using_diffusers_on_numpy_arrays.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zfA87mA93LLP"
      },
      "source": [
        "## Step 1: Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCoVNJtLI9KT"
      },
      "source": [
        "Run the following cell to install the diffusers library as well as a few other requirements:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Jw6-w4TB_7wg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34fb1504-bf40-4fdb-b6ac-7c085120439b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.5/43.5 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.4/166.4 kB\u001b[0m \u001b[31m16.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "bigframes 1.27.0 requires pyarrow>=10.0.1, but you have pyarrow 9.0.0 which is incompatible.\n",
            "cudf-cu12 24.10.1 requires pyarrow<18.0.0a0,>=14.0.0, but you have pyarrow 9.0.0 which is incompatible.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2023.10.0 which is incompatible.\n",
            "pylibcudf-cu12 24.10.1 requires pyarrow<18.0.0a0,>=14.0.0, but you have pyarrow 9.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "%pip install -qq -U diffusers datasets transformers accelerate ftfy pyarrow==9.0.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1xjoISGjZQnt"
      },
      "source": [
        "Then you need to install Git-LFS to upload your model checkpoints:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "CrgjP3c0ZQnt"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!sudo apt -qq install git-lfs\n",
        "!git config --global credential.helper store"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9bR764bDIaDh"
      },
      "source": [
        "Finally, let's import the libraries we'll be using and define a few convenience functions which we'll use later in the notebook:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "VXhqi_hNcpk_"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from matplotlib import pyplot as plt\n",
        "from PIL import Image\n",
        "\n",
        "\n",
        "def show_images(x):\n",
        "    \"\"\"Given a batch of images x, make a grid and convert to PIL\"\"\"\n",
        "    x = x * 0.5 + 0.5  # Map from (-1, 1) back to (0, 1)\n",
        "    grid = torchvision.utils.make_grid(x)\n",
        "    grid_im = grid.detach().cpu().permute(1, 2, 0).clip(0, 1) * 255\n",
        "    grid_im = Image.fromarray(np.array(grid_im).astype(np.uint8))\n",
        "    return grid_im\n",
        "\n",
        "\n",
        "def make_grid(images, size=64):\n",
        "    \"\"\"Given a list of PIL images, stack them together into a line for easy viewing\"\"\"\n",
        "    output_im = Image.new(\"RGB\", (size * len(images), size))\n",
        "    for i, im in enumerate(images):\n",
        "        output_im.paste(im.resize((size, size)), (i * size, 0))\n",
        "    return output_im\n",
        "\n",
        "\n",
        "# Mac users may need device = 'mps' (untested)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QOvw1ej7suWg"
      },
      "source": [
        "## Step 2: Creating a training dataset\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import time\n",
        "import concurrent.futures\n",
        "# @- black tiles, 1\n",
        "# e- blue tiles, -1\n",
        "# .- white tiles, 0\n",
        "\n",
        "def create_numpy_arrays(grid_size,num_blue_tiles):\n",
        "\n",
        "    def create_empty_grid():\n",
        "        # Initialize grid with all white tiles ('.')\n",
        "        grid = [['.' for _ in range(grid_size)] for _ in range(grid_size)]\n",
        "        return grid\n",
        "\n",
        "    # Function to print the grid\n",
        "    def print_grid(grid):\n",
        "        for row in grid:\n",
        "            print(''.join(row))\n",
        "        print()\n",
        "\n",
        "    # Step 1: Place blue tiles ('e') sparsely across the grid\n",
        "    def place_blue_tiles(grid, num_blue_tiles=num_blue_tiles):\n",
        "        count = 0\n",
        "        while count < num_blue_tiles:\n",
        "            x, y = random.randint(0, grid_size - 1), random.randint(0, grid_size - 1)\n",
        "            if grid[x][y] == '.':\n",
        "                grid[x][y] = 'e'\n",
        "                count += 1\n",
        "\n",
        "    # Step 2: Place black tiles ('@') adjacent to blue tiles\n",
        "    def place_black_tiles(grid):\n",
        "        directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]\n",
        "        for x in range(grid_size):\n",
        "            for y in range(grid_size):\n",
        "                if grid[x][y] == 'e':\n",
        "                    shuffled_directions = random.sample(directions, len(directions))\n",
        "                    for dx, dy in shuffled_directions:\n",
        "                        nx, ny = x + dx, y + dy\n",
        "                        if 0 <= nx < grid_size and 0 <= ny < grid_size and grid[nx][ny] == '.':\n",
        "                            grid[nx][ny] = '@'\n",
        "                            break\n",
        "\n",
        "    # Modified Step 3: Ensure black tiles ('@') have at least two adjacent blue tiles and can have more\n",
        "    def validate_black_tile_adjacency(grid):\n",
        "        directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]\n",
        "        for x in range(grid_size):\n",
        "            for y in range(grid_size):\n",
        "                if grid[x][y] == '@':\n",
        "                    blue_count = 0\n",
        "                    # Randomly check directions to count adjacent blue tiles\n",
        "                    shuffled_directions = random.sample(directions, len(directions))\n",
        "                    for dx, dy in shuffled_directions:\n",
        "                        nx, ny = x + dx, y + dy\n",
        "                        if 0 <= nx < grid_size and 0 <= ny < grid_size and grid[nx][ny] == 'e':\n",
        "                            blue_count += 1\n",
        "                    while blue_count < 2:\n",
        "                        for dx, dy in shuffled_directions:\n",
        "                            nx, ny = x + dx, y + dy\n",
        "                            if blue_count == 4:\n",
        "                                break\n",
        "                            if 0 <= nx < grid_size and 0 <= ny < grid_size and grid[nx][ny] == '.':\n",
        "                                grid[nx][ny] = 'e'\n",
        "                                blue_count += 1\n",
        "                                if blue_count >= 2 and random.random() < 0.5:\n",
        "                                    break\n",
        "\n",
        "    # Step 4: Ensure all blue tiles are connected by non-black paths\n",
        "    def validate_blue_connectivity(grid):\n",
        "        visited = [[False for _ in range(grid_size)] for _ in range(grid_size)]\n",
        "\n",
        "        def dfs(x, y):\n",
        "            if not (0 <= x < grid_size and 0 <= y < grid_size) or grid[x][y] == '@' or visited[x][y]:\n",
        "                return\n",
        "            visited[x][y] = True\n",
        "            directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]\n",
        "            for dx, dy in directions:\n",
        "                dfs(x + dx, y + dy)\n",
        "\n",
        "        # Start DFS from the first blue tile\n",
        "        start_found = False\n",
        "        for x in range(grid_size):\n",
        "            for y in range(grid_size):\n",
        "                if grid[x][y] == 'e':\n",
        "                    dfs(x, y)\n",
        "                    start_found = True\n",
        "                    break\n",
        "            if start_found:\n",
        "                break\n",
        "\n",
        "        # Check if all blue tiles are visited\n",
        "        for x in range(grid_size):\n",
        "            for y in range(grid_size):\n",
        "                if grid[x][y] == 'e' and not visited[x][y]:\n",
        "                    return False\n",
        "        return True\n",
        "\n",
        "    # Step 5: Correct the layout if needed\n",
        "    def correct_layout(grid):\n",
        "        grid_i = grid.copy()\n",
        "        # Ensure blue tiles are connected\n",
        "        while not validate_blue_connectivity(grid_i):\n",
        "            print(\"Re-validating and adjusting layout...\")\n",
        "            grid_i = create_empty_grid()  # Reset the grid\n",
        "            place_blue_tiles(grid_i)  # Place initial blue tiles\n",
        "            place_black_tiles(grid_i)  # Place black tiles\n",
        "            validate_black_tile_adjacency(grid_i)\n",
        "\n",
        "        return grid_i\n",
        "\n",
        "    # Function to convert grid characters to numerical values\n",
        "    def convert_grid_to_numerical(grid):\n",
        "        conversion = {'.': 0, 'e': -1, '@': 1}\n",
        "        for x in range(grid_size):\n",
        "            for y in range(grid_size):\n",
        "                grid[x][y] = conversion.get(grid[x][y], grid[x][y])\n",
        "\n",
        "    # Main program\n",
        "    grid = create_empty_grid()\n",
        "    place_blue_tiles(grid)\n",
        "    place_black_tiles(grid)\n",
        "    validate_black_tile_adjacency(grid)\n",
        "    grid_i = correct_layout(grid)\n",
        "\n",
        "    # Convert the final grid to numerical values\n",
        "    convert_grid_to_numerical(grid_i)\n",
        "\n",
        "    # Convert the grid to a NumPy array\n",
        "    grid_array = np.array(grid_i)\n",
        "\n",
        "    #print(\"Final Grid Layout as NumPy Array:\")\n",
        "    #print(grid_array)\n",
        "\n",
        "    return grid_array\n",
        "\n",
        "\n",
        "\n",
        "# Define grid size\n",
        "grid_size = 32\n",
        "num_blue_tiles = 10\n",
        "\n",
        "# Create a list of dictionaries with a timeout check\n",
        "\n",
        "import multiprocessing\n",
        "import time\n",
        "\n",
        "def create_grid_with_timeout(index, grid_size, num_blue_tiles, timeout=1):\n",
        "    \"\"\"\n",
        "    Creates a grid with a timeout. Skips if creation takes too long.\n",
        "    Args:\n",
        "        index: Current index of the grid.\n",
        "        grid_size: Size of the grid to generate.\n",
        "        num_blue_tiles: Number of blue tiles in the grid.\n",
        "        timeout: Timeout in seconds for each grid creation.\n",
        "    Returns:\n",
        "        Dictionary with the grid or None if it times out.\n",
        "    \"\"\"\n",
        "    def create_grid(result_queue):\n",
        "        try:\n",
        "            result = create_numpy_arrays(grid_size, num_blue_tiles)\n",
        "            result_queue.put(result)\n",
        "        except Exception as e:\n",
        "            result_queue.put(None)\n",
        "            print(f\"Error at index {index}: {e}\")\n",
        "\n",
        "    result_queue = multiprocessing.Queue()\n",
        "    process = multiprocessing.Process(target=create_grid, args=(result_queue,))\n",
        "    process.start()\n",
        "    process.join(timeout)\n",
        "\n",
        "    # If the process is still alive after the timeout, terminate it\n",
        "    if process.is_alive():\n",
        "        process.terminate()\n",
        "        process.join()\n",
        "        print(f\"Skipping index {index} due to timeout.\")\n",
        "        return None\n",
        "\n",
        "    # Retrieve the result if available\n",
        "    if not result_queue.empty():\n",
        "        return {'image': result_queue.get()}\n",
        "    else:\n",
        "        print(f\"Skipping index {index} due to an error.\")\n",
        "        return None\n",
        "\n",
        "# Main loop\n",
        "grid_list = []\n",
        "for idx in range(10000):\n",
        "    grid_data = create_grid_with_timeout(idx, grid_size, num_blue_tiles, timeout=1)\n",
        "    if grid_data:\n",
        "        grid_list.append(grid_data)\n",
        "    print(f\"Processed index: {idx}\")\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "len_grid_list = len(grid_list)\n",
        "print(f\"Length of grid is {len_grid_list}\")\n",
        "# Print the list of dictionaries\n",
        "# for i, grid_dict in enumerate(grid_list):\n",
        "#     print(f\"Grid {i+1}:\")\n",
        "#     print(grid_dict['image'])\n",
        "#     print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o09JU5Au5mZR",
        "outputId": "b54182e6-e872-4218-a753-777eb19f7c59"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "-yX-MZhSsxwp"
      },
      "outputs": [],
      "source": [
        "import torchvision\n",
        "from datasets import load_dataset\n",
        "from datasets import Dataset\n",
        "from torchvision import transforms\n",
        "\n",
        "#dataset = grid_list\n",
        "\n",
        "# Convert to Hugging Face Dataset\n",
        "dataset = Dataset.from_dict({\"image\": [entry[\"image\"] for entry in grid_list]})\n",
        "\n",
        "# Or load images from a local folder\n",
        "# dataset = load_dataset(\"imagefolder\", data_dir=\"path/to/folder\")\n",
        "\n",
        "# We'll train on 32-pixel square images, but you can try larger sizes too\n",
        "image_size = 32\n",
        "# You can lower your batch size if you're running out of GPU memory\n",
        "batch_size = 256\n",
        "\n",
        "# Define data augmentations\n",
        "# preprocess = transforms.Compose(\n",
        "#     [\n",
        "        #transforms.Resize((image_size, image_size)),  # Resize\n",
        "        #transforms.RandomHorizontalFlip(),  # Randomly flip (data augmentation)\n",
        "        #transforms.ToTensor(),  # Convert to tensor (0, 1)\n",
        "        #transforms.Normalize([0.5], [0.5]),  # Map to (-1, 1)\n",
        "#     ]\n",
        "# )\n",
        "\n",
        "def transform(examples):\n",
        "    images = [\n",
        "        torch.tensor(image).unsqueeze(0)\n",
        "        for image in examples[\"image\"]\n",
        "        if image is not None  # Skip NoneType images\n",
        "    ]\n",
        "    return {\"images\": images}\n",
        "\n",
        "\n",
        "dataset.set_transform(transform)\n",
        "\n",
        "# Create a dataloader from the dataset to serve up the transformed images in batches\n",
        "train_dataloader = torch.utils.data.DataLoader(\n",
        "    dataset, batch_size=batch_size, shuffle=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-T5nDp9XN4FR"
      },
      "source": [
        "We can grab a batch of images and view some of them like so:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IEjN9CJpcrxd",
        "outputId": "aef9ea02-1b4b-4b17-eada-d42d65cc32a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X shape: torch.Size([8, 1, 32, 32])\n"
          ]
        }
      ],
      "source": [
        "xb = next(iter(train_dataloader))[\"images\"].to(device)[:8]\n",
        "print(\"X shape:\", xb.shape)\n",
        "# show_images(xb).resize((8 * 64, 64), resample=Image.NEAREST)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FSPVe2DOOHXq"
      },
      "source": [
        "We're sticking to a small dataset with 32 pixel images to keep training times manageable in this notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUImroTysoxp"
      },
      "source": [
        "## Step 3: Define the Scheduler\n",
        "\n",
        "Our plan for training is to take these input images and add noise to them, then feed the noisy images to the model. And during inference, we will use the model predictions to iteratively remove noise. In `diffusers`,  these processes are both handled by the **scheduler**.\n",
        "\n",
        "The noise schedule determines how much noise is added at different timesteps. Here's how we might create a scheduler using the default settings for 'DDPM' training and sampling (based on the paper [\"Denoising Diffusion Probabilistic Models\"](https://arxiv.org/abs/2006.11239)):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "ETMq70tpyEtj"
      },
      "outputs": [],
      "source": [
        "from diffusers import DDPMScheduler\n",
        "\n",
        "noise_scheduler = DDPMScheduler(num_train_timesteps=1000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4995PVTlyIm1"
      },
      "source": [
        "The DDPM paper describes a corruption process that adds a small amount of noise for every 'timestep'. Given $x_{t-1}$ for some timestep, we can get the next (slightly more noisy) version $x_t$ with:<br><br>\n",
        "\n",
        "$q(\\mathbf{x}_t \\vert \\mathbf{x}_{t-1}) = \\mathcal{N}(\\mathbf{x}_t; \\sqrt{1 - \\beta_t} \\mathbf{x}_{t-1}, \\beta_t\\mathbf{I}) \\quad\n",
        "q(\\mathbf{x}_{1:T} \\vert \\mathbf{x}_0) = \\prod^T_{t=1} q(\\mathbf{x}_t \\vert \\mathbf{x}_{t-1})$<br><br>\n",
        "\n",
        "\n",
        "That is, we take $x_{t-1}$, scale it by $\\sqrt{1 - \\beta_t}$ and add noise scaled by $\\beta_t$. This $\\beta$ is defined for every t according to some schedule, and determines how much noise is added per timestep. Now, we don't necessarily want to do this operation 500 times to get $x_{500}$ so we have another formula to get $x_t$ for any t given $x_0$: <br><br>\n",
        "\n",
        "$\\begin{aligned}\n",
        "q(\\mathbf{x}_t \\vert \\mathbf{x}_0) &= \\mathcal{N}(\\mathbf{x}_t; \\sqrt{\\bar{\\alpha}_t} \\mathbf{x}_0, {(1 - \\bar{\\alpha}_t)} \\mathbf{I})\n",
        "\\end{aligned}$ where $\\bar{\\alpha}_t = \\prod_{i=1}^T \\alpha_i$ and $\\alpha_i = 1-\\beta_i$<br><br>\n",
        "\n",
        "The maths notation always looks scary! Luckily the scheduler handles all that for us. We can plot $\\sqrt{\\bar{\\alpha}_t}$ (labelled as `sqrt_alpha_prod`) and $\\sqrt{(1 - \\bar{\\alpha}_t)}$ (labelled as `sqrt_one_minus_alpha_prod`) to view how the input (x) and the noise are scaled and mixed across different timesteps:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "oP-rFQUzdx9h",
        "outputId": "b084159d-6123-4a93-bcac-79f767a94363"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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dDHVbwj3L4OLHwE3TTImI61IxIsDJNiTdi8chuXX2apIycs2OJSelH4QPB8Jvrzl+7nY33LsC6nUwN5eISAVQMSLFgn1tfHJ3DxoG+RCfmsPw9zSXjVPYuQhm9YYDqxyjqN74IQx8BTy8zE4mIlIhVIxICWEBXnxydxT1A7zYk5zF7e+vISO3wOxYNVNRIfz8NHw2zDHbbv1OcN9KaHOd2clERCqUihE5TUSQD5/c04NgXxvbDmdwz0fryC0oMjtWzZJ9FP57HfzxpuPnqPsdI6kGNTY3l4hIJVAxImfUOLgWH93ZDT+bO6v3pTJuXixFdsPsWDXDoVh499ITY4fUghs/gqumagAzEam2VIzIWbWpH8C7I7ri6WZl0dZEJn2zBcNQQVKpNn8BcwZAejwENYF7lkKba81OJSJSqVSMyDlFN63D6zd3xGKBT1fHM33JbrMjVU/2IvjpKfj6HijMheb94Z5fIKSV2clERCqdihE5r6va1eP5IW0BeH3pbuau+svkRNVMXhbMGw4xMxw/93kUbpkH3oGmxhIRqSoaKUlK5bYejTiSmcfrS3cz+Zst1PX15Mq29cyO5frSExy9ZRL/BDcbXDcL2l5vdioRkSqlKyNSauP6NefWqIYYBoydF8vG+GNmR3Jth2LhvcsdhUitujDyBxUiIlIjqRiRUrNYLPxrcBv6tqhLXqGduz9ax4FUDRtfLjsWwgdXQeZhx7Dudy+FiG5mpxIRMYWKESkTdzcrM27tTOt6/hzNzmfkB2tIz9GgaGWy/iP4fDgU5ECTvnDXz1C7kdmpRERMo2JEyqyWzZ05I7tRL8CLvUeyGf3f9eQX2s2O5fwMA1a+DN89DIYdOt0Gw78ErwCzk4mImErFiJRLWIAXc0Z2w9fmTkzcUZ74erPGIDkXux1+/D9Y9m/Hz30egcEzwM3D3FwiIk5AxYiUW6t6/rw1vDNuVgtfb0jgjaV7zI7knArz4H93wZp3HT9f+RJcPhksFnNziYg4CRUjckEuuahu8Rgkry3ZxTexCSYncjL5OfDZzbD1a7B6wND3ocdos1OJiDgVFSNywW6Nash9FzcB4P++2symA2nmBnIWeZnwyQ2wd5ljjpnhX0C7G8xOJSLidFSMSIX4vytbcnnLEPIK7dzz8TqSMnLNjmSu3HSYez389TvY/OH2+dD0MrNTiYg4JRUjUiHcrBam39yRi0J9Sc7M496P15FbUGR2LHPkpMJHg+HgGvAKhBHfQMMos1OJiDgtFSNSYfy8PHhvRDdq+3iw6WA6j/+vBvawyToCHw2Cw7HgUwdGfg8NOpudSkTEqakYkQrVsI4Pbw/vgrvVwjexh5i5Yq/ZkapOVjJ8OBCStoBvKIxcCGHtzE4lIuL0VIxIhYtuWodnB7cB4OWfdrJ4W5LJiapA9lHHrZmUneDfAEb9CCEtzU4lIuISVIxIpbitRyNu79EIw4Bx8zayIzHD7EiV5/gxmDsEjmwHv3qOWzN1mpqdSkTEZagYkUozeVBrejatQ3Z+Efd+vL56zmGTm+HoNXNy5t0R30JQE7NTiYi4lHIVI2+99RaRkZF4eXkRFRXFmjVrzrn99OnTadGiBd7e3kRERDB+/Hhyc2t4188awMPNylu3diYiyJv41BzGfr4Ru70aNWjNy3KMI3JoA3gHOQqRuheZnUpExOWUuRj5/PPPmTBhAs888wwbNmygQ4cODBgwgOTk5DNu/+mnn/LEE0/wzDPPsH37dt5//30+//xznnzyyQsOL86vdi1PZt3WBS8PK8t3HmH6kl1mR6oYJ0dWPbDaMdHd7fMhtLXZqUREXFKZi5FXX32Ve+65h1GjRtG6dWtmzZqFj48Pc+bMOeP2f/zxB7169eLWW28lMjKS/v37c8stt5z3aopUH23qBzDlekevkjeW7eHnrYkmJ7pARQXwxQjY/yt4+sFtX0P9jmanEhFxWWUqRvLz81m/fj39+vU79QZWK/369SMmJuaM+/Ts2ZP169cXFx9xcXEsXLiQq6+++qzHycvLIyMjo8RDXNt1ncIZ2TMSgAlfbGLvkSxzA5WX3Q7fjIE9i8HdG4Z/CeFdzU4lIuLSylSMpKSkUFRURGhoaIn1oaGhJCae+X+7t956K//617/o3bs3Hh4eNG3alEsvvfSct2mmTJlCQEBA8SMiIqIsMcVJPTWwFd0jg8jKK+S+uevJyis0O1LZGAb8/DRs/hwsbnDTx9Ao2uxUIiIur9J70yxfvpwXX3yRt99+mw0bNvD111/zww8/8Pzzz591n4kTJ5Kenl78OHDgQGXHlCrg4WZlxvBOhPrb2JOcxWNfbnKtEVp/nw6r3nIsX/s2XNTf1DgiItVFmYqR4OBg3NzcSEoqOYhVUlISYWFhZ9xn0qRJ3H777dx99920a9eO6667jhdffJEpU6Zgt9vPuI/NZsPf37/EQ6qHED8vZt7WBQ83Cz9uSWTWijizI5XOhrmw5FnHcv8XoMPNpsYREalOylSMeHp60qVLF5YuXVq8zm63s3TpUqKjz3y5OicnB6u15GHc3NwAXOt/xVJhOjeszXOD2wLw8k87+G13ismJzmPHQvjuYcdyr7HQ80Fz84iIVDNlvk0zYcIEZs+ezUcffcT27du5//77yc7OZtSoUQCMGDGCiRMnFm8/aNAgZs6cybx589i3bx+LFy9m0qRJDBo0qLgokZrnlu4RDOsagd2AsfM2kpjupOPOHFgLX40Cww4db4N+z5mdSESk2nEv6w7Dhg3jyJEjTJ48mcTERDp27MiiRYuKG7XGx8eXuBLy9NNPY7FYePrpp0lISKBu3boMGjSIF154oeI+hbgci8XCc0Pa8GdCOtsOZ/Dgpxv47N4eeLg50aDAx/Y7xhIpzIXmA2DQ62CxmJ1KRKTasRgucK8kIyODgIAA0tPT1X6kmvnraDbXvPEbmXmF3HtxE568upXZkRyOH4P3+0PKLghr75j4zuZrdioREZdS2u9vJ/pvqNREjerU4uUb2wPw7so45xgQrTDfMahZyi7HDLy3fqFCRESkEqkYEdNd2bYed/VuDMAjX24i/miOeWEMA74fD/tWgqcv3Po5+NczL4+ISA2gYkScwhNXtaRzw0Aycwt54NP15BYUmRPk11cg9r9gscKNH0JYO3NyiIjUICpGxCl4uFmZcWtnavt4sCUhg+e/31b1IbZ8DctODMZ39cvQ/IqqzyAiUgOpGBGnUT/Qm9eGdcRigU9Wx/NNbELVHfzwJljwgGO5xxjodnfVHVtEpIZTMSJO5dIWITzYtxkAE7/+kz3JmZV/0KwjMG84FB6HZv2g/9mnKhARkYqnYkSczrh+F9GzaR1y8ou4/78byMmvxAn1TvacST8AQU1h6Ptg1WB8IiJVScWIOB03q4XXb+5EiJ+N3clZPPvt1so72KInIP4PsPnDLfPAO7DyjiUiImekYkScUl0/G9NvdrQf+WLdwcppP7JuDqx7H7DA9bOh7kUVfwwRETkvFSPitHo2Deahy5oD8OTXf7I/Jbvi3vyvP2DhY47lyydBiysr7r1FRKRMVIyIU3v4smZ0jwwiO7+IBz/bQF5hBYw/knHY0U7EXghtrofeEy78PUVEpNxUjIhTc3ez8votHQk8Mf7ISz/uvLA3LCqAL0dC9hEIaQNDZmjyOxERk6kYEadXL8CbaTd0AGDO7/tYsi2p/G+25Fk4sMrRYHXYXPCsVTEhRUSk3FSMiEvo1zqUO3s55q959KtNHE4/XvY32fYNxMxwLF/7NtRpWoEJRUSkvFSMiMt4/KoWtG3gT1pOAWM/i6WwyF76nVP2wIIxjuWeD0GrQZUTUkREykzFiLgMm7sbM27pTC1PN9bsT+WNZXtKt2N+NnxxO+RnQqNecPmzlZpTRETKRsWIuJTI4Fq8eL1jJt03l+3mj70p597BMOD7CZC8DWqFwA1zwM29CpKKiEhpqRgRlzOkYwNu6hqOYcC4ebEczco7+8Yb58LmeWBxgxs/AL+wqgsqIiKlomJEXNKzg9vQLMSX5Mw8Hv1yE4ZhnL5R8g5Y+H+O5csnQWTvqg0pIiKlomJEXJKPpzszbu2Ep7uVX3Ye4eOYv0puUHAcvrrTMRNv08ug51hzgoqIyHmpGBGX1TLMn4lXtQTghYXb2ZmYeerFn56C5K2OdiLXvQNW/VUXEXFW+g0tLm1kz0gubVGX/EI7D3+2kdyCItj27YkJ8IDrZoFviLkhRUTknFSMiEuzWCxMu7EDwb6e7EzKZOaCX+DbBx0v9hoLzS43N6CIiJyXihFxecG+Nl6+sQNuFNH7z4mQmw4NusJlk8yOJiIipaBiRKqFvi1CmBO5lG7WXWThw9GrZoKbh9mxRESkFFSMSPUQv5qLkz4G4In8u5jwcxp2+xm6+4qIiNNRMSKuLy8T5t+LxbCTftENLLb2YsWuI3z4x36zk4mISCmoGBHX99OTcGw/BEQQcP2rPDWwFQBTf9zB9sMZ5mYTEZHzUjEirm3HQtjwMWBxdOP1CuD2Ho24vGUI+UV/6+4rIiJOS8WIuK6sZPj2Icdyz4eKh3u3WCy8dEN7gn1t7E7O4oUftpsYUkREzkfFiLgmw4BvH4acFAhpA5c9XeLlYF8br9zUAYC5q/5iybYkM1KKiEgpqBgR17ThI9j1I7h5wtDZ4G47bZNLLqrLXb0bA/B//9tMckZuVacUEZFSUDEirid1Hyx60rF8+WQIbXPWTf/vyha0qudPanY+j361+cyz+4qIiKlUjIhrsdsd7UQKsqFRb+gx5pyb29zdeOPmjtjcrazcdYbZfUVExHQqRsS1rHsf9v8KHj4wZEapZuNtHupXPLvviwu3szsp8zx7iIhIVVIxIq7j2H5Y/Ixjud9zENS41LuOiI7k4ovqkldoZ9znseQX2isno4iIlJmKEXENdjt88+CJ2zO9oNvdZdrdarXw8g3tCfTxYOuhDKYv2VVJQUVEpKxUjIhrWP+B4/aMu3epb8/8U6i/F1OuawfAzBV7WbMvtaJTiohIOagYEed37C9YPNmx3O9ZCGpS7re6ql09bugSjmHA+M9jycgtqJiMIiJSbipGxLkZhqP3TH4WNOwJ3e+94Ld8ZlBrwmt7k5B2nGe/3VoBIUVE5EKoGBHntv5D2LcC3L3KfXvmn/y8PHhtWEesFvh6QwI/bD584TlFRKTcVIyI80pPgJ8nOZYvnwx1mlbYW3eLDOL+Sx3v9+T8P0lM1+isIiJmUTEizskwYOGjkJ8J4d0hanSFH2Ls5RfRrkEA6ccLeOyrTdjtGp1VRMQMKkbEOW3/DnYuBKsHDH4DrG4VfghPdyuvDeuIl4eVX3en8FHM/go/hoiInJ+KEXE+uemw8DHHcu9xENKq0g7VLMSXp652vP+UH3ewS6OziohUORUj4nyWPAdZiRDUFPo8WumHu61HIy5tUZf8Qjvj5sWSV1hU6ccUEZFTVIyIc4lf5Zh/BmDQ6+DhVemHtFgs/OeG9gTV8mTb4QxeXazRWUVEqpKKEXEehfnw3VjHcqfboHGfKjt0iJ8XU653jM767so4VsUdrbJji4jUdCpGxHn8/joc2QE+wXDF81V++AFtwripq2N01ke+2KTRWUVEqoi72QFEAEjZDSv/41i+6iXwCTIlxuRBbVgVl0p8ag7PfLOV14Z1NCWHyLkUFBRQVKS2TVL13Nzc8PDwqPD3VTEi5jMM+H48FOVDs37QdqhpUXxt7rw2rCM3zvqD+RsTuKxlCIM61Dctj8jfZWRkkJKSQl5entlRpAaz2WwEBwfj7+9fYe+pYkTMt/mLUzPyDnwFLBZT43RpVJsH+zbjjWV7eGr+n3SNrE29AG9TM4lkZGSQkJCAr68vwcHBeHh4YDH534rULIZhUFBQQHp6OgkJCQAVVpCoGBFzHU+Dn592LF/8KNSONDNNsYcub86KXUfYdDCdR7/cxNw7o7Ba9YtfzJOSkoKvry/h4eEqQsQ03t7e+Pn5cfDgQVJSUiqsGFEDVjHX8imQnQx1mkHPh8xOU8zD7dTorL/vOcoHf+w3O5LUYAUFBeTl5REQEKBCRExnsVgICAggLy+PgoKKaeivYkTMc3gzrHnXsXz1NHC3mZvnH5rU9eXpga0BeGnRDnYmanRWMcfJxqqV0XBQpDxO/l2sqIbUKkbEHHY7/PAIGHZocx007Wt2ojMaHtWQy1qGkF9oZ+y8jRqdVUylqyLiLCr676KKETFH7CdwcA14+sKAF81Oc1YWi4WpQ9sRVMuTHYmZvPqzRmcVEalo5SpG3nrrLSIjI/Hy8iIqKoo1a9acc/u0tDTGjBlDvXr1sNlsXHTRRSxcuLBcgaUayEmFJc84li99Avydu+tsiJ8XU0+OzvqrRmcVEaloZS5GPv/8cyZMmMAzzzzDhg0b6NChAwMGDCA5OfmM2+fn53PFFVewf/9+vvrqK3bu3Mns2bNp0KDBBYcXF7X0X5BzFOq2gqjRZqcplf5twhjWNUKjs4qIVIIyFyOvvvoq99xzD6NGjaJ169bMmjULHx8f5syZc8bt58yZQ2pqKgsWLKBXr15ERkZyySWX0KFDhwsOLy4oYT2s/9CxPPAVcHOdBnmTBrWmYZAPCWnHeeabrWbHERGpNspUjOTn57N+/Xr69et36g2sVvr160dMTMwZ9/n222+Jjo5mzJgxhIaG0rZtW1588cVztsDNy8sjIyOjxEOqAbsdfngUMKD9zRDZy+xEZXJydFarBeZvTOD7zYfMjiQiUi2UqRhJSUmhqKiI0NDQEutDQ0NJTEw84z5xcXF89dVXFBUVsXDhQiZNmsQrr7zCv//977MeZ8qUKQQEBBQ/IiIiyhJTnNWmT+HQBvD0gyv+ZXaacjk5OivAU/O3cDj9uMmJRORcLBZLlT2k/Cq9N43dbickJIR3332XLl26MGzYMJ566ilmzZp11n0mTpxIenp68ePAgQOVHVMqW24GLHnOsXzJ/4Ff6Lm3d2IPXd6cDuEBpB8v4NEvN2G3G2ZHEpEzsNvt9O/fH8MwquQh5VemYiQ4OBg3NzeSkpJKrE9KSiIsLOyM+9SrV4+LLroINze34nWtWrUiMTGR/Pz8M+5js9nw9/cv8RAX9+s0x0irQU1dptHq2Xi4WXlVo7OKOL3Vq1cTFRVldgwphTIVI56ennTp0oWlS5cWr7Pb7SxdupTo6Ogz7tOrVy/27NmD3W4vXrdr1y7q1auHp6dnOWOLSzm6F2LedixfOQXcXf/PvWldX57S6KwiTu27775j0KBBZseQUijzbZoJEyYwe/ZsPvroI7Zv3879999PdnY2o0aNAmDEiBFMnDixePv777+f1NRUxo4dy65du/jhhx948cUXGTNmTMV9CnFuPz0F9gJo1g+a9zc7TYW5LaohfVvUJb/QzrjPYzU6q4iTWbduHV27djU7hpRCmWftHTZsGEeOHGHy5MkkJibSsWNHFi1aVNyoNT4+Hqv1VI0TERHBTz/9xPjx42nfvj0NGjRg7NixPP744xX3KcR57VkCu34Eq7tjpNVq1MjLYrHw0g3tuXL6r2w/nMGri3cx8apWZscSEWD//v1ERkaqYamLsBgu0OomIyODgIAA0tPT1X7ElRQVwMxekLITejzguEVTDf20NZH75q7HYoHP7ulBjyZ1zI4k1Uxubi779u2jcePGeHl5mR3HJbz55ptERkae9zbN9OnTSUtLO239s88+WznBqonS/p0s7fd3ma+MiJTa2vcchYhPHbik+l4JG3BidNbP1x3gkS828eO4Pvh7uc5gbiLV0ZIlS5g3b955t5s+fTp//fXXaetVjFQtTZQnlSM7BX45cSXkskngHWhqnMqm0VlFnEdmZibu7u54e3ufd9v9+/erm64TUDEilWPZvyEvHcLaQecRZqepdBqdVcR5/PTTTwwYMMDsGFIGKkak4iVthQ0fOZavfAmsbufevpro0qg2Y/42Omtieq7JiURqpu+//55rrrnG7BhSBipGpOL9PAkMO7Qa7HLzz1yohy9vTnuNzipiGrvdTlJSEvXr1z/ndgUFBUyfPp2OHTvi7e1NeHg448ePJz8/n5ycHEJDQxk+fHgVpRYVI1Kx9iyBvUvB6gFXPGd2mirn4WbltROjs/62J4UPNTqrSKVYtWrVGdfHxMScdRDOk1JTU+nVqxfjx48nIiKChx9+mICAAKZPn84bb7zBrFmzSE1N5bnnat7vMLOoN41UHHsR/DzZsdz9XghqYm4ek5wcnXXSgi1MXbSD3s2DuSjUz+xYUk0ZhsHxAtcYcM/bw+2Cx/0oLCxk4sSJTJs2jb1799KkScnfM9999x3Dhg0753sMGzaMtWvX8vrrr/Pwww8D8NhjjxEeHs6PP/7Itm3bGDlyJM2aNbugrFJ6Kkak4sR+CslbwSsALn7U7DSmui2qIcu2J/HLziOMmxfLgjG98HTXhUipeMcLimg9+SezY5TKtn8NwMfzwr52tm3bxhVXXMF7773HN998w/jx40u8vnHjRqZOnXrW/ZcsWcKSJUvo06cPDz30UPH64OBgIiMjWbZsGTabjcmTJ19QTikb/XaUipGX5ehBA3Dx/4FPkLl5THZydNagWp5sOzE6q4hcuPbt29O/f39uuOEGvv322xKvxcXFnXal5J/mzp0LwLhx4067SnNy8K777ruPiIiIEq899thj6qFTiXRlRCpGzAzISoTakdD9HrPTOIUQPy+mXN+O++au552Ve7m0RV2NzioVztvDjW3/co0vSW+PiutZN3jwYK6//nqOHTtG7dq1gdJNjLdixQo8PDy48sorz/i6j48PTz755Gnr16xZw8UXX3zhweWMdGVELlxmIvz+umO537PgbjM1jjMZ0CaMm7qGYxjwyBebyMgtMDuSVDMWiwUfT3eXeFTkPDH9+vXD09OThQsXFq9btmwZl1122Vn3OX78OPHx8TRq1AgfH58Sr8XFxbFjxw6ioqKK51oDyM/Px9PTk5UrV/Lvf/8bi8VC69atK+xziIOKEblwv7wABTkQ3g1aX2t2GqczeVCb4tFZn9XorCIVwtvbmyuuuKL4Vk16ejo2m+2c86QcP34cwzBKTOZ60vjx48nLy8PdveQNA3d3d2JiYgBYvXo1hw8f5vfff6/ATyKgYkQuVNJW2Phfx3L/F6rVrLwVxTE6awesFvh6YwI/bD5sdiSRamHw4MEsWrSIgoICFi1adNZbLyfVrl0bX19f9uzZw+bNm4vXz5w5s7io+eekeVarlcOHD+Pn50e3bt0ICwsrvi0kFUfFiFyYkwOctb4WGkaZncZpdWkUVDw665Pz/9TorCIV4JprriErK4vly5fzww8/MHDgwHNub7FYGDlyJHa7nX79+jFmzBiGDh3KmDFjGDJkCJdeeilr165l9OjRrF27tni/jRs30qFDhwq9zSQlqRiR8tuz9NQAZ/2eMTuN0/v76KyPfaXRWUUuVEhICD169GD+/PmkpKSUaOtxNi+//DKPPPIINpuNd999l19//ZXx48fz5Zdf8tJLL9GiRQveeeedEldIYmNj6dSpUyV+ElExIuVjt8OSZx3L3e+psQOclcXfR2f9dXcKH8XsNzuSiMsbPHgw77//Pr16lW7qCS8vL6ZNm8aBAwcoKCggOTmZV155BQ8PD7p3786OHTswDIMrrriieJ9NmzbRvn37yvoIgooRKa9t8yFxM3j6QZ+aPcBZWTSt68tTV7cCYOqPO9iVlGlyIhHXNmTIEPLz88/bpfdCFBYWsmPHDg4dOnRamxKpGCpGpOyKCk4NcNbrYailsTPK4rYejbi0RV3yCu2MmxdLfqHd7EgiLqtly5bceuutlXrl4oUXXmDevHmEh4czceLESjtOTaZiRMpuw8eQGge16kKPB8xO43IsFgv/Gdqe2j4eGp1VpAJ88sknlfr+w4cP5+DBg9jtdmbOnFmpx6qpVIxI2eRnw4qXHMsX/x/YfM3N46JC/L2Ycr3jf3LvrNzL6rijJicSETGPihEpm9WzICvJMex7l5Fmp3FpV7Y9NTrrhC82kX5co7OKSM2kYkRKLycVfjsx7Hvfp8Hd09w81cDkQW1oVMcxOutT8//EMNTdV0RqHhUjUnq/vQp56RDaDtoONTtNteBrc+f1mzvhbrXw/ebD/G9DgtmRRESqnIoRKZ30BFj9rmO53zNwhrkdpHw6RgQy/oqLAJj8zRb2pWSbnEhEpGrpG0VKZ/kUKMqDRr2gWT+z01Q7oy9pSo8mQeTkFzF23kZ19xWRGkXFiJzfkV0Qe6LrXL9nNRleJXCzWnhtWEcCvD3YfDBd3X1FpEZRMSLnt+xfjsnwWgyEiO5mp6m26gV489LQdoCju+/ve1JMTiQiUjVUjMi5JayH7d+BxQqXTzI7TbV3Zdt63NK94YnuvrGkZuebHUlEpNKpGJFz++VFx3P7YRDSytwsNcSka1rRtG4tkjLyePx/m9XdV0SqPRUjcnbxq2DPErC6wyWPm52mxvDxdOeNWzrh6WZl8bYkPlkdb3YkEZFKpWJEzu7kZHidboOgxuZmqWHa1A/g8ataAvD899vYrdl9RaQaUzEiZxa3Avb/Cm6ecPFjZqepkUb1jOSSixyz+z702UZyC4rMjiQiUilUjMjpDAN+ecGx3GUUBISbm6eGslotTLuxA8G+nuxIzGTqjzvMjiQiUilUjMjp9iyBA6vB3Qv6TDA7TY1W18/Gyzd0AODDP/azbEeSyYlERCqeihEp6e9XRbrdDX5h5uYR+rYMYVSvSAAe+3IzyZm55gYScSEWi0WPcj6qkooRKWnnQji0ETxqQe/xZqeREx6/siUtw/w4mp3PI19swm5Xd1+R87Hb7fTv3x/DMPQox6MqqRiRU+x2WHbiqkiP0VAr2Nw8UszLw403b+mEl4eVX3enMGvlXrMjiTi91atXExUVZXYMKQUVI3LKtgWQvBVs/hD9oNlp5B+ah/rxr8FtAXjl512s/yvV5EQizu27775j0KBBZseQUlAxIg72IsfMvOAoRHyCzM0jZ3Rj13CGdKxPkd3goU83kpaj4eJFzmbdunV07drV7BhSCipGxOHPLyFlF3jXhh73m51GzsJisfDCde2IrOPDofRcHvtKw8WLnMn+/fuJjIys8oaYUj7uZgcQJ1BUCMunOpZ7jQUvf3PzyDn52tyZcWtnrn/7DxZvS+LDP/YzqpdGyBX5u+p8i2b69OmkpaWdtv7ZZ5+t8iwVRcWIwJ9fwLF94BMM3e81O42UQtsGATx5dUue/W4bUxbuoGujINqFB5gdS8RpLFmyhHnz5p1zmzvuuIMff/yRffv2UatWrSpKduGmT5/OX3/9ddr60hQj69evp2vXrsyePZu77767EtKVj27T1HRFhbDyZcdyz4fA03X+QdZ0d/SMpH/rUPKL7Dz42QYycwvMjiTiFDIzM3F3d8fb2/us26xdu5a5c+fyxBNPlChEvvrqKx566CH69OmDv78/FouF2267rSpil9r+/fvL3RW3S5cuXHvttUyaNImsrKxKTlp6KkZqui1fQWoc+NRxDHImLsNisfCfG9rTINCbv47m8OT8LWo/IgL89NNPDBgw4JzbPPXUU/j7+3P//SXbyP373/9mxowZxMbG0qBBg8qMaZqJEyeSmJjIG2+8YXaUYipGajJ70amrItEPgs3X3DxSZoE+nrxxS0fcrBa+23SIz9ceMDuSiOm+//57rrnmmrO+vmvXLpYsWcJNN9102tWT1157jV27dpGRkcHMmTMrO6opunfvTsuWLXnnnXew2+1mxwFUjNRsW/4HR/c4etB0v8fsNFJOXRoF8Uj/iwB49rut7ErKNDmRiHnsdjtJSUnUr1//rNvMmTMHwzAYNmzYaa/17duX5s2bO2UvnIKCAqZPn07Hjh3x9vYmPDyc8ePHk5+fT05ODqGhoQwfPrxU73XzzTcTHx/P4sWLKzl16agYqalKXBUZAzY/c/PIBRl9cVP6NA8mt8DOmE82cDy/yOxIIpVq1apVZ1wfExNDdHT0OfddsmQJbm5u9OjRozKiVYrU1FR69erF+PHjiYiI4OGHHyYgIIDp06fzxhtvMGvWLFJTU3nuuedK9X69evUCcJpiRL1paqqt8x3jingFQvf7zE4jF8hqtfDasI5c9fqv7E7O4tlvt/LSDe3NjiVVwTCgIMfsFKXj4QMXeMWhsLCQiRMnMm3aNPbu3UuTJk1KvP7dd9+d8YrHSdnZ2cTGxtKqVSuX6kEzbNgw1q5dy+uvv87DDz8MwGOPPUZ4eDg//vgj27ZtY+TIkTRr1qxU79etWzcAVq5cWWmZy0LFSE1kt5e8KqJxRaqFYF8b04d15Lb3V/P5ugNENQni+s7hZseSylaQAy+e/ZaEU3ny0AX32Nu2bRtXXHEF7733Ht988w3jx5ec0HPjxo1MnTr1rPsnJCRQVFREvXr1LihHVVqyZAlLliyhT58+PPTQQ8Xrg4ODiYyMZNmyZdhsNiZPnlzq9wwICMDLy4v4+PjKiFxmuk1TE21bAEd2gC0AonRVpDrp1SyYhy5rDsBT87eo/YhUO+3bt6d///7ccMMNfPvttyVei4uLO+1KyT8dPXoUgNq1a1daxpMjv5b2cb6uw3PnzgVg3Lhxp7Vl8fLyAuC+++4jIiKixGuPPfbYOXsVBQUFkZKSUp6PWOF0ZaSmsdthxX8cyz3uBy8NlFXdjL28ORv+OsZve1IY/d/1fPtgb3xt+qdebXn4OK44uAIPnwp7q8GDB3P99ddz7Nix4sKiNKOunuw9k5ubW2FZ/qlp06bFRUJpnKuxLcCKFSvw8PDgyiuvPOPrPj4+PPnkk6etX7NmDRdffPFZ3/f48ePnHIulKuk3VE2z/Vs4st0xM2+P0WankUrgZrUw/eaODHzjV+KOZDPx6z954+aOTtk7QCqAxVIjByvs168fnp6eLFy4sLgHybJly7jvvnNf7Q0JCQFOXSGpDEuXLq2w9zp+/Djx8fE0bdoUH5+SxVxcXBw7duygZ8+ehIaGFq/Pz8/H19eXgoICVq5cyb///W9atWrFtm3birex2+2kpaXRuLFzTCWh2zQ1yd/bikSNdnTplWop2NfGW7d2Lh5/5L+rTh86WsSVeXt7c8UVVxTfqklPT8dms533ikS9evWoW7cuO3furIqYF+z48eMYhoHVevrX9fjx48nLy8PdveR1BXd3d2JiYgBYvXo1hw8f5vfffy+xzc6dOzEMg44dO1Za9rJQMVKT7PwBkraAp59m5q0BukYGMfGqlgD86/ttbDqQZm4gkQo2ePBgFi1aREFBAYsWLTrrbYy/s1gsXHzxxaSkpLBnz54qSHlhateuja+vL3v27GHz5s3F62fOnFlciP1z0jyr1crhw4fx8/OjW7duhIWFndZG5mTX6L59+1buByglFSM1hWHAipccy1H3gk+QuXmkStzVuzED2oRSUGTwwCcbSMvJNzuSSIW55ppryMrKYvny5fzwww8MHDiwVPsNHToUcAwb/08LFixg5MiRjBw5srhXTkxMTPG6Rx99tOI+QClYLBZGjhyJ3W6nX79+jBkzhqFDhzJmzBiGDBnCpZdeytq1axk9ejRr164t3m/jxo106NDhrLdnf/75Z9zc3BgyZEhVfZRzUpuRmmLXIkj8Ezx9HUO/S43gmL+mAzsSf+OvozlM+GIT743oitWq9iPi+kJCQujRowfz588nJSWlRLuJcxk6dCghISF8/PHHjBkzpsRrsbGxfPTRRyXWxcXFERcXB0CjRo2YNm1axXyAUnr55Zex2Wx8/vnnvPvuu9SuXZvx48czdepUNm7cyIgRI3jnnXeKi6yTn6NTp05nfL/09HQWLFjANddcc1oPHLOU68rIW2+9RWRkJF5eXkRFRbFmzZpS7Tdv3jwsFgvXXntteQ4r5WUYsPLEP55ud+mqSA0T4O3B28M74+luZdmOZGat3Gt2JJEKM3jwYN5///3iEUVLw9PTk3HjxrFmzRo2btxY4rVnn332jDPinnzs37+/gj/B+Xl5eTFt2jQOHDhAQUEBycnJvPLKK3h4eNC9e3d27NiBYRhcccUVxfts2rSJ9u3PPPDhxx9/TG5ubpVf5TmXMhcjn3/+ORMmTOCZZ55hw4YNdOjQgQEDBpCcnHzO/fbv38+jjz5Knz59yh1WymnfSkhYB2426DHm/NtLtdOmfgD/GtwGgGk/7SRmb+X1JBCpSkOGDCE/P/+8XXr/afz48TRs2LBMA4W5ksLCQnbs2MGhQ4dKtCk5fvw4U6ZMYejQofTu3du8gP9Q5mLk1Vdf5Z577mHUqFG0bt2aWbNm4ePjw5w5c866T1FREcOHD+e5554774A0Ugl+fcXx3HkE+JXuMqZUP8O6RTC0czh2Ax76bCPJGZU3zoJIVWnZsiW33nrrWa8CnI2Xlxdz586la9euZGdnV1I687zwwgvMmzeP8PBwJk6cWLx+//793HvvvVV+q+l8LIZhGKXdOD8/Hx8fH7766qsSt1ruuOMO0tLS+Oabb8643zPPPMPmzZuZP38+I0eOJC0tjQULFpz1OHl5eeTl5RX/nJGRQUREBOnp6fj7a+jyMjm4Dt67HKzu8PBGCGxodiIx0fH8Iq57+3d2JGbSvXEQn9wdhYeb2rE7u9zcXPbt20fjxo3LNJiWSGUp7d/JjIwMAgICzvv9XabfQikpKRQVFZ3WSCg0NJTExMQz7vPbb7/x/vvvM3v27FIfZ8qUKQQEBBQ/nKWBjUs6eVWk/TAVIoK3pxtvD++Mr82dNftSeXHhdrMjiYhUbtfezMxMbr/9dmbPnk1wcHCp95s4cSLp6enFjwMHDlRiymosaSvsXAhYoPf4824uNUOTur68clMHAD74fT8LNiaYnEhEaroyde0NDg7Gzc2NpKSkEuuTkpIICws7bfu9e/eyf//+Eg2L7Ha748Du7uzcuZOmTZuetp/NZsNms5UlmpzJr686nlsPgeDm5mYRpzKgTRgP9m3GjF/28MTXm2ke6kub+pqnSETMUaYrI56ennTp0qXEuPt2u52lS5cSHR192vYtW7bkzz//JDY2tvgxePBg+vbtS2xsrG6/VKaje2Hr147lPo+Ym0Wc0vgrLuKSi+qSW2Bn9H/Xa0A0ETFNmQc9mzBhAnfccQddu3ale/fuTJ8+nezsbEaNGgXAiBEjaNCgAVOmTMHLy4u2bduW2D8wMBDgtPVSwX57DQw7NO8P9crWylxqBjerhddv7sjgGb8Tn5rDw/Ni+WBkN9w0IJqIVLEytxkZNmwY06ZNY/LkyXTs2JHY2FgWLVpU3Kg1Pj6ew4cPV3hQKYP0g7BpnmO5j/MMaiPOJ9DHk1m3dcHLw8rKXUd4dbFrTB5WU5Wh86NIparov4tl6tprltJ2DZITfnwcVs+CRr1h1A9mpxEX8E1sAmPnxQIw67YuXNn29DZgYp6CggL27NlDgwYN9DtQnEJGRgYJCQk0a9YMDw+Pc25X4V17xQVkHYH1J+ZVuFhtRaR0hnRswF29GwPwyBex7EnONDmR/J2Hhwc2m4309HRdHRHTGYZBeno6NpvtnIVIWWiivOpm1dtQeBzqd4ImzjE1tLiGJ65qyZaEdFbvS+Xeuev5Zkwv/Lwq5heNXLjg4GASEhI4ePAgAQEBeHh4nHVGVpHKYBgGBQUFpKenk5WVRYMGDSrsvVWMVCfH02Dte47lPo+CflFJGXi4WXlreGcGvfkbcUeymfDFJt65rYtm+HUSJy9xp6SkkJCgsWHEPDabrcJvGaoYqU7Wzoa8DKjbClpcbXYacUHBvjZm3taFm2bFsHhbEq8t2cUj/VuYHUtO8Pf3x9/fn4KCAoqKisyOIzWQm5tbhd2a+TsVI9VFfg6smulY7jMBrGoOJOXTMSKQKde345EvN/Hmsj1cFOrHoA71zY4lf+Ph4VEpXwgiZtE3VnUR+wnkHHXMP9PmerPTiIsb2iWcey92zLD92Feb2JKQbnIiEanOVIxUB0WF8MebjuXoh8BNF7zkwj1+ZUsubeEYofWej9eRnJlrdiQRqaZUjFQH2xZA2l/gUwc63WZ2Gqkm3KwW3rilE03r1uJwei6j564nr1DtFESk4qkYcXWGAb+/7ljufh94+pibR6oVfy8P3rujG/5e7myIT+PJr7donAsRqXAqRlxd3C+QuBk8fKD7PWankWqocXAt3hreGTerhf9tOMj7v+0zO5KIVDMqRlzdb9Mdz51HgE+QqVGk+urTvC5PD2wFwIsLt/PLzmSTE4lIdaJixJUd2gj7VoDFDaLHmJ1GqrmRPSMZ1jUCuwEPf7qRPclZZkcSkWpCxYgrO9lWpO1QR5dekUpksVh4/tq2dIusTWZeIXd+uJajWXlmxxKRakDFiKtKjYNt3ziWe401N4vUGJ7uVmbd1oWIIG/iU3O4d+56cgvUw0ZELoyKEVf1xwww7NDsCghra3YaqUHq+Nr4YGQ3/LzcWf/XMR77ajN2u3rYiEj5qRhxRVlHHCOugq6KiCmahfjxzm1dcLda+G7TIV5bssvsSCLiwlSMuKLVs6AwFxp0gcjeZqeRGqpns2BevK4dAG8u28NX6w+anEhEXJWKEVeTl+mYnReg1ziwaHp3Mc9N3SJ44NKmAEz8ejMxe4+anEhEXJGKEVez4WPITYc6zaDlQLPTiPBo/xYMbFePgiKD++auU5dfESkzFSOupDAfYt5yLPd8CKxu5uYRAaxWC6/c1IFODQPJyFWXXxEpOxUjrmTLV5CRAL6h0P5ms9OIFPPycGP2iK7FXX7v+Xgdx/PV5VdESkfFiKswDPj9Dcdyj/vBw8vcPCL/EHyiy+/JSfUe+mwjhUV2s2OJiAtQMeIq9iyBI9vB0w+6jDI7jcgZNQvx4707uuHpbmXJ9iQmf7tVs/yKyHmpGHEVf7zpeO5yB3gHmhpF5Fy6Nw7ijZs7YrHAp6vjmbFsj9mRRMTJqRhxBYc3n5oQL2q02WlEzuvKtvV4dlAbAF5ZvIsv1h4wOZGIODMVI64gZobjuc21EBhhahSR0rqjZyT3nxyDZP6f/LIj2eREIuKsVIw4u/QE2PI/x3L0g+ZmESmj/xvQgus7N6DIbvDAJxuIPZBmdiQRcUIqRpzdmnfAXgiNekODzmanESkTi8XCS0Pbc/FFdTleUMSdH65lX0q22bFExMmoGHFmeZmw7kPHck9dFRHX5OFmZebwzrRrEEBqdj63vbeaxPRcs2OJiBNRMeLMNsyFvHSo0xyaDzA7jUi51bK5M2dkNxoH1yIh7Ti3vb+a1Ox8s2OJiJNQMeKsigph1UzHcvQDYNUflbi2un425t7VnXoBXuxJzmLkB2vIzC0wO5aIOAF9wzmr7d9Cejz41IEOt5idRqRChNf2Ye5dUQTV8mTzwXTu+XgduQUaNl6kplMx4owM49QgZ93uAQ9vc/OIVKBmIb58NKo7vjZ3VsWl8uCnGyjQsPEiNZqKEWcUHwOHNoCbDbrdbXYakQrXLjyA9+7ois3dypLtyfzfV5ux2zVsvEhNpWLEGf1xYpCzDjeDb11zs4hUkh5N6vD28M64Wy3M35jAs99pHhuRmkrFiLM5uhd2LnQsa5AzqeYubxXKKzd1wGKBj2P+YuqiHSpIRGogFSPOJuYtwICLroS6F5mdRqTSDenYgH8NaQvAOyvieG3xLpMTiUhVUzHiTLKPQuwnjmVdFZEa5PYejZh8TWsA3li2hzeW7jY5kYhUJRUjzmTd+1CYC/U6QGRvs9OIVKk7ezfmyatbAvDq4l3MXL7X5EQiUlVUjDiLglxY865juefDYLGYm0fEBPde3JTHBrQA4KVFO3jv1ziTE4lIVVAx4iz+/AKyj4B/OLQeYnYaEdOM6duMsZc3B+DfP2znoz/2mxtIRCqdihFnYBinuvP2GA1uHubmETHZuH7NeeDSpgA88+1WPln9l8mJRKQyqRhxBnuXQspO8PSDziPMTiNiOovFwmMDWnDvxU0AeGr+FuauUkEiUl2pGHEGJyfE63w7eAWYm0XESVgsFiZe1ZI7ezUGYNKCLcz5bZ/JqUSkMqgYMduRnbBnCWCB7veanUbEqVgsFiZd04r7LnFcIfnX99t4Z4V62YhUNypGzHbyqkjLgRDU2NwsIk7IYrHwxJUtefiyZgBM+XEHM5ZpHBKR6kTFiJlyUmHTPMdyjwfMzSLixCwWCxP6t2DCFY5Riaf9vItXF+/S0PEi1YSKETOt/xAKj0NYe2jU0+w0Ik7v4cub88RVjoHR3li6m//8tFMFiUg1oGLELEUFsGa2Y7nHAxrkTKSURl/SlEknho6fuXwvz323DbtdBYmIK1MxYpZt30DmIagVAm2vNzuNiEu5q3djnr/WMbneh3/s55EvN1FQZDc5lYiUl4oRMxjGidl5ge73gLvN3DwiLuj2Ho14/eaOuFstzN+YwOi568ktKDI7loiUg4oRMxxcC4c2gJsNuowyO42IyxrSsQHvjuiCzd3K0h3JjJizhozcArNjiUgZqRgxw6q3Hc/tbwTfuuZmEXFxl7UM5eM7u+Nnc2fNvlRueXcVKVl5ZscSkTJQMVLV0g7Atm8dy+rOK1IhoprU4bN7e1CnlidbD2Vw06wYDh7LMTuWiJSSipGqtuZdMIqg8SUQ2sbsNCLVRtsGAXw5OpoGgd7EpWQzdOYfbDuUYXYsESkFFSNVKS8LNnzkWNZVEZEK16SuL1+OjqZ5iC9JGXnc9E4Mv+1OMTuWiJyHipGqtOkzyE2HoCbQvL/ZaUSqpfqB3nw1uidRjYPIyitk5Adr+N/6g2bHEpFzKFcx8tZbbxEZGYmXlxdRUVGsWbPmrNvOnj2bPn36ULt2bWrXrk2/fv3OuX21Zbefmocm6n6wqg4UqSwBPh58fFd3BnWoT6Hd4JEvNzFj2W6N1iripMr8jfj5558zYcIEnnnmGTZs2ECHDh0YMGAAycnJZ9x++fLl3HLLLfzyyy/ExMQQERFB//79SUhIuODwLmXPYkjdC7YA6Hir2WlEqj2buxuvD+tYPOPvtJ938eT8PynU4GgiTsdilPG/ClFRUXTr1o0ZM2YAYLfbiYiI4KGHHuKJJ5447/5FRUXUrl2bGTNmMGLEiFIdMyMjg4CAANLT0/H39y9LXOfx8RCIWw49H4L+/zY7jUiN8nHMfp75diuGAX1b1OWNWzrh5+VhdiyRaq+0399lujKSn5/P+vXr6dev36k3sFrp168fMTExpXqPnJwcCgoKCAoKOus2eXl5ZGRklHi4tKRtjkLEYoXu95qdRqTGGREdyazbHIOj/bLzCDfMjOFAqrr+ijiLMhUjKSkpFBUVERoaWmJ9aGgoiYmJpXqPxx9/nPr165coaP5pypQpBAQEFD8iIiLKEtP5rD7RVqTVIAhsaG4WkRpqQJswvrgvmhA/GzuTMrn2rd9Zuz/V7FgiQhX3ppk6dSrz5s1j/vz5eHl5nXW7iRMnkp6eXvw4cOBAFaasYNkpsOlzx7K684qYqkNEIN882Is29f05mp3P8Nmr+Uo9bURMV6ZiJDg4GDc3N5KSkkqsT0pKIiws7Jz7Tps2jalTp/Lzzz/Tvn37c25rs9nw9/cv8XBZ6z6Aojyo3xkiosxOI1Lj1Qvw5svR0VzVNoz8IjuPfrmJKT9up8iunjYiZilTMeLp6UmXLl1YunRp8Tq73c7SpUuJjo4+637/+c9/eP7551m0aBFdu3Ytf1pXU5gPa99zLPd4ACwWc/OICAA+nu68dWtnHrqsGQDvrIjjvrnrycorNDmZSM1U5ts0EyZMYPbs2Xz00Uds376d+++/n+zsbEaNcsw+O2LECCZOnFi8/UsvvcSkSZOYM2cOkZGRJCYmkpiYSFZWVsV9Cme1/VvISgTfMGg9xOw0IvI3VquFR/q34PWbO+LpbmXJ9iSGzPiNPck14HeTiJMpczEybNgwpk2bxuTJk+nYsSOxsbEsWrSouFFrfHw8hw8fLt5+5syZ5Ofnc8MNN1CvXr3ix7Rp0yruUzir1bMcz93uAndPc7OIyBkN6diAz+/tQZi/F3uPZHPtW7+zaEvpGuSLSMUo8zgjZnDJcUYS1sPsy8DNE8ZvBd8QsxOJyDkcyczjwU83sHqfo4fNA5c25ZH+LXCz6vaqSHlVyjgjUgar33U8t7lehYiIC6jrZ+O/d0dxd+/GALy9fC8jP1jDsex8k5OJVH8qRipDZhJs+Z9jOUqDnIm4Cg83K09f05o3bumEt4cbv+5OYdCM39iSkG52NJFqTcVIZVj/IdgLILw7NOhidhoRKaPBHeozf0xPGtXx4eCx41z/9h989Md+TbQnUklUjFS0wnxYN8exHHWfuVlEpNxahvnz7YO96d86lPwiO898u5XR/11Pek6B2dFEqh0VIxXt7915Ww02O42IXIAAbw/eub0LzwxqjYebhZ+2JjHwzV/ZGH/M7Ggi1YqKkYp2sjtv1zvVnVekGrBYLIzq1Zj/3d+ThkGO2zY3zoph9so47Bq1VaRCqBipSAnr4eBasHpA11FmpxGRCtQ+PJDvH+7NwPb1KLQbvLBwO3d9tJYjmXlmRxNxeSpGKtLJ7rxth6o7r0g15O/lwYxbOvHCdW3xdLfyy84jDJi+kp+3apA0kQuhYqSiZCWrO69IDWCxWBge1YjvHuxNyzA/UrPzuXfueh7/arPmthEpJxUjFaW4O283decVqQFahPnxzYO9uO+SJlgs8Pm6A1z9+q+s/yvV7GgiLkfFSEUozIe17zuWo0abm0VEqozN3Y2JV7Xis3t60CDQm/jUHG6cFcPLP+0gv9BudjwRl6FipCIUd+cNVXdekRqoR5M6/DiuD9d3aoDdgLd+2cvgGb+x+WCa2dFEXIKKkYqw+h3Hc1fNzitSU/l7efDqsI68PbwzQbU82ZGYybVv/c6UH7eTW1BkdjwRp6Zi5EIlrIeDa9SdV0QAuLpdPRaPv5jBHepjN+CdFXFc/fqvrN2vtiQiZ6Ni5EIVd+fV7Lwi4lDH18Ybt3Ri9oiuhPjZiEvJ5qZ3Ynj2261kq8eNyGlUjFyIrGTY+rVjWfPQiMg/XNE6lMUTLuGmruEYBnz4x36ueHUFi7YkatI9kb9RMXIh1n8IRfnqzisiZxXg7cF/bujA3Lu6ExHkzaH0XEb/dz13fbSOA6k5ZscTcQoqRsrr7915u+uqiIicW5/mdfl53CU82LcZHm4Wlu1Ipt+rK5ixbDd5hWrgKjWbipHy+nt33tZDzE4jIi7A29ONRwe04MexF9OzaR3yCu1M+3kXV73+K7/vSTE7nohpVIyUl7rzikg5NQvx5ZO7o3j95o4E+9qIO5LN8PdWc+/H69ifkm12PJEqp2KkPBI2nOrO22Wk2WlExAVZLBaGdGzA0kcuYWTPSNysFn7elsQVr63gxYXbycgtMDuiSJVRMVIea/7Wndcv1NwsIuLSArw9eHZwGxaN7cPFF9WloMjg3ZVx9H15OZ+ujqfIrl43Uv2pGCmr7JRTs/Oq4aqIVJDmoX58fGd3PhjVjaZ1a3E0O58n5//JwDd+5ZcdyeoKLNWaipGy2vCxoztv/c4Qru68IlKx+rYIYdG4i3l2UGsCvD3YkZjJqA/XMuzdVZoRWKotFSNlYS+CdXMcy93vMTeLiFRbHm5WRvZqzIrHLuW+i5vg6W5lzb5Uhs6M4e6P1rEzMdPsiCIVSsVIWexaBOkHwDsI2lxvdhoRqeYCfTyZeHUrVjx2KTd3i8BqgSXbk7jy9ZVM+CKW+KMaNE2qBxUjZbFmtuO58+3g4WVuFhGpMeoFeDN1aHt+Hn8JV7cLwzDg6w0J9H1lOY9+uYl96g4sLs5iuECrqIyMDAICAkhPT8ff39+cECm7YUZXwAJjN0HtRubkEJEab9OBNF5ZvIuVu44AYLXAtR0bMOayZjSt62tyOpFTSvv9rSsjpbX2PcfzRVeqEBERU3WICOTjO7sz/4Ge9G1RF7sBX29MoN+rK3j4s43sSlKbEnEtujJSGnlZ8GoryMuA2/4HzfpVfQYRkbPYfDCNN5buYcn2pOJ1l7UM4Z4+TejRJAiLxWJiOqnJSvv97V6FmVzX5s8dhUhQU2hymdlpRERKaB8eyHt3dGVLQjozlu3hp22JLNuRzLIdybQPD+Dei5twZZsw3N10MVyck66MnI9hwMyekLwNBkyB6Aeq9vgiImW0LyWb936N46v1B8krtAMQEeTNXb0ac2PXCGrZ9P9QqRql/f5WMXI++3+HD68GDx+YsB28A6v2+CIi5XQ0K4+PY/7i45j9HMtxzHXjZ3NnaJdwbo9upMauUulUjFSUL0fC1vmOCfEGvV61xxYRqQDH84v4asNB5vy2r0Q34N7Ngrk9uhGXtwzRLRypFCpGKuTAh2F6W7AXwujfIaxt1R1bRKSC2e0Gv+5JYW7MfpbuSObkb/8Ggd7cGtWQG7uGE+KnMZSk4qgYqQi/TIEVU6FhNNy5qOqOKyJSyQ6k5vDJ6ng+XxtffAvHzWqhb4sQbuoaTt+WIXjoaolcIBUjF6ow33FVJCsJbpgDbYdWzXFFRKpQbkER328+zKer/2JDfFrx+mBfT67vHM5NXcNpFuJnXkBxaSpGLtSW/8FXd4JvKIzbAu6eVXNcERGT7EnO4st1B/jfhgRSsvKK13dqGMi1HRtwdbt61PWzmZhQXI2KkQs15yqI/wMueRz6Plk1xxQRcQIFRXaW7zzCF+sOsGxHMkV2x9eE1QK9mgUzuEN9BrQNw9/Lw+Sk4uxUjFyIxC0wqxdY3R1XRfzrVf4xRUSc0JHMPL7ffIhvYg8ReyCteL2nu5W+LeoyuEMD+rasi4+nxi6R06kYuRDfj4d1c6D1tXDTR5V/PBERF/DX0Wy+2+QoTHYnZxWv9/Kw0qd5XQa0CePyliHUrqXb2uKgYqS88rLglZaQnwkjvoUml1Tu8UREXIxhGOxIzOSb2EP88OchDqQeL37NzWqhe2QQA9qE0r9NGPUDvU1MKmZTMVJe6z+E78ZCnWbw4DrQBFMiImdlGAbbD2fy09ZEftqayI7EkjMGt67nz6Ut6nLJRXXp3Ki2ugvXMCpGyuudi+HwJuj/b+j5UOUeS0Skmok/mlNcmKyPP8bfv2H8vNzp3Sz4RHESQliABlir7lSMlEfCBpjdF9w8YcIOqFWn8o4lIlLNpWTl8evuIyzfeYSVu44UD652UsswP3o2DSa6aR26Nw4iwFu9c6obFSPl8c2DsHEutLsJhs6uvOOIiNQwRXaDzQfTWL7zCCt2HWHTwbQSV02sFmhTP4DopnWIblqHbpFB+Gp2YZenYqSsctMdDVcLcmDUj9CoZ+UcR0RESM3O5/c9KcTEHWXV3qPE/W0CP3A0hG3bIIAuDWvTpZHjods6rkfFSFmtmQ0LH4XgFjBmtRquiohUocT0XGLiUojZe5SYuKMleuic1CDQm86NatOlYSBdGgXRsp6fGsQ6udJ+f+saGIBhwLoPHMtd71QhIiJSxcICvLiuUzjXdQoHHBP5rf/rGBvij7H+r2NsP5xBQtpxEtKO892mQwDY3K20qudPuwYBtG3gT9sGAVwUqgLFFakYATi0EZK3grsXdBhmdhoRkRovIsiHiCAfru3UAIDsvEI2HUgrLlA2xKeRfryA2ANpp40M2yrMj7YNAmjbIIBW9fxpHuJLLbU/cWr60wHY9JnjueVA8K5tbhYRETlNLZs7PZsF07NZMAB2u8FfqTn8mZDOlhOPPxPSycwtZNPBdDYdTC+xf8MgHy4K9aNFmO+JZz+aBPvi6a6rKM5AxUhhPvz5lWO5w63mZhERkVKxWi00Dq5F4+BaDO5QH3AMwBZ/okD5MyGdrQkZ7EzK5EhmHvGpOcSn5rBke1Lxe7ifeI+mdX1pXNfxXk1OvGdQLU8sumVfZVSM7P4ZjqeCbyg0udTsNCIiUk4Wi4VGdWrRqE4trmlfv3h9anY+OxMz2ZWUyc6kTHYlOp4zcwvZnZxVYp6dk/y93Glc15fGdXxoHOxLZLAP4bV9iAjypq6vTYVKBVMxcvIWTfubwE2nQ0Skugmq5Vk8fslJhmFwOD2XnUmZ7DuSzb6UbPYfzSbuSDaH0o+Tketoo7Lpb+1RTrK5W2lQ25vw2j6E1/Y+8XAsNwj0JtjXhptVxUpZ1Oxv3/xs2L3YsdxeDVdFRGoKi8VC/UBv6gd607dFyddyC4r462gO+1Ky2JfieN5/NIeEY8c5nH6cvEI7cUcchcuZuFkt1PW1ERrgRZi/jVB/L0L9vQjz9yIswIvQE+v8vDTi7Ek1uxiJWwFFeRDQEELbmp1GREScgJeHGy3CHI1c/6mgyE5iei4HjuVw8NhxxyP15HIOiRm5FNkNEjNySczIZdM5juPt4UYdX0/q1PKkjq/tH88ll4NqeWJzd6u8D22ychUjb731Fi+//DKJiYl06NCBN998k+7du591+y+//JJJkyaxf/9+mjdvzksvvcTVV19d7tAVZtcix3OLKzW2iIiInJeHm7W42/GZFBbZOZqdT2K6oxhJOvFITM9zPJ/4OTO3kOMFRcUFTWn4eLoR6O2Bv7cHAX97BPqcWvb39iDQx7P4Zz8vd3xt7tjcrU7dzqXMxcjnn3/OhAkTmDVrFlFRUUyfPp0BAwawc+dOQkJCTtv+jz/+4JZbbmHKlClcc801fPrpp1x77bVs2LCBtm1NvBphGKdu0Vw0wLwcIiJSbbi7WYtvy3Q4x3bZeYUczconJTuPo1n5HM3K42h2vmP5xLqUrDxSs/M5mp1Pkd0gJ7+InPwiDqXnlj2X1YLvicKk+OHlTi2bO34nfr49uhGN6tQq/4e/AGUeDj4qKopu3boxY8YMAOx2OxERETz00EM88cQTp20/bNgwsrOz+f7774vX9ejRg44dOzJr1qxSHbPShoPPOAS7foIOt4CH5jwQERHnY7cbZOQWkJZTQPrxU4+04wVknPw558zrs/IKS32c+Q/0pFPDih1rq1KGg8/Pz2f9+vVMnDixeJ3VaqVfv37ExMSccZ+YmBgmTJhQYt2AAQNYsGBBWQ5dOfzrQ9dRZqcQERE5K6vVQqCPJ4E+nmXe1243yM4vJCuvkOy8QjJzHctZJ5//tlw/0LsS0pdOmYqRlJQUioqKCA0NLbE+NDSUHTt2nHGfxMTEM26fmJh41uPk5eWRl5dX/HNGRkZZYoqIiAiOQsbPy8Ppe+445Ti4U6ZMISAgoPgRERFhdiQRERGpJGUqRoKDg3FzcyMpKanE+qSkJMLCws64T1hYWJm2B5g4cSLp6enFjwMHDpQlpoiIiLiQMhUjnp6edOnShaVLlxavs9vtLF26lOjo6DPuEx0dXWJ7gMWLF591ewCbzYa/v3+Jh4iIiFRPZe7aO2HCBO644w66du1K9+7dmT59OtnZ2Ywa5WgIOmLECBo0aMCUKVMAGDt2LJdccgmvvPIKAwcOZN68eaxbt4533323Yj+JiIiIuKQyFyPDhg3jyJEjTJ48mcTERDp27MiiRYuKG6nGx8djtZ664NKzZ08+/fRTnn76aZ588kmaN2/OggULzB1jRERERJxGmccZMUOljTMiIiIilaa0399O2ZtGREREag4VIyIiImIqFSMiIiJiKhUjIiIiYioVIyIiImIqFSMiIiJiKhUjIiIiYqoyD3pmhpNDoWj2XhEREddx8nv7fEOauUQxkpmZCaDZe0VERFxQZmYmAQEBZ33dJUZgtdvtHDp0CD8/PywWS4W9b0ZGBhERERw4cEAju1YyneuqofNcNXSeq4bOc9WprHNtGAaZmZnUr1+/xFQx/+QSV0asVivh4eGV9v6aGbjq6FxXDZ3nqqHzXDV0nqtOZZzrc10ROUkNWEVERMRUKkZERETEVDW6GLHZbDzzzDPYbDazo1R7OtdVQ+e5aug8Vw2d56pj9rl2iQasIiIiUn3V6CsjIiIiYj4VIyIiImIqFSMiIiJiKhUjIiIiYqoaXYy89dZbREZG4uXlRVRUFGvWrDE7ksuYMmUK3bp1w8/Pj5CQEK699lp27txZYpvc3FzGjBlDnTp18PX1ZejQoSQlJZXYJj4+noEDB+Lj40NISAiPPfYYhYWFVflRXMrUqVOxWCyMGzeueJ3Oc8VJSEjgtttuo06dOnh7e9OuXTvWrVtX/LphGEyePJl69erh7e1Nv3792L17d4n3SE1NZfjw4fj7+xMYGMhdd91FVlZWVX8Up1VUVMSkSZNo3Lgx3t7eNG3alOeff77E3CU6z+WzcuVKBg0aRP369bFYLCxYsKDE6xV1Xjdv3kyfPn3w8vIiIiKC//znPxce3qih5s2bZ3h6ehpz5swxtm7datxzzz1GYGCgkZSUZHY0lzBgwADjgw8+MLZs2WLExsYaV199tdGwYUMjKyureJvRo0cbERERxtKlS41169YZPXr0MHr27Fn8emFhodG2bVujX79+xsaNG42FCxcawcHBxsSJE834SE5vzZo1RmRkpNG+fXtj7Nixxet1nitGamqq0ahRI2PkyJHG6tWrjbi4OOOnn34y9uzZU7zN1KlTjYCAAGPBggXGpk2bjMGDBxuNGzc2jh8/XrzNlVdeaXTo0MFYtWqV8euvvxrNmjUzbrnlFjM+klN64YUXjDp16hjff/+9sW/fPuPLL780fH19jddff714G53n8lm4cKHx1FNPGV9//bUBGPPnzy/xekWc1/T0dCM0NNQYPny4sWXLFuOzzz4zvL29jXfeeeeCstfYYqR79+7GmDFjin8uKioy6tevb0yZMsXEVK4rOTnZAIwVK1YYhmEYaWlphoeHh/Hll18Wb7N9+3YDMGJiYgzDcPzDsVqtRmJiYvE2M2fONPz9/Y28vLyq/QBOLjMz02jevLmxePFi45JLLikuRnSeK87jjz9u9O7d+6yv2+12IywszHj55ZeL16WlpRk2m8347LPPDMMwjG3bthmAsXbt2uJtfvzxR8NisRgJCQmVF96FDBw40LjzzjtLrLv++uuN4cOHG4ah81xR/lmMVNR5ffvtt43atWuX+N3x+OOPGy1atLigvDXyNk1+fj7r16+nX79+xeusViv9+vUjJibGxGSuKz09HYCgoCAA1q9fT0FBQYlz3LJlSxo2bFh8jmNiYmjXrh2hoaHF2wwYMICMjAy2bt1ahemd35gxYxg4cGCJ8wk6zxXp22+/pWvXrtx4442EhITQqVMnZs+eXfz6vn37SExMLHGuAwICiIqKKnGuAwMD6dq1a/E2/fr1w2q1snr16qr7ME6sZ8+eLF26lF27dgGwadMmfvvtN6666ipA57myVNR5jYmJ4eKLL8bT07N4mwEDBrBz506OHTtW7nwuMVFeRUtJSaGoqKjEL2eA0NBQduzYYVIq12W32xk3bhy9evWibdu2ACQmJuLp6UlgYGCJbUNDQ0lMTCze5kx/BidfE4d58+axYcMG1q5de9prOs8VJy4ujpkzZzJhwgSefPJJ1q5dy8MPP4ynpyd33HFH8bk607n8+7kOCQkp8bq7uztBQUE61yc88cQTZGRk0LJlS9zc3CgqKuKFF15g+PDhADrPlaSizmtiYiKNGzc+7T1Ovla7du1y5auRxYhUrDFjxrBlyxZ+++03s6NUOwcOHGDs2LEsXrwYLy8vs+NUa3a7na5du/Liiy8C0KlTJ7Zs2cKsWbO44447TE5XfXzxxRd88sknfPrpp7Rp04bY2FjGjRtH/fr1dZ5rsBp5myY4OBg3N7fTehwkJSURFhZmUirX9OCDD/L999/zyy+/EB4eXrw+LCyM/Px80tLSSmz/93McFhZ2xj+Dk6+J4zZMcnIynTt3xt3dHXd3d1asWMEbb7yBu7s7oaGhOs8VpF69erRu3brEulatWhEfHw+cOlfn+r0RFhZGcnJyidcLCwtJTU3VuT7hscce44knnuDmm2+mXbt23H777YwfP54pU6YAOs+VpaLOa2X9PqmRxYinpyddunRh6dKlxevsdjtLly4lOjraxGSuwzAMHnzwQebPn8+yZctOu2zXpUsXPDw8SpzjnTt3Eh8fX3yOo6Oj+fPPP0v85V+8eDH+/v6nfSnUVJdffjl//vknsbGxxY+uXbsyfPjw4mWd54rRq1ev07qn79q1i0aNGgHQuHFjwsLCSpzrjIwMVq9eXeJcp6WlsX79+uJtli1bht1uJyoqqgo+hfPLycnBai351ePm5obdbgd0nitLRZ3X6OhoVq5cSUFBQfE2ixcvpkWLFuW+RQPU7K69NpvN+PDDD41t27YZ9957rxEYGFiix4Gc3f33328EBAQYy5cvNw4fPlz8yMnJKd5m9OjRRsOGDY1ly5YZ69atM6Kjo43o6Oji1092Oe3fv78RGxtrLFq0yKhbt666nJ7H33vTGIbOc0VZs2aN4e7ubrzwwgvG7t27jU8++cTw8fEx/vvf/xZvM3XqVCMwMND45ptvjM2bNxtDhgw5Y9fITp06GatXrzZ+++03o3nz5jW+y+nf3XHHHUaDBg2Ku/Z+/fXXRnBwsPF///d/xdvoPJdPZmamsXHjRmPjxo0GYLz66qvGxo0bjb/++sswjIo5r2lpaUZoaKhx++23G1u2bDHmzZtn+Pj4qGvvhXjzzTeNhg0bGp6enkb37t2NVatWmR3JZQBnfHzwwQfF2xw/ftx44IEHjNq1axs+Pj7GddddZxw+fLjE++zfv9+46qqrDG9vbyM4ONh45JFHjIKCgir+NK7ln8WIznPF+e6774y2bdsaNpvNaNmypfHuu++WeN1utxuTJk0yQkNDDZvNZlx++eXGzp07S2xz9OhR45ZbbjF8fX0Nf39/Y9SoUUZmZmZVfgynlpGRYYwdO9Zo2LCh4eXlZTRp0sR46qmnSnQV1Xkun19++eWMv5fvuOMOwzAq7rxu2rTJ6N27t2Gz2YwGDRoYU6dOveDsFsP427B3IiIiIlWsRrYZEREREeehYkRERERMpWJERERETKViREREREylYkRERERMpWJERERETKViREREREylYkRERERMpWJERERETKViREREREylYkRERERMpWJERERETPX/w5iv7rAuZPUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(noise_scheduler.alphas_cumprod.cpu() ** 0.5, label=r\"${\\sqrt{\\bar{\\alpha}_t}}$\")\n",
        "plt.plot((1 - noise_scheduler.alphas_cumprod.cpu()) ** 0.5, label=r\"$\\sqrt{(1 - \\bar{\\alpha}_t)}$\")\n",
        "plt.legend(fontsize=\"x-large\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "KXDGwKRJyK_s"
      },
      "outputs": [],
      "source": [
        "# One with too little noise added:\n",
        "# noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_start=0.001, beta_end=0.004)\n",
        "# The 'cosine' schedule, which may be better for small image sizes:\n",
        "# noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule='squaredcos_cap_v2')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D0uCdW7UzTvB"
      },
      "source": [
        "Whichever scheduler you've chosen, we can now use it to add noise in different amounts using the `noise_scheduler.add_noise` function like so:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0IPVOilDdzVa",
        "outputId": "9e14db17-c811-4e6d-b51e-61e321fbb554"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Noisy X shape torch.Size([8, 1, 32, 32])\n"
          ]
        }
      ],
      "source": [
        "timesteps = torch.linspace(0, 999, 8).long().to(device)\n",
        "xb = xb.float()\n",
        "noise = torch.randn_like(xb)\n",
        "noisy_xb = noise_scheduler.add_noise(xb, noise, timesteps)\n",
        "print(\"Noisy X shape\", noisy_xb.shape)\n",
        "#show_images(noisy_xb).resize((8 * 64, 64), resample=Image.NEAREST)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import torchvision\n",
        "\n",
        "# Rescale from [-1, 1] to [0, 1]\n",
        "def visualize_noised_samples(noised_xb):\n",
        "    noised_xb = (noised_xb + 1) / 2  # Rescale to [0, 1]\n",
        "    noised_xb = noised_xb.clamp(0, 1)  # Ensure valid range for display\n",
        "    noised_xb = noised_xb.cpu()  # Move to CPU if on GPU\n",
        "\n",
        "    # Create a grid of images\n",
        "    grid = torchvision.utils.make_grid(noised_xb, nrow=8)  # Arrange 8 samples in a row\n",
        "    np_img = grid.permute(1, 2, 0).numpy()  # Convert to (H, W, C) for matplotlib\n",
        "\n",
        "    # Plot the images\n",
        "    plt.figure(figsize=(16, 8))\n",
        "    plt.imshow(np_img)\n",
        "    plt.axis(\"off\")\n",
        "    plt.show()\n",
        "\n",
        "# Call the function to visualize\n",
        "visualize_noised_samples(noisy_xb)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "id": "AWHkeRg_HrZu",
        "outputId": "fe6ee0d9-24ad-4643-b6aa-b8985db51eff"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7hsX6nDZiYm"
      },
      "source": [
        "Again, explore the effect of using different noise schedules and parameters here. [This video](https://www.youtube.com/watch?v=fbLgFrlTnGU) does a great job explaining some of the maths above in more detail, and is a great introduction to some of these concepts."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o5RvxO2ss23-"
      },
      "source": [
        "## Step 4: Define the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gHiMIrLRTk3K"
      },
      "source": [
        "Now we come to the core component: the model itself.\n",
        "\n",
        "Most diffusion models use architectures that are some variant of a [U-net](https://arxiv.org/abs/1505.04597) and that's what we'll use here.\n",
        "\n",
        "![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/unet-model.png)\n",
        "\n",
        "In a nutshell:\n",
        "- the model has the input image go through several blocks of ResNet layers, each of which halves the image size by 2\n",
        "- then through the same number of blocks that upsample it again.\n",
        "- there are skip connections linking the features on the downsample path to the corresponding layers in the upsample path.\n",
        "\n",
        "A key feature of this model is that it predicts images of the same size as the input, which is exactly what we need here.\n",
        "\n",
        "Diffusers provides us a handy `UNet2DModel` class which creates the desired architecture in PyTorch.\n",
        "\n",
        "Let's create a U-net for our desired image size.\n",
        "Note that `down_block_types` correspond to the downsampling blocks (green on the diagram above), and `up_block_types` are the upsampling blocks (red on the diagram):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "fRGXiotOs4Mc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
            "5dd8e00a226b4741ba11f13d90ed0979",
            "3e95915c3c3e4e4a87548ee104153544",
            "dcef6273fb904c5d8398507b8400f637",
            "3f2fea39b0b44eaca89720144760befb",
            "b394e2a28e534dd8b2e4c496368b203c",
            "97cf5f2d17514c99bc7bc32613f6daa2",
            "ae19c1cce75840a3af3c507730cf222c",
            "04b1b9a88b0e400f983e2164fc3e108c",
            "8bda206146ec471bb75a0d63b80e4c1d",
            "8333362c21184a9eaeabf74c9aa3d8db",
            "d943361cf1ae428faaf5b0a276c92425"
          ]
        },
        "outputId": "b912f641-bc71-40db-d51b-5c2376817517"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5dd8e00a226b4741ba11f13d90ed0979"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from diffusers import UNet2DModel\n",
        "\n",
        "# Create a model\n",
        "model = UNet2DModel(\n",
        "    sample_size=image_size,  # the target image resolution\n",
        "    in_channels=1,  # the number of input channels, 3 for RGB images\n",
        "    out_channels=1,  # the number of output channels\n",
        "    layers_per_block=2,  # how many ResNet layers to use per UNet block\n",
        "    block_out_channels=(64, 128, 128, 256),  # More channels -> more parameters\n",
        "    down_block_types=(\n",
        "        \"DownBlock2D\",  # a regular ResNet downsampling block\n",
        "        \"DownBlock2D\",\n",
        "        \"AttnDownBlock2D\",  # a ResNet downsampling block with spatial self-attention\n",
        "        \"AttnDownBlock2D\",\n",
        "    ),\n",
        "    up_block_types=(\n",
        "        \"AttnUpBlock2D\",\n",
        "        \"AttnUpBlock2D\",  # a ResNet upsampling block with spatial self-attention\n",
        "        \"UpBlock2D\",\n",
        "        \"UpBlock2D\",  # a regular ResNet upsampling block\n",
        "    ),\n",
        ")\n",
        "model.to(device);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y7lkJym26QRo"
      },
      "source": [
        "When dealing with higher-resolution inputs you may want to use more down and up-blocks, and keep the attention layers only at the lowest resolution (bottom) layers to reduce memory usage. We'll talk later about how you might experiment to find the best settings for your use-case."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "483LRBfs0C5_"
      },
      "source": [
        "We can check that passing in a batch of data and some random timesteps produces an output the same shape as the input data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmmxZJYM0KwA",
        "outputId": "e40d18f9-2993-4063-eef6-ee5b07f061ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([8, 1, 32, 32])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "with torch.no_grad():\n",
        "    model_prediction = model(noisy_xb, timesteps).sample\n",
        "model_prediction.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mIhFUbFMZxj7"
      },
      "source": [
        "In the next section we'll see how to train this model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vbcdWagYs7tc"
      },
      "source": [
        "## Step 5: Create a Training Loop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGQjvwOi64Sl"
      },
      "source": [
        "Time to train! Below is a typical optimization loop in PyTorch, where we run through the data batch by batch and update the parameters of our model each step using an optimizer - in this case the AdamW optimizer with a learning rate of 0.0004.\n",
        "\n",
        "For each batch of data, we\n",
        "- Sample some random timesteps\n",
        "- Noise the data accordingly\n",
        "- Feed the noisy data through the model\n",
        "- Compare the model predictions with the target (i.e. the noise in this case) using mean squared error as our loss function\n",
        "- Update the model parameters via `loss.backward()` and `optimizer.step()`\n",
        "\n",
        "During this process we also log the losses over time for later plotting.\n",
        "\n",
        "NB: This code takes nearly 10 minutes to run - feel free to skip these two cells and use the pretrained model if you are in a hurry. Alternatively, you can explore how reducing the number of channels in each layer via the model definition above can speed things up."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Waw8nGdO4S-O"
      },
      "source": [
        "The [official diffusers training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) trains a larger model on this dataset at higher resolution, and is a good reference for what a less minimal training loop looks like:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UFRXBcyGs_uO",
        "outputId": "883de50b-8e07-4a64-8f2a-405053f7cdf1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute `num_train_timesteps` directly via 'DDPMScheduler' object attribute is deprecated. Please access 'num_train_timesteps' over 'DDPMScheduler's config object instead, e.g. 'scheduler.config.num_train_timesteps'.\n",
            "  deprecate(\"direct config name access\", \"1.0.0\", deprecation_message, standard_warn=False)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch:5, loss: 0.033800930809229614\n",
            "Epoch:10, loss: 0.026959038339555264\n",
            "Epoch:15, loss: 0.024174899468198417\n",
            "Epoch:20, loss: 0.022550026839599013\n",
            "Epoch:25, loss: 0.021933485567569733\n",
            "Epoch:30, loss: 0.022044007293879987\n"
          ]
        }
      ],
      "source": [
        "from torch.nn import functional as F\n",
        "\n",
        "# Set the noise scheduler\n",
        "noise_scheduler = DDPMScheduler(\n",
        "    num_train_timesteps=1000, beta_schedule=\"squaredcos_cap_v2\"\n",
        ")\n",
        "\n",
        "# Training loop\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=4e-4)\n",
        "\n",
        "losses = []\n",
        "\n",
        "for epoch in range(30):\n",
        "    for step, batch in enumerate(train_dataloader):\n",
        "        # Move clean images to the correct device\n",
        "        clean_images = batch[\"images\"].to(device).float()\n",
        "\n",
        "        # Sample noise and timesteps\n",
        "        noise = torch.randn(clean_images.shape).to(clean_images.device)\n",
        "        bs = clean_images.shape[0]\n",
        "        timesteps = torch.randint(\n",
        "            0, noise_scheduler.num_train_timesteps, (bs,), device=clean_images.device\n",
        "        ).long()\n",
        "\n",
        "        # Add noise to the clean images\n",
        "        noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)\n",
        "\n",
        "        # Get the model prediction\n",
        "        noise_pred = model(noisy_images, timesteps, return_dict=False)[0]\n",
        "\n",
        "        # Calculate the loss\n",
        "        loss = F.mse_loss(noise_pred, noise)\n",
        "\n",
        "        # Zero gradients, backpropagate, and update model weights\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # Log the loss\n",
        "        losses.append(loss.item())\n",
        "\n",
        "    # Log the average loss every 5 epochs\n",
        "    if (epoch + 1) % 5 == 0:\n",
        "        loss_last_epoch = sum(losses[-len(train_dataloader):]) / len(train_dataloader)\n",
        "        print(f\"Epoch:{epoch+1}, loss: {loss_last_epoch}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5g3B7J2y36o-"
      },
      "source": [
        "Plotting the loss, we see that the model rapidly improves initially and then continues to get better at a slower rate (which is more obvious if we use a log scale as shown on the right):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "DoZP2oHHfL3R",
        "outputId": "b706a16c-007e-414f-ae25-116f83e47002"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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ADEwXVLNONQAAwc0WVvbzprDUTg4VAECjM11QDQAAgltkeU+1YUhFpfRWAwAal+mCaotzUjWzqgEACErO4d+SVMQQcABAIzNdUA0AAIJbWIhVoeUZwAtJVgYAaGSmC6pd2b/pqAYAIGg5e6sLigmqAQCNy3RBNQAACH4RbsnKAABoTKYNqumoBgAgeEWGl/VU59NTDQBoZKYLqi0VmcoAAECQiosMkyRlF5QEuCYAgGBnuqDaiTnVAAAEr/jIcElSVn5xgGsCAAh2pguq6acGACD4xUWV9VRn5dNTDQBoXKYLqgEAQPBrWR5UnySoBgA0MtMF1c4p1QapygAACFrRtrKgOr+oNMA1AQAEO9MF1QAAIPg5l9QqKCH7NwCgcZkwqC7rqiZRGQAAwSsirGxJrcISR4BrAgAIdiYMqgEAQLCLdAbVpfRUAwAal+mCatecanqqAQAIWs7h34XFBNUAgMZluqAaAAAEvwh6qgEAfmK6oNq5TjXZvwEACF62UOZUAwD8w3RBNQAACH6R4WVBdQHDvwEAjcx0QTVzqgEACH4RoeVzqhn+DQBoZKYLqgEAQPBzzqkuYvg3AKCRmS6otrhmVQMAgGDlHP5dWEJPNQCgcZkuqAYAAMEvojxRWQFBNQCgkZkuqGZONQAAwc+1TnWJXQaNPgCgEZkuqAYAAMHPVj6n2mFIJXaCagBA4zFdUM061QAABL/I8qBaYgg4AKBxmS6oBgAAwS8sxCJr+Z30lXuOy+HgZjoAoHGYNqhmehUAAMHLYrG4ltW69Z21mv3DngDXCAAQrEwXVFssLKkFAIAZRIVXDAGf/cPeANYEABDMTBdUO9FRDQBAcOvYMsr13Mo9dQBAIzFtUA0AAIJbj7bRrudWRqoBABqJaYNq1qwEAOD07du3T3/84x/VtWtXRUZGqnv37poxY4aKi4sDXTV1aV3RUx1CVzUAoJGEBroC/saNagAAfGfbtm1yOBx69dVX1aNHD23atEm33HKL8vLy9Oyzzwa0bp1at3A9t5q2GwEA0NhMF1Q70U8NAMDpu/TSS3XppZe6Xnfr1k3bt2/XK6+8EvigupX7nGruqgMAGkeD7tu+9NJL6tKliyIiIjR8+HCtWrWqxvJZWVmaNGmS2rVrJ5vNpl69eumrr75qUIVPF20qAACNKzs7W61ataqxTFFRkXJycjwevtbZLaguKXX4/PwAAEgNCKrnzZunKVOmaMaMGVq3bp0GDhyosWPH6ujRo17LFxcX6+KLL9a+ffv0n//8R9u3b9fs2bPVoUOH06786WBKNQAAvrdr1y698MIL+tOf/lRjuZkzZyouLs71SE5O9nld4qPCXM/josJ9fn4AAKQGBNXPPfecbrnlFk2cOFF9+/bVrFmzFBUVpTfffNNr+TfffFMnTpzQp59+qlGjRqlLly46//zzNXDgwNOufENYRFc1AAC1mTp1qiwWS42Pbdu2eRxz6NAhXXrppbrmmmt0yy231Hj+adOmKTs72/U4cOCAz6/BYrHorpSekqTYCNPOeAMANLJ6tTDFxcVau3atpk2b5tpmtVqVkpKi5cuXez3mf//7n0aMGKFJkybps88+U5s2bXTdddfpvvvuU0hIiNdjioqKVFRU5HrdGEPCAABA9e655x7deOONNZbp1q2b6/nhw4c1ZswYjRw5Uq+99lqt57fZbLLZbKdbzVoN7BgvSVqflqXiUofCQ8lYBgDwrXoF1ZmZmbLb7UpMTPTYnpiYWOVutdOePXu0ePFijR8/Xl999ZV27dqlO+64QyUlJZoxY4bXY2bOnKmHH364PlWrs4o51Yz/BgCgOm3atFGbNm3qVPbQoUMaM2aMhgwZorfeekvWJpRqe1SPBElSsd2ho7mF6tgyqpYjAACon0Zv9RwOh9q2bavXXntNQ4YM0bhx4/TAAw9o1qxZ1R7jjyFhAADg9B06dEgXXHCBOnXqpGeffVbHjh1Tenq60tPTA101SVJ4qNU1t7qwxB7g2gAAglG9eqoTEhIUEhKijIwMj+0ZGRlKSkryeky7du0UFhbmMdT7jDPOUHp6uoqLixUeXjVxSGMOCXN2VJOoDACA07dw4ULt2rVLu3btUseOHT32GU2ksS0qKcv8/b/Uw5pySW+vZXZk5Cq5ZZQiw71PTQMAoDr16qkODw/XkCFDtGjRItc2h8OhRYsWacSIEV6PGTVqlHbt2iWHo2Ipix07dqhdu3ZeA2oAANB83HjjjTIMw+ujqSgo76H+1+JdXvcv2pqhS/75vX79yjJ/VgsAECTqPfx7ypQpmj17tt5++21t3bpVt99+u/Ly8jRx4kRJ0oQJEzwSmd1+++06ceKE7rzzTu3YsUNffvmlnnjiCU2aNMl3V1EPlvJJ1U2nqQcAAIH08bqDkqStR0iMCgCov3qvLzFu3DgdO3ZM06dPV3p6ugYNGqT58+e7kpelpaV5JChJTk7WggULdPfdd2vAgAHq0KGD7rzzTt13332+uwoAAAAAAAKgQYs2Tp48WZMnT/a6b+nSpVW2jRgxQitWrGjIW/kcc6oBAAAAAL7SdNa8AAAACACL65Y7AAD1Z76gurzdbEoJVAAAQOO5O6VXoKsAAAhi5guqAQCAqdw4sovreXGpo/qCAAA0gGmDavqpAQAwhxa2EIWFlA1VO5JdULUAo78BAKfBdEE17SYAAOYSGmLVgI7xkqSVe08EtjIAgKBjuqDaiSnVAACYx/CurSRJH689qKO5hQGuDQAgmJguqLZY6KsGAMBsBndqKamsp3rY44sCXBsAQDAxXVDtZDCrGgAA0+jRNtrjdVGp3fWc2+0AgNNhuqCahhMAAPPp2DLS4/WpwtIA1QQAEGxMF1S70FENAIBphIV4/uTJPFUcoJoAAIKN6YJqplQDAICxz3/vek6+FQDA6TBdUO1ERzUAAOYy5eJeHq8dDn4NAABOn+mCaguzqgEAMKW/XNTT4/XR3KIA1QQAEExMF1Q7sU41AADm87uhHV3P75y7PoA1AQAEC9MF1UybAgDAvB67ur/r+cq9JwJYEwBAsDBdUO3EOtUAAJhPeKhpf/oAABoJLQsAADCVey/tLUlqHxchSWRbAQCcFtMG1cypBgDAnC7r106SdDi7UCv3HGdqGADgtJguqGYtSgAAzC0uMsz1fNxrKzz2rd7HPGsAQP2YLqgGAADmFhsRWu2+a2Yt92NNAADBwHRBtbOfmtHfAACYU2iI6X7+AAAaEa0KAAAAAAANZLqg2jml2iBTGQAApvX4r/q5nucX2wNYEwBAc2e6oBoAAGD88M5Kii1bUmvd/pMBrg0AoDkzbVBNPzUAAOZ2bs8ESdLxvOIA1wQA0JyZLqhmRS0AACBJbWNsga4CACAImC6odqGrGgAAU2thq35pLQAA6sp0QbVFdFUDAAAAAHzDdEG1k0FXNQAApmZ38FsAAHD6TBdUM6caAABIUilBNQDAB0wXVDuxTDUAAObWKzHa63YHwTYAoB5MF1TTUQ0AACTp8n7tdP/lfapsX7ztaABqAwBorkwXVDvRUw0AgLlZrRbdel73Kj3WezJPKT27MEC1AgA0N6YLqi3lk6qJqQEAgCTNu3WEx+snvtqmc2Yu0qZD2QGqEQCgOTFdUG0tH//toKsaAABIatki3Ov2/6w96OeaAACaI9MF1a6eaoJqAABQg2K7I9BVAAA0A6YLqp091cTUAACgJu+vTFNhiT3Q1QAANHGmC6qdPdWslgEAAJz+cE4nr9s/Ygg4AKAWpguqmVMNAAAqe/SX/fSf20ZU2X78VFEAagMAaE4aFFS/9NJL6tKliyIiIjR8+HCtWrWqTsfNnTtXFotFV199dUPe1icscvZUE1QDAIAyFotFvZNiFOK8+17OztA2AEAt6h1Uz5s3T1OmTNGMGTO0bt06DRw4UGPHjtXRo0drPG7fvn3661//qtGjRze4sr5gNV3fPAAAqIuYiDB1bBnpsa3Y7tDz3+7QXz/aQJJTAIBX9Q4xn3vuOd1yyy2aOHGi+vbtq1mzZikqKkpvvvlmtcfY7XaNHz9eDz/8sLp163ZaFT5dVgs91QAAwLtJF/TweJ2dX6Lnv92p/6w9qA0HWbcaAFBVvYLq4uJirV27VikpKRUnsFqVkpKi5cuXV3vcI488orZt2+qPf/xjnd6nqKhIOTk5Hg9fc7BKBgAAqCTKFuLxeu7qA67nBcVkAgcAVFWvoDozM1N2u12JiYke2xMTE5Wenu71mB9//FFvvPGGZs+eXef3mTlzpuLi4lyP5OTk+lSzRs6eavqpAQBAZVHhIdXuY5QbAMCbRp1hnJubq+uvv16zZ89WQkJCnY+bNm2asrOzXY8DBw7UflAdkf0bAABUZ3Byy2r38dsBAOBNaH0KJyQkKCQkRBkZGR7bMzIylJSUVKX87t27tW/fPl155ZWubY7ycdehoaHavn27unfvXuU4m80mm81Wn6rVmXOdapKNAACAylq2CNeqBy5SYbFD5z2zxGNfiZ25YwCAqurVUx0eHq4hQ4Zo0aJFrm0Oh0OLFi3SiBFV13bs06ePNm7cqNTUVNfjqquu0pgxY5SamurTYd115eypJqYGAADetI2JUKfWUUqIDvfYns+cagCAF/Ue/j1lyhTNnj1bb7/9trZu3arbb79deXl5mjhxoiRpwoQJmjZtmiQpIiJC/fr183jEx8crJiZG/fr1U3h4eE1v1Sgsruzffn9rAACC0lVXXaVOnTopIiJC7dq10/XXX6/Dhw8Hulqn7Z/jBnm8nvz+enWZ+qUWbPaeRwYAYE71DqrHjRunZ599VtOnT9egQYOUmpqq+fPnu5KXpaWl6ciRIz6vqK+Ud1QzLwoAAB8ZM2aMPvzwQ23fvl0ff/yxdu/erd/+9reBrtZpG92zjS7o3abK9j+9s1ZZ+cUe25ZsP6rrZq/QgRP5/qoeAKCJqNecaqfJkydr8uTJXvctXbq0xmPnzJnTkLf0GbJ/AwDgW3fffbfreefOnTV16lRdffXVKikpUVhYWABrdvqibd5/Kq3Yc0KX9qvIJzPxrdWSpMkfrNdnk0b5pW4AgKahUbN/N0XW8ismURkAAL534sQJvffeexo5cmSNAXVRUZFycnI8Hk1RdUH1be+uVXZBSZXtGw5kNXKNAABNjemCakv5AHAHk6oBAPCZ++67Ty1atFDr1q2Vlpamzz77rMbyM2fOVFxcnOsRiOSldXHTuV2r3Xf7u2v9WBMAQFNlvqDatU51YOsBAEBTNnXqVFkslhof27Ztc5X/29/+pvXr1+ubb75RSEiIJkyYUOOosGnTpik7O9v1OHDggD8uq956JcZo3UMXe923bPdx/bDzmCQpKTbCtX3lnuN+qRsAoGlo0Jzq5ow51QAA1O6ee+7RjTfeWGOZbt26uZ4nJCQoISFBvXr10hlnnKHk5GStWLHC65KbkmSz2WSz2XxZ5UbTqkW45t16ju54b52O53kmKLv+jVUa1qWV0nMKXdvGvbZCax5MUUJ02fUZhuFafQQAEHxMGFSX/cmcagAAqtemTRu1aVM183VdOBwOSWXzpoPF8G6t9c3d52nIY99W2bdq34kq24Y+9q2+/Mu5mvPTPq3ce0Jf3zlaLaqZnw0AaN5MOPzbuU41QTUAAKdr5cqVevHFF5Wamqr9+/dr8eLFuvbaa9W9e/dqe6mbq9bRNnVuHVXn8u+u2K+P1h5U2ol8fbOFta0BIFiZMKgu+5OYGgCA0xcVFaVPPvlEF110kXr37q0//vGPGjBggL777rtmM7y7PoZ3bVXnsiHWiiHfzkSpOYUlWrX3BCPmACCImG4cktXVUx3gigAAEAT69++vxYsXB7oafvPgL/rq4MkCLdtdezKyd1ekuZ47A+xrX1uhzYdz9M9xA/WrwR0brZ4AAP8xX091+Z8M/wYAAPUVGxGm9285p97HzV2dpucW7tDmw2XrcX+4+qCvqwYACBDTBdVWsm8CAAA/+2nXcf1r0U7X64ISu4pLHco81TyTuTkchlbvO6G8otJAVwUAAs58QXX5FTsY/w0AAHxkxbSLtOvxy+pcvrDErqte/FFDH/tWezPzJElbj+ToqfnblFNY0ljV9Jl3V+7XNbOWa8KbqwJdFQAIONPNqXYOACemBgAAvjDrD0OUFBchSercOkr7j+fXekxWfolrbesnvtoqh8PQom1HJUkn84r15G8GNF6FfeDDNQckSWv3nwxwTQAg8EwXVLvWqRZRNQAAOH2X9kuq9zHOgFqSFm7J8NiXeiDrtOrz0KebFGK16O9XnXla56lJWEjFYMdDWQXqEB/ZaO8FAE2d+YZ/k/0bAACcpvblPdOhVs9cLYHO3HL8VJHeWbFfc5btU3ZB4w0jdw+qn1+4o9HeBwCaA9MF1RXrVBNVAwCAhplz0zCN6d1Gn9wx0ufn3paeq1v+vUbZ+fUPiu1uv29K7A5fVstDuFtQ/dFaMpkDMDfTBdUVPdUE1QAAoGF6JcborYnDNKBjfKOcf+GWDP3fop0qLnVozb4TdQ6QS+wVv28KS+yNUjdJCgvx7JOnswKAmZkuqK7oqQ5sPQAAQPCxuC3d+ZeLeirGFqqrBrbX789Orve5MnIK9fDnm/XbWcv12BdbVFhir3UJq+LSiuC7sKTxeqrdh39LUl5x4wXwANDUmTBRGXOqAQBA4+jeJtq1RNaUi3tpysW9JEnvLN9X73M5DEPvrUyTJL29fL9+2JmpY7lFWvVAiiLDQ7we496j3Zg91ZV/R53MK1a0zXQ/KwFAkhl7qsv/ZJgSAADwtSd+3U+/HtxBH9/uOdc6Piq83uf6elO6x+s9mXnKLSrV1vScao9x76me8mGqXl66q97vWxcxEZ4B9PG84kZ5HwBoDkwXVFvLs3QSUgMAAF9rGxOh58YN0pDOLT22j+je2mfvcaqw+iHgRW5B9Y6MU3p6/nYt25Xps/d2io8K83h9Iq/I5+8B+MumQ9n6ttLSdkB9mC6odk51cjD+GwAA+ElCtM31vEU1Q7frKsNtjesTlXqI3Xuqna57feVpvZ83pXbP31En8hpv+S6gsf3ihR9187/XaFsNo0CAmpgvqBZzqgEAQPP1t//8rIJiuz5LPaSzHl2o13/YI0ma+vHPunb2Cr/UobTSD6m/frRBmw5l++W9gcay+2heoKuAZsp0QbXVmf2bAeAAACAAzmwfp1Yt6j/H2t0Nb67SnXNTJUmPfblVp4pKNXf1gWrLHzyZr//7dqdOlvdsV84tszMjV9/tOCZJKiiuPct4qZclvh75fEt9LgFocogP0FAmDKrL51TzbwYAAARAWKhFP913odd9Z3dp6XV7Zav2nXB1FEjShzUE1JL07ILt+ue3O/Srl3/S5sPZGvTIQr31014dyiqQ3WHo4n9+rxveXKXNh7M15LGFGvzIwhrXxraX91T3aBvtUaf3Vu6vU/2bEofD0KT31+mZBdsCXRUEGCNZ0VCmC6pdc6qJqgEAQADYHUa1S2LZ6/Gr3r3oI1/U3Ev8aephSdK+4/ma+vFGZReU6OHPt2jUk4v15o97XeVW7jmh/GK7iu0OZZ6qPvlYSfmbjxuarGuGdHRtf+C/m+pc/8byvw2H9crS3XUuv/5Alr78+YheWlL3YxCcWB0IDWXCoJqeagAAEDiVk3y5q2GXz+QUeiYVe/yrra7n7oG0M8Cf89Nevfrdbk16b532HDtVvq+sF9sWZlVK30SP8xWV2jXnp72usv72lw/W66n527TxYN3meLsndyOoMjc63dBQobUXCS5WeqoBAEAAndW56hDvTq2i9OuzOmh7eq421DyS+7TtP57v8TomIlS55ct0vezWw1tY4tAbP+7Vo2694DuP5uqbu89XSXn0H2q1KjbCc3mtx77YqndWlA0DX3X/RWobG+G1HkWlds3flK6R3RPUJsbmtczpOHaqUFJcvY6xOwyFhlhqL4igRHiAhjJdUE32bwAAEAjfTjlf32xJ140ju0iS3v3jcN3zUapm/rq/LuxT1tubnl2oghK7bhjZRVn5xbp73gZJ0lO/6a/fDU3WtvRczV2VpreX1zx3OSo8RFMu7qXPfz6iDQeyaiybW82617e+s0Z7jnlmQ95d/trZix1qtSgx1jMgdgbUkjTsiUXa8dhlCg+tOjjyhUW79OKSXerWpoUW33NBjXWsK/ee5pI6dvtb3GLoYrtDoSGmG8hpau7fGeIDNJTpgmpX9m9uRQEAAD/q0TZaPdr2cL0+t2eCVt6f4lEmKS5CcyYOc712BtV2R9kUtjPaxerhX/ZT97bRmv7Z5mrfa/PDY2WxWPR/i3Y2uL6VA2pJCi8POJ3ZwUNDLOrSukWN57nl32v09k3Dqmz/auORat+nodyX+qppmH11SkoN6fQSs6OZcc9jwEhWNJTpbsVZrcypBgAAzUvlH/vt4yJrLO/MIVNQbPdpPcJDrdp4MFsr956QJIWFWF2/rarjXKqrah19WjVJnoF0qaP67OXu3D/bo7mFuvT57zXz6601HIFgYnf7+6fTDQ1luqDa+R+4nX80AACgmaj8Y99ax19wpT4ez5pdUKJfvfyT63VUeRbzv17Sq8bjznlikbLyi12vF2xOdw0ldzqZV6zdp5ncrNhtGbC6rpvtHoh/sOqAtqXn6tXv9ngkMGtODpzI1w87vd/IQFXu914ID9BQpguqw8pbodIa1l4EAABoSs7u2srjtTNHTG2c87d9yT1QjwwrC6onjelRXXFJUnpOoX7YmSmpLOj70ztrPfZ/8fNhDX50oS76x3faf7z64eA/7DymAycqEq2V2B06nFXg8drpeF5ZEL//eJ5umrNaq8p71ytzP8b9XGknfDcs3Z9GP71E17+xSiv2HA90VarIKSxpcr3BduZUwwdMF1RHlN9RLSjx7XAoAAAAX1v9QIq++PO56pMU67E9JqJuaXGmXtZHT/92gMJCLPrV4A4+r59zvW2LxaLbzu9eY9lVe0/o7WX7NPrpJVX2TX5/vev50u3HdPBkvn7xwg/6eO1B1/blu4/r+jdWeRx/x3vrNPLJxVqzryxgXrPvZJVz//mD9Vq87ah+9+pyr/VyD6oPuQXVhSW1d8AUFNv188GsJhcoSqr2JkKgLNudqQF//0YPfRb4tczdMacavmC6oNp5R7Uu/1ECAAAEUpsYm/p1qLos1JDOLfWHczppUHK8a9sL1w7WLwa001sTz3ZtiwgL0e+GJmvTw2P13O8G1vhezgzdvz6r7sG3M6iWag9I3lmxXzP+V31yNacZ/9us577ZoU2HcnTPRxtc25ftznQ9tzsMHc4q0MItGZKkN37cK0m67V3PHnBJ2ltLIrRit+HfGw9VrG1dVIfh39e/sVJXvfiTPk09VGvZutienqvr31ip1FoytteFr4f+n65/fLNDkvTuirQA18STw+1zWrA5XUeyC2ooDXhnuuzfzqCanmoAANBcWSwWPXZ1f9kdhh7+fLOGdmmlKwe215UD23stbwsN8brd3ZaHxyo0xKp9mXn6ZF1FkNi9TQvde2mfKkO2pYrfVZIUUkvCsvrwFtDuyMh1Pb9u9gpXsjTJs7fRqVOrqLJ9lYL9E3nFio0IdS2dVd2UwNzCklrruWZ/Wc/4B6sO6FeDO9ZavjYT31qlw9llQ+X3PXnFaZ3LXsdEbWbn/v34YWemzn9mqXY8dlkAa4TmyHQ91RFhZZdcRFANAACauRCrRY/8sp+uqiaYrqxNjK3afc4gs3KZUKtVEW7B82X9klzP3YPqP57b1RXInq4ct4B20nvr9OHqA1qwOcO1bWWloc3eeskLS+wyDEP5bhnQ9xw7pbMeXajfv7ZCkvTcwh16dsF2r3W48a3VHvO3C2v47diQ+wmfbzis13/Y47HtcHZh/U9UjaacPij1QFaNn6c/OSrdkGmuCeoQWKYLqumpBgAAZvXZpFF64lf9Xa+TW1VdmquFLVSL7zlfAzqWDTt/4Ioz1NVtLepfn1XRI+scMi5JCdE2fX/vGE27rM9p19OZ1EySvtx4RPd+/HON5b2NdD6aW6SzH1/kse3T9WU98Gv2n9SmQ9n616KdNQayl/zze0nSku1H1Xf6fL1ZPsy8MmfiuFe/263r31ipwhK7ftqVqRvfWuURmLv78wfr9diXW7XraK7X/U6v/7BHryzd7XXfgRP51Qany92Gyzc1V7/0k15cvCvQ1ZDUtFcEKrE79P2OYzpVvi48mi7TBdUkKgMAAGbVPj5S1w3v5Hp96+hu+vdNw/TtlPM9ynVrE633bzlHi+85X+f1aqNOraP07DUDdc/FvXRRn7Y6o12s+neIU2xEWJX3+NP53dU+LqLRr8Xd4m1HtcltPrRT5qkij9f/cgvkxlWTuMxdQYldP+w8ptveWSuHIT3yRcUyXbO/r+hltlrLejxnfr1NP+zM1OcbDmv86yu1dPsx/WXueo9zZp4q0ouLd7pe5xaWatnuTK3df7LKEPqCYrse+3Krnpq/TccrXcv6tJMa/fQS/XbWMtc294RpGw5m68ufj9R6jYEyf3N6oKsgyfvUgabiX4t2asKbq/Snd9YEuiqohenmVEeEkqgMAACY2+J7ztfKvSd0zZCOrmHflUXbQhXdJtr1+rdDKnqov/jzubJIslYz7vmaocn6v0U7ve5z17ddrLYcyalf5avxixd+rFf5vOKqHSxxkWHKLvCcS339G6s8XucUlujGN1dpXVqWa9vqfSd1xvT5rtfuw9E3HvQM9p/4cqs+WV8xZ72g2K7rXl8pyXNe+ps/7tXFfRNdr19aslvTr+zrev2f8szomw5VfH4bK91YmLs6TVcMaOex7addmUqItql3UozqKju/RKUOh1pHVz99oL6ibWVhyGeph7QvM19/uaiHLBbfzcuvqybcUa0PVpUldftpV9NbHg2eTBdUR5X3VOczjAIAAJhUtzbR6uYWMNdXbUnJJo3poc6toxQXGaan5m9Tenah2sVF6t5Le+v5b3e6gr8v/3Kuuk77qsH18KWxZybqxevOUs8Hvq6x3GNfbPEIqKWq83BDrBU3Kkodht5Zvk+xkWFKjI3wCKglKbN8PW3Js9f0kS+2aP6mit7cN3/aq98PS1avxBj9d/1BvbfSM4t2dn6JrnrxJ49tx0+VndswDN327lodP1XsSq5W10RohmFo4CPfSJK2PnKpR8b3+qj8jXEuC3fn3FRJ0uheCTqrU8sGnbs669NOKiHapuQa5vo3tKf6wIl82cKsahtT/1EZJXaHFm7J0LCurZRQw40KawBuMjS2olK7wkOsAbmB0pgaNPz7pZdeUpcuXRQREaHhw4dr1apV1ZadPXu2Ro8erZYtW6ply5ZKSUmpsXxja9kiXFLZ3cmmkiABAAAgmISHWvXrszrqojMS9c3d5+vnv4/VgrvP00VnJCo2sqJPx2Kx6PFf9av1fN0SWtRa5nSFWq0Kq6bX3t3mw7X3rLsP75akhz7brDvnproSpLl7d/n+as+zap9nQrat5b36d8/bUKXssVNV54ZvOZKj/64/qIlzVmvB5gxXQF3Z7mOn9Nr3u1XgpffePRN7eo7vEqlVzvDuvAFQ2b8W7dQd762tUwKxnMISZeWXnWfX0VP61cvLNPrpJVq6/aiKSr3/7vc2p3rZ7kwdzS3U3/+32euc9+yCEo1+eomGVZqzX1ev/7BXd7y3TtfMqpiC4K1+pxtUr91/Qj8fzDqtc9QmO79ET369TTszas4NIJVl3h/8yELd/u66Rq1TINQ7qJ43b56mTJmiGTNmaN26dRo4cKDGjh2ro0ePei2/dOlSXXvttVqyZImWL1+u5ORkXXLJJTp0yDfr+dVXbESowkLKvqAn8rz/4wUAAEDjmDSmhyTp8v5lWcRD65A6e0jnuvdgOocV11ddlwSrPDzcm33HvScn86Zy4FyTomqmLxaV2lVQ7H3f3fM2aOn2Y1W2F5c6XIHcRf/4Tk98tU1nTJ+vPcdOSSpbV/yeDzfoUJb3dZuLSu0ec7jra9XeEx498dXlO3pu4Q59tTFdn6w76HX/Mwu26brZK1RYYtfQR7/VkMe+VVGp3SOYvPGt1Xr8y62u15+sO6gF5XO6K2f/lqTrZq/UXXNTNWfZPl314k86mlOo13/Yo+z8sr/7/ccr1j5vSE/3Fz8fliTtzSw7z2eph9T7wfn6rNJ656ezTF1WfrF+88pyXfXiT16v0Vce+HSjZn23W1e/9FOtZT9df0j5xfYmM5/el+odVD/33HO65ZZbNHHiRPXt21ezZs1SVFSU3nzzTa/l33vvPd1xxx0aNGiQ+vTpo9dff10Oh0OLFjXszs7pslgsahlV1ltNUA0AAOBfI7sn6Mf7xuhfvx8sSV6TnXnTr0Nsncr18TJXuH+HuDrUq3Wdzn/wpPcg0x+K7A6vmcBzC0vrnYT37WX7dOb0BVq4JcNj+9Pzy5YYe+jTTfp43UFd9I/vqhyblV+s4U8scq1dXmJ36MPVB6rNdF6d296tWPv8Lx+s15Jt3jvppKrzxZ1eWrJby3Yf19xVaSq2O2R3GDqaU6T1lYbo/3v5fuUWluhIdoGmfLhBf3pnrRwOo9rs38t2l81jzi+268a3VuuxL7fqvvIs9Ba3wexHcwvr1EvrrnKM6xwC7/zTyXoaKaXdk/QVN+L6as5M/d5yFDjZHYYcDkP16XjPzi/Rgs3p9Vri7LXvd7tuWPhbvf6qiouLtXbtWqWkpFScwGpVSkqKli+vPYOiJOXn56ukpEStWrWqtkxRUZFycnI8Hr7UqnwIeFZ+7XcaAQAA4FsdW0a5EqRd3DdRKWck6q6Unvr3TcOqPeaT20dp/UMX13pu9wRcj/7yTL3zx2F6/5bhtR73u6HJkqR/XTtYLaPCdO+lvWs9xt+y84v11PxtVbZ/seGwTuQVeTmieo9/tVWlDkO3/Nszs3R1w6QlaXt62W/yhVsylJVfom/KA/K5q9J078c/a/TTS/T3/212BddHcwp1LLfu9Zo4Z7WksnnchSV2j4DqVFGpq0Nsz7FTmvjWKq3dX9HLn3ai4mbH6KeX6J0VVYfVz/x6m0enWkGJvU49zc5kevM3p1e5cXDViz/p4n9+ry01TAtIzy706NWvSw9/YYlddntFucqZ7GvjflkZPhy2X1l+cc15qopLHbr4n9/p2tkrPObVP+0lo31+cam+/PmIcgtLdNPbq/Wnd9Zq1nfel5OrbPPhbD3x1TZNfn997YUbQb2C6szMTNntdiUmJnpsT0xMVHp63brx77vvPrVv394jMK9s5syZiouLcz2Sk5PrU81aORMs1PYlAAAAQOMKDbHq9RuG6q6UXhrVI8FrGUNl87SduXGc3pp4dpWyA5PjXc+vHtxBo3u2UUxEmMea2k6/G9pRj/7yTP143xhXJvOrBrbXuocu1h0X9NCqBy5q+IU1gme/2eF1+98/36LbfDRP1W6UDc325rZ312nSe+v0t/9UrBv+wao0rd5XMVd7zrJ9Gv30En2345hGPrlYo55arBN5xdXO5/Zm6scbNeDhb3Tp89+7tn2WelhnPbpQ+cWlum72Si3Zfkw3v11xQ+BwNcPU3X2/45j+u65iiHVecanXeeQ1+cULP3rceHDeNKhuePr7K9N0zsxFmvrxRtc290B+h5de7hK7Q+c/s8RjDfV3aph77417kH/+M0u16+gpffHzYT33zfbTGrZfta4V55r03roqS9ttOZKjPcfytLLSd+rlpbt1738815+f/tlmTXp/ne6am6q15d8X98/V7jBcc+bd/Xv5Pt3x3jqPcv7m1+zfTz75pObOnaulS5cqIqL6THnTpk3TlClTXK9zcnJ8GlhHhrFWNQAAQFMTYrVoZPfWrqG33ozumaAfdmbqr5f00pjebfX938YoItyqd5fv1/oDWbq8fzuNPTNJxaUOxbgNLV/y1ws06snFkqR/jhsoq8WiS/slyRZaNZu1MzNxQzI7N3cHT+brdzWs4f3lRs+1r6d9slFntq86NP+GN8sTEzsMr2uIV+fGt1a55oDvycyrsv/AiQJXwrSTbqNOt6XXPrL14MkCvf7jXtfrQycL9NtZdRtt65RdUFJlmTVJOl5pWumy3ZmKDAvR/f8tC6bnrTmg2y7orq4JLTyWXLvkn9+rsiNZhcrI8ezFPZpbpE2HstWvmqkMa/ad0LjXVigyLEQvXDdYd81L9dj/6fpDenFJ2TrtF56RqGhbiL7ZkqGJI7sqMjxE6dmFemXpLt1yXjd1bFl9tvSafLnxiL7ceESp0y9WfPl0W/cAN6tSPoJF247qzR/36qZzu6qwxO5aJm6R2zSAFm45Em5+e7WWbD+mJX+9QF3LkxemZxdq+mebPc6bV1xa52klvlKvoDohIUEhISHKyPCce5GRkaGkpKQaj3322Wf15JNP6ttvv9WAAQNqLGuz2WSz+W4dvMqcy2qR/RsAAKBpee/m4frfhsN686d92nAgS5LnWsKv/GGI1qed1IhuZXOgO7UuCwCmXFLzcO0O8ZFKnX6xosJDvfZao8yeY1UD2drUlhF92icba9zvzltSNXfVDYOuT3I4J/e11NvHRahrmxZ1WhPaW8dcidu85beX7dOM/22uUmb+pnTdfkH3WtfGzvMymvaDVWn6YFWa7r+8j+Ijw/W7s8s6HLMLSrQ3M891c+BUUakmvrW6yvEHT1Z8PjkFJa7EYtkFJZp22Rn6xQs/KPNUsfafyNcL1w7Wg59u0i8HtdeY3m3rvfzVuyv2a/KFPSV5BtXPf1t17fpHvtii35zV0bVsW2VRbku4LSn/bsxdlaZpl5+hOT/t1YLNGVWOuegf32nSBd1146iu9ar36ajX/yjh4eEaMmSIR5IxZ9KxESNGVHvc008/rUcffVTz58/X0KFDG15bH4kIcw7/JqgGAABoSiwWi345qIM+mzTK6/5oW6hG92zjmpNdH/FR4fUOqL+dcr76tovVOd1aadKY7lX2j+pRFtx3a9NCD/2ir3Y/cbnuvKhnjed0jpp0uvW8bvWqU3NTOYN4XZZRq87411eebnVc3AP4w9mFeu/mc/Tq9UMadK5dR08p5bnv9NgXW7wG1JK08VCWdmTk1hiDnPHQfK3cU31g/8RX23Tvxz8rvXxo+DWzltUt83ZqRQKvk25DqJduO6Zt6TnKLF/SbHt6rv7v2536LPWwbpqzRl2nfaUuU79UQbFd81an6cFPN9Y6vPpIdqEmvb9OXaZ+qc831J44rLqAWpLXgP7V7/fIMAz9/fMtWu7lszqWW6QjjTiP3Jt6D/+eMmWKbrjhBg0dOlTDhg3T888/r7y8PE2cOFGSNGHCBHXo0EEzZ86UJD311FOaPn263n//fXXp0sU19zo6OlrR0dE+vJS6c97xYPg3AAAAatKjbbS+unO0pLIEU785q6OeWbBdX5cvB/XGDWfr4MkC9Whb8bv23J4JHr2gLaPCPIYq/3DfGA197FtJUlxkmH5zVke99v2eetXr+nM6a8PBLP180PvQ6id/3V+PfLGlSXYiDXKb995UdCkf8ZAQHV5LSe+2pZfNjd519FS1Zb7amK6vNtach6qgxK6/f76l1vdLzynUgs3p2pFR/ftVx5mxW5K2Z+Tq0ud/cL0+kl2oDV7Wtn6wPBu8JI09M0mxEWH6cM0Br+d/b2Wa67m3hHH1UWp36INVaZq7Ks1je23f61ZRDft7bKh6B9Xjxo3TsWPHNH36dKWnp2vQoEGaP3++K3lZWlqarG7531955RUVFxfrt7/9rcd5ZsyYob///e+nV/sGcs2pboL/yQAAAMCTIf8nHvLGYrGoW5toj/WDI8JCPAJqSRrauaX+de1g9WwbreJShwYmx6vH/V+ptLyHLyHapr+N7a1nFmzXM78doN5JMa7XTme2j61xWPWjV/eT3WFo9FOLPRJaOf1+WCf9flgndZn65elets/F+zngqYslf71AkhQZ5teUUw12339+1vZ6LuXl5Jy7XB33xHNOH7slDLvj3XXKLfJPwue0EwVepw+cOWNBjcc1ZCTL6WjQt2by5MmaPHmy131Lly71eL1v376GvEWjiggnqAYAAEDD1DbH1GKx6KqB7WssM2lMD10/orMrodKkMT1cQfXQzi31zDUDNebZpTWeI8Rq0Vd3jtYjn2/Rb4d01K3vrNWpSsFO59ZR2t+A+cbV6d6mhUodhuuco3smaNnu4/XKuBxta3qBq/PvNDy06t/t+OGdPHpfm4KGBtS+4K+AWqr/UmJOdkfjrc3tTdP7RvtBdHjZZXtLAgAAAICmJczatBKL1S9tU/UqZyiOtoXqVFGpHrjiDFd2Y28GdKzIAB0fFa7nxg2S5H0pIZvbHPJzeyTox12ZVcrUVefWUfriz6N1LLdIr3y3S7eM7qZubaJVWGLXtvRc19zeKwe2r3EurXtQfVGfth7Znk+HxaJak4B58/uzK1YZCq30XTurU7zuvbRPkwuqUbPqludrLKYMqmMjy/4Dy66U1h0AAABNx32X9tF7K/frrotrTvzlb1ZfRdWV/HDvGB3KKnAtm/Sn87vp1e/26PUJQ9UrMUYtbCH6z9qDunpwB6/HJ8baqmTBdk/M9s9xg5R2Il/f7zim/1u0UwnR4a4EVZL06aRRSoy1acTMxV7PHxsRpsjwEHVqHaWZv65YzSciLKTO86SX/PUCj+Hz/TvGuYLqF68brA/XHNS1Zyfr9vdqX3fbubyaU10C6qTYCNeSXE5/v+pM1/POraP0iwHtFGK16I4Leqh3UozX8/xv8igZhjT1k43aeqT25byuHZasD1Z5zkEe1aN1nbKNo/7ObO996bHG0rRu+/lJHEE1AABAk3f7Bd31430Xql1cZKCr4sFazyWG6qpli3CPdYinXtpH6x+6WCl9E9WpdZRaR9v0p/O7KzHW+/rZr14/VMO6ttLcW89xbXNfh7tNjE1DOrfUnRf11MK7z9Oq+1O0bOqFrv0d4iPVLi5SUy7u5do2vGsrndOtlSTpwSvOqLH+zt/YF/Zp43X/wI5xVXrgQ9w+y3Zxkfr3TcN0Wf92enn8WTW+l6R6Zer+dsp52vj3S7R82oXq3LpiHeYLerdxrQwklQ0Df/G6s/R/vx/sEVD/5PY5SdKAjvEamByvr8uT2FW2/bFL9daNZ+v6czpr+2OXKszLHN/fDulY5/rXhfvfmy/85qz61e+By2v+fkhlieAqZ78PBqYMqmMjyzrof9p1vE5p3gEAAACXBsTUDYnDLRaLWraoe1Kv3kkx+vBPI3RO+Rrekufwbyer1aKeiTGyWi1q7Zbt2rlCzl8u6qntj12qj24boXdvHq65t47Q1kcu1XC383qz8O7z9MYNQ/XLgR308e0j1cctKP392cn6903DXa9/Oai94iLDNG5YstsZKrqaL+/frtbrjQwL8Qh2K48guHpQxbz2Hm1jFBMRJovFolJ7xfucW8dhwh3iI3Vx38Q6lZXKbmaM6dNWj17dT7bQkCpfmf/7/aAqQ80b6tweCXr7pmEeS7OFh1jVqvy78/RvB1R3aI1qWvospnwIf4f4SF3Yp61uGd1VN4zsUuP5LujdRmsevFjrp1/coPpI0id3jGzwsY3JlMO/nXfRJOnPH6xX59ZRGtAxPnAVAgAAQLNh8dms6sZ350U9tWz3cV1TTa+oLTRE/71jpEodhlq4zXW2hYbo7C6tXK8jw2vvXWwbG6GLynvRh3RuqU8njVKfh+ZLKltmLC6q4jf48+MGqdRhKLSOY+mHdW2lEItFJXaH1uwvy05tsVjUIb5iFEOI1SJHecDcr0Osbh7dzWN9ZqfOraNca2fXFgi6+/tVZ2pvZl6VYxqSyGzsmUla4yXL9oCOcdUuk3ZF/3baeiRHezLzPLb3aBut83t5jg4IsVo0/67R2ngwWxf2aat7//NzrXXq3yFOg5LjXctgRYSFqEvrqCpTCiaO6qLpv+grqfakfU7n92qjt24823Ved7ZQq4pKa04s9vqEoWrZIkxndWpZ63uNqOXmT2MwZU91+3jPIUR7juVVUxIAAADw1FhzqhvD8G6ttf6hi2vsrRzcqaVHAO0r4W5DnisPf7ZYLAoLscpisbh6VPskxVZ7rg//NEIf3HqO7kzpKYtFmjymR5Uy7nO1O8RHql+HOH102wiPIe6S9NRvBujivon68E8jvA7Lrk6H+Eh9O+V8XX9OZ4/tM648U5/cMVJjensf9l7ZT1MvVERYiEZ2b63fnNVR0y7r49r3+NX91bFlpNcM6S+NP0uL7jlfo3t69q67D2d3CrFa1DYmQhedkegR+D5UHgw7uX+OgzvF65bR3RQZFqLxwztJ8pzqcM2Qjtrx2GWaceWZslgsXgNq5xD0DvGR2v7Ypa7tYSGe5V8ef5Z6JUbrm7vP8/h7k6QL+7Stco0pfRM1pLP37+j//X6QplzcS+sfuljPXjNQr/yh9qkDvmbKnup2cZFq1SJcJ/LKEjOEhjSj/xkBAAAQUFcP7qCP1h5U9zbVZ+iurCFZqX2lPkPIfcnqFiyF1fB7e9nUC1Vid3j0lFdndM82Sp1+iWIjqpZ1n5/t/Ly93SxIbhWl2ROG1vpedRUeatVZnVrWuDaye0AZU153q9Wif/xuoCRpZPcEHTyZr/4d4/TN3edJkg5nFWjV3pPqnRSjxFib6zyz/jBEX2484up97tTKe1DttR4qC6yfWbBN7918jjrER+rFJbsklWU+79Q6SuunX+yaNlBsr+hBfuaagbV+Fn++sIeuGdrRlQdhYMc4bTiYrWuGJnuUu7x/O9cQ/8o5Ckb1SNC1w5LVd7r3tajf+eMwPf7lVl3Qu63+cE4ndWxZcf2+nqdeV6YMqiWpV2K0Vuw5Icn78gMAAKB+ioqKNHz4cG3YsEHr16/XoEGDAl0loFGM6pGgBXedp+RWTSuBWlPmLfBziggLqTIkuCbuUzndWQM8hKCu7+4t0V3/jnHqX75UWlT58r892saoR9uq2cdb2EI9hjh7+2yrG1YfYrXohpFdNGFEZ4WFWHUst8htX9mf7n8X1w7rpGcWbK/z3HOLxeKRWPCDW8/R3sw89W1X/SiEyh+Hw2EoIrT678Ponm00/666jQrwFxMH1TGuoPqUHxcwBwAgWN17771q3769NmzYEOiqAI2uuqWW4GnerefoaG6R1+DQ16rrnfWXuiaj80U13ZdKS65DT/X153TW0h1H9auzypZjcw57dw++vQX7fzqvmwYlx9d5ybTKosJDa13eqvL72g0j4DdI6suUc6ol6Y4LKuYPrN1fNUkAAACou6+//lrffPONnn322UBXBUATMrxba105sH3tBU/DxFFdJEn312FJp8ZU1wR2vliSLTE2QlMu7qVHfnmmR8/y788uG2ZdeXmtR6/up+//NkaxEZ69/CHuw/K9VCs0xKpRPRLqNDS/oSrfAHCUj92/qE/bRntPXzNtT3VSXIR+N7SjPlxzUBurybAHAABql5GRoVtuuUWffvqpoqKqH+LprqioSEVFFcMOc3JyGqt6QJPQSEtbQ9L0X/TVzaO7qUN8pFIPZOn9lWn684U9/V6Puv4d++q78JeLql7jE7/qrzsu6KFOXpKXeUssVtfs642pchXiI8tyADSnCbqm7amWpHFnl2W123n0lHYfO+W1zKGsAr28dJey8ov9WTUAAJoFwzB044036rbbbtPQoXVP/DNz5kzFxcW5HsnJybUfBABeuC+t9fjV/bTlkbGu+cn+VNceaF/0VFd7bqvFa0AdiLrUVXxURSK9Xw/u4Eo2ZgQyu189mTqo7tiyYhJ9dWu3jXt1uZ6ev11TP97or2oBABBwU6dOdS2ZUt1j27ZteuGFF5Sbm6tp06bV6/zTpk1Tdna263HgwIFGuhIAZmKxWFyJvvzNuQxUeC3LdDWFQNapKfRUv3jdYPVOjNGsPwzRc+MGecwXby5MO/xbkuLdFqDfesT7sLODJ8sWhv9uxzG/1AkAgKbgnnvu0Y033lhjmW7dumnx4sVavny5bDabx76hQ4dq/Pjxevvtt70ea7PZqhwDAO56tI3WrqOndFan+EBXpU6uGZqsmIgwDaqlvk0gjnVxn89c1znhvtYnKVYLypcRc9d8+qlNHlTb3FK1t4wKzPp9AAA0RW3atFGbNrUvWfKvf/1Ljz32mOv14cOHNXbsWM2bN0/Dhw9vzCoCCHL/vmmY5q5K0x/O6RzoqtRJiNWiKwa0q7Wct7nNgdKU6lJZMxr9be6gWpJuHNlFc5btkyQVlzqqHW5gNKt7JQAA+EenTp08XkdHR0uSunfvro4dOwaiSgCCRPv4SE25pHegq2EaTS3e6dE2utmMFm5+A9Z97G9jeysh2qZDWQXq9eDX+mTdQa/lmtOdEgAAAABozu6+uJduHNlF/7ltRKCrUivTB9UtbKH603ndXK+nfLhBxaWOANYIAIDmq0uXLjIMQ4MGDQp0VQAAzVi0LVR/v+pMDe3SKtBVqZXpg2pJurhvosfrx77cUqUMHdUAAAAAglWgEpUFA4JqSZ1bR2nsmRWB9b+X79f29NwA1ggAAAAA0BwQVKss692r1w/VpDHdXdvGv77SY8Fxu4O+agAAADQMvYDm1oSTbMMHCKrd/G1sH/VtFytJyjxVpAWb01377A5DK/ccD1TVAAAAAABNEEF1JY//qp/r+dzVBzz23TUv1c+1AQAAAIDGR296wxFUVzKwY7wGJsdLkpZu91wXjazgAAAAaIjQECIWIFgRVFditVr02aRRXvcdzyv2c20AAAAQDOZMHKaE6HC9PP6sQFcF8MoghVSDEVRX44oB7bxuf/PHvfri58N+rg0AAACas2FdW2n1Aym6vL/335hAoLVqERboKjRboYGuQFM189f9FWa16NNUzwD6kS/K1rDOzC3SjaO6BqJqAAAAaIYsTFpFE/Tc7wbq260ZmjCiS6Cr0mzRU12N2IgwPf/7wdr9xOVe9//98y36+WCW/v6/zZq/Kd1rGQAAAABoyn59Vke9PH6IIsJCAl2VZouguhYhVou++9sFevX6IVX2XfXiT5qzbJ9ue3et8otLSWQGAAAAACZDUF0HnVu30Ngzk/TRbSOqLdN3+gL1evBrdZn6pb7aeMRj39Pzt+mPc1Yru6CksasKAAAAAPAj5lTXw9ldWmnPE5fLkDTru916ZsF2r+XueG+d1+0DH/5GY89M1FmdWurW87rpxcW71K1NtCspWkGxXaUOh2IiSBIAAAAAAM0BQXU9Wa1lCSYmjemh3w1N1rMLtmvh1gydqONyWws2Z2jB5gzN/Hqba9uk96W/XtJL/11/SDmFpfriz+dq/qZ0JUTbdFbneGXkFMlhGDqZV6yLzkg8rfpnnipSTESobKGNN2diR0au5izbp0ljeqhDfGSjvQ8AAAAABBpB9WloE2PTU78doKckGYahNftP6ppZyxt0rme/2eF6PvyJRdWW69I6SkO7tNLNo7sqM7dYGw9l60/nddOCzenqlRSj7m2iqz128+FsXfXiT/rNWR10xwU9lBQX0SgJCa5+6SflF9v1/so0PXvNQP12SEefvwfgLw6H4bqZBgAAAFRGUO0jFotFZ3dppX1PXqHZ3+/RPxZu17TLztCEEZ11NLdIbaJt6nb/V6f9PvuO52vf8Xz9Z+1B17an5m/zKPPc7wZqXdpJ7c3M082juykju1Dfbj2qb7dmSJI+XHNQH64pO/71CUMVFmrVyj3HddO5XVVQbNeCzek6t2eCDmcVaEzvtnp/VZriIsN0eb92em/lfg3t0kpntIuVJB3KKlDLqDDlFdnVJsYmScovtrvq8tePNug/aw9oVPcEXT+is2IjwnQ8r9hV1t+2HslR6+hwtY2JqFN5u8PQf9Ye0Pm92ioprm7HIHjkFJbosud/0PCurfTcuEGBrg4AAGimLOIGfTCzGIZhBLoStcnJyVFcXJyys7MVGxsb6Oo02O5jpzR/U7puHt1VB04UyDAMRYSF6MDJfF03e6UkqX1chA5nFwa4prULD7VWyXZ+06iuchiG5izbV+fzjO6ZoGuHdVKvxBjFRYZpb2ae9mae0vPf7tSR7ELdel43/XJQey3ffVxtYmy6on87Fdsd2nw4R0M7t3St91hc6tDS7Uc1ontr2UJDZHcYSjuRL7vDUN/2Zd+ZVXtP6Hevlo0kWPXARWobE6GjOYVas/+kLuuX5HXtyJeW7NIzC7YrxGrRP8cN0jeb0zV+eGeN6N661ms7kVesErtDibG+CcaLSx3KLSxR6+iG35D4dkuG4qLCdHaXVj6pk6/kFpZofVqWRnZvrdCQppM/8dP1h3TXvFRJ0obplyguinwHKBMs7VJTwmcKIJg98vkWvfnTXknSvievCHBtUFd1bZsIqpuIRz7foqO5hXp+3CDtPHpKs7/fo0/WH9I1QzoqPadQ44d3Vk5Bid78aa9G90zQ7B/2ehx/dpeW+vlgtoqa0LJei+45X88t3KEvfz5Se+HT0C4uQkdquRFx/Tmd9c6K/dXuD7VaVOqo+KcwfngnfbzuoApLav48Q60WtY+PlNUiTRzVVX2SYvTR2oMKC7Hog1UHJElTLu6lPcdOKbugRD3aRqtH22h9tOag4iLDdMeYHlq8LUN928Vp0dYM7c7MU2xEqPYcy1NirE13X9xLX21MV5vocH287pAycgo19bI+Mgypb/tYPbdwhzJyCnXvpX105YB2Oplfoo2HsvXuiv0qLLHrvkv7qF+HOOUXl+r+Tzbq09TDkqTzerXRrwa3V9eEaO3LzNOWIznq2y5Wu46e0jVDOyoxNkJZ+SWKjwrT4awC7cjIVa/EGHUrn16wNzNPtlCrbKFWzfput64a2EH9O8ZJKpsKsSczT11bt5DVapFhGMopLFWMLVR2w1BYedBcYne4nt/89hrXSIqOLSP14BVnqE9SrBZtO6oSu0OX9UtS59Ytqnz+K/YcV7QtVP06xLm2GYahUkfZ++w+dkqxEWFKiA7X6n0n1SsxWpHhISq1G/rv+kMa2qWlcgtL1bNttOKjwiVJ29Jz1LlVC0WGh+i/6w/q7nkbXOcelByvt28apryiUu05lqdzeyYoO79EJ/OL1alVlKxWixwOQ5//fFjndGutaFuowkKsCg+t/kaBYRgeN3SO5RZpyfaj+vXgDgoNser4qSIdyS6sco2Ltx3VB6vS9ORvBiihATdaVu45rt5JMa7rbgwn84q1Lu2kLuzT1utNK6ksQaMt1KpSh6H5m9N1YZ+2igoL8Trk3vlZFZXaVVTqUGw9kjoezSlU62ibCkrsirad/iAtM7RL/sZnCiCYEVQ3TwTVJrBoa4ZatQjX4E4tPbZn5RfrmQXbNbRLS32y7pB6JcboaG6RWoSH6O6Le7l6Trel5+jjtQdltVh0yZmJevSLrUo9kFXlfUKtFlktFkWElf3wdR/eXZ0rB7bXC9cOliQdPJmvSe+v1wYv5wY6t47S/uP5dS4fGRaigpLav4Pu+iTFaFt6bn2r1iAtwkOU5/ZvJDzUqohQq3IKS6s9pk9SjHIKSnRerzaau/pAteXObB+rzYdzFBZiUYm94r/ullFhSoqLVGKsTQM7xqtbmxY6eLJAx3KLtP94npZsPyZJ6pUYrSNZhcot8qxLiNUie/lNpRtGdNbby/erb7tYRdtCNaZPW+05dkofrzuozq1bqHdijNamndSx3CJJUv8OcerWpoVO5BUrvTz4zzxVpL2ZeTp4ssDjfboltFDn1lGu+tR2Q6xtjE0dW0ZqXVqW1/03juyi/OJSGYa0Nu2kDp4o0Hm92qh1i3DNW1P95/j938aoU+uoavfXBe2S7/GZAghmBNXNE0E1GizzVNmP5YRom04VlXrt1SksD2qcic6OnypSXGSYQkOsOppbqPjIcK+9c3aHoYISu9annVTPtjFauv2oqwfXYZQNcV6974SGd22lbm2i9e/l+/TeyjRJUnxUmLLyq671fd3wTlq89ahCrBYdyiqost+pTYxNXRNaKO14vtJzChUeYpXdMJQQHa6MnKJqjxvcKV5P/WaAWthC9dXPR/TE11vV9P/VAKiOLxIo0i75Hp8pgGBGUN08EVQjKBmGoRK7IUOG3ltRFmzfdG5Xj/12h1FlXm7lIbbe2B2GQsqHnNalvFR2c+FwVoFatQiX3WHoaG6RVuw5roc/36IXrxusEd1aq9RhqKDYrsTYCOUWlqjY7tC6tCzFRoTK7jDUwhaq37+2QpJ0Rf92mnpZHx3JLtSpohKd0621juUWaeuRXG0+nK3wEKsiw0N0fq82+uLnI/ri58PqlRijw9mFys4v1ojurXVZv3YqdTgUHhKi9WknlVtUqtiIUO3NzFdhqV2P/bKfjmQXKvNUkd74ca82HMzSY1f309Ltx5RfXKpDJwvUOtqmPkkxev3HvSoudSgpNkIjurdWenahlu857rr+XonR2pFxSpJ0zZCOchhlN2V6JUYrKS5Sh04WKO1EnromtNA3WzKUU1CiSWN66Kddma7eSnevTxiqAR3jNPWTjVqx57iGdG6pH3ZmqlWLcA3p3FJHsgvUKzFGJ/OKvR5fk7tSeur5b3dKKrth5Lx5JEnRtlCdKipV14QW2puZ59repXWUJl/YU5f3T5JhSFkFJZoyL1Ur955QiNWia4cl60hWoTJyC7Uz45SKSh3qllA2dHx419bKKihWRk6hftp13KMuHVtG6uDJAvVoG63EWJvH/hhbqC7v367GntaaWCzyetOn8rV5K195GkRN4iLDlF1Q9SaXe693VHiI8ovtGtgxToeyCnSqqLTWKRWtWoTXeYlC97qEWC11Ou72C7rrvkv71Ov83tAu+R6fKYBgRlDdPDVqUP3SSy/pmWeeUXp6ugYOHKgXXnhBw4YNq7b8Rx99pIceekj79u1Tz5499dRTT+nyyy+v8/vR0KI5MQxDWfklatmi7nNVhzy6UMfzivXPcQP1q8HmWYJsX2aeWrYIV1xk/ROAzd90RCv2nNADV5zhmpstSaV2h8dNlb2ZecrIKdQ53VprXdpJldoNDetac6K2ut5UqSvDKEuc9/ay/frzhT2qfDe6TP3S9dy9oXXWo8TukNVicQWsIVaLiksdriDYfVSIw1E2p3zDwSx1ad3CI9P+3sw8HT9VpKGVEtXtzcxTi/AQtY2NUGGJXSV2h2IiwnQoq0CjnlwsSdr6yKXl89ErPt/CErvr5kBdPjPnfPdQq0UWi0X5xaWyOwzF1DA3elt6juZvSlevxBglRNtcf3evfb9bc1cf0Kt/GKKeiTGu8iV2hwpL7AoLsbpG0vj671OiXWoMfKYAghlBdfPUaEH1vHnzNGHCBM2aNUvDhw/X888/r48++kjbt29X27Ztq5RftmyZzjvvPM2cOVO/+MUv9P777+upp57SunXr1K9fP59eDNBcHc4qUOqBLF16ZhJrIpvQ419u0ewf9uqOC7rrXh/0ovrSt1syFBUeopE9EgJdlSaFdsn3+EwBBLMVe47r96+tUOsW4Vr70MWBrg7qqNGC6uHDh+vss8/Wiy++KElyOBxKTk7Wn//8Z02dOrVK+XHjxikvL09ffPGFa9s555yjQYMGadasWT69GABojopLHdp4KEsDOsZ79Lqj6aJd8j0+UwDBbvPhbCW3iqrX6hUIrLq2TfX69VZcXKy1a9cqJSWl4gRWq1JSUrR8+XKvxyxfvtyjvCSNHTu22vKSVFRUpJycHI8HAASr8FCrhnRuRUANAEAQO7N9HAF1kKrXL7jMzEzZ7XYlJiZ6bE9MTFR6errXY9LT0+tVXpJmzpypuLg41yM5Obk+1QQAAAAAwC+aZLfItGnTlJ2d7XocONCwLLgAAAAAADSmqgsQ1yAhIUEhISHKyMjw2J6RkaGkpCSvxyQlJdWrvCTZbDbZbLZq9wMAAAAA0BTUq6c6PDxcQ4YM0aJFi1zbHA6HFi1apBEjRng9ZsSIER7lJWnhwoXVlgcAAAAAoLmoV0+1JE2ZMkU33HCDhg4dqmHDhun5559XXl6eJk6cKEmaMGGCOnTooJkzZ0qS7rzzTp1//vn6xz/+oSuuuEJz587VmjVr9Nprr/n2SgAAAAAA8LN6B9Xjxo3TsWPHNH36dKWnp2vQoEGaP3++KxlZWlqarNaKDvCRI0fq/fff14MPPqj7779fPXv21KefflrnNaoBAAAAAGiq6r1OdSCwdiUAoCmhXfI9PlMAQFPTKOtUAwAAAACACgTVAAAAAAA0EEE1AAAAAAANVO9EZYHgnPadk5MT4JoAAFDRHjWDtCTNBm09AKCpqWt73yyC6tzcXElScnJygGsCAECF3NxcxcXFBboaQYG2HgDQVNXW3jeL7N8Oh0OHDx9WTEyMLBbLaZ0rJydHycnJOnDggCmzi3L9XD/Xz/Wb9fol330GhmEoNzdX7du391hGEg1HW+87XL+5r1/iM+D6uX5fXX9d2/tm0VNttVrVsWNHn54zNjbWlF8yJ66f6+f6uX4z88VnQA+1b9HW+x7Xb+7rl/gMuH6u3xfXX5f2ntvrAAAAAAA0EEE1AAAAAAANZLqg2mazacaMGbLZbIGuSkBw/Vw/18/1m/X6JT4DszD73zPXb+7rl/gMuH6u39/X3ywSlQEAAAAA0BSZrqcaAAAAAABfIagGAAAAAKCBCKoBAAAAAGgggmoAAAAAABqIoBoAAAAAgAYyVVD90ksvqUuXLoqIiNDw4cO1atWqQFfJJ2bOnKmzzz5bMTExatu2ra6++mpt377do0xhYaEmTZqk1q1bKzo6Wr/5zW+UkZHhUSYtLU1XXHGFoqKi1LZtW/3tb39TaWmpPy/FJ5588klZLBbdddddrm3Bfv2HDh3SH/7wB7Vu3VqRkZHq37+/1qxZ49pvGIamT5+udu3aKTIyUikpKdq5c6fHOU6cOKHx48crNjZW8fHx+uMf/6hTp075+1LqzW6366GHHlLXrl0VGRmp7t2769FHH5X7wgbBdP3ff/+9rrzySrVv314Wi0Wffvqpx35fXevPP/+s0aNHKyIiQsnJyXr66acb+9LqrKbPoKSkRPfdd5/69++vFi1aqH379powYYIOHz7scY7m/hmgZsHY3tPWe6Ktp60P5rZeor1vdm29YRJz5841wsPDjTfffNPYvHmzccsttxjx8fFGRkZGoKt22saOHWu89dZbxqZNm4zU1FTj8ssvNzp16mScOnXKVea2224zkpOTjUWLFhlr1qwxzjnnHGPkyJGu/aWlpUa/fv2MlJQUY/369cZXX31lJCQkGNOmTQvEJTXYqlWrjC5duhgDBgww7rzzTtf2YL7+EydOGJ07dzZuvPFGY+XKlcaePXuMBQsWGLt27XKVefLJJ424uDjj008/NTZs2GBcddVVRteuXY2CggJXmUsvvdQYOHCgsWLFCuOHH34wevToYVx77bWBuKR6efzxx43WrVsbX3zxhbF3717jo48+MqKjo43/+7//c5UJpuv/6quvjAceeMD45JNPDEnGf//7X4/9vrjW7OxsIzEx0Rg/fryxadMm44MPPjAiIyONV1991V+XWaOaPoOsrCwjJSXFmDdvnrFt2zZj+fLlxrBhw4whQ4Z4nKO5fwaoXrC297T1FWjraeuDva03DNr75tbWmyaoHjZsmDFp0iTXa7vdbrRv396YOXNmAGvVOI4ePWpIMr777jvDMMq+eGFhYcZHH33kKrN161ZDkrF8+XLDMMq+uFar1UhPT3eVeeWVV4zY2FijqKjIvxfQQLm5uUbPnj2NhQsXGueff76roQ3267/vvvuMc889t9r9DofDSEpKMp555hnXtqysLMNmsxkffPCBYRiGsWXLFkOSsXr1aleZr7/+2rBYLMahQ4car/I+cMUVVxg33XSTx7Zf//rXxvjx4w3DCO7rr9zI+OpaX375ZaNly5Ye3/377rvP6N27dyNfUf15+6FR2apVqwxJxv79+w3DCL7PAJ7M0t7T1tPWuwvmts4wzN3WGwbtfXNo600x/Lu4uFhr165VSkqKa5vValVKSoqWL18ewJo1juzsbElSq1atJElr165VSUmJx/X36dNHnTp1cl3/8uXL1b9/fyUmJrrKjB07Vjk5Odq8ebMfa99wkyZN0hVXXOFxnVLwX////vc/DR06VNdcc43atm2rwYMHa/bs2a79e/fuVXp6usf1x8XFafjw4R7XHx8fr6FDh7rKpKSkyGq1auXKlf67mAYYOXKkFi1apB07dkiSNmzYoB9//FGXXXaZpOC/fne+utbly5frvPPOU3h4uKvM2LFjtX37dp08edJPV+M72dnZslgsio+Pl2TOz8AszNTe09bT1tPWm7Otl2jvvQl0Wx96+pfQ9GVmZsput3v8JypJiYmJ2rZtW4Bq1TgcDofuuusujRo1Sv369ZMkpaenKzw83PUlc0pMTFR6erqrjLfPx7mvqZs7d67WrVun1atXV9kX7Ne/Z88evfLKK5oyZYruv/9+rV69Wn/5y18UHh6uG264wVV/b9fnfv1t27b12B8aGqpWrVo1+eufOnWqcnJy1KdPH4WEhMhut+vxxx/X+PHjJSnor9+dr641PT1dXbt2rXIO576WLVs2Sv0bQ2Fhoe677z5de+21io2NlWS+z8BMzNLe09bT1tPWm7etl2jvK2sKbb0pgmozmTRpkjZt2qQff/wx0FXxmwMHDujOO+/UwoULFREREejq+J3D4dDQoUP1xBNPSJIGDx6sTZs2adasWbrhhhsCXLvG9+GHH+q9997T+++/rzPPPFOpqam666671L59e1NcP6pXUlKi3/3udzIMQ6+88kqgqwP4DG09bT1tPW09yjSVtt4Uw78TEhIUEhJSJQNkRkaGkpKSAlQr35s8ebK++OILLVmyRB07dnRtT0pKUnFxsbKysjzKu19/UlKS18/Hua8pW7t2rY4ePaqzzjpLoaGhCg0N1Xfffad//etfCg0NVWJiYlBff7t27dS3b1+PbWeccYbS0tIkVdS/pu9/UlKSjh496rG/tLRUJ06caPLX/7e//U1Tp07V73//e/Xv31/XX3+97r77bs2cOVNS8F+/O19da3P+9+DkbGT379+vhQsXuu5cS+b5DMzIDO09bT1tvRNtvTnbeon23qkptfWmCKrDw8M1ZMgQLVq0yLXN4XBo0aJFGjFiRABr5huGYWjy5Mn673//q8WLF1cZxjBkyBCFhYV5XP/27duVlpbmuv4RI0Zo48aNHl8+55ez8n/iTc1FF12kjRs3KjU11fUYOnSoxo8f73oezNc/atSoKsuq7NixQ507d5Ykde3aVUlJSR7Xn5OTo5UrV3pcf1ZWltauXesqs3jxYjkcDg0fPtwPV9Fw+fn5slo9/ysLCQmRw+GQFPzX785X1zpixAh9//33KikpcZVZuHChevfu3SyGgjkb2Z07d+rbb79V69atPfab4TMwq2Bu72nraetp62nrnWjvm2BbX+/UZs3U3LlzDZvNZsyZM8fYsmWLceuttxrx8fEeGSCbq9tvv92Ii4szli5dahw5csT1yM/Pd5W57bbbjE6dOhmLFy821qxZY4wYMcIYMWKEa79zmYlLLrnESE1NNebPn2+0adOmWSwz4Y17RlDDCO7rX7VqlREaGmo8/vjjxs6dO4333nvPiIqKMt59911XmSeffNKIj483PvvsM+Pnn382fvnLX3pddmHw4MHGypUrjR9//NHo2bNnk11mwt0NN9xgdOjQwbXMxieffGIkJCQY9957r6tMMF1/bm6usX79emP9+vWGJOO5554z1q9f78p26YtrzcrKMhITE43rr7/e2LRpkzF37lwjKiqqSSyxYRg1fwbFxcXGVVddZXTs2NFITU31+D/RPbtnc/8MUL1gbe9p66uiraetD9a23jBo75tbW2+aoNowDOOFF14wOnXqZISHhxvDhg0zVqxYEegq+YQkr4+33nrLVaagoMC44447jJYtWxpRUVHGr371K+PIkSMe59m3b59x2WWXGZGRkUZCQoJxzz33GCUlJX6+Gt+o3NAG+/V//vnnRr9+/QybzWb06dPHeO211zz2OxwO46GHHjISExMNm81mXHTRRcb27ds9yhw/fty49tprjejoaCM2NtaYOHGikZub68/LaJCcnBzjzjvvNDp16mREREQY3bp1Mx544AGP/1SD6fqXLFni9d/7DTfcYBiG7651w4YNxrnnnmvYbDajQ4cOxpNPPumvS6xVTZ/B3r17q/0/ccmSJa5zNPfPADULxvaetr4q2nra+mBt6w2D9r65tfUWwzCM+vVtAwAAAAAAySRzqgEAAAAAaAwE1QAAAAAANBBBNQAAAAAADURQDQAAAABAAxFUAwAAAADQQATVAAAAAAA0EEE1AAAAAAANRFANAAAAAEADEVQDAAAAANBABNUAAAAAADQQQTUAAAAAAA30/9mwbrISHMd7AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(12, 4))\n",
        "axs[0].plot(losses)\n",
        "axs[1].plot(np.log(losses))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7woNwcna3gkU"
      },
      "source": [
        "As an alternative to running the training code above, you can use the model from the pipeline like so:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "IN21Roo5fVnB"
      },
      "outputs": [],
      "source": [
        "# Uncomment to instead load the model I trained earlier:\n",
        "# model = butterfly_pipeline.unet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVdGxVJOtBPb"
      },
      "source": [
        "## Step 6: Generate Images\n",
        "\n",
        "How do we get images with this model?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LTBbByMl7ri3"
      },
      "source": [
        "### Option 1: Creating a pipeline:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "p3mJYitMtDeB"
      },
      "outputs": [],
      "source": [
        "from diffusers import DDPMPipeline\n",
        "\n",
        "image_pipe = DDPMPipeline(unet=model, scheduler=noise_scheduler)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "754594b3d1fb4cd595c355204a5c5dbf",
            "1d0eca98049a4286ba9b3fa5d926f9ad",
            "cbced86ad1364a94b7986e74a620dbb4",
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        "outputId": "a3ac4021-8d0b-4d77-a9a5-9b9953e85385"
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      "outputs": [
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          "output_type": "display_data",
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            ],
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NumPy array shape: (32, 32)\n",
            "NumPy array dtype: uint8\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Run the pipeline to generate images\n",
        "pipeline_output = image_pipe()\n",
        "\n",
        "# Access the first image\n",
        "generated_image_pil = pipeline_output.images[0]  # PIL image\n",
        "\n",
        "# Convert to NumPy array\n",
        "generated_image_np = np.array(generated_image_pil)\n",
        "\n",
        "# Check the shape and dtype\n",
        "print(\"NumPy array shape:\", generated_image_np.shape)\n",
        "print(\"NumPy array dtype:\", generated_image_np.dtype)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "from torchvision.transforms import ToTensor\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def visualize_pipeline_output(pipeline_output, nrow=8):\n",
        "    \"\"\"\n",
        "    Visualizes images generated by the DDPMPipeline in a grid format.\n",
        "    Args:\n",
        "        pipeline_output: The output from the DDPMPipeline, expected to contain PIL images.\n",
        "        nrow: Number of images per row in the grid.\n",
        "    \"\"\"\n",
        "    # Convert PIL images to tensors\n",
        "    tensors = [ToTensor()(image) for image in pipeline_output.images]  # Converts to [0, 1]\n",
        "\n",
        "    # Stack tensors into a batch and rescale to [-1, 1] if needed\n",
        "    image_batch = torch.stack(tensors) * 2 - 1  # Rescale from [0, 1] to [-1, 1]\n",
        "\n",
        "    # Clamp to ensure values are in [-1, 1]\n",
        "    image_batch = image_batch.clamp(-1, 1)\n",
        "\n",
        "    # Rescale from [-1, 1] to [0, 1] for visualization\n",
        "    image_batch = (image_batch + 1) / 2\n",
        "    image_batch = image_batch.clamp(0, 1)\n",
        "\n",
        "    # Apply thresholding\n",
        "    image_batch = torch.where(image_batch < 0.25, torch.zeros_like(image_batch), image_batch)  # Set < 0.25 to 0\n",
        "    image_batch = torch.where(image_batch > 0.75, torch.ones_like(image_batch), image_batch)   # Set > 0.75 to 1\n",
        "    image_batch = torch.where((image_batch >= 0.25) & (image_batch <= 0.75), torch.full_like(image_batch, 0.5), image_batch)  # Set [0.25, 0.75] to 0.5\n",
        "\n",
        "    # Create a grid of images\n",
        "    grid = torchvision.utils.make_grid(image_batch, nrow=nrow)\n",
        "    np_img = grid.permute(1, 2, 0).numpy()  # Convert to (H, W, C) for matplotlib\n",
        "\n",
        "    # Plot the images\n",
        "    plt.figure(figsize=(4, 4))\n",
        "    plt.imshow(np_img)\n",
        "    plt.axis(\"off\")\n",
        "    plt.show()\n",
        "\n",
        "# Run the pipeline to generate images\n",
        "pipeline_output = image_pipe()\n",
        "\n",
        "# Visualize the output\n",
        "visualize_pipeline_output(pipeline_output)\n"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 377,
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        "outputId": "e4907c2c-d3e2-4b3b-c278-6db23a2421c1"
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      "execution_count": 20,
      "outputs": [
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          "data": {
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          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UHvu25neFJHh"
      },
      "source": [
        "We can save a pipeline to a local folder like so:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "a8C0_2UVhiWb"
      },
      "outputs": [],
      "source": [
        "image_pipe.save_pretrained(\"my_pipeline\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DHgonGi3FN4J"
      },
      "source": [
        "Inspecting the folder contents:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "F5hcwUSJhsnv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc3998f4-66f6-48d9-adff-338240a7f2c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model_index.json  scheduler  unet\n"
          ]
        }
      ],
      "source": [
        "!ls my_pipeline/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vypV6Joh2vE7"
      },
      "source": [
        "The `scheduler` and `unet` subfolders contain everything needed to re-create those components. For example, inside the `unet` folder you'll find the model weights (`diffusion_pytorch_model.bin`) alongside a config file which specifies the UNet architecture."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "dbFq6QXJFUdD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de1ea977-6189-487e-8960-3d8ab535544d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "config.json  diffusion_pytorch_model.safetensors\n"
          ]
        }
      ],
      "source": [
        "!ls my_pipeline/unet/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L2_tiRgS7-qh"
      },
      "source": [
        "Together, these files contain everything needed to recreate the pipeline. You can manually upload them to the hub to share the pipeline with others, or check out the code to do this via the API in the next section."
      ]
    }
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