Upload fusion_t2i_CLIP_interrogator.ipynb

Google Colab Jupyter Notebooks/fusion_t2i_CLIP_interrogator.ipynb

@@ -175,6 +175,92 @@
         "id": "Xf9zoq-Za3wi"
       }
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title ⚄ 📷💭 Use pre-encoded image+prompt pair\n",
+        "loaded_ref = False\n",
+        "try:\n",
+        "  ref\n",
+        "  loaded_ref = True\n",
+        "except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
+        "if loaded_ref : prev_ref = ref.clone().detach()\n",
+        "\n",
+        "try:prompt\n",
+        "except: prompt = ''\n",
+        "\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",
+        "url = target_urls[f'{PROMPT_INDEX}']\n",
+        "if url.find('perchance')>-1:\n",
+        "  image = Image.open(requests.get(url, stream=True).raw)\n",
+        "#------#\n",
+        "%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n",
+        "references = torch.load('reference_text_and_image_encodings.pt'  , weights_only=False)\n",
+        "# @markdown ⚖️ 🖼️ encoding <-----?-----> 📝 encoding </div> <br>\n",
+        "C = 0.3 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
+        "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
+        "method = 'Add to existing ref' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
+        "image_size = 0.57 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
+        "show_encoding = True # @param {type:\"boolean\"}\n",
+        "\n",
+        "if(not method == 'Do nothing'):\n",
+        "  if method == 'Refresh': ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
+        "  if method == 'Subtract from existing ref':\n",
+        "    ref = torch.sub(ref, math.pow(10 ,log_strength-1) * C * references[index][0].dequantize().to(dtype = torch.float32))\n",
+        "    ref = torch.sub(ref, math.pow(10 ,log_strength-1) * (1-C) * references[index][1].dequantize().to(dtype = torch.float32))\n",
+        "  else:\n",
+        "    ref = torch.add(ref, math.pow(10 ,log_strength-1) * C * references[index][0].dequantize().to(dtype = torch.float32))\n",
+        "    ref = torch.add(ref, math.pow(10 ,log_strength-1) * (1-C) * references[index][1].dequantize().to(dtype = torch.float32))\n",
+        "  #---------#\n",
+        "  references = '' # Clear up memory\n",
+        "  ref = ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
+        "  ref = ref.clone().detach()\n",
+        "  #------#\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 = 1\n",
+        "  if show_encoding: columns = columns+1\n",
+        "  if show_encoding and loaded_ref : columns = columns+1\n",
+        "  fig.add_subplot(rows, columns, 1)\n",
+        "  plt.imshow(image)\n",
+        "  plt.axis('off')\n",
+        "  plt.title(f\"Reference image at index={index}\" ,  color='white' , fontsize=round(20*image_size))\n",
+        "  #-----#\n",
+        "  if show_encoding and loaded_ref:\n",
+        "    fig.add_subplot(rows, columns, columns-1)\n",
+        "    plt.imshow( visualize(prev_ref))\n",
+        "    plt.axis('off')\n",
+        "    plt.title(\"Encoding (before)\" ,  color='white' , fontsize=round(20*image_size))\n",
+        "  print(f'Prompt for this image : \\n\\n \"{prompt} \" \\n\\n')\n",
+        "\n",
+        "  if show_encoding:\n",
+        "    fig.add_subplot(rows, columns, columns)\n",
+        "    plt.imshow( visualize(ref))\n",
+        "    plt.axis('off')\n",
+        "    plt.title(\"Encoding (now)\" ,  color='white' , fontsize=round(20*image_size))\n",
+        "  #------#\n"
+      ],
+      "metadata": {
+        "id": "BwrEs5zVB0Sb",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Other methods"
+      ],
+      "metadata": {
+        "id": "f9_AcquM7AYZ"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
@@ -289,92 +375,6 @@
       "execution_count": null,
       "outputs": []
     },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 🖼️ Image encoders (optional)"
-      ],
-      "metadata": {
-        "id": "f9_AcquM7AYZ"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# @title ⚄ 📷💭 Use pre-encoded image+prompt pair\n",
-        "loaded_ref = False\n",
-        "try:\n",
-        "  ref\n",
-        "  loaded_ref = True\n",
-        "except:ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
-        "if loaded_ref : prev_ref = ref.clone().detach()\n",
-        "\n",
-        "try:prompt\n",
-        "except: prompt = ''\n",
-        "\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",
-        "url = target_urls[f'{PROMPT_INDEX}']\n",
-        "if url.find('perchance')>-1:\n",
-        "  image = Image.open(requests.get(url, stream=True).raw)\n",
-        "#------#\n",
-        "%cd {home_directory + 'fusion-t2i-generator-data/' + 'reference'}\n",
-        "references = torch.load('reference_text_and_image_encodings.pt'  , weights_only=False)\n",
-        "# @markdown ⚖️ 🖼️ encoding <-----?-----> 📝 encoding </div> <br>\n",
-        "C = 0.3 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
-        "log_strength = 1 # @param {type:\"slider\", min:-5, max:5, step:0.01}\n",
-        "method = 'Add to existing ref' # @param [\"Refresh\" , \"Add to existing ref\" , \"Subtract from existing ref\" , \"Do nothing\"]\n",
-        "image_size = 0.57 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
-        "show_encoding = True # @param {type:\"boolean\"}\n",
-        "\n",
-        "if(not method == 'Do nothing'):\n",
-        "  if method == 'Refresh': ref = torch.zeros(dim).to(dtype = dot_dtype)\n",
-        "  if method == 'Subtract from existing ref':\n",
-        "    ref = torch.sub(ref, math.pow(10 ,log_strength-1) * C * references[index][0].dequantize().to(dtype = torch.float32))\n",
-        "    ref = torch.sub(ref, math.pow(10 ,log_strength-1) * (1-C) * references[index][1].dequantize().to(dtype = torch.float32))\n",
-        "  else:\n",
-        "    ref = torch.add(ref, math.pow(10 ,log_strength-1) * C * references[index][0].dequantize().to(dtype = torch.float32))\n",
-        "    ref = torch.add(ref, math.pow(10 ,log_strength-1) * (1-C) * references[index][1].dequantize().to(dtype = torch.float32))\n",
-        "  #---------#\n",
-        "  references = '' # Clear up memory\n",
-        "  ref = ref/ref.norm(p=2, dim=-1, keepdim=True)\n",
-        "  ref = ref.clone().detach()\n",
-        "  #------#\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 = 1\n",
-        "  if show_encoding: columns = columns+1\n",
-        "  if show_encoding and loaded_ref : columns = columns+1\n",
-        "  fig.add_subplot(rows, columns, 1)\n",
-        "  plt.imshow(image)\n",
-        "  plt.axis('off')\n",
-        "  plt.title(f\"Reference image at index={index}\" ,  color='white' , fontsize=round(20*image_size))\n",
-        "  #-----#\n",
-        "  if show_encoding and loaded_ref:\n",
-        "    fig.add_subplot(rows, columns, columns-1)\n",
-        "    plt.imshow( visualize(prev_ref))\n",
-        "    plt.axis('off')\n",
-        "    plt.title(\"Encoding (before)\" ,  color='white' , fontsize=round(20*image_size))\n",
-        "  print(f'Prompt for this image : \\n\\n \"{prompt} \" \\n\\n')\n",
-        "\n",
-        "  if show_encoding:\n",
-        "    fig.add_subplot(rows, columns, columns)\n",
-        "    plt.imshow( visualize(ref))\n",
-        "    plt.axis('off')\n",
-        "    plt.title(\"Encoding (now)\" ,  color='white' , fontsize=round(20*image_size))\n",
-        "  #------#\n"
-      ],
-      "metadata": {
-        "id": "BwrEs5zVB0Sb",
-        "cellView": "form"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
     {
       "cell_type": "markdown",
       "source": [
@@ -540,12 +540,10 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# CLIP Interrogator\n",
-        "\n",
-        "**Save the reference prior to running the Interrogator**"
+        "# Search prompts using CLIP"
       ],
       "metadata": {
-        "id": "zeu6JcM-mk9z"
+        "id": "UqrYOkhlEQdM"
       }
     },
     {