],
+      "metadata": {
+        "id": "KI9Ho6CG7m3Z"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
@@ -337,6 +469,15 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Use an image as a reference via uploading it to the /content/ folder (optional)**"
+      ],
+      "metadata": {
+        "id": "MBPi7F8S7tg3"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
@@ -463,103 +604,27 @@
         "  #------#"
       ],
       "metadata": {
-        "id": "lOQuTPfBMK82"
+        "id": "lOQuTPfBMK82",
+        "cellView": "form"
       },
       "execution_count": null,
       "outputs": []
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
       "source": [
-        "# @title ⚄ Evaluate saved  reference similarity to select items (optional)\n",
-        "EVAL = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
-        "\n",
-        "# @markdown 📝 Enhance/Penalize Similarity and skip items containing word(s)\n",
-        "POS = '' # @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
-        "NEG = ''# @param {type:'string' ,placeholder:'item1 , item2 , ...'}\n",
-        "# @markdown -----\n",
-        "# @markdown logarithmic prompt strength x for value 10^(x-1)\n",
-        "_POS = 0 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
-        "_NEG = 0  # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "**Run the interrogator**\n",
         "\n",
-        "show_local_reference = True # @param {type:\"boolean\"}\n",
-        "show_encoding = True # @param {type:\"boolean\"}\n",
-        "\n",
-        "%cd /content/\n",
-        "_ref = load_file('reference.safetensors' )\n",
-        "ref = _ref['weights'].to(dot_dtype)\n",
-        "\n",
-        "if EVAL.strip() != '':\n",
-        "  print(\"Saved Reference:\\n\")\n",
-        "  for item in EVAL.split(','):\n",
-        "    if item.strip()=='':continue\n",
-        "    inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
-        "    test = model.get_text_features(**inputs)[0]\n",
-        "    test = test/test.norm(p=2 , dim = -1 , keepdim = True)\n",
-        "    ref= ref/ref.norm(p=2 , dim=-1 , keepdim=True)\n",
-        "    eval = torch.dot(ref , test)\n",
-        "    print(f'{item.strip()} : {round(eval.item()*100, 2)}%')\n",
-        "  #-----#\n",
-        "\n",
-        "  if(show_local_reference):\n",
-        "    print(\"\\n---------\\nLocal Reference with enchancements added :\\n\")\n",
-        "\n",
-        "    for _item in POS.split(','):\n",
-        "      item = _item.strip()\n",
-        "      if item == '':continue\n",
-        "      inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
-        "      ref = ref + math.pow(10,_POS-1) * model.get_text_features(**inputs)[0]\n",
-        "    #-------#\n",
-        "\n",
-        "    for _item in NEG.split(','):\n",
-        "      item = _item.strip()\n",
-        "      if item == '':continue\n",
-        "      inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
-        "      ref = ref + math.pow(10,_NEG-1) * model.get_text_features(**inputs)[0]\n",
-        "    #-------#\n",
-        "\n",
-        "    ref= ref/ref.norm(p=2 , dim=-1 , keepdim=True)\n",
-        "    for item in EVAL.split(','):\n",
-        "      if item.strip()=='':continue\n",
-        "      inputs = tokenizer(text = item.strip(), truncation = True  , padding=True, return_tensors=\"pt\")\n",
-        "      test = model.get_text_features(**inputs)[0]\n",
-        "      test = test/test.norm(p=2 , dim = -1 , keepdim = True)\n",
-        "      eval = torch.dot(ref , test)\n",
-        "      print(f'{item.strip()} : {round(eval.item()*100, 2)}%')\n",
-        "    #-----#\n",
-        "\n",
-        "  if show_encoding:\n",
-        "    # create figure\n",
-        "    fig = plt.figure(figsize=(10*image_size, 10*image_size))\n",
-        "    fig.patch.set_facecolor((56/255,56/255,56/255))\n",
-        "    rows = 1\n",
-        "    columns = 3\n",
-        "    fig.add_subplot(rows, columns, 1)\n",
-        "    plt.imshow( visualize(ref))\n",
-        "    plt.axis('off')\n",
-        "    plt.title( \"Encoding (local variable)\",  color='white',  fontsize=round(20*image_size))\n",
-        "    if num_plots>1:\n",
-        "      fig.add_subplot(rows, columns, 2)\n",
-        "      plt.imshow( visualize( _ref['weights'].to(dot_dtype)))\n",
-        "      plt.axis('off')\n",
-        "      plt.title(\"Encoding (saved file)\",  color='white',  fontsize=round(20*image_size))\n",
-        "\n",
-        "      fig.add_subplot(rows, columns, 3)\n",
-        "      plt.imshow( visualize(ref - _ref['weights'].to(dot_dtype)))\n",
-        "      plt.axis('off')\n",
-        "      plt.title(\"Changes\",  color='white',  fontsize=round(20*image_size))\n",
-        "    #------#\n"
+        " Since the list of items is large (>1 million items) you will need to select a range within the sorted results to print."
       ],
       "metadata": {
-        "id": "Oxi6nOyrUTAe"
-      },
-      "execution_count": null,
-      "outputs": []
+        "id": "ROKsoZrt7zMe"
+      }
     },
     {
       "cell_type": "code",
       "source": [
-        "# @title ⚄ Run the CLIP interrogator on the saved reference\n",
+        "# @title ⚄ CLIP Interrogator\n",
         "LIST_SIZE = 1000  # @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",
@@ -757,6 +822,17 @@
       "execution_count": null,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Evaluate Similarities**\n",
+        "\n",
+        "Run this cell to see how far down the list you can go before similarity to the reference is lost."
+      ],
+      "metadata": {
+        "id": "yl1DYzUn8YCC"
+      }
+    },
     {
       "cell_type": "code",
       "source": [