StableKeypoints.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_diMq-1WMIl1",
        "outputId": "88c6fbde-8ee6-4881-995b-182087a00857"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate) (12.4.127)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.29.3\n"
          ]
        }
      ],
      "source": [
        "# Installing PyTorch with CUDA support (matching Colab's CUDA version, usually the latest supported by PyTorch)\n",
        "!pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113\n",
        "\n",
        "# Installing Diffusers and Transformers\n",
        "!pip install diffusers==0.8.0 transformers\n",
        "\n",
        "# Installing essential and commonly used libraries\n",
        "!pip install numpy pandas scipy scikit-learn matplotlib opencv-python-headless\n",
        "\n",
        "# Additional utilities that might be useful\n",
        "!pip install tqdm requests pillow\n",
        "\n",
        "!pip install \"jax[cuda12_local]==0.4.23\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html\n",
        "\n",
        "!pip install wandb\n",
        "\n",
        "!pip install accelerate\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mibKn7XLDGp7"
      },
      "outputs": [],
      "source": [
        "num_keypoints = 10\n",
        "num_optimization_steps=500  # recommended higher for more complex images\n",
        "batch_size=1 # recommended higher for more complex images\n",
        "furthest_point_num_samples=10 # ratio of num_keypoints/furthest_point_num_samples determines the spread of kpts\n",
        "\n",
        "image_dir = \"/content/uploaded_images\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Rc8Z8j6_DGp8",
        "outputId": "1b426fe1-852a-4ffa-f319-d791d0dcf78e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-46408a62-909c-4a93-99a2-3f4f1c4b9b72\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-46408a62-909c-4a93-99a2-3f4f1c4b9b72\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Saving output_image_0101.jpg to output_image_0101.jpg\n",
            "Saving output_image_0100.jpg to output_image_0100.jpg\n",
            "Saving output_image_0099.jpg to output_image_0099.jpg\n",
            "Saving output_image_0098.jpg to output_image_0098.jpg\n",
            "Saving output_image_0097.jpg to output_image_0097.jpg\n",
            "Saving output_image_0096.jpg to output_image_0096.jpg\n",
            "Saving output_image_0095.jpg to output_image_0095.jpg\n",
            "Saving output_image_0094.jpg to output_image_0094.jpg\n",
            "Saving output_image_0093.jpg to output_image_0093.jpg\n",
            "Saving output_image_0092.jpg to output_image_0092.jpg\n",
            "Saving output_image_0091.jpg to output_image_0091.jpg\n",
            "Saving output_image_0090.jpg to output_image_0090.jpg\n",
            "Saving output_image_0089.jpg to output_image_0089.jpg\n",
            "Saving output_image_0088.jpg to output_image_0088.jpg\n",
            "Saving output_image_0087.jpg to output_image_0087.jpg\n",
            "Saving output_image_0086.jpg to output_image_0086.jpg\n",
            "Saving output_image_0085.jpg to output_image_0085.jpg\n",
            "Saving output_image_0084.jpg to output_image_0084.jpg\n",
            "Saving output_image_0083.jpg to output_image_0083.jpg\n",
            "Saving output_image_0082.jpg to output_image_0082.jpg\n",
            "Saving output_image_0081.jpg to output_image_0081.jpg\n",
            "Saving output_image_0080.jpg to output_image_0080.jpg\n",
            "Saving output_image_0079.jpg to output_image_0079.jpg\n",
            "Saving output_image_0078.jpg to output_image_0078.jpg\n",
            "Saving output_image_0077.jpg to output_image_0077.jpg\n",
            "Saving output_image_0076.jpg to output_image_0076.jpg\n",
            "Saving output_image_0075.jpg to output_image_0075.jpg\n",
            "Saving output_image_0074.jpg to output_image_0074.jpg\n",
            "Saving output_image_0073.jpg to output_image_0073.jpg\n",
            "Saving output_image_0072.jpg to output_image_0072.jpg\n",
            "Saving output_image_0071.jpg to output_image_0071.jpg\n",
            "Saving output_image_0070.jpg to output_image_0070.jpg\n",
            "Saving output_image_0069.jpg to output_image_0069.jpg\n",
            "Saving output_image_0068.jpg to output_image_0068.jpg\n",
            "Saving output_image_0067.jpg to output_image_0067.jpg\n",
            "Saving output_image_0066.jpg to output_image_0066.jpg\n",
            "Saving output_image_0065.jpg to output_image_0065.jpg\n",
            "Saving output_image_0064.jpg to output_image_0064.jpg\n",
            "Saving output_image_0063.jpg to output_image_0063.jpg\n",
            "Saving output_image_0062.jpg to output_image_0062.jpg\n",
            "Saving output_image_0061.jpg to output_image_0061.jpg\n",
            "Saving output_image_0060.jpg to output_image_0060.jpg\n",
            "Saving output_image_0059.jpg to output_image_0059.jpg\n",
            "Saving output_image_0058.jpg to output_image_0058.jpg\n",
            "Saving output_image_0057.jpg to output_image_0057.jpg\n",
            "Saving output_image_0056.jpg to output_image_0056.jpg\n",
            "Saving output_image_0055.jpg to output_image_0055.jpg\n",
            "Saving output_image_0054.jpg to output_image_0054.jpg\n",
            "Saving output_image_0053.jpg to output_image_0053.jpg\n",
            "Saving output_image_0052.jpg to output_image_0052.jpg\n",
            "Saving output_image_0051.jpg to output_image_0051.jpg\n",
            "Saving output_image_0050.jpg to output_image_0050.jpg\n",
            "Saving output_image_0049.jpg to output_image_0049.jpg\n",
            "Saving output_image_0048.jpg to output_image_0048.jpg\n",
            "Saving output_image_0047.jpg to output_image_0047.jpg\n",
            "Saving output_image_0046.jpg to output_image_0046.jpg\n",
            "Saving output_image_0045.jpg to output_image_0045.jpg\n",
            "Saving output_image_0044.jpg to output_image_0044.jpg\n",
            "Saving output_image_0043.jpg to output_image_0043.jpg\n",
            "Saving output_image_0042.jpg to output_image_0042.jpg\n",
            "Saving output_image_0041.jpg to output_image_0041.jpg\n",
            "Saving output_image_0040.jpg to output_image_0040.jpg\n",
            "Saving output_image_0039.jpg to output_image_0039.jpg\n",
            "Saving output_image_0038.jpg to output_image_0038.jpg\n",
            "Saving output_image_0037.jpg to output_image_0037.jpg\n",
            "Saving output_image_0036.jpg to output_image_0036.jpg\n",
            "Saving output_image_0035.jpg to output_image_0035.jpg\n",
            "Saving output_image_0034.jpg to output_image_0034.jpg\n",
            "Saving output_image_0033.jpg to output_image_0033.jpg\n",
            "Saving output_image_0032.jpg to output_image_0032.jpg\n",
            "Saving output_image_0031.jpg to output_image_0031.jpg\n",
            "Saving output_image_0030.jpg to output_image_0030.jpg\n",
            "Saving output_image_0029.jpg to output_image_0029.jpg\n",
            "Saving output_image_0028.jpg to output_image_0028.jpg\n",
            "Saving output_image_0027.jpg to output_image_0027.jpg\n",
            "Saving output_image_0026.jpg to output_image_0026.jpg\n",
            "Saving output_image_0025.jpg to output_image_0025.jpg\n",
            "Saving output_image_0024.jpg to output_image_0024.jpg\n",
            "Saving output_image_0023.jpg to output_image_0023.jpg\n",
            "Saving output_image_0022.jpg to output_image_0022.jpg\n",
            "Saving output_image_0021.jpg to output_image_0021.jpg\n",
            "Saving output_image_0020.jpg to output_image_0020.jpg\n",
            "Saving output_image_0019.jpg to output_image_0019.jpg\n",
            "Saving output_image_0018.jpg to output_image_0018.jpg\n",
            "Saving output_image_0017.jpg to output_image_0017.jpg\n",
            "Saving output_image_0016.jpg to output_image_0016.jpg\n",
            "Saving output_image_0015.jpg to output_image_0015.jpg\n",
            "Saving output_image_0014.jpg to output_image_0014.jpg\n",
            "Saving output_image_0013.jpg to output_image_0013.jpg\n",
            "Saving output_image_0012.jpg to output_image_0012.jpg\n",
            "Saving output_image_0011.jpg to output_image_0011.jpg\n",
            "Saving output_image_0010.jpg to output_image_0010.jpg\n",
            "Saving output_image_0009.jpg to output_image_0009.jpg\n",
            "Saving output_image_0008.jpg to output_image_0008.jpg\n",
            "Saving output_image_0007.jpg to output_image_0007.jpg\n",
            "Saving output_image_0006.jpg to output_image_0006.jpg\n",
            "Saving output_image_0005.jpg to output_image_0005.jpg\n",
            "Saving output_image_0004.jpg to output_image_0004.jpg\n",
            "Saving output_image_0003.jpg to output_image_0003.jpg\n",
            "Saving output_image_0002.jpg to output_image_0002.jpg\n",
            "Saving output_image_0001.jpg to output_image_0001.jpg\n",
            "Saved file output_image_0230.jpg to uploaded_images/\n",
            "Saved file output_image_0229.jpg to uploaded_images/\n",
            "Saved file output_image_0228.jpg to uploaded_images/\n",
            "Saved file output_image_0227.jpg to uploaded_images/\n",
            "Saved file output_image_0226.jpg to uploaded_images/\n",
            "Saved file output_image_0225.jpg to uploaded_images/\n",
            "Saved file output_image_0224.jpg to uploaded_images/\n",
            "Saved file output_image_0223.jpg to uploaded_images/\n",
            "Saved file output_image_0222.jpg to uploaded_images/\n",
            "Saved file output_image_0221.jpg to uploaded_images/\n",
            "Saved file output_image_0220.jpg to uploaded_images/\n",
            "Saved file output_image_0219.jpg to uploaded_images/\n",
            "Saved file output_image_0218.jpg to uploaded_images/\n",
            "Saved file output_image_0217.jpg to uploaded_images/\n",
            "Saved file output_image_0216.jpg to uploaded_images/\n",
            "Saved file output_image_0215.jpg to uploaded_images/\n",
            "Saved file output_image_0214.jpg to uploaded_images/\n",
            "Saved file output_image_0213.jpg to uploaded_images/\n",
            "Saved file output_image_0212.jpg to uploaded_images/\n",
            "Saved file output_image_0211.jpg to uploaded_images/\n",
            "Saved file output_image_0210.jpg to uploaded_images/\n",
            "Saved file output_image_0209.jpg to uploaded_images/\n",
            "Saved file output_image_0208.jpg to uploaded_images/\n",
            "Saved file output_image_0207.jpg to uploaded_images/\n",
            "Saved file output_image_0206.jpg to uploaded_images/\n",
            "Saved file output_image_0205.jpg to uploaded_images/\n",
            "Saved file output_image_0204.jpg to uploaded_images/\n",
            "Saved file output_image_0203.jpg to uploaded_images/\n",
            "Saved file output_image_0202.jpg to uploaded_images/\n",
            "Saved file output_image_0201.jpg to uploaded_images/\n",
            "Saved file output_image_0200.jpg to uploaded_images/\n",
            "Saved file output_image_0199.jpg to uploaded_images/\n",
            "Saved file output_image_0198.jpg to uploaded_images/\n",
            "Saved file output_image_0197.jpg to uploaded_images/\n",
            "Saved file output_image_0196.jpg to uploaded_images/\n",
            "Saved file output_image_0195.jpg to uploaded_images/\n",
            "Saved file output_image_0194.jpg to uploaded_images/\n",
            "Saved file output_image_0193.jpg to uploaded_images/\n",
            "Saved file output_image_0192.jpg to uploaded_images/\n",
            "Saved file output_image_0191.jpg to uploaded_images/\n",
            "Saved file output_image_0190.jpg to uploaded_images/\n",
            "Saved file output_image_0189.jpg to uploaded_images/\n",
            "Saved file output_image_0188.jpg to uploaded_images/\n",
            "Saved file output_image_0187.jpg to uploaded_images/\n",
            "Saved file output_image_0186.jpg to uploaded_images/\n",
            "Saved file output_image_0185.jpg to uploaded_images/\n",
            "Saved file output_image_0184.jpg to uploaded_images/\n",
            "Saved file output_image_0183.jpg to uploaded_images/\n",
            "Saved file output_image_0182.jpg to uploaded_images/\n",
            "Saved file output_image_0181.jpg to uploaded_images/\n",
            "Saved file output_image_0180.jpg to uploaded_images/\n",
            "Saved file output_image_0179.jpg to uploaded_images/\n",
            "Saved file output_image_0178.jpg to uploaded_images/\n",
            "Saved file output_image_0177.jpg to uploaded_images/\n",
            "Saved file output_image_0176.jpg to uploaded_images/\n",
            "Saved file output_image_0175.jpg to uploaded_images/\n",
            "Saved file output_image_0174.jpg to uploaded_images/\n",
            "Saved file output_image_0173.jpg to uploaded_images/\n",
            "Saved file output_image_0172.jpg to uploaded_images/\n",
            "Saved file output_image_0171.jpg to uploaded_images/\n",
            "Saved file output_image_0170.jpg to uploaded_images/\n",
            "Saved file output_image_0169.jpg to uploaded_images/\n",
            "Saved file output_image_0168.jpg to uploaded_images/\n",
            "Saved file output_image_0167.jpg to uploaded_images/\n",
            "Saved file output_image_0166.jpg to uploaded_images/\n",
            "Saved file output_image_0165.jpg to uploaded_images/\n",
            "Saved file output_image_0164.jpg to uploaded_images/\n",
            "Saved file output_image_0163.jpg to uploaded_images/\n",
            "Saved file output_image_0162.jpg to uploaded_images/\n",
            "Saved file output_image_0161.jpg to uploaded_images/\n",
            "Saved file output_image_0160.jpg to uploaded_images/\n",
            "Saved file output_image_0159.jpg to uploaded_images/\n",
            "Saved file output_image_0158.jpg to uploaded_images/\n",
            "Saved file output_image_0157.jpg to uploaded_images/\n",
            "Saved file output_image_0156.jpg to uploaded_images/\n",
            "Saved file output_image_0155.jpg to uploaded_images/\n",
            "Saved file output_image_0154.jpg to uploaded_images/\n",
            "Saved file output_image_0153.jpg to uploaded_images/\n",
            "Saved file output_image_0152.jpg to uploaded_images/\n",
            "Saved file output_image_0151.jpg to uploaded_images/\n",
            "Saved file output_image_0150.jpg to uploaded_images/\n",
            "Saved file output_image_0149.jpg to uploaded_images/\n",
            "Saved file output_image_0148.jpg to uploaded_images/\n",
            "Saved file output_image_0147.jpg to uploaded_images/\n",
            "Saved file output_image_0146.jpg to uploaded_images/\n",
            "Saved file output_image_0145.jpg to uploaded_images/\n",
            "Saved file output_image_0144.jpg to uploaded_images/\n",
            "Saved file output_image_0143.jpg to uploaded_images/\n",
            "Saved file output_image_0142.jpg to uploaded_images/\n",
            "Saved file output_image_0141.jpg to uploaded_images/\n",
            "Saved file output_image_0140.jpg to uploaded_images/\n",
            "Saved file output_image_0139.jpg to uploaded_images/\n",
            "Saved file output_image_0138.jpg to uploaded_images/\n",
            "Saved file output_image_0137.jpg to uploaded_images/\n",
            "Saved file output_image_0136.jpg to uploaded_images/\n",
            "Saved file output_image_0135.jpg to uploaded_images/\n",
            "Saved file output_image_0134.jpg to uploaded_images/\n",
            "Saved file output_image_0133.jpg to uploaded_images/\n",
            "Saved file output_image_0132.jpg to uploaded_images/\n",
            "Saved file output_image_0131.jpg to uploaded_images/\n",
            "Saved file output_image_0130.jpg to uploaded_images/\n",
            "Saved file output_image_0129.jpg to uploaded_images/\n",
            "Saved file output_image_0128.jpg to uploaded_images/\n",
            "Saved file output_image_0127.jpg to uploaded_images/\n",
            "Saved file output_image_0126.jpg to uploaded_images/\n",
            "Saved file output_image_0125.jpg to uploaded_images/\n",
            "Saved file output_image_0124.jpg to uploaded_images/\n",
            "Saved file output_image_0123.jpg to uploaded_images/\n",
            "Saved file output_image_0122.jpg to uploaded_images/\n",
            "Saved file output_image_0121.jpg to uploaded_images/\n",
            "Saved file output_image_0120.jpg to uploaded_images/\n",
            "Saved file output_image_0119.jpg to uploaded_images/\n",
            "Saved file output_image_0118.jpg to uploaded_images/\n",
            "Saved file output_image_0117.jpg to uploaded_images/\n",
            "Saved file output_image_0116.jpg to uploaded_images/\n",
            "Saved file output_image_0115.jpg to uploaded_images/\n",
            "Saved file output_image_0114.jpg to uploaded_images/\n",
            "Saved file output_image_0113.jpg to uploaded_images/\n",
            "Saved file output_image_0112.jpg to uploaded_images/\n",
            "Saved file output_image_0111.jpg to uploaded_images/\n",
            "Saved file output_image_0110.jpg to uploaded_images/\n",
            "Saved file output_image_0109.jpg to uploaded_images/\n",
            "Saved file output_image_0108.jpg to uploaded_images/\n",
            "Saved file output_image_0107.jpg to uploaded_images/\n",
            "Saved file output_image_0106.jpg to uploaded_images/\n",
            "Saved file output_image_0105.jpg to uploaded_images/\n",
            "Saved file output_image_0104.jpg to uploaded_images/\n",
            "Saved file output_image_0103.jpg to uploaded_images/\n",
            "Saved file output_image_0102.jpg to uploaded_images/\n",
            "Saved file output_image_0101.jpg to uploaded_images/\n",
            "Saved file output_image_0100.jpg to uploaded_images/\n",
            "Saved file output_image_0099.jpg to uploaded_images/\n",
            "Saved file output_image_0098.jpg to uploaded_images/\n",
            "Saved file output_image_0097.jpg to uploaded_images/\n",
            "Saved file output_image_0096.jpg to uploaded_images/\n",
            "Saved file output_image_0095.jpg to uploaded_images/\n",
            "Saved file output_image_0094.jpg to uploaded_images/\n",
            "Saved file output_image_0093.jpg to uploaded_images/\n",
            "Saved file output_image_0092.jpg to uploaded_images/\n",
            "Saved file output_image_0091.jpg to uploaded_images/\n",
            "Saved file output_image_0090.jpg to uploaded_images/\n",
            "Saved file output_image_0089.jpg to uploaded_images/\n",
            "Saved file output_image_0088.jpg to uploaded_images/\n",
            "Saved file output_image_0087.jpg to uploaded_images/\n",
            "Saved file output_image_0086.jpg to uploaded_images/\n",
            "Saved file output_image_0085.jpg to uploaded_images/\n",
            "Saved file output_image_0084.jpg to uploaded_images/\n",
            "Saved file output_image_0083.jpg to uploaded_images/\n",
            "Saved file output_image_0082.jpg to uploaded_images/\n",
            "Saved file output_image_0081.jpg to uploaded_images/\n",
            "Saved file output_image_0080.jpg to uploaded_images/\n",
            "Saved file output_image_0079.jpg to uploaded_images/\n",
            "Saved file output_image_0078.jpg to uploaded_images/\n",
            "Saved file output_image_0077.jpg to uploaded_images/\n",
            "Saved file output_image_0076.jpg to uploaded_images/\n",
            "Saved file output_image_0075.jpg to uploaded_images/\n",
            "Saved file output_image_0074.jpg to uploaded_images/\n",
            "Saved file output_image_0073.jpg to uploaded_images/\n",
            "Saved file output_image_0072.jpg to uploaded_images/\n",
            "Saved file output_image_0071.jpg to uploaded_images/\n",
            "Saved file output_image_0070.jpg to uploaded_images/\n",
            "Saved file output_image_0069.jpg to uploaded_images/\n",
            "Saved file output_image_0068.jpg to uploaded_images/\n",
            "Saved file output_image_0067.jpg to uploaded_images/\n",
            "Saved file output_image_0066.jpg to uploaded_images/\n",
            "Saved file output_image_0065.jpg to uploaded_images/\n",
            "Saved file output_image_0064.jpg to uploaded_images/\n",
            "Saved file output_image_0063.jpg to uploaded_images/\n",
            "Saved file output_image_0062.jpg to uploaded_images/\n",
            "Saved file output_image_0061.jpg to uploaded_images/\n",
            "Saved file output_image_0060.jpg to uploaded_images/\n",
            "Saved file output_image_0059.jpg to uploaded_images/\n",
            "Saved file output_image_0058.jpg to uploaded_images/\n",
            "Saved file output_image_0057.jpg to uploaded_images/\n",
            "Saved file output_image_0056.jpg to uploaded_images/\n",
            "Saved file output_image_0055.jpg to uploaded_images/\n",
            "Saved file output_image_0054.jpg to uploaded_images/\n",
            "Saved file output_image_0053.jpg to uploaded_images/\n",
            "Saved file output_image_0052.jpg to uploaded_images/\n",
            "Saved file output_image_0051.jpg to uploaded_images/\n",
            "Saved file output_image_0050.jpg to uploaded_images/\n",
            "Saved file output_image_0049.jpg to uploaded_images/\n",
            "Saved file output_image_0048.jpg to uploaded_images/\n",
            "Saved file output_image_0047.jpg to uploaded_images/\n",
            "Saved file output_image_0046.jpg to uploaded_images/\n",
            "Saved file output_image_0045.jpg to uploaded_images/\n",
            "Saved file output_image_0044.jpg to uploaded_images/\n",
            "Saved file output_image_0043.jpg to uploaded_images/\n",
            "Saved file output_image_0042.jpg to uploaded_images/\n",
            "Saved file output_image_0041.jpg to uploaded_images/\n",
            "Saved file output_image_0040.jpg to uploaded_images/\n",
            "Saved file output_image_0039.jpg to uploaded_images/\n",
            "Saved file output_image_0038.jpg to uploaded_images/\n",
            "Saved file output_image_0037.jpg to uploaded_images/\n",
            "Saved file output_image_0036.jpg to uploaded_images/\n",
            "Saved file output_image_0035.jpg to uploaded_images/\n",
            "Saved file output_image_0034.jpg to uploaded_images/\n",
            "Saved file output_image_0033.jpg to uploaded_images/\n",
            "Saved file output_image_0032.jpg to uploaded_images/\n",
            "Saved file output_image_0031.jpg to uploaded_images/\n",
            "Saved file output_image_0030.jpg to uploaded_images/\n",
            "Saved file output_image_0029.jpg to uploaded_images/\n",
            "Saved file output_image_0028.jpg to uploaded_images/\n",
            "Saved file output_image_0027.jpg to uploaded_images/\n",
            "Saved file output_image_0026.jpg to uploaded_images/\n",
            "Saved file output_image_0025.jpg to uploaded_images/\n",
            "Saved file output_image_0024.jpg to uploaded_images/\n",
            "Saved file output_image_0023.jpg to uploaded_images/\n",
            "Saved file output_image_0022.jpg to uploaded_images/\n",
            "Saved file output_image_0021.jpg to uploaded_images/\n",
            "Saved file output_image_0020.jpg to uploaded_images/\n",
            "Saved file output_image_0019.jpg to uploaded_images/\n",
            "Saved file output_image_0018.jpg to uploaded_images/\n",
            "Saved file output_image_0017.jpg to uploaded_images/\n",
            "Saved file output_image_0016.jpg to uploaded_images/\n",
            "Saved file output_image_0015.jpg to uploaded_images/\n",
            "Saved file output_image_0014.jpg to uploaded_images/\n",
            "Saved file output_image_0013.jpg to uploaded_images/\n",
            "Saved file output_image_0012.jpg to uploaded_images/\n",
            "Saved file output_image_0011.jpg to uploaded_images/\n",
            "Saved file output_image_0010.jpg to uploaded_images/\n",
            "Saved file output_image_0009.jpg to uploaded_images/\n",
            "Saved file output_image_0008.jpg to uploaded_images/\n",
            "Saved file output_image_0007.jpg to uploaded_images/\n",
            "Saved file output_image_0006.jpg to uploaded_images/\n",
            "Saved file output_image_0005.jpg to uploaded_images/\n",
            "Saved file output_image_0004.jpg to uploaded_images/\n",
            "Saved file output_image_0003.jpg to uploaded_images/\n",
            "Saved file output_image_0002.jpg to uploaded_images/\n",
            "Saved file output_image_0001.jpg to uploaded_images/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "import os\n",
        "\n",
        "# Create a directory if it doesn't exist\n",
        "save_dir = 'uploaded_images'\n",
        "if not os.path.exists(save_dir):\n",
        "    os.makedirs(save_dir)\n",
        "\n",
        "# Upload files\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Save uploaded files\n",
        "for filename in uploaded.keys():\n",
        "    with open(os.path.join(save_dir, filename), 'wb') as f:\n",
        "        f.write(uploaded[filename])\n",
        "    print(f'Saved file {filename} to {save_dir}/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xobzNzxMKS-",
        "outputId": "22550248-a099-49e4-ae59-9561dd38158f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:jax._src.xla_bridge:CUDA backend failed to initialize: Found CUDA version 12010, but JAX was built against version 12020, which is newer. The copy of CUDA that is installed must be at least as new as the version against which JAX was built. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import abc\n",
        "import torch.nn.functional as F\n",
        "from diffusers import StableDiffusionPipeline, DDIMScheduler\n",
        "\n",
        "\n",
        "\n",
        "class AttentionControl(abc.ABC):\n",
        "    def step_callback(self, x_t):\n",
        "        return x_t\n",
        "\n",
        "    def between_steps(self):\n",
        "        return\n",
        "\n",
        "    @property\n",
        "    def num_uncond_att_layers(self):\n",
        "        return 0\n",
        "\n",
        "    @abc.abstractmethod\n",
        "    def forward(self, dict, is_cross: bool, place_in_unet: str):\n",
        "        raise NotImplementedError\n",
        "\n",
        "    def __call__(self, dict, is_cross: bool, place_in_unet: str):\n",
        "\n",
        "        dict = self.forward(dict, is_cross, place_in_unet)\n",
        "\n",
        "        return dict['attn']\n",
        "\n",
        "    def reset(self):\n",
        "        self.cur_step = 0\n",
        "        self.cur_att_layer = 0\n",
        "\n",
        "    def __init__(self):\n",
        "        self.cur_step = 0\n",
        "        self.num_att_layers = -1\n",
        "        self.cur_att_layer = 0\n",
        "\n",
        "class AttentionStore(AttentionControl):\n",
        "    @staticmethod\n",
        "    def get_empty_store():\n",
        "        return {\n",
        "            \"attn\": [],\n",
        "        }\n",
        "\n",
        "    def forward(self, dict, is_cross: bool, place_in_unet: str):\n",
        "        key = f\"{place_in_unet}_{'cross' if is_cross else 'self'}\"\n",
        "        # if attn.shape[1] <= 32**2:  # avoid memory overhead\n",
        "        self.step_store[\"attn\"].append(dict['attn'])\n",
        "\n",
        "        return dict\n",
        "\n",
        "    def reset(self):\n",
        "        super(AttentionStore, self).reset()\n",
        "        self.step_store = self.get_empty_store()\n",
        "\n",
        "    def __init__(self):\n",
        "        super(AttentionStore, self).__init__()\n",
        "        self.step_store = self.get_empty_store()\n",
        "\n",
        "def register_attention_control(model, controller, feature_upsample_res=256):\n",
        "    def ca_forward(self, place_in_unet):\n",
        "        to_out = self.to_out\n",
        "        if type(to_out) is torch.nn.modules.container.ModuleList:\n",
        "            to_out = self.to_out[0]\n",
        "        else:\n",
        "            to_out = self.to_out\n",
        "\n",
        "        def forward(x, context=None, mask=None):\n",
        "            batch_size, sequence_length, dim = x.shape\n",
        "            h = self.heads\n",
        "            q = self.to_q(x)\n",
        "            is_cross = context is not None\n",
        "\n",
        "            context = context if is_cross else x\n",
        "            k = self.to_k(context)\n",
        "            v = self.to_v(context)\n",
        "            q = self.reshape_heads_to_batch_dim(q)\n",
        "            k = self.reshape_heads_to_batch_dim(k)\n",
        "            v = self.reshape_heads_to_batch_dim(v)\n",
        "\n",
        "            sim = torch.einsum(\"b i d, b j d -> b i j\", q, k) * self.scale\n",
        "            # sim = torch.matmul(q, k.permute(0, 2, 1)) * self.scale\n",
        "\n",
        "            if mask is not None:\n",
        "                mask = mask.reshape(batch_size, -1)\n",
        "                max_neg_value = -torch.finfo(sim.dtype).max\n",
        "                mask = mask[:, None, :].repeat(h, 1, 1)\n",
        "                sim = sim.masked_fill(~mask, max_neg_value)\n",
        "\n",
        "            # attention, what we cannot get enough of\n",
        "            attn = torch.nn.Softmax(dim=-1)(sim)\n",
        "            attn = attn.clone()\n",
        "\n",
        "            out = torch.matmul(attn, v)\n",
        "\n",
        "            if (\n",
        "                is_cross\n",
        "                and sequence_length <= 32**2\n",
        "                and len(controller.step_store[\"attn\"]) < 4\n",
        "            ):\n",
        "                x_reshaped = x.reshape(\n",
        "                    batch_size,\n",
        "                    int(sequence_length**0.5),\n",
        "                    int(sequence_length**0.5),\n",
        "                    dim,\n",
        "                ).permute(0, 3, 1, 2)\n",
        "                # upsample to feature_upsample_res**2\n",
        "                x_reshaped = (\n",
        "                    F.interpolate(\n",
        "                        x_reshaped,\n",
        "                        size=(feature_upsample_res, feature_upsample_res),\n",
        "                        mode=\"bicubic\",\n",
        "                        align_corners=False,\n",
        "                    )\n",
        "                    .permute(0, 2, 3, 1)\n",
        "                    .reshape(batch_size, -1, dim)\n",
        "                )\n",
        "\n",
        "                q = self.to_q(x_reshaped)\n",
        "                q = self.reshape_heads_to_batch_dim(q)\n",
        "\n",
        "                sim = torch.einsum(\"b i d, b j d -> b i j\", q, k) * self.scale\n",
        "                attn = torch.nn.Softmax(dim=-1)(sim)\n",
        "                attn = attn.clone()\n",
        "\n",
        "                attn = controller({\"attn\": attn}, is_cross, place_in_unet)\n",
        "\n",
        "            out = self.reshape_batch_dim_to_heads(out)\n",
        "            return to_out(out)\n",
        "\n",
        "        return forward\n",
        "\n",
        "    class DummyController:\n",
        "        def __call__(self, *args):\n",
        "            return args[0]\n",
        "\n",
        "        def __init__(self):\n",
        "            self.num_att_layers = 0\n",
        "\n",
        "    if controller is None:\n",
        "        controller = DummyController()\n",
        "\n",
        "    def register_recr(net_, count, place_in_unet):\n",
        "        if net_.__class__.__name__ == \"CrossAttention\":\n",
        "            net_.forward = ca_forward(net_, place_in_unet)\n",
        "            return count + 1\n",
        "        elif hasattr(net_, \"children\"):\n",
        "            for net__ in net_.children():\n",
        "                count = register_recr(net__, count, place_in_unet)\n",
        "        return count\n",
        "\n",
        "    cross_att_count = 0\n",
        "    sub_nets = model.named_children()\n",
        "    for net in sub_nets:\n",
        "        if \"up\" in net[0]:\n",
        "            cross_att_count += register_recr(net[1], 0, \"up\")\n",
        "\n",
        "    controller.num_att_layers = cross_att_count\n",
        "\n",
        "    # create assertion with message\n",
        "    assert cross_att_count != 0, \"No cross attention layers found in the model. Please check to make sure you're using diffusers==0.8.0.\"\n",
        "\n",
        "def load_ldm(device, type=\"CompVis/stable-diffusion-v1-4\", feature_upsample_res=256):\n",
        "    scheduler = DDIMScheduler(\n",
        "        beta_start=0.00085,\n",
        "        beta_end=0.012,\n",
        "        beta_schedule=\"scaled_linear\",\n",
        "        clip_sample=False,\n",
        "        set_alpha_to_one=False,\n",
        "    )\n",
        "\n",
        "    MY_TOKEN = \"\"\n",
        "    NUM_DDIM_STEPS = 50\n",
        "    scheduler.set_timesteps(NUM_DDIM_STEPS)\n",
        "\n",
        "\n",
        "    ldm = StableDiffusionPipeline.from_pretrained(\n",
        "        type, use_auth_token=MY_TOKEN, scheduler=scheduler\n",
        "    ).to(device)\n",
        "\n",
        "    if device != \"cpu\":\n",
        "        ldm.unet = nn.DataParallel(ldm.unet)\n",
        "        ldm.vae = nn.DataParallel(ldm.vae)\n",
        "\n",
        "        controllers = {}\n",
        "        for device_id in ldm.unet.device_ids:\n",
        "            device = torch.device(\"cuda\", device_id)\n",
        "            controller = AttentionStore()\n",
        "            controllers[device] = controller\n",
        "    else:\n",
        "        controllers = {}\n",
        "        _device = torch.device(\"cpu\")\n",
        "        controller = AttentionStore()\n",
        "        controllers[_device] = controller\n",
        "\n",
        "        # patched_devices = set()\n",
        "\n",
        "    def hook_fn(module, input):\n",
        "        _device = input[0].device\n",
        "        # if device not in patched_devices:\n",
        "        register_attention_control(module, controllers[_device], feature_upsample_res=feature_upsample_res)\n",
        "        # patched_devices.add(device)\n",
        "\n",
        "    if device != \"cpu\":\n",
        "        ldm.unet.module.register_forward_pre_hook(hook_fn)\n",
        "    else:\n",
        "        ldm.unet.register_forward_pre_hook(hook_fn)\n",
        "\n",
        "    num_gpus = torch.cuda.device_count()\n",
        "\n",
        "    for param in ldm.vae.parameters():\n",
        "        param.requires_grad = False\n",
        "    for param in ldm.text_encoder.parameters():\n",
        "        param.requires_grad = False\n",
        "    for param in ldm.unet.parameters():\n",
        "        param.requires_grad = False\n",
        "\n",
        "    return ldm, controllers, num_gpus\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 977,
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              "diffusion_pytorch_model.safetensors:   0%|          | 0.00/335M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0951b1b15c22486e81ac020721e0fbec"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "ldm, controllers, num_gpus = load_ldm(\"cuda:0\", \"runwayml/stable-diffusion-v1-5\", feature_upsample_res=128)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h6Im6OvUZcT-"
      },
      "outputs": [],
      "source": [
        "from PIL import Image as PILImage\n",
        "import os\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, data_root, image_size):\n",
        "        super().__init__()\n",
        "        self.data_root = data_root\n",
        "        self.image_files = [f for f in os.listdir(data_root) if os.path.isfile(os.path.join(data_root, f))]\n",
        "        # sort by name\n",
        "        self.image_files.sort()\n",
        "        self.transform = transforms.Compose([\n",
        "            # transforms.Lambda(lambda img: img.crop((280, 0, img.width - 280, img.height))),\n",
        "            transforms.Resize((image_size, image_size)),\n",
        "            transforms.ToTensor()\n",
        "        ])\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_path = os.path.join(self.data_root, self.image_files[idx])\n",
        "        img = PILImage.open(img_path).convert('RGB')\n",
        "        img = self.transform(img)\n",
        "        sample = {'img': img, 'kpts': torch.zeros(15, 2), 'visibility': torch.zeros(15)}\n",
        "        return sample\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.image_files)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HMKs6pSbbM6U"
      },
      "outputs": [],
      "source": [
        "import math\n",
        "\n",
        "\n",
        "class RandomAffineWithInverse:\n",
        "    def __init__(\n",
        "        self,\n",
        "        degrees=0,\n",
        "        scale=(1.0, 1.0),\n",
        "        translate=(0.0, 0.0),\n",
        "    ):\n",
        "        self.degrees = degrees\n",
        "        self.scale = scale\n",
        "        self.translate = translate\n",
        "\n",
        "        # Initialize self.last_params to 0s\n",
        "        self.last_params = {\n",
        "            \"theta\": torch.eye(2, 3).unsqueeze(0),\n",
        "        }\n",
        "\n",
        "    def create_affine_matrix(self, angle, scale, translations_percent):\n",
        "        angle_rad = math.radians(angle)\n",
        "\n",
        "        # Create affine matrix\n",
        "        theta = torch.tensor(\n",
        "            [\n",
        "                [math.cos(angle_rad), math.sin(angle_rad), translations_percent[0]],\n",
        "                [-math.sin(angle_rad), math.cos(angle_rad), translations_percent[1]],\n",
        "            ],\n",
        "            dtype=torch.float,\n",
        "        )\n",
        "\n",
        "        theta[:, :2] = theta[:, :2] * scale\n",
        "        theta = theta.unsqueeze(0)  # Add batch dimension\n",
        "        return theta\n",
        "\n",
        "    def __call__(self, img_tensor, theta=None):\n",
        "\n",
        "        if theta is None:\n",
        "            theta = []\n",
        "            for i in range(img_tensor.shape[0]):\n",
        "                # Calculate random parameters\n",
        "                angle = torch.rand(1).item() * (2 * self.degrees) - self.degrees\n",
        "                scale_factor = torch.rand(1).item() * (self.scale[1] - self.scale[0]) + self.scale[0]\n",
        "                translations_percent = (\n",
        "                    torch.rand(1).item() * (2 * self.translate[0]) - self.translate[0],\n",
        "                    torch.rand(1).item() * (2 * self.translate[1]) - self.translate[1],\n",
        "                    # 1.0,\n",
        "                    # 1.0,\n",
        "                )\n",
        "\n",
        "                # Create the affine matrix\n",
        "                theta.append(self.create_affine_matrix(\n",
        "                    angle, scale_factor, translations_percent\n",
        "                ))\n",
        "            theta = torch.cat(theta, dim=0).to(img_tensor.device)\n",
        "\n",
        "        # Store them for inverse transformation\n",
        "        self.last_params = {\n",
        "            \"theta\": theta,\n",
        "        }\n",
        "\n",
        "        # Apply transformation\n",
        "        grid = F.affine_grid(theta, img_tensor.size(), align_corners=False).to(\n",
        "            img_tensor.device\n",
        "        )\n",
        "        transformed_img = F.grid_sample(img_tensor, grid, align_corners=False)\n",
        "\n",
        "        return transformed_img\n",
        "\n",
        "    def inverse(self, img_tensor):\n",
        "\n",
        "        # Retrieve stored parameters\n",
        "        theta = self.last_params[\"theta\"]\n",
        "\n",
        "        # Augment the affine matrix to make it 3x3\n",
        "        theta_augmented = torch.cat(\n",
        "            [theta, torch.Tensor([[0, 0, 1]]).expand(theta.shape[0], -1, -1)], dim=1\n",
        "        )\n",
        "\n",
        "        # Compute the inverse of the affine matrix\n",
        "        theta_inv_augmented = torch.inverse(theta_augmented)\n",
        "        theta_inv = theta_inv_augmented[:, :2, :]  # Take the 2x3 part back\n",
        "\n",
        "        # Apply inverse transformation\n",
        "        grid_inv = F.affine_grid(theta_inv, img_tensor.size(), align_corners=False).to(\n",
        "            img_tensor.device\n",
        "        )\n",
        "        untransformed_img = F.grid_sample(img_tensor, grid_inv, align_corners=False)\n",
        "\n",
        "        return untransformed_img\n",
        "\n",
        "\n",
        "\n",
        "def return_theta(scale, pixel_loc, rotation_angle_degrees=0):\n",
        "    \"\"\"\n",
        "    Pixel_loc between 0 and 1\n",
        "    Rotation_angle_degrees between 0 and 360\n",
        "    \"\"\"\n",
        "\n",
        "    rescaled_loc = pixel_loc * 2 - 1\n",
        "\n",
        "    rotation_angle_radians = math.radians(rotation_angle_degrees)\n",
        "    cos_theta = math.cos(rotation_angle_radians)\n",
        "    sin_theta = math.sin(rotation_angle_radians)\n",
        "\n",
        "    theta = torch.tensor(\n",
        "        [\n",
        "            [scale * cos_theta, -scale * sin_theta, rescaled_loc[1]],\n",
        "            [scale * sin_theta, scale * cos_theta, rescaled_loc[0]],\n",
        "        ]\n",
        "    )\n",
        "    theta = theta.unsqueeze(0)\n",
        "    return theta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rh61tFGObYPr"
      },
      "outputs": [],
      "source": [
        "def init_random_noise(device, num_words=77):\n",
        "    return torch.randn(1, num_words, 768).to(device)\n",
        "\n",
        "def image2latent(model, image, device):\n",
        "    with torch.no_grad():\n",
        "        if type(image) is PILImage:\n",
        "            image = np.array(image)\n",
        "        if type(image) is torch.Tensor and image.dim() == 4:\n",
        "            latents = image\n",
        "        else:\n",
        "            # print the max and min values of the image\n",
        "            image = torch.from_numpy(image).float() * 2 - 1\n",
        "            image = image.permute(0, 3, 1, 2).to(device)\n",
        "            if device != \"cpu\":\n",
        "                latents = model.vae.module.encode(image)[\"latent_dist\"].mean\n",
        "            else:\n",
        "                latents = model.vae.encode(image)[\"latent_dist\"].mean\n",
        "            latents = latents * 0.18215\n",
        "    return latents\n",
        "\n",
        "def find_pred_noise(\n",
        "    ldm,\n",
        "    image,\n",
        "    context,\n",
        "    noise_level=-1,\n",
        "    device=\"cuda\",\n",
        "):\n",
        "    # if image is a torch.tensor, convert to numpy\n",
        "    if type(image) == torch.Tensor:\n",
        "        image = image.permute(0, 2, 3, 1).detach().cpu().numpy()\n",
        "\n",
        "    with torch.no_grad():\n",
        "        latent = image2latent(ldm, image, device)\n",
        "\n",
        "    noise = torch.randn_like(latent)\n",
        "\n",
        "    noisy_image = ldm.scheduler.add_noise(\n",
        "        latent, noise, ldm.scheduler.timesteps[noise_level]\n",
        "    )\n",
        "\n",
        "    # import ipdb; ipdb.set_trace()\n",
        "\n",
        "    pred_noise = ldm.unet(noisy_image,\n",
        "                          ldm.scheduler.timesteps[noise_level].repeat(noisy_image.shape[0]),\n",
        "                          context.repeat(noisy_image.shape[0], 1, 1))[\"sample\"]\n",
        "\n",
        "    return noise, pred_noise\n",
        "\n",
        "def collect_maps(\n",
        "    controller,\n",
        "    from_where=[\"up_cross\"],\n",
        "    upsample_res=512,\n",
        "    layers=[0, 1, 2, 3],\n",
        "    indices=None,\n",
        "):\n",
        "    \"\"\"\n",
        "    returns the bilinearly upsampled attention map of size upsample_res x upsample_res for the first word in the prompt\n",
        "    \"\"\"\n",
        "\n",
        "    attention_maps = controller.step_store['attn']\n",
        "\n",
        "    attention_maps_list = []\n",
        "\n",
        "    layer_overall = -1\n",
        "\n",
        "    for layer in range(len(attention_maps)):\n",
        "        layer_overall += 1\n",
        "\n",
        "        if layer_overall not in layers:\n",
        "            continue\n",
        "\n",
        "        data = attention_maps[layer]\n",
        "\n",
        "        data = data.reshape(\n",
        "            data.shape[0], int(data.shape[1] ** 0.5), int(data.shape[1] ** 0.5), data.shape[2]\n",
        "        )\n",
        "\n",
        "        # import ipdb; ipdb.set_trace()\n",
        "\n",
        "        if indices is not None:\n",
        "            data = data[:, :, :, indices]\n",
        "\n",
        "        data = data.permute(0, 3, 1, 2)\n",
        "\n",
        "        if upsample_res != -1 and data.shape[1] ** 0.5 != upsample_res:\n",
        "            # bilinearly upsample the image to attn_sizexattn_size\n",
        "            data = F.interpolate(\n",
        "                data,\n",
        "                size=(upsample_res, upsample_res),\n",
        "                mode=\"bilinear\",\n",
        "                align_corners=False,\n",
        "            )\n",
        "\n",
        "        attention_maps_list.append(data)\n",
        "\n",
        "\n",
        "    attention_maps_list = torch.stack(attention_maps_list, dim=0).mean(dim=(0, 1))\n",
        "\n",
        "    controller.reset()\n",
        "\n",
        "    return attention_maps_list\n",
        "\n",
        "def run_and_find_attn(\n",
        "    ldm,\n",
        "    image,\n",
        "    context,\n",
        "    noise_level=-1,\n",
        "    device=\"cuda\",\n",
        "    from_where=[\"down_cross\", \"mid_cross\", \"up_cross\"],\n",
        "    layers=[0, 1, 2, 3, 4, 5],\n",
        "    upsample_res=32,\n",
        "    indices=None,\n",
        "    controllers=None,\n",
        "):\n",
        "    _, _ = find_pred_noise(\n",
        "        ldm,\n",
        "        image,\n",
        "        context,\n",
        "        noise_level=noise_level,\n",
        "        device=device,\n",
        "    )\n",
        "\n",
        "    attention_maps=[]\n",
        "\n",
        "    for controller in controllers:\n",
        "\n",
        "        _attention_maps = collect_maps(\n",
        "            controllers[controller],\n",
        "            from_where=from_where,\n",
        "            upsample_res=upsample_res,\n",
        "            layers=layers,\n",
        "            indices=indices,\n",
        "        )\n",
        "\n",
        "        attention_maps.append(_attention_maps)\n",
        "\n",
        "        controllers[controller].reset()\n",
        "\n",
        "\n",
        "\n",
        "    return attention_maps\n",
        "\n",
        "def gaussian_circle(pos, size=64, sigma=16, device=\"cuda\"):\n",
        "    \"\"\"Create a batch of 2D Gaussian circles with a given size, standard deviation, and center coordinates.\n",
        "\n",
        "    pos is in between 0 and 1 and has shape [batch_size, 2]\n",
        "\n",
        "    \"\"\"\n",
        "    batch_size = pos.shape[0]\n",
        "    _pos = pos * size  # Shape [batch_size, 2]\n",
        "    _pos = _pos.unsqueeze(1).unsqueeze(1)  # Shape [batch_size, 1, 1, 2]\n",
        "\n",
        "    grid = torch.meshgrid(torch.arange(size).to(device), torch.arange(size).to(device))\n",
        "    grid = torch.stack(grid, dim=-1) + 0.5  # Shape [size, size, 2]\n",
        "    grid = grid.unsqueeze(0)  # Shape [1, size, size, 2]\n",
        "\n",
        "    dist_sq = (grid[..., 1] - _pos[..., 1]) ** 2 + (\n",
        "        grid[..., 0] - _pos[..., 0]\n",
        "    ) ** 2  # Shape [batch_size, size, size]\n",
        "    dist_sq = -1 * dist_sq / (2.0 * sigma**2.0)\n",
        "    gaussian = torch.exp(dist_sq)  # Shape [batch_size, size, size]\n",
        "\n",
        "    return gaussian\n",
        "\n",
        "def gaussian_circles(pos, size=64, sigma=16, device=\"cuda\"):\n",
        "    \"\"\"In the case of multiple points, pos has shape [batch_size, num_points, 2]\n",
        "    \"\"\"\n",
        "\n",
        "    circles = []\n",
        "\n",
        "    for i in range(pos.shape[0]):\n",
        "        _circles = gaussian_circle(\n",
        "            pos[i], size=size, sigma=sigma, device=device\n",
        "        )  # Assuming H and W are the same\n",
        "\n",
        "        circles.append(_circles)\n",
        "\n",
        "    circles = torch.stack(circles)\n",
        "    circles = torch.mean(circles, dim=0)\n",
        "\n",
        "    return circles\n",
        "\n",
        "def find_max_pixel(map):\n",
        "    \"\"\"\n",
        "    finds the pixel of the map with the highest value\n",
        "    map shape [batch_size, h, w]\n",
        "\n",
        "    output shape [batch_size, 2]\n",
        "    \"\"\"\n",
        "\n",
        "    batch_size, h, w = map.shape\n",
        "\n",
        "    map_reshaped = map.view(batch_size, -1)\n",
        "\n",
        "    max_indices = torch.argmax(map_reshaped, dim=-1)\n",
        "\n",
        "    max_indices = max_indices.view(batch_size, 1)\n",
        "\n",
        "    max_indices = torch.cat([max_indices // w, max_indices % w], dim=-1)\n",
        "\n",
        "    # offset by a half a pixel to get the center of the pixel\n",
        "    max_indices = max_indices + 0.5\n",
        "\n",
        "    return max_indices\n",
        "\n",
        "def mask_radius(map, max_coords, radius):\n",
        "    \"\"\"\n",
        "    Masks all values within a given radius of the max_coords in the map.\n",
        "\n",
        "    Args:\n",
        "    map (Tensor): The attention map with shape [batch_size, h, w].\n",
        "    max_coords (Tensor): The coordinates of the point to mask around, shape [batch_size, 2].\n",
        "    radius (float): The radius within which to mask the values.\n",
        "\n",
        "    Returns:\n",
        "    Tensor: The masked map.\n",
        "    \"\"\"\n",
        "    batch_size, h, w = map.shape\n",
        "\n",
        "    # Create a meshgrid to compute the distance for each pixel\n",
        "    x_coords = torch.arange(w).view(1, -1).repeat(h, 1).to(map.device)\n",
        "    y_coords = torch.arange(h).view(-1, 1).repeat(1, w).to(map.device)\n",
        "    x_coords = x_coords.unsqueeze(0).repeat(batch_size, 1, 1)\n",
        "    y_coords = y_coords.unsqueeze(0).repeat(batch_size, 1, 1)\n",
        "\n",
        "    # Calculate squared Euclidean distance from the max_coords\n",
        "    squared_dist = (x_coords - max_coords[:, 1].unsqueeze(1).unsqueeze(2))**2 + \\\n",
        "                   (y_coords - max_coords[:, 0].unsqueeze(1).unsqueeze(2))**2\n",
        "\n",
        "    # Mask out pixels within the specified radius\n",
        "    mask = squared_dist > radius**2\n",
        "    masked_map = map * mask.float()\n",
        "\n",
        "    return masked_map\n",
        "\n",
        "def find_k_max_pixels(map, num=3):\n",
        "    \"\"\"\n",
        "    finds the pixel of the map with the highest value\n",
        "    map shape [batch_size, h, w]\n",
        "\n",
        "    output shape [num, batch_size, 2]\n",
        "    \"\"\"\n",
        "\n",
        "    batch_size, h, w = map.shape\n",
        "\n",
        "    points = []\n",
        "\n",
        "    for i in range(num):\n",
        "\n",
        "        point = find_max_pixel(map)\n",
        "        points.append(point)\n",
        "\n",
        "        map = mask_radius(map, point, 0.05*h)\n",
        "\n",
        "    return torch.stack(points)\n",
        "\n",
        "def find_top_k_gaussian(attention_maps, top_k, sigma = 3, epsilon = 1e-5, num_subjects = 1):\n",
        "    \"\"\"\n",
        "    attention_maps is of shape [batch_size, image_h, image_w]\n",
        "\n",
        "    min_dist set to 0 becomes a simple top_k\n",
        "    \"\"\"\n",
        "\n",
        "    device = attention_maps.device\n",
        "\n",
        "    batch_size, image_h, image_w = attention_maps.shape\n",
        "\n",
        "    max_pixel_locations = find_k_max_pixels(attention_maps, num=num_subjects)/image_h\n",
        "\n",
        "    # Normalize the activation maps to represent probability distributions\n",
        "    attention_maps_softmax = torch.softmax(attention_maps.view(batch_size, image_h * image_w)+epsilon, dim=-1)\n",
        "\n",
        "    target = gaussian_circles(max_pixel_locations, size=image_h, sigma=sigma, device=attention_maps.device)\n",
        "\n",
        "    target = target.reshape(batch_size, image_h * image_w)+epsilon\n",
        "    target/=target.sum(dim=-1, keepdim=True)\n",
        "\n",
        "    # sort the kl distances between attention_maps_softmax and target\n",
        "    kl_distances = torch.sum(target * (torch.log(target) - torch.log(attention_maps_softmax)), dim=-1)\n",
        "    # get the argsort for kl_distances\n",
        "    kl_distances_argsort = torch.argsort(kl_distances, dim=-1, descending=False)\n",
        "\n",
        "    return torch.tensor(kl_distances_argsort[:top_k]).to(device)\n",
        "\n",
        "def furthest_point_sampling(attention_maps, top_k, top_initial_candidates):\n",
        "    \"\"\"\n",
        "    attention_maps is of shape [batch_size, image_h, image_w]\n",
        "\n",
        "    min_dist set to 0 becomes a simple top_k\n",
        "    \"\"\"\n",
        "\n",
        "    device = attention_maps.device\n",
        "\n",
        "    batch_size, image_h, image_w = attention_maps.shape\n",
        "\n",
        "    # Assuming you have a function find_max_pixel to get the pixel locations\n",
        "    max_pixel_locations = find_max_pixel(attention_maps)/image_h  # You'll need to define find_max_pixel\n",
        "\n",
        "    # Find the furthest two points from top_initial_candidates\n",
        "    max_dist = -1\n",
        "\n",
        "    for i in range(len(top_initial_candidates)):\n",
        "        for j in range(i+1, len(top_initial_candidates)):\n",
        "            dist = torch.sqrt(torch.sum((max_pixel_locations[top_initial_candidates[i]] - max_pixel_locations[top_initial_candidates[j]])**2))\n",
        "            if dist > max_dist:\n",
        "                max_dist = dist\n",
        "                furthest_pair = (top_initial_candidates[i].item(), top_initial_candidates[j].item())\n",
        "\n",
        "    # Initialize the furthest point sampling with the furthest pair\n",
        "    selected_indices = [furthest_pair[0], furthest_pair[1]]\n",
        "\n",
        "    for _ in range(top_k - 2):\n",
        "        max_min_dist = -1\n",
        "        furthest_point = None\n",
        "\n",
        "        for i in top_initial_candidates:\n",
        "            if i.item() in selected_indices:\n",
        "                continue\n",
        "\n",
        "            this_min_dist = torch.min(torch.sqrt(torch.sum((max_pixel_locations[i] - torch.index_select(max_pixel_locations, 0, torch.tensor(selected_indices).to(device)))**2, dim=-1)))\n",
        "\n",
        "            if this_min_dist > max_min_dist:\n",
        "                max_min_dist = this_min_dist\n",
        "                furthest_point = i.item()\n",
        "\n",
        "        if furthest_point is not None:\n",
        "            selected_indices.append(furthest_point)\n",
        "\n",
        "    return torch.tensor(selected_indices).to(device)\n",
        "\n",
        "def find_gaussian_loss_at_point(\n",
        "    attn_map, pos, sigma=1.0, temperature=1e-1, device=\"cuda\", indices=None, num_subjects=1\n",
        "):\n",
        "    \"\"\"\n",
        "    pos is a location between 0 and 1\n",
        "    \"\"\"\n",
        "\n",
        "    # attn_map is of shape (T, H, W)\n",
        "    T, H, W = attn_map.shape\n",
        "\n",
        "    # Create Gaussian circle at the given position\n",
        "    target = gaussian_circles(\n",
        "        pos, size=H, sigma=sigma, device=attn_map.device\n",
        "    )  # Assuming H and W are the same\n",
        "    target = target.to(attn_map.device)\n",
        "\n",
        "    # possibly select a subset of indices\n",
        "    if indices is not None:\n",
        "        attn_map = attn_map[indices]\n",
        "        target = target[indices]\n",
        "\n",
        "    # Compute loss\n",
        "    loss = F.mse_loss(attn_map, target)\n",
        "\n",
        "    return loss\n",
        "\n",
        "def sharpening_loss(attn_map, sigma=1.0, temperature=1e1, device=\"cuda\", num_subjects = 1):\n",
        "\n",
        "    pos = find_k_max_pixels(attn_map, num=num_subjects)/attn_map.shape[-1]\n",
        "\n",
        "    loss = find_gaussian_loss_at_point(\n",
        "        attn_map,\n",
        "        pos,\n",
        "        sigma=sigma,\n",
        "        temperature=temperature,\n",
        "        device=device,\n",
        "        num_subjects=num_subjects,\n",
        "    )\n",
        "\n",
        "    return loss\n",
        "\n",
        "def equivariance_loss(embeddings_initial, embeddings_transformed, transform, index):\n",
        "    # untransform the embeddings_transformed\n",
        "    embeddings_initial_prime = transform.inverse(embeddings_transformed)[index]\n",
        "\n",
        "    loss = F.mse_loss(embeddings_initial, embeddings_initial_prime)\n",
        "\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-ducalFEZBaL"
      },
      "outputs": [],
      "source": [
        "from tqdm import tqdm\n",
        "import wandb\n",
        "\n",
        "def optimize_embedding(\n",
        "    ldm,\n",
        "    context=None,\n",
        "    device=\"cuda\",\n",
        "    num_steps=2000,\n",
        "    from_where=[\"down_cross\", \"mid_cross\", \"up_cross\"],\n",
        "    upsample_res=256,\n",
        "    layers=[0, 1, 2, 3, 4, 5],\n",
        "    lr=5e-3,\n",
        "    noise_level=-1,\n",
        "    num_tokens=1000,\n",
        "    top_k=10,\n",
        "    augment_degrees=30,\n",
        "    augment_scale=(0.9, 1.1),\n",
        "    augment_translate=(0.1, 0.1),\n",
        "    dataset_loc=\"~\",\n",
        "    sigma=1.0,\n",
        "    sharpening_loss_weight=100,\n",
        "    equivariance_attn_loss_weight=100,\n",
        "    batch_size=4,\n",
        "    num_gpus=1,\n",
        "    max_len=-1,\n",
        "    min_dist=0.05,\n",
        "    furthest_point_num_samples=50,\n",
        "    controllers=None,\n",
        "    validation = False,\n",
        "    num_subjects=1,\n",
        "):\n",
        "    dataset = CustomDataset(data_root=dataset_loc, image_size=512)\n",
        "\n",
        "\n",
        "    invertible_transform = RandomAffineWithInverse(\n",
        "        degrees=augment_degrees,\n",
        "        scale=augment_scale,\n",
        "        translate=augment_translate,\n",
        "    )\n",
        "\n",
        "\n",
        "    # every iteration return image, pixel_loc\n",
        "\n",
        "    if context is None:\n",
        "        context = init_random_noise(device, num_words=num_tokens)\n",
        "\n",
        "    context.requires_grad = True\n",
        "\n",
        "    # optimize context to maximize attention at pixel_loc\n",
        "    optimizer = torch.optim.Adam([context], lr=lr)\n",
        "\n",
        "    # time the optimization\n",
        "    import time\n",
        "\n",
        "    start = time.time()\n",
        "\n",
        "    running_equivariance_attn_loss = 0\n",
        "    running_sharpening_loss = 0\n",
        "    running_total_loss = 0\n",
        "\n",
        "    # create dataloader for the dataset\n",
        "    dataloader = torch.utils.data.DataLoader(dataset, batch_size=num_gpus, shuffle=True, drop_last=True)\n",
        "\n",
        "    dataloader_iter = iter(dataloader)\n",
        "\n",
        "    # import ipdb; ipdb.set_trace()\n",
        "\n",
        "    for iteration in tqdm(range(int(num_steps*batch_size))):\n",
        "\n",
        "        try:\n",
        "            mini_batch = next(dataloader_iter)\n",
        "        except StopIteration:  # Explicitly catch StopIteration\n",
        "            dataloader_iter = iter(dataloader)\n",
        "            mini_batch = next(dataloader_iter)\n",
        "\n",
        "        image = mini_batch[\"img\"]\n",
        "\n",
        "        attn_maps = run_and_find_attn(\n",
        "            ldm,\n",
        "            image,\n",
        "            context,\n",
        "            layers=layers,\n",
        "            noise_level=noise_level,\n",
        "            from_where=from_where,\n",
        "            upsample_res=-1,\n",
        "            device=device,\n",
        "            controllers=controllers,\n",
        "        )\n",
        "\n",
        "        # import ipdb; ipdb.set_trace()\n",
        "\n",
        "        transformed_img = invertible_transform(image)\n",
        "\n",
        "        attention_maps_transformed = run_and_find_attn(\n",
        "            ldm,\n",
        "            transformed_img,\n",
        "            context,\n",
        "            layers=layers,\n",
        "            noise_level=noise_level,\n",
        "            from_where=from_where,\n",
        "            upsample_res=-1,\n",
        "            device=device,\n",
        "            controllers=controllers,\n",
        "        )\n",
        "\n",
        "        _sharpening_loss = []\n",
        "        _loss_equivariance_attn = []\n",
        "\n",
        "        for index, attn_map, attention_map_transformed in zip(torch.arange(num_gpus), attn_maps, attention_maps_transformed):\n",
        "\n",
        "\n",
        "            top_embedding_indices = find_top_k_gaussian(\n",
        "                attn_map, furthest_point_num_samples, sigma=sigma, num_subjects = num_subjects\n",
        "            )\n",
        "\n",
        "            top_embedding_indices = furthest_point_sampling(attention_map_transformed, top_k, top_embedding_indices)\n",
        "\n",
        "            _sharpening_loss.append(sharpening_loss(attn_map[top_embedding_indices], device=device, sigma=sigma, num_subjects = num_subjects))\n",
        "\n",
        "            _loss_equivariance_attn.append(equivariance_loss(\n",
        "                attn_map[top_embedding_indices], attention_map_transformed[top_embedding_indices][None].repeat(num_gpus, 1, 1, 1), invertible_transform, index\n",
        "            ))\n",
        "\n",
        "\n",
        "\n",
        "        _sharpening_loss = torch.stack([loss.to('cuda:0') for loss in _sharpening_loss]).mean()\n",
        "        _loss_equivariance_attn = torch.stack([loss.to('cuda:0') for loss in _loss_equivariance_attn]).mean()\n",
        "\n",
        "\n",
        "        # use the old loss for the first 1000 iterations\n",
        "        # new loss is unstable for early iterations\n",
        "        loss = (\n",
        "            + _loss_equivariance_attn * equivariance_attn_loss_weight\n",
        "            + _sharpening_loss * sharpening_loss_weight\n",
        "        )\n",
        "\n",
        "        running_equivariance_attn_loss += _loss_equivariance_attn / (batch_size//num_gpus) * equivariance_attn_loss_weight\n",
        "        running_sharpening_loss += _sharpening_loss / (batch_size//num_gpus) * sharpening_loss_weight\n",
        "        running_total_loss += loss / (batch_size//num_gpus)\n",
        "\n",
        "        loss = loss / (batch_size//num_gpus)\n",
        "\n",
        "        loss.backward()\n",
        "        if (iteration + 1) % (batch_size//num_gpus) == 0:\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "            running_equivariance_attn_loss = 0\n",
        "            running_sharpening_loss = 0\n",
        "            running_total_loss = 0\n",
        "\n",
        "    print(f\"optimization took {time.time() - start} seconds\")\n",
        "\n",
        "    return context.detach()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R1Rd0usPdfoG",
        "outputId": "716deee6-9016-4204-fa71-c254b0d7ec58"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/500 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/functional.py:507: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3549.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "<ipython-input-20-d782888e078d>:283: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  return torch.tensor(kl_distances_argsort[:top_k]).to(device)\n",
            "100%|██████████| 500/500 [15:20<00:00,  1.84s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "optimization took 920.2602188587189 seconds\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "embedding = optimize_embedding(\n",
        "        ldm,\n",
        "        num_steps=num_optimization_steps,\n",
        "        batch_size=batch_size,\n",
        "        top_k = num_keypoints,\n",
        "        controllers=controllers,\n",
        "        num_gpus=num_gpus,\n",
        "        furthest_point_num_samples=furthest_point_num_samples,\n",
        "        num_tokens=100,\n",
        "        dataset_loc=image_dir,\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HuiQgjhNDGqG",
        "outputId": "266c7384-bca3-4c2c-bc06-0160be2f4a1b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/100 [00:00<?, ?it/s]<ipython-input-20-d782888e078d>:283: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  return torch.tensor(kl_distances_argsort[:top_k]).to(device)\n",
            "100%|██████████| 100/100 [01:14<00:00,  1.34it/s]\n"
          ]
        }
      ],
      "source": [
        "@torch.no_grad()\n",
        "def find_best_indices(\n",
        "    ldm,\n",
        "    context,\n",
        "    num_steps=100,\n",
        "    noise_level=-1,\n",
        "    upsample_res=256,\n",
        "    layers=[0, 1, 2, 3, 4, 5],\n",
        "    from_where=[\"down_cross\", \"mid_cross\", \"up_cross\"],\n",
        "    top_k=10,\n",
        "    dataset_loc=\"~\",\n",
        "    furthest_point_num_samples=50,\n",
        "    controllers=None,\n",
        "    num_gpus=1,\n",
        "    sigma = 3,\n",
        "    num_subjects=1,\n",
        "):\n",
        "\n",
        "    dataset = CustomDataset(data_root=dataset_loc, image_size=512)\n",
        "\n",
        "\n",
        "    maps = []\n",
        "    indices_list = []\n",
        "\n",
        "    # create dataloader for the dataset\n",
        "    dataloader = torch.utils.data.DataLoader(dataset, batch_size=num_gpus, shuffle=True, drop_last=True)\n",
        "\n",
        "    dataloader_iter = iter(dataloader)\n",
        "\n",
        "    mini_batch = dataset[0]\n",
        "\n",
        "    for _ in tqdm(range(num_steps//num_gpus)):\n",
        "\n",
        "        try:\n",
        "            mini_batch = next(dataloader_iter)\n",
        "        except StopIteration:  # Explicitly catch StopIteration\n",
        "            dataloader_iter = iter(dataloader)\n",
        "            mini_batch = next(dataloader_iter)\n",
        "\n",
        "        image = mini_batch[\"img\"]\n",
        "\n",
        "        attention_maps = run_and_find_attn(\n",
        "            ldm,\n",
        "            image,\n",
        "            context,\n",
        "            layers=layers,\n",
        "            noise_level=noise_level,\n",
        "            from_where=from_where,\n",
        "            upsample_res=upsample_res,\n",
        "            controllers=controllers,\n",
        "        )\n",
        "\n",
        "        for attention_map in attention_maps:\n",
        "\n",
        "            top_initial_candidates = find_top_k_gaussian(\n",
        "                attention_map, furthest_point_num_samples, sigma=sigma, num_subjects = num_subjects\n",
        "            )\n",
        "\n",
        "            top_embedding_indices = furthest_point_sampling(attention_map, top_k, top_initial_candidates)\n",
        "\n",
        "            indices_list.append(top_embedding_indices.cpu())\n",
        "\n",
        "    # find the top_k most common indices\n",
        "    indices_list = torch.cat([index for index in indices_list])\n",
        "    # indices_list = indices_list.reshape(-1)\n",
        "    indices, counts = torch.unique(indices_list, return_counts=True)\n",
        "    indices = indices[counts.argsort(descending=True)]\n",
        "    indices = indices[:top_k]\n",
        "\n",
        "    return indices\n",
        "\n",
        "indices = find_best_indices(\n",
        "        ldm,\n",
        "        embedding,\n",
        "        dataset_loc=image_dir,\n",
        "        controllers = controllers,\n",
        "        furthest_point_num_samples=furthest_point_num_samples,\n",
        "        top_k=num_keypoints,\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G4RM5ch9rBot"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import imageio\n",
        "import torch\n",
        "from tqdm import tqdm\n",
        "from IPython.display import HTML\n",
        "from base64 import b64encode\n",
        "import numpy as np\n",
        "from io import BytesIO\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "@torch.no_grad()\n",
        "def run_image_with_context_augmented(\n",
        "    ldm,\n",
        "    image,\n",
        "    context,\n",
        "    indices,\n",
        "    device=\"cuda\",\n",
        "    from_where=[\"down_cross\", \"mid_cross\", \"up_cross\"],\n",
        "    layers=[0, 1, 2, 3, 4, 5],\n",
        "    augmentation_iterations=20,\n",
        "    noise_level=-1,\n",
        "    augment_degrees=30,\n",
        "    augment_scale=(0.9, 1.1),\n",
        "    augment_translate=(0.1, 0.1),\n",
        "    controllers=None,\n",
        "    num_gpus=1,\n",
        "    upsample_res=512,\n",
        "):\n",
        "    # if image is a torch.tensor, convert to numpy\n",
        "    if type(image) == torch.Tensor:\n",
        "        image = image.permute(1, 2, 0).detach().cpu().numpy()\n",
        "\n",
        "    num_samples = torch.zeros(len(indices), upsample_res, upsample_res).to(device)\n",
        "    sum_samples = torch.zeros(len(indices), upsample_res, upsample_res).to(device)\n",
        "\n",
        "    invertible_transform = RandomAffineWithInverse(\n",
        "        degrees=augment_degrees,\n",
        "        scale=augment_scale,\n",
        "        translate=augment_translate,\n",
        "    )\n",
        "\n",
        "    for i in range(augmentation_iterations//num_gpus):\n",
        "\n",
        "        augmented_img = (\n",
        "            invertible_transform(torch.tensor(image)[None].repeat(num_gpus, 1, 1, 1).permute(0, 3, 1, 2))\n",
        "            .permute(0, 2, 3, 1)\n",
        "            .numpy()\n",
        "        )\n",
        "\n",
        "        attn_maps = run_and_find_attn(\n",
        "            ldm,\n",
        "            augmented_img,\n",
        "            context,\n",
        "            layers=layers,\n",
        "            noise_level=noise_level,\n",
        "            from_where=from_where,\n",
        "            upsample_res=upsample_res,\n",
        "            device=device,\n",
        "            controllers=controllers,\n",
        "            indices=indices.cpu(),\n",
        "        )\n",
        "\n",
        "        attn_maps = torch.stack([map.to(\"cuda:0\") for map in attn_maps])\n",
        "\n",
        "        _num_samples = invertible_transform.inverse(torch.ones_like(attn_maps))\n",
        "        _sum_samples = invertible_transform.inverse(attn_maps)\n",
        "\n",
        "        num_samples += _num_samples.sum(dim=0)\n",
        "        sum_samples += _sum_samples.sum(dim=0)\n",
        "\n",
        "    # visualize sum_samples/num_samples\n",
        "    attention_maps = sum_samples / num_samples\n",
        "\n",
        "    # replace all nans with 0s\n",
        "    attention_maps[attention_maps != attention_maps] = 0\n",
        "\n",
        "    return attention_maps\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KnvRpJvcDGqI"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import torch\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm.auto import tqdm\n",
        "from IPython.display import display\n",
        "\n",
        "# Assuming CustomDataset and other necessary modules are defined elsewhere\n",
        "\n",
        "@torch.no_grad()\n",
        "def visualize_keypoints(\n",
        "    ldm,\n",
        "    contexts,\n",
        "    indices,\n",
        "    device=\"cuda\",\n",
        "    from_where=[\"down_cross\", \"mid_cross\", \"up_cross\"],\n",
        "    layers=[0, 1, 2, 3, 4, 5],\n",
        "    noise_level=-1,\n",
        "    augment_degrees=30,\n",
        "    augment_scale=(0.9, 1.1),\n",
        "    augment_translate=(0.1, 0.1),\n",
        "    augmentation_iterations=20,\n",
        "    dataset_loc=\"~\",\n",
        "    controllers=None,\n",
        "    num_gpus=1,\n",
        "    sample_size=4  # Number of random samples\n",
        "):\n",
        "\n",
        "    dataset = CustomDataset(data_root=dataset_loc, image_size=512)\n",
        "\n",
        "    if len(dataset) < sample_size:\n",
        "        raise ValueError(\"sample_size larger than dataset size.\")\n",
        "\n",
        "    # Randomly sample indices from the dataset\n",
        "    sampled_indices = random.sample(range(len(dataset)), sample_size)\n",
        "\n",
        "    # Setting up the subplot\n",
        "    fig, axes = plt.subplots(1, sample_size, figsize=(20, 5))\n",
        "\n",
        "    for idx, i in enumerate(sampled_indices):\n",
        "\n",
        "        batch = dataset[i]\n",
        "        img = batch[\"img\"]\n",
        "\n",
        "        maps = []\n",
        "        for j in range(len(contexts)):\n",
        "            map = run_image_with_context_augmented(\n",
        "                ldm,\n",
        "                img,\n",
        "                contexts[j],\n",
        "                indices.cpu(),\n",
        "                device=device,\n",
        "                from_where=from_where,\n",
        "                layers=layers,\n",
        "                noise_level=noise_level,\n",
        "                augment_degrees=augment_degrees,\n",
        "                augment_scale=augment_scale,\n",
        "                augment_translate=augment_translate,\n",
        "                augmentation_iterations=augmentation_iterations,\n",
        "                controllers=controllers,\n",
        "                num_gpus=num_gpus,\n",
        "                upsample_res=512,\n",
        "            )\n",
        "            maps.append(map)\n",
        "\n",
        "        maps = torch.stack(maps)\n",
        "        map = torch.mean(maps, dim=0)\n",
        "\n",
        "        point = find_max_pixel(map) / 512.0\n",
        "        point = point.unsqueeze(0).cpu()  # Prepare the point for plotting\n",
        "\n",
        "        # Plot points on the image\n",
        "        ax = axes[idx]\n",
        "        ax.imshow(img.numpy().transpose(1, 2, 0))\n",
        "        num_people, num_points, _ = point.shape\n",
        "        prop_cycle = plt.rcParams['axes.prop_cycle']\n",
        "        colors = prop_cycle.by_key()['color']\n",
        "        for i in range(num_people):\n",
        "            for j in range(num_points):\n",
        "                color = \"yellow\" if j == 16 else colors[j % len(colors)]\n",
        "                x, y = point[i, j, 1] * 512, point[i, j, 0] * 512\n",
        "                ax.scatter(x, y, color=color, marker=f\"${j}$\", s=300)\n",
        "        ax.axis(\"off\")\n",
        "\n",
        "    plt.subplots_adjust(wspace=0.1, hspace=0)\n",
        "    plt.show()\n",
        "\n",
        "# Make sure to define or import plot_point_single, CustomDataset, and other used functions or classes.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "id": "2Q6B6hk-DGqJ",
        "outputId": "ad2aedf2-2d78-4f39-8362-33332944e487"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 4 Axes>"
            ],
            "image/png": 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          "metadata": {}
        }
      ],
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        "visualize_keypoints(\n",
        "    ldm,\n",
        "    embedding,\n",
        "    indices,\n",
        "    dataset_loc=image_dir,\n",
        "    controllers=controllers,\n",
        "    augmentation_iterations=10,\n",
        ")"
      ]
    },
    {
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      },
      "outputs": [],
      "source": []
    },
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      "execution_count": null,
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        "id": "AyCMLoSUDGqJ"
      },
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      "source": []
    }
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