StyleAligned_Metrics.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alessioborgi/StyleAligned_DiffModels/blob/main/StyleAligned_Metrics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74VTMLiY2Gbc"
      },
      "source": [
        "### SINGLE-STYLE METRICS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4wNSrX_MUTY",
        "outputId": "195d1f84-c8c9-405a-d200-318f0b121085"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "zsh:1: no matches found: https://drive.google.com/drive/folders/1Rdb3XkwW1H_IMFVh3tShj4adh-XUNt03?usp=sharing\n"
          ]
        }
      ],
      "source": [
        "# Download the file\n",
        "!gdown --folder https://drive.google.com/drive/folders/1Rdb3XkwW1H_IMFVh3tShj4adh-XUNt03?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ra1kzd-HWj_x",
        "outputId": "0a2c9b19-967b-402c-9a32-3efcbcd22819"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "unzip:  cannot find or open AlignZipFolder/OnlyPromptFile.zip, AlignZipFolder/OnlyPromptFile.zip.zip or AlignZipFolder/OnlyPromptFile.zip.ZIP.\n"
          ]
        }
      ],
      "source": [
        "# Unzip the file if it's a zip\n",
        "!unzip -q AlignZipFolder/OnlyPromptFile.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJvxCnjsW6fj",
        "outputId": "68d0ab81-855f-4bb8-9f70-808944dbd26c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Collecting wcwidth<0.3.0,>=0.2.12 (from ftfy)\n",
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            "\u001b[?25hUsing cached wcwidth-0.2.13-py2.py3-none-any.whl (34 kB)\n",
            "Installing collected packages: wcwidth, ftfy\n",
            "  Attempting uninstall: wcwidth\n",
            "    Found existing installation: wcwidth 0.2.5\n",
            "    Uninstalling wcwidth-0.2.5:\n",
            "      Successfully uninstalled wcwidth-0.2.5\n",
            "Successfully installed ftfy-6.2.3 wcwidth-0.2.13\n",
            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
            "Collecting git+https://github.com/openai/CLIP.git\n",
            "  Cloning https://github.com/openai/CLIP.git to /private/var/folders/mx/xlmhp5ln7l5g33gb6w32s46m0000gn/T/pip-req-build-e12_6icu\n",
            "  Running command git clone --quiet https://github.com/openai/CLIP.git /private/var/folders/mx/xlmhp5ln7l5g33gb6w32s46m0000gn/T/pip-req-build-e12_6icu\n",
            "  Resolved https://github.com/openai/CLIP.git to commit dcba3cb2e2827b402d2701e7e1c7d9fed8a20ef1\n",
            "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25hRequirement already satisfied: ftfy in /Users/alessioborgi/opt/anaconda3/lib/python3.9/site-packages (from clip==1.0) (6.2.3)\n",
            "Requirement already satisfied: packaging in /Users/alessioborgi/opt/anaconda3/lib/python3.9/site-packages (from clip==1.0) (21.3)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /Users/alessioborgi/opt/anaconda3/lib/python3.9/site-packages (from requests->torchvision->clip==1.0) (3.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /Users/alessioborgi/opt/anaconda3/lib/python3.9/site-packages (from sympy->torch->clip==1.0) (1.2.1)\n",
            "Building wheels for collected packages: clip\n",
            "  Building wheel for clip (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Created wheel for clip: filename=clip-1.0-py3-none-any.whl size=1369520 sha256=04371c464313409990a7b4eaa9bd42a4b16b042448dd5a0aeb7579cd0d75273d\n",
            "  Stored in directory: /private/var/folders/mx/xlmhp5ln7l5g33gb6w32s46m0000gn/T/pip-ephem-wheel-cache-jc29bb_n/wheels/c8/e4/e1/11374c111387672fc2068dfbe0d4b424cb9cdd1b2e184a71b5\n",
            "Successfully built clip\n",
            "Installing collected packages: clip\n",
            "Successfully installed clip-1.0\n",
            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "# Install required libraries\n",
        "!pip install transformers ftfy regex tqdm\n",
        "!pip install git+https://github.com/openai/CLIP.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312,
          "referenced_widgets": [
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        "id": "vYwQ3J3zMSvu",
        "outputId": "e95db155-5dc6-41c7-e528-1d4c5f5d4a50"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|███████████████████████████████████████| 338M/338M [00:05<00:00, 61.8MiB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
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              "model_id": "22f427fcfec842b4aed8c65f91255b51",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/244 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dff0720975d64f6eaaec35e5850def18",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/453 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "861bd732d48b4381a8e8c1aa6216f6d2",
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            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/343M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of ViTModel were not initialized from the model checkpoint at facebook/dino-vitb8 and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'clip_aligned': 0.34217529296875, 'clip_non_aligned': 0.3454345703125, 'dino_aligned': 0.5018175, 'dino_non_aligned': 0.34539917}\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import torch\n",
        "import clip\n",
        "from PIL import Image\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import numpy as np\n",
        "from transformers import AutoImageProcessor, AutoModel\n",
        "from torchvision import transforms\n",
        "\n",
        "# Load CLIP model and preprocess\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "clip_model, clip_preprocess = clip.load(\"ViT-B/32\", device=device)\n",
        "\n",
        "# Load DINO model and processor from Hugging Face\n",
        "dino_processor = AutoImageProcessor.from_pretrained(\"facebook/dino-vitb8\")\n",
        "dino_model = AutoModel.from_pretrained(\"facebook/dino-vitb8\").to(device)\n",
        "\n",
        "def get_clip_similarity(image_path, prompt):\n",
        "    image = clip_preprocess(Image.open(image_path)).unsqueeze(0).to(device)\n",
        "    text = clip.tokenize([prompt]).to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        image_features = clip_model.encode_image(image)\n",
        "        text_features = clip_model.encode_text(text)\n",
        "\n",
        "    image_features /= image_features.norm(dim=-1, keepdim=True)\n",
        "    text_features /= text_features.norm(dim=-1, keepdim=True)\n",
        "\n",
        "    similarity = (image_features @ text_features.T).item()\n",
        "    return similarity\n",
        "\n",
        "def get_dino_embeddings(image_paths):\n",
        "    images = [Image.open(image_path) for image_path in image_paths]\n",
        "    inputs = dino_processor(images=images, return_tensors=\"pt\").to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        outputs = dino_model(**inputs)\n",
        "        embeddings = outputs.last_hidden_state[:, 0, :].cpu().numpy()  # Taking the CLS token\n",
        "\n",
        "    return embeddings\n",
        "\n",
        "def compute_pairwise_similarity(embeddings):\n",
        "    similarities = cosine_similarity(embeddings)\n",
        "    upper_tri_indices = np.triu_indices_from(similarities, k=1)\n",
        "    return similarities[upper_tri_indices].mean()\n",
        "\n",
        "def process_folder(folder_path):\n",
        "    subfolders = [f.path for f in os.scandir(folder_path) if f.is_dir()]\n",
        "    clip_results = {'aligned': [], 'non_aligned': []}\n",
        "    dino_results = {'aligned': [], 'non_aligned': []}\n",
        "\n",
        "    for subfolder in subfolders:\n",
        "        aligned_images = []\n",
        "        non_aligned_images = []\n",
        "        subfolder_name = os.path.basename(subfolder)\n",
        "\n",
        "        for file in os.listdir(subfolder):\n",
        "            if file.endswith(\".jpg\") or file.endswith(\".png\"):\n",
        "                file_path = os.path.join(subfolder, file)\n",
        "                if file.startswith(\"znon\"):\n",
        "                    non_aligned_images.append(file_path)\n",
        "                else:\n",
        "                    aligned_images.append(file_path)\n",
        "\n",
        "        for image_path in aligned_images:\n",
        "            image_name = os.path.basename(image_path)\n",
        "            prompt = f\"{image_name[:-4]}, {subfolder_name}\"\n",
        "            clip_similarity = get_clip_similarity(image_path, prompt)\n",
        "            clip_results['aligned'].append(clip_similarity)\n",
        "\n",
        "        for image_path in non_aligned_images:\n",
        "            image_name = os.path.basename(image_path)[5:]\n",
        "            prompt = f\"{image_name[:-4]}, {subfolder_name}\"\n",
        "            clip_similarity = get_clip_similarity(image_path, prompt)\n",
        "            clip_results['non_aligned'].append(clip_similarity)\n",
        "\n",
        "        if aligned_images:\n",
        "            aligned_embeddings = get_dino_embeddings(aligned_images)\n",
        "            dino_similarity = compute_pairwise_similarity(aligned_embeddings)\n",
        "            dino_results['aligned'].append(dino_similarity)\n",
        "\n",
        "        if non_aligned_images:\n",
        "            non_aligned_embeddings = get_dino_embeddings(non_aligned_images)\n",
        "            dino_similarity = compute_pairwise_similarity(non_aligned_embeddings)\n",
        "            dino_results['non_aligned'].append(dino_similarity)\n",
        "\n",
        "    avg_clip_aligned = np.mean(clip_results['aligned'])\n",
        "    avg_clip_non_aligned = np.mean(clip_results['non_aligned'])\n",
        "    avg_dino_aligned = np.mean(dino_results['aligned'])\n",
        "    avg_dino_non_aligned = np.mean(dino_results['non_aligned'])\n",
        "\n",
        "    return {\n",
        "        \"clip_aligned\": avg_clip_aligned,\n",
        "        \"clip_non_aligned\": avg_clip_non_aligned,\n",
        "        \"dino_aligned\": avg_dino_aligned,\n",
        "        \"dino_non_aligned\": avg_dino_non_aligned\n",
        "    }\n",
        "\n",
        "# Example usage\n",
        "folder_path = \"OnlyPrompt\"\n",
        "results = process_folder(folder_path)\n",
        "print(results)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AnWlZ0jbahCs",
        "outputId": "65d5a0e7-346c-4882-a5cf-c10b0a409fb0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'clip_aligned': 0.34217529296875,\n",
              " 'clip_non_aligned': 0.3454345703125,\n",
              " 'dino_aligned': 0.5018175,\n",
              " 'dino_non_aligned': 0.34539917}"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHsdokxA2Gbh"
      },
      "source": [
        "### MULTI-STYLE DINO VIT-B/8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t9OPeE0x2Gbh",
        "outputId": "bd279b90-b057-41eb-d240-7cd943c70121"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'StyleAlignedDiffModels'...\n",
            "remote: Enumerating objects: 1919, done.\u001b[K\n",
            "remote: Counting objects: 100% (51/51), done.\u001b[K\n",
            "remote: Compressing objects: 100% (30/30), done.\u001b[K\n",
            "remote: Total 1919 (delta 21), reused 46 (delta 18), pack-reused 1868 (from 1)\u001b[K\n",
            "Receiving objects: 100% (1919/1919), 1.26 GiB | 24.11 MiB/s, done.\n",
            "Resolving deltas: 100% (791/791), done.\n",
            "Updating files: 100% (454/454), done.\n",
            "/content/StyleAlignedDiffModels\n",
            " \u001b[0m\u001b[01;34maudio\u001b[0m/\n",
            " \u001b[01;34mimgs\u001b[0m/\n",
            " LICENSE\n",
            " \u001b[01;34mmusic\u001b[0m/\n",
            " README.md\n",
            " requirements.txt\n",
            " \u001b[01;34msrc\u001b[0m/\n",
            " StyleAligned_ControlNet.ipynb\n",
            " StyleAligned_Explanation.ipynb\n",
            " StyleAligned_Focused_Attention_Explanation.ipynb\n",
            " StyleAligned_Metrics.ipynb\n",
            " StyleAligned_MultiReference.ipynb\n",
            " StyleAligned_MultiReference_Slerp.ipynb\n",
            " StyleAligned_MultiScale_AdaIN_Explanation.ipynb\n",
            " StyleAligned_Prompts.ipynb\n",
            "'StyleAligned_Ref&Content(Audio).ipynb'\n",
            "'StyleAligned_Ref&Content(Audio+Music).ipynb'\n",
            "'StyleAligned_Ref&Content(Img+Audio).ipynb'\n",
            "'StyleAligned_Ref&Content(Img+Audio+Music).ipynb'\n",
            "'StyleAligned_Ref&Content(Img).ipynb'\n",
            "'StyleAligned_Ref&Content(Img+Music).ipynb'\n",
            "'StyleAligned_Ref&Content(Img+Weather).ipynb'\n",
            " StyleAligned_Ref.ipynb\n",
            " To_Do.txt\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/alessioborgi/StyleAlignedDiffModels.git\n",
        "\n",
        "# Change directory to the cloned repository\n",
        "%cd StyleAlignedDiffModels\n",
        "%ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FbkD6ANq2Gbh",
        "outputId": "47203bdb-565c-4dc7-9897-e85a7d74816c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Retrieving folder contents\n",
            "Processing file 1qQ7MkB89aJtzAeljDxcsDW86fvFsyWOx OnlyPromptFile.zip\n",
            "Retrieving folder contents completed\n",
            "Building directory structure\n",
            "Building directory structure completed\n",
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1qQ7MkB89aJtzAeljDxcsDW86fvFsyWOx\n",
            "From (redirected): https://drive.google.com/uc?id=1qQ7MkB89aJtzAeljDxcsDW86fvFsyWOx&confirm=t&uuid=499d023e-2f1f-4bc9-8c49-5d872d8bc88c\n",
            "To: /content/StyleAlignedDiffModels/AlignZipFolder/OnlyPromptFile.zip\n",
            "100% 125M/125M [00:06<00:00, 19.8MB/s]\n",
            "Download completed\n"
          ]
        }
      ],
      "source": [
        "# Download the file\n",
        "!gdown --folder https://drive.google.com/drive/folders/1Rdb3XkwW1H_IMFVh3tShj4adh-XUNt03?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "XyIxTA6m2Gbi"
      },
      "outputs": [],
      "source": [
        "# Unzip the file if it's a zip\n",
        "!unzip -q AlignZipFolder/OnlyPromptFile.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wH8Hgi7m2Gbi",
        "outputId": "55a65875-2e87-40c6-fad0-a5cede98013b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.42.4)\n",
            "Collecting ftfy\n",
            "  Downloading ftfy-6.2.3-py3-none-any.whl.metadata (7.8 kB)\n",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.5)\n",
            "Requirement already satisfied: numpy<2.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.4)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: wcwidth<0.3.0,>=0.2.12 in /usr/local/lib/python3.10/dist-packages (from ftfy) (0.2.13)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.6.1)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.8)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.7.4)\n",
            "Downloading ftfy-6.2.3-py3-none-any.whl (43 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.0/43.0 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: ftfy\n",
            "Successfully installed ftfy-6.2.3\n",
            "Collecting git+https://github.com/openai/CLIP.git\n",
            "  Cloning https://github.com/openai/CLIP.git to /tmp/pip-req-build-82szrw17\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/openai/CLIP.git /tmp/pip-req-build-82szrw17\n",
            "  Resolved https://github.com/openai/CLIP.git to commit dcba3cb2e2827b402d2701e7e1c7d9fed8a20ef1\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: ftfy in /usr/local/lib/python3.10/dist-packages (from clip==1.0) (6.2.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from clip==1.0) (24.1)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from clip==1.0) (2024.5.15)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from clip==1.0) (4.66.5)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from clip==1.0) (2.4.0+cu121)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from clip==1.0) (0.19.0+cu121)\n",
            "Requirement already satisfied: wcwidth<0.3.0,>=0.2.12 in /usr/local/lib/python3.10/dist-packages (from ftfy->clip==1.0) (0.2.13)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (3.15.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (1.13.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->clip==1.0) (2024.6.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision->clip==1.0) (1.26.4)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->clip==1.0) (9.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->clip==1.0) (2.1.5)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->clip==1.0) (1.3.0)\n",
            "Building wheels for collected packages: clip\n",
            "  Building wheel for clip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for clip: filename=clip-1.0-py3-none-any.whl size=1369490 sha256=be8de78deeb9b2a071c4ddf6837e82665af7c0fc24009407e04b5a6d2094f726\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-6r0y20w0/wheels/da/2b/4c/d6691fa9597aac8bb85d2ac13b112deb897d5b50f5ad9a37e4\n",
            "Successfully built clip\n",
            "Installing collected packages: clip\n",
            "Successfully installed clip-1.0\n"
          ]
        }
      ],
      "source": [
        "# Install required libraries\n",
        "!pip install transformers ftfy regex tqdm\n",
        "!pip install git+https://github.com/openai/CLIP.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149,
          "referenced_widgets": [
            "ad1d08247c844f668273eae03a49fccb",
            "be5499110fc94853adccdde213ba9df7",
            "c40f05ec71994b18b3f6731c171ca1df",
            "27de4729356348518635dddf76566ae8",
            "5ccbd53c40a9485a897843d45d520d64",
            "204c4f2dfc5240aca64d41dffaa7935d",
            "5112aaaaee6d45b59325a732e6dbd5dc",
            "359e1e2e6d2542c5a0b0eceb64930d5e",
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            "1dc53297d9c0446b8a74b3db7eff7895",
            "8150ad9c178c4197aca70aae7c02d41d",
            "7d86bc660206438abca3875d036905ae",
            "18ec543719f049ad8605a8847470105a",
            "75b95568a2bc475e8e97826124d4b068",
            "3d935708f4794a45a3c3e61b1ddd5e2d",
            "76c79470b6a240dea37bebc04204afc5",
            "8c76f584966141f882aa79d7f5ebed8a",
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            "db310c7f84684d6996a78eb0c53955b6",
            "fce3eda85d754bc98f1e0962dc2333fa",
            "bf1e1e9e2ba94cabad54cefae28e1bb2",
            "926cdd07e247417c8a9cf163fc885e2a",
            "ee1083dbdc384996b9a5746cfe504de8"
          ]
        },
        "id": "NQDJx-1i2Gbi",
        "outputId": "5ea841d0-760b-4b1a-b1a6-a1cd2e871ca4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/244 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ad1d08247c844f668273eae03a49fccb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/453 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8150ad9c178c4197aca70aae7c02d41d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/343M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9cec18bab6124856b2539994ec338c98"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of ViTModel were not initialized from the model checkpoint at facebook/dino-vitb8 and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import torch\n",
        "from PIL import Image\n",
        "from transformers import AutoImageProcessor, AutoModel\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "# Load DINO model and processor from Hugging Face\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "dino_processor = AutoImageProcessor.from_pretrained(\"facebook/dino-vitb8\")\n",
        "dino_model = AutoModel.from_pretrained(\"facebook/dino-vitb8\").to(device)\n",
        "\n",
        "def get_dino_embeddings(image_paths):\n",
        "    \"\"\"\n",
        "    Extract DINO VIT-B/8 embeddings from a list of image paths.\n",
        "\n",
        "    :param image_paths: List of paths to images.\n",
        "    :return: List of embeddings corresponding to each image.\n",
        "    \"\"\"\n",
        "    images = [Image.open(image_path) for image_path in image_paths]\n",
        "    inputs = dino_processor(images=images, return_tensors=\"pt\").to(device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        outputs = dino_model(**inputs)\n",
        "        embeddings = outputs.last_hidden_state[:, 0, :].cpu().numpy()  # Taking the CLS token\n",
        "\n",
        "    return embeddings\n",
        "\n",
        "def compute_similarity(generated_embedding, reference_embedding):\n",
        "    \"\"\"\n",
        "    Compute the similarity between the generated image and a single reference style.\n",
        "\n",
        "    :param generated_embedding: Embedding of the generated image (1D numpy array).\n",
        "    :param reference_embedding: Embedding of the reference image (1D numpy array).\n",
        "    :return: Similarity score.\n",
        "    \"\"\"\n",
        "    similarity = cosine_similarity(generated_embedding.reshape(1, -1), reference_embedding.reshape(1, -1)).item()\n",
        "    return similarity\n",
        "\n",
        "def process_main_folder(main_folder, reference_image_paths):\n",
        "    \"\"\"\n",
        "    Process the main folder and compute the weighted DINO metric for each subfolder.\n",
        "    \"\"\"\n",
        "    final_results = {}\n",
        "\n",
        "    subfolders = [f.path for f in os.scandir(main_folder) if f.is_dir()]\n",
        "\n",
        "    for subfolder in subfolders:\n",
        "        subfolder_name = os.path.basename(subfolder)\n",
        "        # Extract weights from the subfolder name\n",
        "        try:\n",
        "            parts = subfolder_name.split('_')\n",
        "            weight1 = float(parts[-2])\n",
        "            weight2 = float(parts[-1])\n",
        "            weights = [weight1, weight2]\n",
        "        except Exception as e:\n",
        "            print(f\"Error parsing weights from {subfolder_name}: {e}\")\n",
        "            continue\n",
        "\n",
        "        # Print the header with reference image information\n",
        "        print(f\"Results for {subfolder_name} (reference_image 1: {os.path.basename(reference_image_paths[0])}, reference_image 2: {os.path.basename(reference_image_paths[1])}):\")\n",
        "\n",
        "        # Calculate the mean DINO similarity for all images in the subfolder w.r.t. both references\n",
        "        similarities_ref1 = []\n",
        "        similarities_ref2 = []\n",
        "\n",
        "        for image_file in os.listdir(subfolder):\n",
        "            if image_file.endswith(\".jpg\") or image_file.endswith(\".png\"):\n",
        "                # Skip the reference image file\n",
        "                if image_file == \"ref_img.png\":\n",
        "                    continue\n",
        "\n",
        "                image_path = os.path.join(subfolder, image_file)\n",
        "                generated_embedding = get_dino_embeddings([image_path])[0]\n",
        "                reference_embeddings = get_dino_embeddings(reference_image_paths)\n",
        "\n",
        "                similarity_to_ref1 = compute_similarity(generated_embedding, reference_embeddings[0])\n",
        "                similarity_to_ref2 = compute_similarity(generated_embedding, reference_embeddings[1])\n",
        "\n",
        "                similarities_ref1.append(similarity_to_ref1)\n",
        "                similarities_ref2.append(similarity_to_ref2)\n",
        "\n",
        "                # Print similarity results for each image\n",
        "                print(f\"Image: {image_file}, Similarity to Reference 1: {similarity_to_ref1}, Similarity to Reference 2: {similarity_to_ref2}\")\n",
        "\n",
        "        # Calculate the mean similarities for the subfolder\n",
        "        mean_similarity_ref1 = np.mean(similarities_ref1)\n",
        "        mean_similarity_ref2 = np.mean(similarities_ref2)\n",
        "\n",
        "        # Compute the final weighted multi-style DINO metric\n",
        "        weighted_multi_style_dino = mean_similarity_ref1 * weight1 + mean_similarity_ref2 * weight2\n",
        "\n",
        "        final_results[subfolder_name] = {\n",
        "            \"mean_similarity_ref1\": mean_similarity_ref1,\n",
        "            \"mean_similarity_ref2\": mean_similarity_ref2,\n",
        "            \"weighted_multi_style_dino\": weighted_multi_style_dino\n",
        "        }\n",
        "\n",
        "        print(f\"Mean Similarity to Reference 1: {mean_similarity_ref1}\")\n",
        "        print(f\"Mean Similarity to Reference 2: {mean_similarity_ref2}\")\n",
        "        print(f\"Weighted Multi-Style DINO Metric: {weighted_multi_style_dino}\\n\")\n",
        "\n",
        "    return final_results\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### LINEAR WEIGHTED BLENDING"
      ],
      "metadata": {
        "id": "t2YG98q1Rwpq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### GUIDANCE 10"
      ],
      "metadata": {
        "id": "LvbITb7LR8QS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-fRguRRg2Gbj",
        "outputId": "31be22c4-91c6-4dd8-83cf-2723c8c101e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results for medieval_picasso_1_0 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.24083636701107025, Similarity to Reference 2: 0.37675100564956665\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.3318901062011719, Similarity to Reference 2: 0.33703693747520447\n",
            "Image: mountain_landscape.png, Similarity to Reference 1: 0.34509122371673584, Similarity to Reference 2: 0.4812157452106476\n",
            "Image: castle.png, Similarity to Reference 1: 0.2660902142524719, Similarity to Reference 2: 0.31534716486930847\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.3823093771934509, Similarity to Reference 2: 0.2968261241912842\n",
            "Image: golf.png, Similarity to Reference 1: 0.3861086070537567, Similarity to Reference 2: 0.4416363835334778\n",
            "Mean Similarity to Reference 1: 0.3253876492381096\n",
            "Mean Similarity to Reference 2: 0.37480222682158154\n",
            "Weighted Multi-Style DINO Metric: 0.3253876492381096\n",
            "\n",
            "Results for medieval_picasso_0.25_0.75 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.28779250383377075, Similarity to Reference 2: 0.47556257247924805\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.26584383845329285, Similarity to Reference 2: 0.34049779176712036\n",
            "Image: castle.png, Similarity to Reference 1: 0.24293220043182373, Similarity to Reference 2: 0.345846027135849\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.4344305396080017, Similarity to Reference 2: 0.3884338140487671\n",
            "Image: mountains.png, Similarity to Reference 1: 0.4025583863258362, Similarity to Reference 2: 0.4982239007949829\n",
            "Image: golf.png, Similarity to Reference 1: 0.31145042181015015, Similarity to Reference 2: 0.4412105977535248\n",
            "Mean Similarity to Reference 1: 0.32416798174381256\n",
            "Mean Similarity to Reference 2: 0.4149624506632487\n",
            "Weighted Multi-Style DINO Metric: 0.39226383343338966\n",
            "\n",
            "Results for medieval_picasso_0.75_0.25 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.3454701900482178, Similarity to Reference 2: 0.44394317269325256\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.28522539138793945, Similarity to Reference 2: 0.28984975814819336\n",
            "Image: castle.png, Similarity to Reference 1: 0.28056806325912476, Similarity to Reference 2: 0.27575260400772095\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.42465347051620483, Similarity to Reference 2: 0.3683374524116516\n",
            "Image: mountains.png, Similarity to Reference 1: 0.3668120801448822, Similarity to Reference 2: 0.4994980990886688\n",
            "Image: golf.png, Similarity to Reference 1: 0.4073813557624817, Similarity to Reference 2: 0.4240102171897888\n",
            "Mean Similarity to Reference 1: 0.3516850918531418\n",
            "Mean Similarity to Reference 2: 0.383565217256546\n",
            "Weighted Multi-Style DINO Metric: 0.35965512320399284\n",
            "\n",
            "Results for medieval_picasso_0.5_0.5 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.3540186882019043, Similarity to Reference 2: 0.5333502292633057\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.31572282314300537, Similarity to Reference 2: 0.341280460357666\n",
            "Image: castle.png, Similarity to Reference 1: 0.3279207646846771, Similarity to Reference 2: 0.3326939344406128\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.35049664974212646, Similarity to Reference 2: 0.29943060874938965\n",
            "Image: mountains.png, Similarity to Reference 1: 0.3782867193222046, Similarity to Reference 2: 0.47578686475753784\n",
            "Image: golf.png, Similarity to Reference 1: 0.3951759338378906, Similarity to Reference 2: 0.46116209030151367\n",
            "Mean Similarity to Reference 1: 0.35360359648863476\n",
            "Mean Similarity to Reference 2: 0.40728403131167096\n",
            "Weighted Multi-Style DINO Metric: 0.38044381390015286\n",
            "\n",
            "Results for medieval_picasso_0_1 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.24716852605342865, Similarity to Reference 2: 0.4930434823036194\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.25067272782325745, Similarity to Reference 2: 0.3763076066970825\n",
            "Image: castle.png, Similarity to Reference 1: 0.21194788813591003, Similarity to Reference 2: 0.3419573903083801\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.27220815420150757, Similarity to Reference 2: 0.39630812406539917\n",
            "Image: mountains.png, Similarity to Reference 1: 0.34651118516921997, Similarity to Reference 2: 0.5743507146835327\n",
            "Image: golf.png, Similarity to Reference 1: 0.3442821502685547, Similarity to Reference 2: 0.48573020100593567\n",
            "Mean Similarity to Reference 1: 0.2787984386086464\n",
            "Mean Similarity to Reference 2: 0.44461625317732495\n",
            "Weighted Multi-Style DINO Metric: 0.44461625317732495\n",
            "\n",
            "Final Results for medieval_picasso_1_0:\n",
            "Mean Similarity to Reference 1: 0.3253876492381096\n",
            "Mean Similarity to Reference 2: 0.37480222682158154\n",
            "Weighted Multi-Style DINO Metric: 0.3253876492381096\n",
            "\n",
            "Final Results for medieval_picasso_0.25_0.75:\n",
            "Mean Similarity to Reference 1: 0.32416798174381256\n",
            "Mean Similarity to Reference 2: 0.4149624506632487\n",
            "Weighted Multi-Style DINO Metric: 0.39226383343338966\n",
            "\n",
            "Final Results for medieval_picasso_0.75_0.25:\n",
            "Mean Similarity to Reference 1: 0.3516850918531418\n",
            "Mean Similarity to Reference 2: 0.383565217256546\n",
            "Weighted Multi-Style DINO Metric: 0.35965512320399284\n",
            "\n",
            "Final Results for medieval_picasso_0.5_0.5:\n",
            "Mean Similarity to Reference 1: 0.35360359648863476\n",
            "Mean Similarity to Reference 2: 0.40728403131167096\n",
            "Weighted Multi-Style DINO Metric: 0.38044381390015286\n",
            "\n",
            "Final Results for medieval_picasso_0_1:\n",
            "Mean Similarity to Reference 1: 0.2787984386086464\n",
            "Mean Similarity to Reference 2: 0.44461625317732495\n",
            "Weighted Multi-Style DINO Metric: 0.44461625317732495\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Example usage\n",
        "main_folder = \"./imgs/Multi_Reference/Weighted_Latent_Space/medieval_picasso_Guidance_10\"\n",
        "reference_image_paths = [\"./imgs/medieval-bed.jpeg\", \"./imgs/Picasso_Smoking_Water_Pipe.jpeg\"]\n",
        "results = process_main_folder(main_folder, reference_image_paths)\n",
        "\n",
        "# Print or save the results\n",
        "for subfolder, metrics in results.items():\n",
        "    print(f\"Final Results for {subfolder}:\")\n",
        "    print(f\"Mean Similarity to Reference 1: {metrics['mean_similarity_ref1']}\")\n",
        "    print(f\"Mean Similarity to Reference 2: {metrics['mean_similarity_ref2']}\")\n",
        "    print(f\"Weighted Multi-Style DINO Metric: {metrics['weighted_multi_style_dino']}\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### GUIDANCE 25"
      ],
      "metadata": {
        "id": "FrdRvXH2R-5w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "main_folder = \"./imgs/Multi_Reference/Weighted_Latent_Space/medieval_picasso_Guidance_25\"\n",
        "reference_image_paths = [\"./imgs/medieval-bed.jpeg\", \"./imgs/Picasso_Smoking_Water_Pipe.jpeg\"]\n",
        "results = process_main_folder(main_folder, reference_image_paths)\n",
        "\n",
        "# Print or save the results\n",
        "for subfolder, metrics in results.items():\n",
        "    print(f\"Final Results for {subfolder}:\")\n",
        "    print(f\"Mean Similarity to Reference 1: {metrics['mean_similarity_ref1']}\")\n",
        "    print(f\"Mean Similarity to Reference 2: {metrics['mean_similarity_ref2']}\")\n",
        "    print(f\"Weighted Multi-Style DINO Metric: {metrics['weighted_multi_style_dino']}\\n\")"
      ],
      "metadata": {
        "id": "EDQaqDtcRg5A",
        "outputId": "55cdf03f-3a09-42ec-dd02-e9e329fd25af",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results for medieval_picasso_1_0 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: mushrooms.png, Similarity to Reference 1: 0.3140500783920288, Similarity to Reference 2: 0.27019909024238586\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.37879446148872375, Similarity to Reference 2: 0.37161117792129517\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.42154496908187866, Similarity to Reference 2: 0.3311164975166321\n",
            "Image: castle.png, Similarity to Reference 1: 0.27519989013671875, Similarity to Reference 2: 0.3981720209121704\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.30036813020706177, Similarity to Reference 2: 0.29647260904312134\n",
            "Image: mountains.png, Similarity to Reference 1: 0.23457500338554382, Similarity to Reference 2: 0.4646250903606415\n",
            "Image: golf.png, Similarity to Reference 1: 0.3399296700954437, Similarity to Reference 2: 0.43160536885261536\n",
            "Mean Similarity to Reference 1: 0.3234946003981999\n",
            "Mean Similarity to Reference 2: 0.36625740783555166\n",
            "Weighted Multi-Style DINO Metric: 0.3234946003981999\n",
            "\n",
            "Results for medieval_picasso_0.25_0.75 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.32685723900794983, Similarity to Reference 2: 0.5049002170562744\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.2812800407409668, Similarity to Reference 2: 0.3750867545604706\n",
            "Image: castle.png, Similarity to Reference 1: 0.2727503478527069, Similarity to Reference 2: 0.3911522328853607\n",
            "Image: hot_air_baloon.png, Similarity to Reference 1: 0.37391817569732666, Similarity to Reference 2: 0.4017494320869446\n",
            "Image: fishes.png, Similarity to Reference 1: 0.3423689901828766, Similarity to Reference 2: 0.42835235595703125\n",
            "Image: mountains.png, Similarity to Reference 1: 0.2502709627151489, Similarity to Reference 2: 0.4972679316997528\n",
            "Image: athens.png, Similarity to Reference 1: 0.3541560471057892, Similarity to Reference 2: 0.33368992805480957\n",
            "Image: golf.png, Similarity to Reference 1: 0.32638511061668396, Similarity to Reference 2: 0.46735596656799316\n",
            "Mean Similarity to Reference 1: 0.3159983642399311\n",
            "Mean Similarity to Reference 2: 0.42494435235857964\n",
            "Weighted Multi-Style DINO Metric: 0.3977078553289175\n",
            "\n",
            "Results for medieval_picasso_0.75_0.25 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.30658504366874695, Similarity to Reference 2: 0.43001487851142883\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.3034665286540985, Similarity to Reference 2: 0.309512197971344\n",
            "Image: athens_temple.png, Similarity to Reference 1: 0.32903575897216797, Similarity to Reference 2: 0.26167768239974976\n",
            "Image: castle.png, Similarity to Reference 1: 0.2861153781414032, Similarity to Reference 2: 0.3315371870994568\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.3405591547489166, Similarity to Reference 2: 0.3510892391204834\n",
            "Image: mountains.png, Similarity to Reference 1: 0.2695053815841675, Similarity to Reference 2: 0.5170409083366394\n",
            "Image: school_fish.png, Similarity to Reference 1: 0.30854660272598267, Similarity to Reference 2: 0.41985130310058594\n",
            "Image: golf.png, Similarity to Reference 1: 0.27272969484329224, Similarity to Reference 2: 0.41956189274787903\n",
            "Mean Similarity to Reference 1: 0.30206794291734695\n",
            "Mean Similarity to Reference 2: 0.3800356611609459\n",
            "Weighted Multi-Style DINO Metric: 0.3215598724782467\n",
            "\n",
            "Results for medieval_picasso_0.5_0.5 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: woman_saxophone.png, Similarity to Reference 1: 0.22503429651260376, Similarity to Reference 2: 0.29551583528518677\n",
            "Image: dinosaur.png, Similarity to Reference 1: 0.4104658365249634, Similarity to Reference 2: 0.36228305101394653\n",
            "Image: mushrooms.png, Similarity to Reference 1: 0.29216983914375305, Similarity to Reference 2: 0.391776978969574\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.3200189471244812, Similarity to Reference 2: 0.47739601135253906\n",
            "Image: child_pizza.png, Similarity to Reference 1: 0.36053603887557983, Similarity to Reference 2: 0.4114261269569397\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.3026416003704071, Similarity to Reference 2: 0.3531492352485657\n",
            "Image: man_pc.png, Similarity to Reference 1: 0.2235749363899231, Similarity to Reference 2: 0.3502669334411621\n",
            "Image: castle.png, Similarity to Reference 1: 0.31751173734664917, Similarity to Reference 2: 0.39651739597320557\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.3301007151603699, Similarity to Reference 2: 0.4036750793457031\n",
            "Image: fishes.png, Similarity to Reference 1: 0.33505740761756897, Similarity to Reference 2: 0.41745999455451965\n",
            "Image: mountains.png, Similarity to Reference 1: 0.31111830472946167, Similarity to Reference 2: 0.5852283239364624\n",
            "Image: athens.png, Similarity to Reference 1: 0.34347617626190186, Similarity to Reference 2: 0.2712283432483673\n",
            "Image: golf.png, Similarity to Reference 1: 0.33597898483276367, Similarity to Reference 2: 0.457922101020813\n",
            "Mean Similarity to Reference 1: 0.31597575545310974\n",
            "Mean Similarity to Reference 2: 0.39798810848822963\n",
            "Weighted Multi-Style DINO Metric: 0.3569819319706697\n",
            "\n",
            "Results for medieval_picasso_0_1 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: mushrooms.png, Similarity to Reference 1: 0.20605063438415527, Similarity to Reference 2: 0.3535454571247101\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.28555208444595337, Similarity to Reference 2: 0.4914189577102661\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.27242758870124817, Similarity to Reference 2: 0.41516607999801636\n",
            "Image: fish.png, Similarity to Reference 1: 0.26867571473121643, Similarity to Reference 2: 0.43849679827690125\n",
            "Image: castle.png, Similarity to Reference 1: 0.22401416301727295, Similarity to Reference 2: 0.3906632661819458\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.33730196952819824, Similarity to Reference 2: 0.46286728978157043\n",
            "Image: mountains.png, Similarity to Reference 1: 0.27878087759017944, Similarity to Reference 2: 0.5639729499816895\n",
            "Image: athens.png, Similarity to Reference 1: 0.29852479696273804, Similarity to Reference 2: 0.25177648663520813\n",
            "Image: golf.png, Similarity to Reference 1: 0.30851778388023376, Similarity to Reference 2: 0.4371100068092346\n",
            "Mean Similarity to Reference 1: 0.275538401471244\n",
            "Mean Similarity to Reference 2: 0.4227796991666158\n",
            "Weighted Multi-Style DINO Metric: 0.4227796991666158\n",
            "\n",
            "Final Results for medieval_picasso_1_0:\n",
            "Mean Similarity to Reference 1: 0.3234946003981999\n",
            "Mean Similarity to Reference 2: 0.36625740783555166\n",
            "Weighted Multi-Style DINO Metric: 0.3234946003981999\n",
            "\n",
            "Final Results for medieval_picasso_0.25_0.75:\n",
            "Mean Similarity to Reference 1: 0.3159983642399311\n",
            "Mean Similarity to Reference 2: 0.42494435235857964\n",
            "Weighted Multi-Style DINO Metric: 0.3977078553289175\n",
            "\n",
            "Final Results for medieval_picasso_0.75_0.25:\n",
            "Mean Similarity to Reference 1: 0.30206794291734695\n",
            "Mean Similarity to Reference 2: 0.3800356611609459\n",
            "Weighted Multi-Style DINO Metric: 0.3215598724782467\n",
            "\n",
            "Final Results for medieval_picasso_0.5_0.5:\n",
            "Mean Similarity to Reference 1: 0.31597575545310974\n",
            "Mean Similarity to Reference 2: 0.39798810848822963\n",
            "Weighted Multi-Style DINO Metric: 0.3569819319706697\n",
            "\n",
            "Final Results for medieval_picasso_0_1:\n",
            "Mean Similarity to Reference 1: 0.275538401471244\n",
            "Mean Similarity to Reference 2: 0.4227796991666158\n",
            "Weighted Multi-Style DINO Metric: 0.4227796991666158\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### SLERP BLENDING"
      ],
      "metadata": {
        "id": "ZhV3LHN9SPLp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### GUIDANCE 10"
      ],
      "metadata": {
        "id": "HB2Tah9wSYoK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "main_folder = \"./imgs/Multi_Reference/Weighted_Latent_Space_SLERP/medieval_picasso_guidance_10\"\n",
        "reference_image_paths = [\"./imgs/medieval-bed.jpeg\", \"./imgs/Picasso_Smoking_Water_Pipe.jpeg\"]\n",
        "results = process_main_folder(main_folder, reference_image_paths)\n",
        "\n",
        "# Print or save the results\n",
        "for subfolder, metrics in results.items():\n",
        "    print(f\"Final Results for {subfolder}:\")\n",
        "    print(f\"Mean Similarity to Reference 1: {metrics['mean_similarity_ref1']}\")\n",
        "    print(f\"Mean Similarity to Reference 2: {metrics['mean_similarity_ref2']}\")\n",
        "    print(f\"Weighted Multi-Style DINO Metric: {metrics['weighted_multi_style_dino']}\\n\")"
      ],
      "metadata": {
        "id": "83eMPEuSSTzO",
        "outputId": "70a70e21-114a-46dd-a114-89b50749ae09",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results for medieval_picasso_1_0 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.3909943401813507, Similarity to Reference 2: 0.3329729437828064\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.39169394969940186, Similarity to Reference 2: 0.3081279993057251\n",
            "Image: castle.png, Similarity to Reference 1: 0.33056825399398804, Similarity to Reference 2: 0.3134803771972656\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.3788291811943054, Similarity to Reference 2: 0.2098432332277298\n",
            "Image: mountains.png, Similarity to Reference 1: 0.3864254355430603, Similarity to Reference 2: 0.27427464723587036\n",
            "Image: golf.png, Similarity to Reference 1: 0.45199745893478394, Similarity to Reference 2: 0.4151020646095276\n",
            "Mean Similarity to Reference 1: 0.38841810325781506\n",
            "Mean Similarity to Reference 2: 0.30896687755982083\n",
            "Weighted Multi-Style DINO Metric: 0.38841810325781506\n",
            "\n",
            "Results for medieval_picasso_0.25_0.75 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.3154395818710327, Similarity to Reference 2: 0.45143604278564453\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.33677926659584045, Similarity to Reference 2: 0.4655846953392029\n",
            "Image: castle.png, Similarity to Reference 1: 0.31481099128723145, Similarity to Reference 2: 0.3951644003391266\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.4200683832168579, Similarity to Reference 2: 0.4692065715789795\n",
            "Image: mountains.png, Similarity to Reference 1: 0.42991963028907776, Similarity to Reference 2: 0.5517943501472473\n",
            "Image: golf.png, Similarity to Reference 1: 0.42601653933525085, Similarity to Reference 2: 0.4993635416030884\n",
            "Mean Similarity to Reference 1: 0.3738390654325485\n",
            "Mean Similarity to Reference 2: 0.47209160029888153\n",
            "Weighted Multi-Style DINO Metric: 0.4475284665822983\n",
            "\n",
            "Results for medieval_picasso_0.75_0.25 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.32704854011535645, Similarity to Reference 2: 0.30926743149757385\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.28201499581336975, Similarity to Reference 2: 0.3008349537849426\n",
            "Image: castle.png, Similarity to Reference 1: 0.24629315733909607, Similarity to Reference 2: 0.23433691263198853\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.36758124828338623, Similarity to Reference 2: 0.3043646216392517\n",
            "Image: mountains.png, Similarity to Reference 1: 0.36351847648620605, Similarity to Reference 2: 0.3029603958129883\n",
            "Image: golf.png, Similarity to Reference 1: 0.3193948268890381, Similarity to Reference 2: 0.3343489170074463\n",
            "Mean Similarity to Reference 1: 0.31764187415440875\n",
            "Mean Similarity to Reference 2: 0.29768553872903186\n",
            "Weighted Multi-Style DINO Metric: 0.31265279029806453\n",
            "\n",
            "Results for medieval_guidance_0.5_0.5 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.3734681010246277, Similarity to Reference 2: 0.40007832646369934\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.34984058141708374, Similarity to Reference 2: 0.42027047276496887\n",
            "Image: castle.png, Similarity to Reference 1: 0.2689531445503235, Similarity to Reference 2: 0.314014196395874\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.45183688402175903, Similarity to Reference 2: 0.3833264112472534\n",
            "Image: mountains.png, Similarity to Reference 1: 0.43242958188056946, Similarity to Reference 2: 0.46769094467163086\n",
            "Image: golf.png, Similarity to Reference 1: 0.43262237310409546, Similarity to Reference 2: 0.4814569652080536\n",
            "Mean Similarity to Reference 1: 0.3848584443330765\n",
            "Mean Similarity to Reference 2: 0.41113955279191333\n",
            "Weighted Multi-Style DINO Metric: 0.3979989985624949\n",
            "\n",
            "Results for medieval_picasso_0_1 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.2100580930709839, Similarity to Reference 2: 0.5059064626693726\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.25938135385513306, Similarity to Reference 2: 0.3899717330932617\n",
            "Image: castle.png, Similarity to Reference 1: 0.2982625663280487, Similarity to Reference 2: 0.4484243392944336\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.2646210789680481, Similarity to Reference 2: 0.4229709804058075\n",
            "Image: mountains.png, Similarity to Reference 1: 0.2824299931526184, Similarity to Reference 2: 0.5775085091590881\n",
            "Image: golf.png, Similarity to Reference 1: 0.3300178050994873, Similarity to Reference 2: 0.5539506077766418\n",
            "Mean Similarity to Reference 1: 0.2741284817457199\n",
            "Mean Similarity to Reference 2: 0.4831221053997676\n",
            "Weighted Multi-Style DINO Metric: 0.4831221053997676\n",
            "\n",
            "Final Results for medieval_picasso_1_0:\n",
            "Mean Similarity to Reference 1: 0.38841810325781506\n",
            "Mean Similarity to Reference 2: 0.30896687755982083\n",
            "Weighted Multi-Style DINO Metric: 0.38841810325781506\n",
            "\n",
            "Final Results for medieval_picasso_0.25_0.75:\n",
            "Mean Similarity to Reference 1: 0.3738390654325485\n",
            "Mean Similarity to Reference 2: 0.47209160029888153\n",
            "Weighted Multi-Style DINO Metric: 0.4475284665822983\n",
            "\n",
            "Final Results for medieval_picasso_0.75_0.25:\n",
            "Mean Similarity to Reference 1: 0.31764187415440875\n",
            "Mean Similarity to Reference 2: 0.29768553872903186\n",
            "Weighted Multi-Style DINO Metric: 0.31265279029806453\n",
            "\n",
            "Final Results for medieval_guidance_0.5_0.5:\n",
            "Mean Similarity to Reference 1: 0.3848584443330765\n",
            "Mean Similarity to Reference 2: 0.41113955279191333\n",
            "Weighted Multi-Style DINO Metric: 0.3979989985624949\n",
            "\n",
            "Final Results for medieval_picasso_0_1:\n",
            "Mean Similarity to Reference 1: 0.2741284817457199\n",
            "Mean Similarity to Reference 2: 0.4831221053997676\n",
            "Weighted Multi-Style DINO Metric: 0.4831221053997676\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### GUIDANCE 25"
      ],
      "metadata": {
        "id": "phK3kbbWSbZw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Example usage\n",
        "main_folder = \"./imgs/Multi_Reference/Weighted_Latent_Space_SLERP/medieval_picasso_guidance_25\"\n",
        "reference_image_paths = [\"./imgs/medieval-bed.jpeg\", \"./imgs/Picasso_Smoking_Water_Pipe.jpeg\"]\n",
        "results = process_main_folder(main_folder, reference_image_paths)\n",
        "\n",
        "# Print or save the results\n",
        "for subfolder, metrics in results.items():\n",
        "    print(f\"Final Results for {subfolder}:\")\n",
        "    print(f\"Mean Similarity to Reference 1: {metrics['mean_similarity_ref1']}\")\n",
        "    print(f\"Mean Similarity to Reference 2: {metrics['mean_similarity_ref2']}\")\n",
        "    print(f\"Weighted Multi-Style DINO Metric: {metrics['weighted_multi_style_dino']}\\n\")"
      ],
      "metadata": {
        "id": "6SBmex7HSWrm",
        "outputId": "a411b795-9629-4fe3-e344-c89cf936228a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results for medieval_picasso_1_0 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.44854736328125, Similarity to Reference 2: 0.39558112621307373\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.42154496908187866, Similarity to Reference 2: 0.3311164975166321\n",
            "Image: castle.png, Similarity to Reference 1: 0.44291308522224426, Similarity to Reference 2: 0.32869166135787964\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.38328251242637634, Similarity to Reference 2: 0.24222637712955475\n",
            "Image: mountains.png, Similarity to Reference 1: 0.3559674024581909, Similarity to Reference 2: 0.294542133808136\n",
            "Image: golf.png, Similarity to Reference 1: 0.44195836782455444, Similarity to Reference 2: 0.43918943405151367\n",
            "Mean Similarity to Reference 1: 0.41570228338241577\n",
            "Mean Similarity to Reference 2: 0.338557871679465\n",
            "Weighted Multi-Style DINO Metric: 0.41570228338241577\n",
            "\n",
            "Results for medieval_picasso_0.25_0.75 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.3429526686668396, Similarity to Reference 2: 0.4951786696910858\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.35377147793769836, Similarity to Reference 2: 0.4455391764640808\n",
            "Image: ref.png, Similarity to Reference 1: 0.374686598777771, Similarity to Reference 2: 0.931791365146637\n",
            "Image: castle.png, Similarity to Reference 1: 0.31481099128723145, Similarity to Reference 2: 0.3951644003391266\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.4200683832168579, Similarity to Reference 2: 0.4692065715789795\n",
            "Image: mountains.png, Similarity to Reference 1: 0.3991883099079132, Similarity to Reference 2: 0.5742683410644531\n",
            "Image: golf.png, Similarity to Reference 1: 0.42601653933525085, Similarity to Reference 2: 0.4993635416030884\n",
            "Mean Similarity to Reference 1: 0.3759278527327946\n",
            "Mean Similarity to Reference 2: 0.5443588665553502\n",
            "Weighted Multi-Style DINO Metric: 0.5022511130997113\n",
            "\n",
            "Results for medieval_picasso_0.75_0.25 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.3113120198249817, Similarity to Reference 2: 0.37299779057502747\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.30264198780059814, Similarity to Reference 2: 0.35990023612976074\n",
            "Image: castle.png, Similarity to Reference 1: 0.24629315733909607, Similarity to Reference 2: 0.23433691263198853\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.36758124828338623, Similarity to Reference 2: 0.3043646216392517\n",
            "Image: mountains.png, Similarity to Reference 1: 0.39642274379730225, Similarity to Reference 2: 0.4685223698616028\n",
            "Image: golf.png, Similarity to Reference 1: 0.3193948268890381, Similarity to Reference 2: 0.3343489170074463\n",
            "Mean Similarity to Reference 1: 0.3239409973224004\n",
            "Mean Similarity to Reference 2: 0.34574514130751294\n",
            "Weighted Multi-Style DINO Metric: 0.32939203331867856\n",
            "\n",
            "Results for medieval_guidance_0.5_0.5 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: scarecrow.png, Similarity to Reference 1: 0.39400142431259155, Similarity to Reference 2: 0.49862974882125854\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.3444002866744995, Similarity to Reference 2: 0.39859527349472046\n",
            "Image: castle.png, Similarity to Reference 1: 0.2689531445503235, Similarity to Reference 2: 0.314014196395874\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.45183688402175903, Similarity to Reference 2: 0.3833264112472534\n",
            "Image: mountains.png, Similarity to Reference 1: 0.4044628143310547, Similarity to Reference 2: 0.5249118804931641\n",
            "Image: golf.png, Similarity to Reference 1: 0.43262237310409546, Similarity to Reference 2: 0.4814569652080536\n",
            "Mean Similarity to Reference 1: 0.38271282116572064\n",
            "Mean Similarity to Reference 2: 0.4334890792767207\n",
            "Weighted Multi-Style DINO Metric: 0.40810095022122067\n",
            "\n",
            "Results for medieval_picasso_0_1 (reference_image 1: medieval-bed.jpeg, reference_image 2: Picasso_Smoking_Water_Pipe.jpeg):\n",
            "Image: lighthouse.png, Similarity to Reference 1: 0.27242758870124817, Similarity to Reference 2: 0.41516607999801636\n",
            "Image: castle.png, Similarity to Reference 1: 0.37036675214767456, Similarity to Reference 2: 0.4721944332122803\n",
            "Image: air_baloon.png, Similarity to Reference 1: 0.23470112681388855, Similarity to Reference 2: 0.4049878716468811\n",
            "Image: mountains.png, Similarity to Reference 1: 0.3584514856338501, Similarity to Reference 2: 0.5499409437179565\n",
            "Image: scare_crow.png, Similarity to Reference 1: 0.32055988907814026, Similarity to Reference 2: 0.4400254786014557\n",
            "Image: golf.png, Similarity to Reference 1: 0.317926824092865, Similarity to Reference 2: 0.5389372110366821\n",
            "Mean Similarity to Reference 1: 0.31240561107794446\n",
            "Mean Similarity to Reference 2: 0.47020866970221203\n",
            "Weighted Multi-Style DINO Metric: 0.47020866970221203\n",
            "\n",
            "Final Results for medieval_picasso_1_0:\n",
            "Mean Similarity to Reference 1: 0.41570228338241577\n",
            "Mean Similarity to Reference 2: 0.338557871679465\n",
            "Weighted Multi-Style DINO Metric: 0.41570228338241577\n",
            "\n",
            "Final Results for medieval_picasso_0.25_0.75:\n",
            "Mean Similarity to Reference 1: 0.3759278527327946\n",
            "Mean Similarity to Reference 2: 0.5443588665553502\n",
            "Weighted Multi-Style DINO Metric: 0.5022511130997113\n",
            "\n",
            "Final Results for medieval_picasso_0.75_0.25:\n",
            "Mean Similarity to Reference 1: 0.3239409973224004\n",
            "Mean Similarity to Reference 2: 0.34574514130751294\n",
            "Weighted Multi-Style DINO Metric: 0.32939203331867856\n",
            "\n",
            "Final Results for medieval_guidance_0.5_0.5:\n",
            "Mean Similarity to Reference 1: 0.38271282116572064\n",
            "Mean Similarity to Reference 2: 0.4334890792767207\n",
            "Weighted Multi-Style DINO Metric: 0.40810095022122067\n",
            "\n",
            "Final Results for medieval_picasso_0_1:\n",
            "Mean Similarity to Reference 1: 0.31240561107794446\n",
            "Mean Similarity to Reference 2: 0.47020866970221203\n",
            "Weighted Multi-Style DINO Metric: 0.47020866970221203\n",
            "\n"
          ]
        }
      ]
    }
  ],
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