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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "70eef519",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright (c) MONAI Consortium\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63d95da6",
+   "metadata": {},
+   "source": [
+    "# Using ControlNet to control image generation\n",
+    "\n",
+    "This tutorial illustrates how to use MONAI Generative Models to train a ControlNet [1]. ControlNets are hypernetworks that allow for supplying extra conditioning to ready-trained diffusion models. In this example, we will walk through training a ControlNet that allows us to specify a whole-brain mask that the sampled image must respect.\n",
+    "\n",
+    "\n",
+    "\n",
+    "In summary, the tutorial will cover the following:\n",
+    "1. Loading and preprocessing a dataset (we extract the brain MRI dataset 2D slices from 3D volumes from the BraTS dataset)\n",
+    "2. Training a 2D diffusion model\n",
+    "3. Freeze the diffusion model and train a ControlNet\n",
+    "3. Conditional sampling with the ControlNet\n",
+    "\n",
+    "[1] - Zhang et al. [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "022890b1-ea44-4c60-8a80-ed1fc755f90b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n",
+    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b766027",
+   "metadata": {},
+   "source": [
+    "## Setup environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "972ed3f3",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-05-04 18:42:25,456 - A matching Triton is not available, some optimizations will not be enabled.\n",
+      "Error caught was: No module named 'triton'\n",
+      "MONAI version: 1.2.dev2304\n",
+      "Numpy version: 1.23.4\n",
+      "Pytorch version: 1.13.1+cu117\n",
+      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
+      "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
+      "MONAI __file__: /home/mark/Envs/monai-generative/lib/python3.8/site-packages/monai/__init__.py\n",
+      "\n",
+      "Optional dependencies:\n",
+      "Pytorch Ignite version: 0.4.10\n",
+      "ITK version: 5.3.0\n",
+      "Nibabel version: 5.0.0\n",
+      "scikit-image version: 0.19.3\n",
+      "Pillow version: 9.3.0\n",
+      "Tensorboard version: 2.12.0\n",
+      "gdown version: 4.6.0\n",
+      "TorchVision version: 0.14.1+cu117\n",
+      "tqdm version: 4.64.1\n",
+      "lmdb version: 1.4.0\n",
+      "psutil version: 5.9.4\n",
+      "pandas version: 1.5.3\n",
+      "einops version: 0.6.0\n",
+      "transformers version: 4.21.3\n",
+      "mlflow version: 2.1.1\n",
+      "pynrrd version: 1.0.0\n",
+      "\n",
+      "For details about installing the optional dependencies, please visit:\n",
+      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "import time\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "from monai import transforms\n",
+    "from monai.apps import DecathlonDataset\n",
+    "from monai.config import print_config\n",
+    "from monai.data import DataLoader\n",
+    "from monai.utils import first, set_determinism\n",
+    "from torch.cuda.amp import GradScaler, autocast\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "\n",
+    "from generative.inferers import DiffusionInferer\n",
+    "from generative.networks.nets import DiffusionModelUNet, ControlNet\n",
+    "from generative.networks.schedulers import DDPMScheduler\n",
+    "\n",
+    "print_config()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d4ff515",
+   "metadata": {},
+   "source": [
+    "### Setup data directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8b4323e7",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
+    "root_dir = tempfile.mkdtemp() if directory is None else directory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99175d50",
+   "metadata": {},
+   "source": [
+    "### Set deterministic training for reproducibility"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "34ea510f",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "set_determinism(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3f70dd1-236a-47ff-a244-575729ad92ba",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Setup BRATS dataset\n",
+    "\n",
+    "We now download the BraTS dataset and extract the 2D slices from the 3D volumes.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87977bac-ff5e-4612-b9f2-b069d6ad9e9a",
+   "metadata": {},
+   "source": [
+    "### Specify transforms\n",
+    "We create a rough brain mask by thresholding the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c68d2d91-9a0b-4ac1-ae49-f4a64edbd82a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n"
+     ]
+    }
+   ],
+   "source": [
+    "channel = 0\n",
+    "assert channel in [0, 1, 2, 3], \"Choose a valid channel\"\n",
+    "\n",
+    "train_transforms = transforms.Compose(\n",
+    "    [\n",
+    "        transforms.LoadImaged(keys=[\"image\"]),\n",
+    "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
+    "        transforms.Lambdad(keys=[\"image\"], func=lambda x: x[channel, :, :, :]),\n",
+    "        transforms.AddChanneld(keys=[\"image\"]),\n",
+    "        transforms.EnsureTyped(keys=[\"image\"]),\n",
+    "        transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n",
+    "        transforms.Spacingd(keys=[\"image\"], pixdim=(3.0, 3.0, 2.0), mode=\"bilinear\"),\n",
+    "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(64, 64, 44)),\n",
+    "        transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n",
+    "        transforms.RandSpatialCropd(keys=[\"image\"], roi_size=(64, 64, 1), random_size=False),\n",
+    "        transforms.Lambdad(keys=[\"image\"], func=lambda x: x.squeeze(-1)),\n",
+    "        transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"mask\"]),\n",
+    "        transforms.Lambdad(keys=[\"mask\"], func=lambda x: torch.where(x > 0.1, 1, 0)),\n",
+    "        transforms.FillHolesd(keys=[\"mask\"]),\n",
+    "        transforms.CastToTyped(keys=[\"mask\"], dtype=np.float32),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d378ac6",
+   "metadata": {},
+   "source": [
+    "### Load training and validation datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "da1927b0",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-05-04 18:42:34,233 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
+      "2023-05-04 18:42:34,233 - INFO - File exists: /home/mark/data_drive/monai_data_dir/Task01_BrainTumour.tar, skipped downloading.\n",
+      "2023-05-04 18:42:34,233 - INFO - Non-empty folder exists in /home/mark/data_drive/monai_data_dir/Task01_BrainTumour, skipped extracting.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 388/388 [01:36<00:00,  4.02it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Length of training data: 388\n",
+      "Train image shape torch.Size([1, 64, 64])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:24<00:00,  3.88it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Length of val data: 96\n",
+      "Validation Image shape torch.Size([1, 64, 64])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_ds = DecathlonDataset(\n",
+    "    root_dir=root_dir,\n",
+    "    task=\"Task01_BrainTumour\",\n",
+    "    section=\"training\",\n",
+    "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
+    "    num_workers=4,\n",
+    "    download=True,\n",
+    "    seed=0,\n",
+    "    transform=train_transforms,\n",
+    ")\n",
+    "print(f\"Length of training data: {len(train_ds)}\")\n",
+    "print(f'Train image shape {train_ds[0][\"image\"].shape}')\n",
+    "\n",
+    "val_ds = DecathlonDataset(\n",
+    "    root_dir=root_dir,\n",
+    "    task=\"Task01_BrainTumour\",\n",
+    "    section=\"validation\",\n",
+    "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
+    "    num_workers=4,\n",
+    "    download=False,\n",
+    "    seed=0,\n",
+    "    transform=train_transforms,\n",
+    ")\n",
+    "print(f\"Length of val data: {len(val_ds)}\")\n",
+    "print(f'Validation image shape {val_ds[0][\"image\"].shape}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8e4d6164-00e5-4663-a678-1391438574e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4, drop_last=True, persistent_workers=True)\n",
+    "val_loader = DataLoader(val_ds, batch_size=64, shuffle=False, num_workers=4, drop_last=True, persistent_workers=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d86ba60-84d2-49f2-95c1-2ab611310d84",
+   "metadata": {},
+   "source": [
+    "### Visualise the images and masks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "17a5e9a4-9756-400b-8dbd-0f1d457ad3dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Batch shape: torch.Size([64, 1, 64, 64])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "check_data = first(train_loader)\n",
+    "print(f\"Batch shape: {check_data['image'].shape}\")\n",
+    "image_visualisation = torch.cat(\n",
+    "    (\n",
+    "        torch.cat(\n",
+    "            [\n",
+    "                check_data[\"image\"][0, 0],\n",
+    "                check_data[\"image\"][1, 0],\n",
+    "                check_data[\"image\"][2, 0],\n",
+    "                check_data[\"image\"][3, 0],\n",
+    "            ],\n",
+    "            dim=1,\n",
+    "        ),\n",
+    "        torch.cat(\n",
+    "            [check_data[\"mask\"][0, 0], check_data[\"mask\"][1, 0], check_data[\"mask\"][2, 0], check_data[\"mask\"][3, 0]],\n",
+    "            dim=1,\n",
+    "        ),\n",
+    "    ),\n",
+    "    dim=0,\n",
+    ")\n",
+    "plt.figure(figsize=(6, 3))\n",
+    "plt.imshow(image_visualisation, vmin=0, vmax=1, cmap=\"gray\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de29d929-bc99-4235-aea6-d6867c3d360c",
+   "metadata": {},
+   "source": [
+    "## Train the Diffusion model\n",
+    "In general, a ControlNet can be trained in combination with a pre-trained, frozen diffusion model. In this case we will quickly train the diffusion model first."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08428bc6",
+   "metadata": {},
+   "source": [
+    "### Define network, scheduler, optimizer, and inferer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bee5913e",
+   "metadata": {
+    "lines_to_next_cell": 2,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "device = torch.device(\"cuda\")\n",
+    "\n",
+    "model = DiffusionModelUNet(\n",
+    "    spatial_dims=2,\n",
+    "    in_channels=1,\n",
+    "    out_channels=1,\n",
+    "    num_channels=(128, 256, 256),\n",
+    "    attention_levels=(False, True, True),\n",
+    "    num_res_blocks=1,\n",
+    "    num_head_channels=256,\n",
+    ")\n",
+    "model.to(device)\n",
+    "\n",
+    "scheduler = DDPMScheduler(num_train_timesteps=1000)\n",
+    "\n",
+    "optimizer = torch.optim.Adam(params=model.parameters(), lr=2.5e-5)\n",
+    "\n",
+    "inferer = DiffusionInferer(scheduler)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f815ff34",
+   "metadata": {},
+   "source": [
+    "### Run training\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9a4fc901",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|██████████████| 6/6 [00:03<00:00,  1.73it/s, loss=0.987]\n",
+      "Epoch 1: 100%|██████████████| 6/6 [00:02<00:00,  2.44it/s, loss=0.946]\n",
+      "Epoch 2: 100%|██████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.893]\n",
+      "Epoch 3: 100%|██████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.836]\n",
+      "Epoch 4: 100%|███████████████| 6/6 [00:02<00:00,  2.43it/s, loss=0.78]\n",
+      "Epoch 5: 100%|██████████████| 6/6 [00:02<00:00,  2.33it/s, loss=0.723]\n",
+      "Epoch 6: 100%|██████████████| 6/6 [00:02<00:00,  2.34it/s, loss=0.673]\n",
+      "Epoch 7: 100%|██████████████| 6/6 [00:02<00:00,  2.43it/s, loss=0.617]\n",
+      "Epoch 8: 100%|██████████████| 6/6 [00:02<00:00,  2.34it/s, loss=0.567]\n",
+      "Epoch 9: 100%|███████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.52]\n",
+      "Epoch 10: 100%|█████████████| 6/6 [00:02<00:00,  2.32it/s, loss=0.478]\n",
+      "Epoch 11: 100%|█████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.434]\n",
+      "Epoch 12: 100%|█████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.389]\n",
+      "Epoch 13: 100%|█████████████| 6/6 [00:02<00:00,  2.40it/s, loss=0.357]\n",
+      "Epoch 14: 100%|█████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.321]\n",
+      "Epoch 15: 100%|█████████████| 6/6 [00:02<00:00,  2.31it/s, loss=0.284]\n",
+      "Epoch 16: 100%|█████████████| 6/6 [00:02<00:00,  2.39it/s, loss=0.252]\n",
+      "Epoch 17: 100%|█████████████| 6/6 [00:02<00:00,  2.40it/s, loss=0.227]\n",
+      "Epoch 18: 100%|█████████████| 6/6 [00:02<00:00,  2.39it/s, loss=0.205]\n",
+      "Epoch 19: 100%|█████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.197]\n",
+      "Epoch 20: 100%|█████████████| 6/6 [00:02<00:00,  2.31it/s, loss=0.167]\n",
+      "Epoch 21: 100%|█████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.152]\n",
+      "Epoch 22: 100%|█████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.137]\n",
+      "Epoch 23: 100%|█████████████| 6/6 [00:02<00:00,  2.31it/s, loss=0.123]\n",
+      "Epoch 24: 100%|█████████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.112]\n",
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:09<00:00, 101.87it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25: 100%|█████████████| 6/6 [00:02<00:00,  2.31it/s, loss=0.104]\n",
+      "Epoch 26: 100%|████████████| 6/6 [00:02<00:00,  2.35it/s, loss=0.0922]\n",
+      "Epoch 27: 100%|████████████| 6/6 [00:02<00:00,  2.39it/s, loss=0.0875]\n",
+      "Epoch 28: 100%|████████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.0778]\n",
+      "Epoch 29: 100%|████████████| 6/6 [00:02<00:00,  2.30it/s, loss=0.0702]\n",
+      "Epoch 30: 100%|████████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.0606]\n",
+      "Epoch 31: 100%|████████████| 6/6 [00:02<00:00,  2.36it/s, loss=0.0573]\n",
+      "Epoch 32: 100%|████████████| 6/6 [00:02<00:00,  2.33it/s, loss=0.0535]\n",
+      "Epoch 33: 100%|████████████| 6/6 [00:02<00:00,  2.33it/s, loss=0.0452]\n",
+      "Epoch 34: 100%|████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0497]\n",
+      "Epoch 35: 100%|████████████| 6/6 [00:02<00:00,  2.29it/s, loss=0.0469]\n",
+      "Epoch 36: 100%|████████████| 6/6 [00:02<00:00,  2.28it/s, loss=0.0377]\n",
+      "Epoch 37: 100%|████████████| 6/6 [00:02<00:00,  2.30it/s, loss=0.0381]\n",
+      "Epoch 38: 100%|████████████| 6/6 [00:02<00:00,  2.27it/s, loss=0.0413]\n",
+      "Epoch 39: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0318]\n",
+      "Epoch 40: 100%|████████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0379]\n",
+      "Epoch 41: 100%|████████████| 6/6 [00:02<00:00,  2.14it/s, loss=0.0338]\n",
+      "Epoch 42: 100%|██████████████| 6/6 [00:02<00:00,  2.22it/s, loss=0.03]\n",
+      "Epoch 43: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0287]\n",
+      "Epoch 44: 100%|████████████| 6/6 [00:02<00:00,  2.14it/s, loss=0.0269]\n",
+      "Epoch 45: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0255]\n",
+      "Epoch 46: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0304]\n",
+      "Epoch 47: 100%|████████████| 6/6 [00:02<00:00,  2.17it/s, loss=0.0265]\n",
+      "Epoch 48: 100%|████████████| 6/6 [00:02<00:00,  2.13it/s, loss=0.0266]\n",
+      "Epoch 49: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0256]\n",
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:09<00:00, 101.68it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 50: 100%|████████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0249]\n",
+      "Epoch 51: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0228]\n",
+      "Epoch 52: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0297]\n",
+      "Epoch 53: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0228]\n",
+      "Epoch 54: 100%|████████████| 6/6 [00:02<00:00,  2.17it/s, loss=0.0285]\n",
+      "Epoch 55: 100%|████████████| 6/6 [00:02<00:00,  2.19it/s, loss=0.0258]\n",
+      "Epoch 56: 100%|████████████| 6/6 [00:02<00:00,  2.13it/s, loss=0.0205]\n",
+      "Epoch 57: 100%|████████████| 6/6 [00:02<00:00,  2.14it/s, loss=0.0265]\n",
+      "Epoch 58: 100%|████████████| 6/6 [00:02<00:00,  2.08it/s, loss=0.0237]\n",
+      "Epoch 59: 100%|████████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.0226]\n",
+      "Epoch 60: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0272]\n",
+      "Epoch 61: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0236]\n",
+      "Epoch 62: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0234]\n",
+      "Epoch 63: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0211]\n",
+      "Epoch 64: 100%|████████████| 6/6 [00:02<00:00,  2.06it/s, loss=0.0245]\n",
+      "Epoch 65: 100%|████████████| 6/6 [00:02<00:00,  2.08it/s, loss=0.0246]\n",
+      "Epoch 66: 100%|████████████| 6/6 [00:02<00:00,  2.13it/s, loss=0.0195]\n",
+      "Epoch 67: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0227]\n",
+      "Epoch 68: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0251]\n",
+      "Epoch 69: 100%|████████████| 6/6 [00:02<00:00,  2.07it/s, loss=0.0209]\n",
+      "Epoch 70: 100%|████████████| 6/6 [00:02<00:00,  2.08it/s, loss=0.0236]\n",
+      "Epoch 71: 100%|████████████| 6/6 [00:02<00:00,  2.03it/s, loss=0.0261]\n",
+      "Epoch 72: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0255]\n",
+      "Epoch 73: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0232]\n",
+      "Epoch 74: 100%|████████████| 6/6 [00:03<00:00,  1.98it/s, loss=0.0229]\n",
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:09<00:00, 103.82it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 75: 100%|████████████| 6/6 [00:02<00:00,  2.15it/s, loss=0.0198]\n",
+      "Epoch 76: 100%|████████████| 6/6 [00:02<00:00,  2.22it/s, loss=0.0264]\n",
+      "Epoch 77: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0208]\n",
+      "Epoch 78: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0192]\n",
+      "Epoch 79: 100%|████████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0257]\n",
+      "Epoch 80: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0226]\n",
+      "Epoch 81: 100%|████████████| 6/6 [00:02<00:00,  2.07it/s, loss=0.0226]\n",
+      "Epoch 82: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0216]\n",
+      "Epoch 83: 100%|████████████| 6/6 [00:02<00:00,  2.03it/s, loss=0.0228]\n",
+      "Epoch 84: 100%|████████████| 6/6 [00:02<00:00,  2.08it/s, loss=0.0247]\n",
+      "Epoch 85: 100%|████████████| 6/6 [00:03<00:00,  1.99it/s, loss=0.0234]\n",
+      "Epoch 86: 100%|█████████████| 6/6 [00:02<00:00,  2.02it/s, loss=0.022]\n",
+      "Epoch 87: 100%|█████████████| 6/6 [00:02<00:00,  2.12it/s, loss=0.023]\n",
+      "Epoch 88: 100%|████████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0232]\n",
+      "Epoch 89: 100%|█████████████| 6/6 [00:03<00:00,  1.95it/s, loss=0.021]\n",
+      "Epoch 90: 100%|████████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0217]\n",
+      "Epoch 91: 100%|██████████████| 6/6 [00:02<00:00,  2.02it/s, loss=0.02]\n",
+      "Epoch 92: 100%|████████████| 6/6 [00:02<00:00,  2.09it/s, loss=0.0226]\n",
+      "Epoch 93: 100%|████████████| 6/6 [00:02<00:00,  2.07it/s, loss=0.0232]\n",
+      "Epoch 94: 100%|██████████████| 6/6 [00:03<00:00,  1.85it/s, loss=0.02]\n",
+      "Epoch 95: 100%|████████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0179]\n",
+      "Epoch 96: 100%|████████████| 6/6 [00:02<00:00,  2.03it/s, loss=0.0225]\n",
+      "Epoch 97: 100%|████████████| 6/6 [00:02<00:00,  2.00it/s, loss=0.0144]\n",
+      "Epoch 98: 100%|████████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.0214]\n",
+      "Epoch 99: 100%|████████████| 6/6 [00:03<00:00,  1.93it/s, loss=0.0137]\n",
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:09<00:00, 103.43it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 100: 100%|███████████| 6/6 [00:02<00:00,  2.13it/s, loss=0.0221]\n",
+      "Epoch 101: 100%|███████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0137]\n",
+      "Epoch 102: 100%|███████████| 6/6 [00:02<00:00,  2.03it/s, loss=0.0217]\n",
+      "Epoch 103: 100%|███████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.0199]\n",
+      "Epoch 104: 100%|███████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0196]\n",
+      "Epoch 105: 100%|███████████| 6/6 [00:03<00:00,  1.97it/s, loss=0.0167]\n",
+      "Epoch 106: 100%|███████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0199]\n",
+      "Epoch 107: 100%|███████████| 6/6 [00:03<00:00,  1.97it/s, loss=0.0201]\n",
+      "Epoch 108: 100%|███████████| 6/6 [00:03<00:00,  1.99it/s, loss=0.0183]\n",
+      "Epoch 109: 100%|███████████| 6/6 [00:02<00:00,  2.03it/s, loss=0.0213]\n",
+      "Epoch 110: 100%|███████████| 6/6 [00:03<00:00,  1.99it/s, loss=0.0194]\n",
+      "Epoch 111: 100%|███████████| 6/6 [00:03<00:00,  1.90it/s, loss=0.0198]\n",
+      "Epoch 112: 100%|███████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0227]\n",
+      "Epoch 113: 100%|████████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.019]\n",
+      "Epoch 114: 100%|███████████| 6/6 [00:03<00:00,  1.89it/s, loss=0.0189]\n",
+      "Epoch 115: 100%|███████████| 6/6 [00:03<00:00,  1.93it/s, loss=0.0176]\n",
+      "Epoch 116: 100%|███████████| 6/6 [00:03<00:00,  1.90it/s, loss=0.0247]\n",
+      "Epoch 117: 100%|███████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.0226]\n",
+      "Epoch 118: 100%|███████████| 6/6 [00:02<00:00,  2.02it/s, loss=0.0199]\n",
+      "Epoch 119: 100%|███████████| 6/6 [00:03<00:00,  1.84it/s, loss=0.0207]\n",
+      "Epoch 120: 100%|████████████| 6/6 [00:03<00:00,  1.93it/s, loss=0.017]\n",
+      "Epoch 121: 100%|███████████| 6/6 [00:02<00:00,  2.00it/s, loss=0.0213]\n",
+      "Epoch 122: 100%|███████████| 6/6 [00:03<00:00,  1.91it/s, loss=0.0204]\n",
+      "Epoch 123: 100%|███████████| 6/6 [00:03<00:00,  1.98it/s, loss=0.0242]\n",
+      "Epoch 124: 100%|███████████| 6/6 [00:03<00:00,  1.98it/s, loss=0.0196]\n",
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:09<00:00, 100.61it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 125: 100%|███████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0176]\n",
+      "Epoch 126: 100%|█████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.02]\n",
+      "Epoch 127: 100%|███████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0246]\n",
+      "Epoch 128: 100%|███████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0189]\n",
+      "Epoch 129: 100%|███████████| 6/6 [00:03<00:00,  1.99it/s, loss=0.0174]\n",
+      "Epoch 130: 100%|███████████| 6/6 [00:03<00:00,  1.99it/s, loss=0.0165]\n",
+      "Epoch 131: 100%|███████████| 6/6 [00:03<00:00,  1.95it/s, loss=0.0229]\n",
+      "Epoch 132: 100%|███████████| 6/6 [00:03<00:00,  1.89it/s, loss=0.0174]\n",
+      "Epoch 133: 100%|███████████| 6/6 [00:03<00:00,  1.79it/s, loss=0.0172]\n",
+      "Epoch 134: 100%|███████████| 6/6 [00:02<00:00,  2.04it/s, loss=0.0193]\n",
+      "Epoch 135: 100%|████████████| 6/6 [00:03<00:00,  1.94it/s, loss=0.018]\n",
+      "Epoch 136: 100%|█████████████| 6/6 [00:03<00:00,  1.87it/s, loss=0.02]\n",
+      "Epoch 137: 100%|████████████| 6/6 [00:03<00:00,  1.87it/s, loss=0.022]\n",
+      "Epoch 138: 100%|███████████| 6/6 [00:02<00:00,  2.13it/s, loss=0.0204]\n",
+      "Epoch 139: 100%|███████████| 6/6 [00:03<00:00,  1.94it/s, loss=0.0192]\n",
+      "Epoch 140: 100%|███████████| 6/6 [00:03<00:00,  1.88it/s, loss=0.0184]\n",
+      "Epoch 141: 100%|███████████| 6/6 [00:03<00:00,  1.92it/s, loss=0.0175]\n",
+      "Epoch 142: 100%|███████████| 6/6 [00:03<00:00,  1.80it/s, loss=0.0198]\n",
+      "Epoch 143: 100%|███████████| 6/6 [00:03<00:00,  1.88it/s, loss=0.0166]\n",
+      "Epoch 144: 100%|███████████| 6/6 [00:03<00:00,  1.94it/s, loss=0.0237]\n",
+      "Epoch 145: 100%|███████████| 6/6 [00:03<00:00,  1.83it/s, loss=0.0195]\n",
+      "Epoch 146: 100%|███████████| 6/6 [00:03<00:00,  1.94it/s, loss=0.0171]\n",
+      "Epoch 147: 100%|███████████| 6/6 [00:03<00:00,  1.96it/s, loss=0.0187]\n",
+      "Epoch 148: 100%|███████████| 6/6 [00:03<00:00,  1.88it/s, loss=0.0161]\n",
+      "Epoch 149: 100%|████████████| 6/6 [00:03<00:00,  1.93it/s, loss=0.022]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 95.66it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train completed, total time: 489.2791540622711.\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_epochs = 150\n",
+    "val_interval = 25\n",
+    "epoch_loss_list = []\n",
+    "val_epoch_loss_list = []\n",
+    "\n",
+    "scaler = GradScaler()\n",
+    "total_start = time.time()\n",
+    "for epoch in range(n_epochs):\n",
+    "    model.train()\n",
+    "    epoch_loss = 0\n",
+    "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
+    "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
+    "    for step, batch in progress_bar:\n",
+    "        images = batch[\"image\"].to(device)\n",
+    "        optimizer.zero_grad(set_to_none=True)\n",
+    "\n",
+    "        with autocast(enabled=True):\n",
+    "            # Generate random noise\n",
+    "            noise = torch.randn_like(images).to(device)\n",
+    "\n",
+    "            # Create timesteps\n",
+    "            timesteps = torch.randint(\n",
+    "                0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
+    "            ).long()\n",
+    "\n",
+    "            # Get model prediction\n",
+    "            noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
+    "\n",
+    "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+    "\n",
+    "        scaler.scale(loss).backward()\n",
+    "        scaler.step(optimizer)\n",
+    "        scaler.update()\n",
+    "\n",
+    "        epoch_loss += loss.item()\n",
+    "\n",
+    "        progress_bar.set_postfix({\"loss\": epoch_loss / (step + 1)})\n",
+    "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
+    "\n",
+    "    if (epoch + 1) % val_interval == 0:\n",
+    "        model.eval()\n",
+    "        val_epoch_loss = 0\n",
+    "        for step, batch in enumerate(val_loader):\n",
+    "            images = batch[\"image\"].to(device)\n",
+    "            with torch.no_grad():\n",
+    "                with autocast(enabled=True):\n",
+    "                    noise = torch.randn_like(images).to(device)\n",
+    "                    timesteps = torch.randint(\n",
+    "                        0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
+    "                    ).long()\n",
+    "                    noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
+    "                    val_loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+    "\n",
+    "            val_epoch_loss += val_loss.item()\n",
+    "            progress_bar.set_postfix({\"val_loss\": val_epoch_loss / (step + 1)})\n",
+    "        val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
+    "\n",
+    "        # Sampling image during training\n",
+    "        noise = torch.randn((1, 1, 64, 64))\n",
+    "        noise = noise.to(device)\n",
+    "        scheduler.set_timesteps(num_inference_steps=1000)\n",
+    "        with autocast(enabled=True):\n",
+    "            image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler)\n",
+    "\n",
+    "        plt.figure(figsize=(2, 2))\n",
+    "        plt.imshow(image[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "        plt.tight_layout()\n",
+    "        plt.axis(\"off\")\n",
+    "        plt.show()\n",
+    "\n",
+    "total_time = time.time() - total_start\n",
+    "print(f\"train completed, total time: {total_time}.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd2b79a4",
+   "metadata": {},
+   "source": [
+    "## Train the ControlNet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73524090-2924-4967-8774-45e795f45bb4",
+   "metadata": {},
+   "source": [
+    "### Set up models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "06181aa6-1c4b-415d-9973-df6f44693935",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# Create control net\n",
+    "controlnet = ControlNet(\n",
+    "    spatial_dims=2,\n",
+    "    in_channels=1,\n",
+    "    num_channels=(128, 256, 256),\n",
+    "    attention_levels=(False, True, True),\n",
+    "    num_res_blocks=1,\n",
+    "    num_head_channels=256,\n",
+    "    conditioning_embedding_num_channels=(16,),\n",
+    ")\n",
+    "# Copy weights from the DM to the controlnet\n",
+    "controlnet.load_state_dict(model.state_dict(), strict=False)\n",
+    "controlnet = controlnet.to(device)\n",
+    "# Now, we freeze the parameters of the diffusion model.\n",
+    "for p in model.parameters():\n",
+    "    p.requires_grad = False\n",
+    "optimizer = torch.optim.Adam(params=controlnet.parameters(), lr=2.5e-5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94d2e5e7-8633-4d1d-a323-7e74c963641c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Run ControlNet training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "78053aaf-2009-405b-904e-0e5d301018eb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|█████████████| 6/6 [00:02<00:00,  2.42it/s, loss=0.0229]\n",
+      "Epoch 1: 100%|█████████████| 6/6 [00:02<00:00,  2.42it/s, loss=0.0182]\n",
+      "Epoch 2: 100%|█████████████| 6/6 [00:02<00:00,  2.32it/s, loss=0.0206]\n",
+      "Epoch 3: 100%|█████████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.0223]\n",
+      "Epoch 4: 100%|█████████████| 6/6 [00:02<00:00,  2.34it/s, loss=0.0193]\n",
+      "Epoch 5: 100%|█████████████| 6/6 [00:02<00:00,  2.29it/s, loss=0.0216]\n",
+      "Epoch 6: 100%|██████████████| 6/6 [00:02<00:00,  2.22it/s, loss=0.019]\n",
+      "Epoch 7: 100%|█████████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0179]\n",
+      "Epoch 8: 100%|█████████████| 6/6 [00:02<00:00,  2.28it/s, loss=0.0188]\n",
+      "Epoch 9: 100%|█████████████| 6/6 [00:02<00:00,  2.20it/s, loss=0.0219]\n",
+      "Epoch 10: 100%|████████████| 6/6 [00:02<00:00,  2.29it/s, loss=0.0185]\n",
+      "Epoch 11: 100%|████████████| 6/6 [00:02<00:00,  2.32it/s, loss=0.0202]\n",
+      "Epoch 12: 100%|█████████████| 6/6 [00:03<00:00,  1.62it/s, loss=0.021]\n",
+      "Epoch 13: 100%|████████████| 6/6 [00:02<00:00,  2.15it/s, loss=0.0239]\n",
+      "Epoch 14: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0182]\n",
+      "Epoch 15: 100%|████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0192]\n",
+      "Epoch 16: 100%|████████████| 6/6 [00:02<00:00,  2.19it/s, loss=0.0192]\n",
+      "Epoch 17: 100%|████████████| 6/6 [00:02<00:00,  2.29it/s, loss=0.0223]\n",
+      "Epoch 18: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0224]\n",
+      "Epoch 19: 100%|████████████| 6/6 [00:02<00:00,  2.13it/s, loss=0.0215]\n",
+      "Epoch 20: 100%|████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0186]\n",
+      "Epoch 21: 100%|████████████| 6/6 [00:02<00:00,  2.19it/s, loss=0.0191]\n",
+      "Epoch 22: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0159]\n",
+      "Epoch 23: 100%|████████████| 6/6 [00:02<00:00,  2.15it/s, loss=0.0179]\n",
+      "Epoch 24: 100%|█████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.018]\n",
+      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:31<00:00, 31.75it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 400x200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25: 100%|████████████| 6/6 [00:02<00:00,  2.47it/s, loss=0.0192]\n",
+      "Epoch 26: 100%|████████████| 6/6 [00:02<00:00,  2.44it/s, loss=0.0151]\n",
+      "Epoch 27: 100%|████████████| 6/6 [00:02<00:00,  2.50it/s, loss=0.0205]\n",
+      "Epoch 28: 100%|████████████| 6/6 [00:02<00:00,  2.42it/s, loss=0.0161]\n",
+      "Epoch 29: 100%|████████████| 6/6 [00:02<00:00,  2.45it/s, loss=0.0193]\n",
+      "Epoch 30: 100%|████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.0176]\n",
+      "Epoch 31: 100%|████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.0202]\n",
+      "Epoch 32: 100%|████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.0161]\n",
+      "Epoch 33: 100%|████████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.0186]\n",
+      "Epoch 34: 100%|████████████| 6/6 [00:02<00:00,  2.30it/s, loss=0.0204]\n",
+      "Epoch 35: 100%|████████████| 6/6 [00:02<00:00,  2.35it/s, loss=0.0161]\n",
+      "Epoch 36: 100%|████████████| 6/6 [00:02<00:00,  2.21it/s, loss=0.0129]\n",
+      "Epoch 37: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0174]\n",
+      "Epoch 38: 100%|████████████| 6/6 [00:02<00:00,  2.25it/s, loss=0.0201]\n",
+      "Epoch 39: 100%|██████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.02]\n",
+      "Epoch 40: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0183]\n",
+      "Epoch 41: 100%|████████████| 6/6 [00:02<00:00,  2.20it/s, loss=0.0199]\n",
+      "Epoch 42: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0228]\n",
+      "Epoch 43: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0151]\n",
+      "Epoch 44: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0135]\n",
+      "Epoch 45: 100%|█████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.016]\n",
+      "Epoch 46: 100%|████████████| 6/6 [00:02<00:00,  2.28it/s, loss=0.0205]\n",
+      "Epoch 47: 100%|████████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.0194]\n",
+      "Epoch 48: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0188]\n",
+      "Epoch 49: 100%|████████████| 6/6 [00:02<00:00,  2.28it/s, loss=0.0194]\n",
+      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:32<00:00, 30.98it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 400x200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 50: 100%|████████████| 6/6 [00:02<00:00,  2.51it/s, loss=0.0175]\n",
+      "Epoch 51: 100%|█████████████| 6/6 [00:02<00:00,  2.47it/s, loss=0.019]\n",
+      "Epoch 52: 100%|████████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.0191]\n",
+      "Epoch 53: 100%|████████████| 6/6 [00:02<00:00,  2.46it/s, loss=0.0197]\n",
+      "Epoch 54: 100%|████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.0194]\n",
+      "Epoch 55: 100%|████████████| 6/6 [00:02<00:00,  2.42it/s, loss=0.0202]\n",
+      "Epoch 56: 100%|████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.0144]\n",
+      "Epoch 57: 100%|████████████| 6/6 [00:02<00:00,  2.30it/s, loss=0.0186]\n",
+      "Epoch 58: 100%|████████████| 6/6 [00:02<00:00,  2.29it/s, loss=0.0186]\n",
+      "Epoch 59: 100%|████████████| 6/6 [00:02<00:00,  2.33it/s, loss=0.0154]\n",
+      "Epoch 60: 100%|████████████| 6/6 [00:02<00:00,  2.32it/s, loss=0.0152]\n",
+      "Epoch 61: 100%|████████████| 6/6 [00:02<00:00,  2.21it/s, loss=0.0178]\n",
+      "Epoch 62: 100%|████████████| 6/6 [00:02<00:00,  2.17it/s, loss=0.0198]\n",
+      "Epoch 63: 100%|████████████| 6/6 [00:02<00:00,  2.17it/s, loss=0.0176]\n",
+      "Epoch 64: 100%|████████████| 6/6 [00:02<00:00,  2.44it/s, loss=0.0169]\n",
+      "Epoch 65: 100%|████████████| 6/6 [00:03<00:00,  1.86it/s, loss=0.0167]\n",
+      "Epoch 66: 100%|█████████████| 6/6 [00:02<00:00,  2.21it/s, loss=0.017]\n",
+      "Epoch 67: 100%|████████████| 6/6 [00:02<00:00,  2.03it/s, loss=0.0191]\n",
+      "Epoch 68: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0132]\n",
+      "Epoch 69: 100%|█████████████| 6/6 [00:02<00:00,  2.32it/s, loss=0.017]\n",
+      "Epoch 70: 100%|████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0207]\n",
+      "Epoch 71: 100%|████████████| 6/6 [00:02<00:00,  2.25it/s, loss=0.0195]\n",
+      "Epoch 72: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0189]\n",
+      "Epoch 73: 100%|████████████| 6/6 [00:02<00:00,  2.06it/s, loss=0.0144]\n",
+      "Epoch 74: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0199]\n",
+      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:32<00:00, 30.69it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 400x200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 75: 100%|████████████| 6/6 [00:02<00:00,  2.49it/s, loss=0.0199]\n",
+      "Epoch 76: 100%|████████████| 6/6 [00:02<00:00,  2.49it/s, loss=0.0174]\n",
+      "Epoch 77: 100%|████████████| 6/6 [00:02<00:00,  2.45it/s, loss=0.0218]\n",
+      "Epoch 78: 100%|████████████| 6/6 [00:02<00:00,  2.49it/s, loss=0.0138]\n",
+      "Epoch 79: 100%|████████████| 6/6 [00:02<00:00,  2.36it/s, loss=0.0186]\n",
+      "Epoch 80: 100%|████████████| 6/6 [00:02<00:00,  2.33it/s, loss=0.0192]\n",
+      "Epoch 81: 100%|████████████| 6/6 [00:02<00:00,  2.33it/s, loss=0.0174]\n",
+      "Epoch 82: 100%|████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0228]\n",
+      "Epoch 83: 100%|████████████| 6/6 [00:02<00:00,  2.31it/s, loss=0.0183]\n",
+      "Epoch 84: 100%|████████████| 6/6 [00:02<00:00,  2.21it/s, loss=0.0201]\n",
+      "Epoch 85: 100%|████████████| 6/6 [00:02<00:00,  2.27it/s, loss=0.0141]\n",
+      "Epoch 86: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0181]\n",
+      "Epoch 87: 100%|████████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0211]\n",
+      "Epoch 88: 100%|████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0178]\n",
+      "Epoch 89: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0167]\n",
+      "Epoch 90: 100%|████████████| 6/6 [00:02<00:00,  2.30it/s, loss=0.0213]\n",
+      "Epoch 91: 100%|████████████| 6/6 [00:02<00:00,  2.19it/s, loss=0.0114]\n",
+      "Epoch 92: 100%|████████████| 6/6 [00:02<00:00,  2.14it/s, loss=0.0137]\n",
+      "Epoch 93: 100%|████████████| 6/6 [00:02<00:00,  2.15it/s, loss=0.0128]\n",
+      "Epoch 94: 100%|████████████| 6/6 [00:03<00:00,  1.86it/s, loss=0.0183]\n",
+      "Epoch 95: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0125]\n",
+      "Epoch 96: 100%|████████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0176]\n",
+      "Epoch 97: 100%|████████████| 6/6 [00:02<00:00,  2.25it/s, loss=0.0193]\n",
+      "Epoch 98: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0198]\n",
+      "Epoch 99: 100%|████████████| 6/6 [00:03<00:00,  1.90it/s, loss=0.0183]\n",
+      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:32<00:00, 31.08it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 400x200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 100: 100%|███████████| 6/6 [00:02<00:00,  2.50it/s, loss=0.0171]\n",
+      "Epoch 101: 100%|███████████| 6/6 [00:02<00:00,  2.40it/s, loss=0.0172]\n",
+      "Epoch 102: 100%|███████████| 6/6 [00:02<00:00,  2.44it/s, loss=0.0167]\n",
+      "Epoch 103: 100%|███████████| 6/6 [00:02<00:00,  2.46it/s, loss=0.0167]\n",
+      "Epoch 104: 100%|███████████| 6/6 [00:02<00:00,  2.46it/s, loss=0.0146]\n",
+      "Epoch 105: 100%|███████████| 6/6 [00:02<00:00,  2.49it/s, loss=0.0157]\n",
+      "Epoch 106: 100%|███████████| 6/6 [00:02<00:00,  2.28it/s, loss=0.0152]\n",
+      "Epoch 107: 100%|███████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0166]\n",
+      "Epoch 108: 100%|███████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0155]\n",
+      "Epoch 109: 100%|███████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0154]\n",
+      "Epoch 110: 100%|███████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0185]\n",
+      "Epoch 111: 100%|███████████| 6/6 [00:02<00:00,  2.21it/s, loss=0.0159]\n",
+      "Epoch 112: 100%|███████████| 6/6 [00:02<00:00,  2.25it/s, loss=0.0143]\n",
+      "Epoch 113: 100%|████████████| 6/6 [00:02<00:00,  2.30it/s, loss=0.015]\n",
+      "Epoch 114: 100%|███████████| 6/6 [00:02<00:00,  2.19it/s, loss=0.0215]\n",
+      "Epoch 115: 100%|███████████| 6/6 [00:02<00:00,  2.02it/s, loss=0.0169]\n",
+      "Epoch 116: 100%|███████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.0141]\n",
+      "Epoch 117: 100%|███████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0189]\n",
+      "Epoch 118: 100%|███████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0162]\n",
+      "Epoch 119: 100%|███████████| 6/6 [00:02<00:00,  2.09it/s, loss=0.0159]\n",
+      "Epoch 120: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.015]\n",
+      "Epoch 121: 100%|███████████| 6/6 [00:02<00:00,  2.08it/s, loss=0.0177]\n",
+      "Epoch 122: 100%|███████████| 6/6 [00:02<00:00,  2.27it/s, loss=0.0164]\n",
+      "Epoch 123: 100%|████████████| 6/6 [00:02<00:00,  2.02it/s, loss=0.017]\n",
+      "Epoch 124: 100%|███████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0195]\n",
+      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:32<00:00, 30.41it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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v+d1kMmHq1KlgjCGTyWgeUxQFa9euxRVXXIGlS5f28u2OZt54440+rV2KC5HrjyxLcd9QKNRLgAYS8T3PGMOaNWtgNBrxpS99qc/99Xo9vva1r+G5557r89t4V1fXoK31aOj1eiiKglwux7c1NzfjhRde0Oy3ePFiAMATTzyh2V5YvToSr/G4LO4JEybg97//Pa6++mpMmTJFUzn5zjvv4I9//CPPxzzllFOwdOlSrF27FsFgEPPnz8d7772Hp59+GpdddhkWLFhw3Iu9++678cwzz2DJkiX4zne+A5vNhrVr16Kurg7bt2//3LW73W7813/9FxwOB2w2G+bMmdPLvwUc9r8uWLAAP/zhD9Hc3IxTTjkFGzduxIsvvog77rhDE4gcCu6++26sX78eixYtwvLly2Gz2fCrX/0KtbW1CAQCx/Vt4kS4+OKLsXLlStxwww0466yz8NFHH+F3v/tdL3/oBRdcgIqKCpx99tkoLy/Hxx9/jDVr1uCiiy7qM6it0+mwfv16XHbZZbjqqqvw8ssvY+HChYN6LSOB5cuXIx6P4/LLL8fkyZP55+d//ud/UF9fz/2mF1xwAUwmEy655BLceuutiEaj+OUvf4mysjJulQ8kqqrilVdewdKlSzFnzhxs2LABL730Eu69995e8RGRBx98EG+88QbmzJmDW265BVOnTkUgEMAHH3yA119/HYFAYMDXeiwuuugiPPzww1iyZAmuvfZadHZ24vHHH8fEiRM1OjFz5kx87WtfwyOPPAK/348zzzwTf//73/m3DPFzNdKu8bjyuIlPP/2U3XLLLay+vp6ZTCbmcDjY2WefzR577DGWTCb5fplMhj3wwAOsoaGBGY1GVlNTw+655x7NPowdTge66KKLep2nMKWPMca2b9/O5s+fz1RVZdXV1WzVqlXsqaee+tx0QMYYe/HFF9nUqVOZwWDQpAYWpgMyxlgkEmHf/e53WVVVFTMajWzSpEnsoYce0qQnMXY4Neq2227rtfajpTgVpgP297o//PBDdu655zKz2czGjRvH/uM//oP9/Oc/ZwBYe3t7r2OILF26lNlstj7P01f6XuG6kskku/POO1llZSWzWCzs7LPPZu+++26vdT755JNs3rx5rKSkhJnNZjZhwgR21113sVAoxPcpzONm7HDq1vz585ndbmebN28+5rWMBjZs2MBuvPFGNnnyZGa325nJZGITJ05ky5cvZx0dHZp9//znP7MZM2YwVVVZfX09+8///E/261//ut/vpb7en/v27WMA2EMPPcS30XukqamJXXDBBcxqtbLy8nJ2//3390oFRUGqHGOMdXR0sNtuu43V1NQwo9HIKioq2Je+9CW2du3az309jvZZ2bJli2a/vt474tpFnnrqKTZp0iRmNpvZ5MmT2bp16/jzRWKxGLvtttuY1+tldrudXXbZZWz37t0MAHvwwQcH7BoHGoWxIoj+SfrkjjvuwJNPPoloNCrL6SVfiGXLluHZZ59FNBod7qUMO1u3bsVpp52G9evXD3iq8UAhO7sUCYVdF/1+P5555hmcc845UrQlkhOkr26mjzzyCHQ6HebNmzcMK+ofx90dUDI8zJ07F+eddx6mTJmCjo4OPPXUUwiHw7jvvvuGe2kSSdHy05/+FO+//z4WLFgAg8GADRs2YMOGDfjGN77RK198JCGFu0i48MIL8eyzz2Lt2rVQFAWnn346nnrqqRFtFUgkI52zzjoLr732GlatWoVoNIra2lqsWLECP/zhD4d7acdE+rglEomkyJA+bolEIikypHBLJBJJkSGFWyKRSIqMfgcnB7s6TyIhRmrYRVVVMMaQzWYHfUapZGzzeZ8BmVUikfQT6n0xUm8skrGDFG6JpJ9I4ZaMFKRwSyT9RAq2ZKQgg5MSiURSZEjhlkgkkiJDCrdEIpEUGVK4JRKJpMiQwi2RSCRFhhRuiUQiKTKkcEskEkmRIfO4JRKJZITQ39YiUrglEolkhCCFWyKRSIqM/jYvkz5uiUQiKTKkcEskEkmRIYVbIpFIigwp3BKJRFJkSOGWSCSSIkMKt0QikRQZUrglEomkyJDCLZFIJEWGFG6JRCIpMqRwSyQSSZEhhVsikUiKDCncEolEUmRI4ZZIJJIiQwq3RCKRFBmyratEIpGMEGQ/7iFGp9NBr9drtuVyOeTzeSiKAoPBoNkmkRQ7JDKMsT639/WY5Oj0V7QBKdwDxqxZs7BkyRIYjUYAhxuib9q0CX//+99RW1uLyy67DHa7HRs2bMD7778/zKuVSL44haJcUlKCyspKGAwGqKoKAGhtbUVLSwsYY1AUhYuToijI5/NS2AXoNeoPUrgHiNNOOw3f/e534XA4AADpdBqZTAb/+Mc/UFtbi1tuuQUVFRVobW2Vwi0ZcI5m/Q7Wufqysj0eD6ZMmQJVVeF0OqHX6/Hee++hra0NjDHodDq+r6IoyOVyyGazxzwPMLas9v5eqxTufqLT6TB+/HiMGzeu12OKoqCxsRFms5m7S4xGIyZMmIDzzjsP06dPh8PhgMlkwtSpU7FgwQJ0dnbi008/RSaTGepLkYwxSChFUSBR7I/bTlEU6HQ6KIoCk8kEo9EInU7H/1dVFQaDARUVFTCZTNziNhgMKC0tRV1dHXK5HD9eNptFPp9HPB5HMBgc8OsdCyisnxJ/PP6X0YjFYsFdd92Fr3/9632+Fi6XC6WlpZrHAoEAAoEAVFVFeXk59Ho9uru7EQqF8PLLL2PVqlXw+/1DeRlFwUi1sEb6Z0Bcn+iS0Ov13MLN5/PQ6XTc+k2n030eR3y+TqeDyWSCXq+Hz+eD0+mE0+lERUUFzGYzSkpKYLFYkEwmEYvFYDQaUVZWBlVVEQwGEQwGkc/nkU6nkc/nEQqFEIvF0NbWhk8++eSoVndfr/dIfW8MNJ93nUVpcSuKAlVVYTKZhuycNpsNNTU1OOmkk/r9HK/XC6/Xq9lWVlaGsrIy1NbWwuPxHPOrIgAkk0mkUqkTWrNkbCCKM1nGJHo6nQ4Gg6GXcOv1ejDGwBjr9a2PjkHHJgvaaDTCarXCbrfD6XTC4/FAVVWUlJTAZrMhFAohnU7DYDDwm4PVaoVOp0Mul0Mmk+GWt8FgQCQSgaqqSCaTfX4OCoV7rIh2fyhKi9tqteLqq6/GOeecM2TnNBqNmDVrFqZMmTIgx9u3bx82b96MRCJx1H0ymQxeeOEFvPrqq2PqTTtSr3UkfQZEXC4XKisrYTab4XA4YDabARx2g5jNZrjdbhiNRmQyGWSzWU3wMBgMIhaLoaurC/v370cul+PWeD6fRz6fh9vtxrRp0+BwOKCqKsxmM0wmExwOB/R6PYxGo+ZGIFrXonVP0D7BYBAHDx5EIpHAgQMHEAqFkEgkEI/HNaKfTqeRTqf58ccCo9LiNplMmDdvHpYtWzbcSzlhGhoa0NDQcMx9kskk9u7di40bN46ZN6zk+LFYLKisrITdbofP54PNZuPiqKoqqqqqoKoqUqkU0uk0TCYT7HY7GGPo7u5GNBrFnj174Pf7kU6nucWezWaRyWRgt9sxceJE+Hw+Lp5k3TPGkM1mkU6nYbFYYLfbkclkuAhbLBZYrVYoisJ94haLBUajES6XCy6XC9FoVOOyicfjGn863QgkRyhK4R4r6PV6nHrqqbj66qv5tmQyiQ8//BDNzc3DtzDJsEKi6XK5YLVaUV5ejvLyclgsFrjdbm5N5/N5GAwGZLNZJBIJ5HI5MMaQy+U0Qmg0GuF2u9HQ0IBcLgej0ci/XeRyOZSUlKCiogJOp5Nbv+SCIVdLPp/nQqsoCrf8xW8phda3wWCAzWaDTqdDZWUlTCYTkskkqqurYTAYYLfbodfrkUgkkEwm+dqz2SwOHTqEcDg8RK/4yEMK9wjGaDTikksuwfnnn8+3BQIB/Pu//7sU7jEK+bONRiPq6+tRXV0Nj8eDmpoamEwm2Gw2GI1G5HI5/i+RSCCfz0Ov10Ov1yObzXKrVq/Xw2w2o6qqiqfwORwObh0rigKLxcIzRoLBICKRCPR6PY8xkQuGfOcUnMzlcojFYojFYvwx0Q1jNBp56qDBYEA8HofBYIDBYIBer4fVaoVer0cymUQymeSvQTwex//+7/9i586dY/abaFEJN0WwvV4vbDbbcC9nSLDb7bDb7fx3Sr+SjE30ej33M1Og0GazwWw2c1+zmC9NUAUvbSOfNIku+b7p+WKQkzGGVCrF3S96vR4Gg4EXm5F4FmaiFAZLRYubnkOPm81m/g2BrsNsNnN3DEFWNwVAaU1jjaIS7sbGRixfvhz19fWYPHnycC9HIhkSxBxsm82GCRMmwG63o6GhgafdqaoKnU6HfD6PTCbDxVIUTBJqEsZcLoe2tjZ0d3fDaDTCZDJpxD2ZTPL9S0pKYDab4fP54Ha7YTAYYLVaARwWX/EGIAq5wWDgud0Wi0UTbDSbzTAYDPymAoD71QEgGo0COGzRZzIZLtKUhWI2m7l/faxRVMLt9Xoxf/58TJo0abiXMmyIH0hg5GZgSAYG0YoFDgfmvV4vXC4X3G43HA4Hdy8A4GXkoqVL//L5PHK5HE8dVBQFsVgMfr8fqqryb3aUtkcZHuR7VlUVVqsVXq+Xu0QoiEkZKGKhDa2b3CSUliiuU3wvA+DuHbKi6WZDwi0KNYn+WKSohFsCqKqKJUuWwOfz4eOPP8amTZs0/j/J6EF0NZALw+l0orq6Gm63G06nk4tnLpfjFq6Yh03HIYGlmgCycn0+H0wmE3+MhJMEknzhZrMZNpsNHo8HpaWlMBqNPDNFzDIhizoQCCCVSvF1082CfOP0MwU2AWj2zefziMVi/EZgMpmQTqe5v5ss+7FquEjhLjKsViuuvPJKXH755fj973+Pf/7zn1K4RxGihS0W1phMJphMJrjdbtTX18Pr9fIsEbJ0SWApaFjoR85kMvy9Qtkj1dXVqK6uht/vx8GDB7nlnM1mkUwmkUgkeN63w+GAz+dDVVUV90XTuRlj3C0CAKFQCOFwmH8zoMAo+a5NJhMvyhGFm24A6XSa30yo+EdcE1ngUrhHKIqioKKiAuXl5Zg0aRIvLhjLGI1GjU9SMnohIaMMDMoYIT924X4kfn2JGgkrBTLJ1VC4H4m30+mEy+WCzWbj6X3pdBo9PT089VA8L91EqEReDJiSNV3o4hNdJ1T8I37LoHXSMSgw73a7kcvlEIlExmTbiBEv3Hq9HpdddhmWLVsGt9uN8vLy4V6SRDKgFFYWEuT6MBgMcDqdKCkpgd1uR09PD9LpNK9kFBs+kdBRd0ryPyuKApvNBrvdzlPwyMedTCa5S0L0Kc+cORNz5sxBLpdDIBBAOp1GW1sbmpubUVVVhZkzZ/IAI+VskyVfWlrKXTliFgq5bLLZLL8+RVG4W8ZkMnHhdjgcmmwYm80Gk8mEfD6P0tJSpFIpNDU14Z///OeYa9Y2YoWbKq1UVUVdXR1mzpzZa1CBRFIs9NUKVXwM0AaayZIlq9Vms/G0P/I/k9iKPvDCwLVo2ZKoi4Uy4roKm0uRWyaZTCKdTiMajSIQCMDv98NutyObzfLjiDcfSlkVg6CFfnexZ4ronqGiHuDwNwTxhkJWNwCePtjZ2XnUG99oZsQKd3l5Oa688kqMHz8ec+fOHZN/HMno4Vi+WLFKETgciLNYLPD5fFi4cCEqKiqQzWa5H5sEj1wn9LNYhk5+acqNFrM0jEYjT81jjHFrmSx0h8PBLee2tjakUimEQiEu4IwxxGIx7N+/HxaLhbdwFXuUUIoiiXI+n0cqleL/iyl8lEVC7hZ6jLJb6BsApRQqisJTFampFb0+Y4URK9w+nw/XXHMNzjzzzF4pQxLJaKLwva3X62Gz2VBRUYHFixejsbERHR0d6OzsRCKRgN/v53nMFO8QXRWUDmg2m7lQU343Wcnk4qCAJgAeKBSDjZ2dnUin04hEIpq0vHg8jkOHDvGqSnJrUK8Ti8XCqzTpeRRwJDcOWdnkGxdvYCTwFJBMJpN8zTqdjm/LZDLcEh9LE3WGVbjLy8t5t71du3ahs7MTNTU1aGxsxIQJE+D1eqWlfQyqqqqwcOFCtLe34+OPPx6TQZpixeFw8O569PWfsiXcbjdKS0tRUVHBLWGxBJwEUMzUEC1xAFx8AXDLWnRbkGiSoJIgA0eyOwBwC9tqtXKLn1wu0WiUd7cMh8OaoCntC4DnX5Mgk8CKed+iu4c+82IJPQBNDxUaWkKBU4PBgHA4/LltkkcLwyrcp556KlauXAkAuO+++7Bx40bMnz8f99xzD9xuN0pKSoZzeSOeOXPm4KSTTsK+ffvwwx/+EG+99dZwL0nSByRKoqg0NDRg8uTJvFlTLpdDMBhEIpFAeXk5amtr4XQ6ARzuT0MBP+rCR6lyJJRkiZKlm0qleOofWdZk+VK6HQAenBQDlOT6IMvaZDLB5/PBYDBwd0lPTw8OHDiAVCqF/fv38xxz+szSjUB0mYhWNgVN6ZxiLxO6sYg3Croh0Q3IarXyVrWBQIB3GKRqy9HOkAm32O2LqKioQF1dHRhjqKiogM/nQ2VlJWprazX9OSR9Q0ErssQkIxMx4EdWrdVqhdvt1gg3VTU6HA44nU7enpXcG+RHJlHtq/Me3STEwGBh0BEAd1GQmIvZJwSJJ1VlFgY/6cZBNyRVVfnPJNJ0faKvm14D8efCACudj5pRkf+bhF78tiFe61hhyIS7vr4et9xyi2ZmY01NDVwuFxhjuOmmm7B48WJMmjRJNlHqJ1u2bMEzzzyD9vZ27N69e7iXIzkKJLJkFZvNZpSVlaGyslJjjVOAjt7/JNrUL4TasQaDQaTTaR7ko4k0orVKvutcLod4PK5xU5D1m8vlEA6HkUgkNH5mElyqoIzH47yVazAY5L8Dh10WTqdTM+lGFGM6vyi69FoA4FY15YmTD5yOQb526q1ClZ90wwmFQujs7EQsFhtTPUuGTLjLyspw4YUXYvr06X0+Pm/evKFayqhh3759ePbZZxEIBIZ7KZJ+oNfrYbFYYLFYeHEL+ZwzmQz30Ypd98jqFUvR4/E4F+1sNqvJLqH9SBwpS0MUNQr4ZbNZ7qcWfcsk4GQ108/AYbdNKBTSDAR2uVxwOp1IpVL8JgBAI/4AeIEN5ZyLvmzqdkiZJ2IPElG46fUg4U4kEvzmM1b828AgC7eiKJgxYwZOOeUUeDwebNq0CVu3bsXs2bPR2Ng4mKeWSEYMqqrCZrNBVVW43W5YrVbYbDbuGqFKQ5/Pxy1PmhJDVia5NSj9jTJHKPeZLOi+BgLTz2ImRjwe5+JIKXaE+DxRQClDxW638yk6JKrk0zYajfyGU5iXTjck0d9N108iLKY8ioMfxFmViqIgFAohGo0iEonwG9hYySgBBlm4dTodFi1ahLvuugsfffQR7r33XrS1teGBBx6Qwi0ZM9jtdpSXl8Nut6O2thY2mw0ul0tTPUjBPUprI6ESu/SRWFMKIFmmVCkJHPFdU0sE4Ihwx+NxXgEZiUSgKArKysrgdDo14gho/fK5XI5PoLFarbBarbBYLPB6vTynOpVKaXLHKZ+bioDEXPNIJIJEIqFpOCUeg64NOFLJKR6Dvp20t7ejq6uLB0xHQ6fA/vrpB91VoqoqPB4PvF4vSkpKeNMYyYmRz+fR1dWFSCSCjo6OMVV0UKyQQFFbVAomi131SLComKSw4x6JdGHwDtAGPUkoxeBjNBrlrgVRGPrqZyIeRwxsimm5fQUSj9bwqdB3XhgkFd06hecSfybrm1IX4/E4otEoksnkmMrfJobMxz1+/Hjcf//9SKVSGD9+/FCddtQRjUbx61//Gq+99hra2toQi8WGe0ljkmOVsBfup6oq75JXVlamCeTRNHPyUYtNl2igLvXnIEs2FotxN0Oha4KeH4lEEIlEEIvFcODAASSTSZSWlsLr9XJ3CT2H/Mqia4KCgiSoZBmLFrSYq00CK7ZbFYOrdL0kzhaLBalUCuFwGDqdDiUlJTyVkQKX4kxLCnJ2dHQgFothz549aG1t1bR4HUsMmXC7XC7MmTNnqE43KqFqsp07d+KNN94Y7uVI+gFZk5TPTMFJMYhIrg/xOWI3PLF0XK/Xa9wmojUuCncmk+Hi3dXVhUQiAZfLpSmDF4VaFG7KcBHXQu4cOpco4iJ9pfzR8WnNNBGHAqwk+nQOcheRcIvHIr92OBxGMBjsFcgsZo4nnXHElrxLtOzbtw+vvPIKDh06hJ07dw73csY8/bXwGGNIJpPo6enhhS/U0U+cIgMccQeIgbyuri4AR4YMUEqgOOWGilqAI5YvBfsymQw/HwUoqZcIBUdJ+Oi5VBMgCjZ17RMFmK6PslYoeFoYlKT/qYKT/PTZbBadnZ383LFYjN/cRPcJWe2RSATNzc0IBoMIhUKjSrSB45tmJYW7SGhqasKjjz6KvXv3Sr92EcEYQyKRQCAQ4NkflMInuiLI8s1ms3xoQjqdRkdHBx8bRuJJ/8TOeoUCRsJNNwp6rljRCBzpCyL6l0msRSGn5xf2Fcnn8zxbJR6PIxwOAziS/WE2m/nQX7E032QyIZPJoKuri990HA4HbDYb3G43b4RFGSvpdBqhUAhNTU0IBoPcfTJWkcI9hORyORw4cIBbGcfDrl27eKMfSXFBVjLlaFutVt5alXzU4j7iz+S/Fd0NhQE/+p3GfVEZeCQS0eRUA0dytMUCmMJAaF9ViJQBQ+ejtVHGCWWdqKray3Kkc1AJfiqVQiAQQGdnJ6LRKPL5PAKBABKJBGw2G+LxOB9GTN9Skskkuru7eeMp0V0zFpHCPYQkEgk8/fTTeO655477udFoFN3d3YOwKslgQ0E4n8/HR3+JGSSUv0ytU8k1QEFBcpHQTVsUY/FfIpHAjh070NXVxcXObDajtLSUW9h08yB3BFUqkvuiLz8rrVEsBqLUQgqwUvVjeXk5v4GQJU1WeTgc5sMYqPqTjnvw4EFNQFRMHyRXDL2OYpBTfD3GElK4BxiK0vdVxRUOh9Hc3IwdO3YMw8okw0GhuFB+NQk3WdAkzCRQyWSS+7VFq1w8Flnj5IumvtnUJZJ80qKvmtZU2N+jULDF1EPKZhELfFKpFM9uEa+FjikGO0m44/E4kskkt7bFc9GgYtHypw6AFMQUA5WFvVrGGlK4B5iOjg7893//N5qamno9lk6nsWXLlmFYlWQ4IOEtKytDdXU1ysrKEI1G0dnZCavVyjM8SKyoFJ1ylMXAIFmhYlOq5uZmdHV1aQKXnZ2dSCaTqKmp4TNayWdM7hcSRsYYTCYTF0TqAyIW8aiqilQqhY6ODs3U9nw+D6vVytMdTSYT/H4/du7cqRkCLHYBJHGmbxYiYsFNod9dzHLp6zljESncA0xPTw+ef/552WJVwkXW4/Ggrq4Odrud968mMRL9ymRxJ5NJRKNRLpwkZqJ4Z7NZtLa24tNPP9X09KZjer1eTJ8+nQcHFUVBMBhEOBzWWO/k6yZLn0Q3l8vxGY/pdBqBQADxeJxPaCd3C3X9NJvNaG5uxocffsj7hwPQ9Co5HtcG5W+T9S7RIoV7gDlaBZlk7EGWpqqqKCkp4RNrgMN9sOPxOBd3ElNya5CVLbohSFj9fj93p5hMJlitVng8Hk1vEYfDwQOKoh+cOvAVWrFk3ZLgUhDVbrfzdSmKglQqhXg8zm8ger0eJSUlsNlsmlRHsRvhiSI/R0dHCrdEMkiQj9fhcKC+vp4LWi6XQ3t7OwKBAA/C6fV6brmSlUozFk0mEyKRCC8+2b9/P3c1WK1WVFZW4uSTT+Y9PqinCOVxkx+cen1T7xOyhKkKknzglIbndrvh8Xig1+thNpsRj8fh9/sRiUQ0Al5XVwePx8OLfKj17PFWNPaVJSLFu2+kcEskgwgF50T3AaCtKKQxZJTVUVhuLiLmTauqCrPZzP8nfzWgnZBe2IlP7JdNiH1BSOTJhSM2tjKbzTz1L5lMcitfHKZAlnhfr8VYDigOJFK4JZJBhDGGffv2YdOmTXC5XJgwYQJv60oBPSqq8nq9sFqtcDgc3PVBU2bIAs7lcny4b3l5OdxuNywWCyKRCM99ppFeFGyMRqO9GkbRMShbhIQfOOI2SaVS6OrqQiqVQllZGV8TdfOLRqOa3t6qqsLn83E3EFn7YqHMsSxoaV33HyncEskgEwgEsGfPHpSWlqKqqopbyRaLBaFQCMFgkPfcpuKT8vJynsNMokj/qE+3w+GA1+sFAJ4+SAJMKXxk8YvQcURftJguSHEacaq7w+GAoiiw2+2wWq1Ip9M8L7ulpQXBYFBj/VMqX2G72ELGchHNF0EKt0QyyFB2iMFgQCwW4/09aEAwBSCdTiccDgdcLhfsdrsmX5v2sdlsqKysRCaTQUlJCZxOJ9LptGYwgmhZU/EMpQFSlobJZNJMjC98HqXhpVIpTd8QMcWPZklSbno0GkVLSwsSiQRisZhmjNnRkKJ9YkjhlkgGGXFaTDAYRDQaRTgc5iJOVnRpaSlcLhdKSkrg8Xi48GUyGZhMJt7zY/z48WCMwW63w2KxIBwOa6abU3YIlaZTMJF81+R6ocpNsZRezBunGwKlIYrFOAB4hoyqqkin02htbcXWrVs1LWNlKt/gMOjC3d3djY8//hhOpxOVlZXctyaRjBUymQzvm04uEeBIIybqBkiCKXbhIyidj/zYJLLUL4REkjJS6P/CwhYxe0SsdCwcXiCKOFnghS1WKa2QbhRGoxE2m43PnhxLMyCHmkEV7lwuh5deegnbt2/HjBkzcPfdd6OhoWEwTymRDBon0htDURT4/X709PTA5XLBaDTC4XDAbrfD5/NxNwSl6lF/jmg0yi1msRUqza1kjKGrqwt+v5+P7qLnpVIpzWAD6u5HQUMaUkyl9FQlaTabebZIJpPhnfqoN4lomVPvEVqfxWJBbW0tHA4HwuEwtm/fLodYDyKDbnEfPHgQBw8eRD6f539osWWkRDLaIUs3Eonwxk9i9zuysmkKjjizURRLANwKJuuaMjoKp7GLQxbEFrBUFVloaVPRD/UNAaCZ8UjCTf/EEnZyu1itVn4ToopMyeAwZK9uS0sLnnzySVRVVWHJkiWYNWvWUJ1aIhkQTiSQJj4nnU7jwIEDvMCFZk+SuJLvmPqV0Fgzg8GA7u5uLsS0n1hZSZZxIpFANBqFw+HgljkFEa1WK7LZLCwWC09HJP+6OGWd3CvUMyWTyfAe2BRUpcpKOr9Op0MoFMKhQ4f67EUiGViGVLjXrl0Lh8OBsrIyKdySMQdjjHfF8/l8KC8v55YpZX9QL+22tjaYTCaUl5fDarXyKeiJRALd3d1gjGHKlCkYN24cTCYTnxpP/Ugo3RAAd4FQyb1o3bvdbthsNvj9foRCIY1wx2IxRCIRPsRA7PPt8/nQ2NjIhdtgMCCZTKKlpQWxWIxb7ZLBYUi/zxQGTCSSsUosFkN3dzdsNhs8Ho8mE8RiscDtdvNZleSGEK1jADyrREzro+ZP5L8GoMmjFkebkatFzBcXuxWS+wQ4UhZPfnexwIeEmxpVUe63ZPCQjiiJZBhoa2tDNBpFRUUFD0pS8QzlagNHugjSWC/q1AcAFRUV8Hg8iMVi8Pv9YIzBZrPxocCUaUL+cvKtU5YLNa4ia5ncG7SfzWaDxWJBIpHgAU/K76ZxbJQNo9PpEAwGNda2LK4ZPIZFuBOJBEKhkGYSh0QylqDCFvIri5NkxEChONyXRp5RwJKaUymKwq3hwqEJYsGM2CuEskFo6IcYtCRoPWLvEjGxgFIBgSMWOe1Lwc/RItwj7VqGXLhTqRSee+457Ny5E6effjq+/vWvw+VyDfUyJJJhRSyOaW5uhtVqRW1tLdxuN9/HYDDwXtpkkVutVvh8PjDG4Pf70dLSAp1OB5vNhlwuxysWnU4nTCYTcrkc7ylit9s1wiz2yhYzV8i6psHGVFBDpfgul4uvDQDPbHG5XJg5cybC4TC2bds2qtIBR5JoA8Mg3JlMBm+//TbefvttBAIBXH755VK4JWMKst7y+Tzi8Tg6Ojpgt9tRU1MDi8WiscCp5wcFAS0WC3w+HwCgq6sLgUAAVqsVXq+Xp9zGYjFNyiBNe6fBBOL5ydImS5luKAB4z2/yket0OrhcLng8Hl7RSTeGeDwOn8+H6upq9PT0YPfu3cP2+o4FpI9bIhlixEntZrMZ5eXlsNlsYIwhGo1qRn+Ru4L832azmfuQu7u7EQqFuCsFAB8wTIJKA3kzmQwikQiCwSD3T4suFIvFAqfT2cvtQu1cC/uOUD0GcLh/N7lQwuEwIpHIMRtLSb44UrglkmGAhNBms6GxsREWiwXxeBzd3d18qnk2m+XT0qkrH+V/5/N5tLe3o6enB4lEgvu26fFsNovOzk5kMhmEQiFks1k+pCEej6Orq4sPa9DpdCgvL0dZWRl0Oh23smlEGXAkG4UG94pVpB6PB8Dh9rHt7e28ayAx0vzDo4EhF25FUXjuqM/n67PhejESjUYRDAbR3t4uc1glR0XsTUIl7NSpL5fLcdGmoCEFJymYSC4K6j1SOCpPHIVG/8Qqyb4CjfQZFJ8jPq+vafCFzwPAbzRip8L+vB5S1I+fIRduVVVxzTXX4MILL0RlZeWo8W//4x//wLp169DZ2dnnhHfJ2EbMjTYYDKisrITP50NJSQlP2YtGoxr3BqHT6eBwOFBRUaGZgu5wOMAYg9PphN1u5/1GKK+bcqpppFlNTQ0qKyuRSCTg8Xg0DaqMRiPi8TiAI3nflN0i9kyh7YqiwGKxgDHGuw92d3dj9+7dSCaTSCQSmsZUlJ1S2KyqMKOsP61gJUMk3OIfx2g0YsaMGbj44ouH4tRDAmMMzc3N2LBhg6a9pkh/Uh7lG3b0Ilq8VKJeUVHBqxvJ2qaGUVTyTjnSlN9NIp/NZnnJOg0uEIVbTNMjnE4nPB4PF1zKGCEBLyycETsVAtpugjS0gR4ja7u7u/uoBThi/5SjCXdhV0RJ3wyqcCuKgtmzZ+OMM87gPjJVVTF9+vTBPO2QEY/HsWnTJnz22Wd4++23eUVbIdXV1Vi4cCH3BR6NrVu34p133pHtMEcZTqcTbrebi57BYOA9t1VVhcvl4n1AwuEwAGjypQEgFApxF4vYZdBkMvGBwqLrgoYgiILc1tbG28uSQIrVkOSXJlcKuWsA8MdoqjwA3tSqpaUFbW1t3Kfel/jS2ummZbfbefCT8slzuRxCoRACgQDviEjnkJ8JLYMq3DqdDgsWLMA999zDgxz0BxwNhMNhrF+/Hi+++KLmjV9IQ0MDvve97+Gkk0466rHy+TyeeOIJbNmyRb5JRxGKosDr9aKurg4AeJofBQNpyEIikUAymYTf74eqqrDb7Zq+2YFAAN3d3fB4PHC73dw3Tg2fKN9b9IPH43GeWpjP59HS0oJMJgOLxYKSkhJNB79MJsPL38UKS8p+ofekz+fjOeMkqE1NTdi1a9cx/dpiW9q6ujpUV1fDbDZz658aUx08eJCPXKPrpwCt5AiD7ioRu4iNNmgEVTweR0lJCSorK/tsZ9nY2Ai3280tlaMdq6qqCqeccgovPabMAWpMJClODAYD7HY7gCPVhtQfRJysTjMkRfcBuSvIx0x52SKUf11Y9UjPpcAn5V33ZRFTu1ay9At90WK1JPXrFrsHAoDb7UZJSQlPRST/ujh2zWQy8eQE6m7IGOMa4fP5uCuI/qcWsoWB2LGMTAccIObNm4fvfOc7fYqz0+lERUXFMZ+vKAoWLVqEqVOn8uBQKpXCL3/5SzzzzDPyDVvEuFwuVFdX8z4fVPjS3t4Ot9vNqxwnT56M2tpatLe3o7m5GQC475p6iaRSKbS2tnIhpC6AJIAkujQMGDiSuUGphhQgpZayADT7UlVkKpXiYk7NpsLhMPx+P/dn0zcFh8OBc845B1/96leRTCaxefNmdHV1wev1wuv1arJY6IZCfn0AKCsrg8FgQH19PXQ6HWKxGPbt28crS6mdLL1+Y71RnRTuLwC5fcxmM8aNG4eZM2dyy+pEKC8vR3l5Of89lUrh5ZdfhqqqmgCR7LBYXFDFIwk3Wb9kuZLgOhwOWK1WxGIxHmAURZbEjvK2qTWr2AlQDB6S2NIoMtpftKopWCgOCaZz6/V6Xmgj9kSJxWKIRqMIhULcEjaZTCgtLUVjYyPi8Tiam5uRz+e5S0j8NkAuFqrmBMADrNQrnI5P2StGo5HPvAQw5q1vKdxfALvdjmuuuQannXYaTj311AGfp2kwGLB48WJeYgwcDoi++OKL2Lp164CeSzI4MMaQSCQQDAa55aooCmw2G6xWKxwOh8alwRhDSUkJpk6dqunsJ2aJ0HAFEnpVVXn7V8pCEX3bdKO3WCxwuVy8AlPMOKH0QgD8sVwuxysyC0eh0eDidDoNh8PB+5V88MEHvEoTONzHJBgM8hsJBRtpbSTI5A4hMpkM3G43zGYzdx0mk0neF5yqQUX/+1hCCvcXwG6349JLL9WkSQ0ker0e8+bNw7nnnsu3+f1+7NmzRwp3EUGCQxYwxX1EwRULXTweD0pLS5FKpdDW1qYpwhGzSEj8yV1Co8YMBoPGp0352STC5AohC5ss+76sWvpf9ItTGqLJZEImk4HT6eQZKDt27OBZIsDhb42RSIR/66AbAgVpKfZFg4dFS9rlcsFut3OrPhaL8WwXQsxFH0tI4f6CDIZgH+v4qqri5JNPxqJFi3Do0CHs3r17TFocxQQNRDCZTNwipswPKpYRhxmQkNPUGuCwxUuDhKn6UayCpPxw8olTep0YTCQLV8ylJmufAp9ihWRhtaTYYwUAH+wgTpkPh8Oa6svCpAQSWNFlI4ou+f/pmgHwmxQF/jOZDFwuF1KpFK9WHmu9UaRwFxk2mw033ngjrrzySvzpT3/CqlWrEAqFhntZkmNgtVrh8Xh4hoWiKEilUpo0N9qWyWS4KwM4/Pe22Wx8YAGNEcvlcjxtjx4zmUxwOp0wm82IRCLclxwIBJBIJOB0OuF0OjXiSD500VVB4k7iChzJXKFMKqPRyI9FN5NgMIjOzk7ul6a12Ww2AEcqMk0mEy/LF4ch07qorSx9K7DZbFzk6fWj2MCuXbvQ2dnZS7jFm81oRAp3kaHT6VBaWorS0lLU1NTA5/NBURTEYrGjFgBJhg+xt4cY5AOOpOCRpUtf+8n/S4JIIka52mR5iuJEQUQaX0auEvpHNwiycAuDmbQeMQ2wcB9yCVIwldZBNySxAEh0rdA1001ATFHsy6onEaY1kGCTu4W+MeTzeT6lBzgyHGIsIIW7iJk1axZ+/OMf4+DBg/jNb36DnTt3DveSRjXHa8VRNkgymcShQ4fgcDhQXV3Ne2XTPoXNnUiUxD7clLon5muTIIfDYRw8eBA6nY5b3JT5QccSKytFESXEplFUACMGMEXLm4SaBJMsYhq5Jk6Fd7vdvH84+dTJ4u4rt5x83XT9dKOh89ONjZ7n8/kwb948hMNh7Ny5E36/n1/jaBZxKdxFzPjx4zF+/Hg0NTVh48aNUrgHGXEcWH9EgfzO6XQawWCQbyf3hOhfLrRygSM51WTZig2bKHMkn88jGo2iu7sbwGG3DGVixONxGI1GuN1u7lvv65rEAGU+n+f+cbqpiFY5+dUpg4UqLQHwCT2U/x2LxWCz2fjkHbpZkXVOvnFyfdDjYhl+YXGR2Bslk8nAbrdj8uTJCAaDaG5u5sLdF6PJfTKows0Yw0cffYT169ejsrISc+fOHTXdACVjj+P9wJMAhcNh7r6gzAgKDJIIknDSdhJFCmKKAUCywsm1QhNwgMOZTvQYBT1FX3WhO0EMbJO4iha5+A0AgMbPLHYTJBcHZcCUlZUhl8vBYrHwnHW/349cLsfdG+J5aI0UyBXrFeibBYm4eE6x50phf5cv+vcbyQyqcOfzebz66qt48803MWfOHNTX10vhlhQtJF6iz/dYkCh2dXWhp6cH0WgUtbW1AKAJLJLfVuybTX7ceDyuKVahiknKRslkMlBVFR6PBzqdjgcFCbL2KR+chhSLZfeU3ULPE8WPhFzM9FBVlTeEIihNL5VKQVVVNDQ0wGazoaurC36/H36/Hx999BESiQTKysrgdrthsVjgdrt5uT8FWCkI29nZyStGE4kEDAYDrFarJitG7KVCNxVyp4xmBt1VQi98Z2cn9u/fD1VVUVJSwpP9JZJio7AvyOdB1iHlcyuKwifaAEdcB5TNUZh+Jwb5yDIVRV4Ue0KsoBSLe0iAxcrMQj+2eJzCye/izUt0Y4juFDGYSNdG3yLIcqZjiD3KxQpR8XgkzuLgCPHmKVaCimsdTRZ2IUPm425qasKKFStQVlaGW265ZVT145aMPY5XFPL5PAKBAN59912YzWbU1dXxWZM+nw8mkwkOh4Nbs2LVI2MMdrudtz6gfG/RL03WvSjMJJCU2x0IBLiVTI97vV4+z5IKdMjfTI2k6JyUl03/qCEW9REhcRaDnKqqoqKiAk6nE1arVePLNpvNcDgcPGWQrGVyAQHauIIo+OINh85FLqfCv89oDFQOmXCHQiFs3rwZdrsdixcv7nUnP5ZvSiIZDWQyGR5EtFgsfBCB1Wrlvmoxn7mwkMVisXC3iZiqBxzJkSYXAlnwJHJinxPgiE/Z7XZrmj8BR1LyaIya2J0PAE9PFBtZiYMdSFBpejwJPaUrxuNxJJNJzf4035ICo3Q9RytwK/yGUVh9SgxmcdxwMuRZJel0Gq+//rpmUozdbseiRYswadKkoV6ORDIs9PT0cEFubW2F3W7HzJkz+VAFGv1FKXlU+ELWqOjjpeAfAF60UhjIA8B94TTUlwQ6FArxAiGxIVZPTw/2798PvV4Pp9OpsWipEIjcImKPFEDrSgG0VZeU8kc3FrFtbTAY5Dc3suDpWwjluufzeW5xh8NhdHR0IBaLgTEGh8PB+5CL5x5tDItw/+Uvf8HLL7/Mt1VWVmLcuHFSuCVjBgrYEU6nE5MmTeKuj3g8zoNxlG4nWqOU6UFuDFHoyZInsSQxpb4mmUwGHR0dSCaTiEajcDgcqKmp4cMVqDd2PB7Hnj17YLPZMGnSJJ7pAoD3odfr9fD5fHzyvBgUpNxzco3Qz/StgY4nFh/RzcJkMqGqqoqvGQDPWhEn0AcCAezatYu3h6U5nJSSOFqDlMOSx013XCIajWL37t147733UF5ejpqamhHjOolGo2hubuapTwDg8XhQX18/KodDSIaHbDaLaDSKYDCIeDze6ys+CXZhRWNfgTyyXgtb/8ZiMSSTSUQiEd6p0G63w+l08p7flEViMBgQjUa5JU89uKl/uOhXF903tDbxMRJl+iZAQUrqLkhCTDcet9vN+66IA4vJzy0WB1GMIB6P8+yZwmZVoxGF9fPqBtNXZDAYMG7cOLhcLlx11VW44447jjktZijZvn07Vq5cic8++4xvW7BgAe69916UlZUN48qO0NTUhH/913/Fa6+9NtxLGRBG6gduMD8DJpMJp512GqqqquB0OuH1emE0GnleNg0sUBSF+4tpTWT9ik2jCouE8vk8PvvsMxw4cIC3hjWbzZg4cSLKysq4e4ael8/nsXfvXnz66aeaSs5p06ahoaFBk+FitVp5NSS5PyhrhVIEqY8KpTFS8yu6iZSWlsJms2n8+mKQVEz3A8D7upBV3dPTg40bN2Lfvn18vcUs3p+37hFROZnNZvnEjzPOOKPXpHR6Uww25EcUv14FAgF8/PHH2LVrF9/W0NBw1PmSQwkFkBKJhOwQWOQwxhCNRhEIBHjpOnCkYIYsVzGTQgzGFfY/ET/4JMTJZBLhcBhWq5XnUZPFHQ6HEQwGeUUitYOlIcb0LSASiSCZTPJCGUVReCYIcKQSk6z9VCrFrXZ6nHzlqVSKj2+jFD/K46ap8ZS7DoD3AadzUkMuo9HI10yfy2IOSvZn7SNCuEXeffddrFixgt9Z9Xo9vvzlL+P8888f9D+G3+/HH/7wB4113d7ejo6OjkE974nywQcf4E9/+hMOHTqEPXv2DPdyJF+AbDaLjo4ORCIRtLa2Ys+ePbxc3Ww2o76+HjU1NdwSLXSXANDkaRfmbufzeYwbNw4+n48PYSC3BblQKPAn+p2pgyEJc1NTE7q7u1FaWoopU6Zwf7jFYuE+aABcbMWqSprsQzeETCajyTUnwSfjiSxzce4kBXXphtbd3Y3m5mZEIpE+y92LMRWwP+sdccK9Y8cOTc8Nk8kEn8+HhQsXaqyNwSAYDOKFF17A3/72N75tJP/RP/nkE6xduxaBQGBEr1Py+TDG4Pf7EQgENOXlJSUl3I9bUlKCTCaDcDis6QRJ4kRuA5r8TsJNwldWVgaXy6UpryfRJrEUS97JnSH61tvb29He3o5oNIrx48fzsWJmsxnxeJy3hqVZlfTtVcyUIaudbhyFHRPFRlJix0QSbspI0el0OHjwIN555x0kEgnNzUz0+RejeH8eI064Aa1YDvVg0P42EBouMpkMdu7ciZaWFnz44Yea/FpJcVP4dyTXnU6nw4EDB7B161buKxabTJELhVwIYiMq8omLQg4cqYCk/2n6eiqVQkdHB6LRKMLh8DGnwodCIe6DpnxscdgBcCQ4Sd+gxSpP8YYjdgUs7I2STqe5y4WGQvT09CCdTqO7u7tX/5W+XsfRxogUbsnRicViWLduHf70pz8hFotpsl0kxU+hAMViMSQSCWzevBn/93//h+rqapx//vlwuVyIx+N8qAE1ZhLbxZJlLfbxpiBfIQ6HA06nE8FgEB988AGam5sRi8V6pdOROOdyORw8eFAzgLiqqgoNDQ0AwGdCks+c/OlUkCO2cSV/t+i7phsOBTNDoRBvGZBMJrF37150dXUhkUho4k10MxiNYi1SFMIdi8XQ0dGh6alA5cEDCZUAl5eX837GhNhfgtKVhpJ0Oo1wOIzu7m60trbi4MGDQ3p+yfAg5moDhyuQaYZj4cCBwt4m4tR2OhaVpou9Smhfepws377ETwwukuhSql9ha1bxWGRV0/6iRS1eJwm9GOCMx+O82jKRSCAej/PP51gNyo+IdMBjodPpMHXqVEyZMoWvwWaz4frrr8d55503oOeKRqN4//330dbWhhdffBHPPvssf2OYzWZce+21WLJkCaqqqjBz5kzeSH4o2L59O371q1/xr8z79+8fsnMPNSPVWhquz4Doo7VYLKiurobdbsfJJ5+M2tpaXkhD1ipNhnE6nZrgZTgcRjQahd1ux7hx43gGh8FgQHt7O5qampBIJPios0OHDuHAgQOatZDx4nQ6UVFRwfucUNdC8qGLLhu6hsLmWTRLUq/Xw263w2QyIR6Pcx84dQeMRCJcuGnKezQa1czVpOOOFooiHfBY5PN57NixAzt27ODb3G43zj333AEXbrvdjvnz5yOXy2Hfvn14/vnn+WMGgwGnn346rrrqqgE9Z39pb2/HSy+9hL179w7L+SVDQ1+BNPH3RCKBvXv3QlVVTJgwgTduovavJIqF/a4ZY7xIhXzMYmOrtrY2tLS0IJvNwuVywe128xxrsSMgBSJpaILZbOYBSp1Ox3PNRV+2Xq9HNptFJBLhQUtxWLDYI4XK9yORCFpaWhAOh7lvW5y3KXZRFAcOjybxPhYjXrj7Ip1O480339R8TbLb7TjnnHNQU1MzjCv7YuzYsQNbtmzp8+vfzp07EQ6Hh2FVkqGkP8JDAcE9e/YgmUxy8aNe2ZSeR6Lu9/t5IM/v9/OeIOJknq6uLi6EoVCI+9fFoCdwuDS/vLwcbrcbNTU1fMJPoSBbrVbo9Xqeqy3mh1Nfk0QiwXuQ0D+a3JNIJJBKpXhVpjipXkxWEP36Y0W0gSIV7ng8jj/84Q8ai7impgarV68uWuFmjOHNN9/EqlWr+gw4ZrNZ3tlNIslkMti2bRu2bduG0tJSTJs2DRaLBQ6HAzabjQcbw+Ewurq6+AR2v9+PVCqF8vJyqKqKnp4eXuhC48Q6Ojq4X5l86STcPp8PEyZMgNvtRkNDA3Q6HVpbWxEOh3k1pdlshtfrhclk4qXyVCgmtmsldwgV+4jtXCkVkf6nQKlYzj8a0/z6S1EKN3BkQAPR09OD5uZm7N69+wsfO5fLoaura8DfFPl8Hp2dnZrJIQRjDC0tLQgGg5rrkkg+D/JxZ7NZ3kSKAo2U1heNRnmedSwWQyAQgMlkQjAY5KXuZN2SlSx22APQq0qSrHz6Xxw4LFrGZLXTNnJ7UEUmtZsVg5u0nsIAp8hYFW2giIW7EL/fj5///OdYv379Fz5WPp/n/r6BJB6P47e//S1eeumlPs956NChEVFKLykuotEoPvnkE17QIgqqWIlI/U5isRh6enp4LjhZsGKKnphhAhypxAwGgzhw4AAYY5gwYYKm9aroU49EIgDA506SJZ1Op3Ho0CFeNCa2exVL++mcADSW+FgWa5FRI9ypVEoTwBwMyLcoZheQNSG2zzzWGj/55BO8+eabg7pOydiC+pwcDfI7k5shkUhwtxuJrZhGWGghiz7odDqNSCTC++MUzqksbBAlBiHJig6FQrzntph1AqCXVS3pm1Ej3INNOp3Gxo0beTAHOFy0sGTJEkyZMgUffPABXnvttWNazMlkEtu2bRuC1UokRzhWpzzR2iUoYChaxPR7JBLhVjGNCysc8CAej3LFk8kkgsEgd9WI56f/i7kx1FAz4vO4RxKiDw8AKioq8Oijj+Lyyy/H2rVr8YMf/OCYlg9Z5fLr3rEZqa+P/AwcprAPCE2Wz2azvAUD7UMBS7FzHwBNmqGkN0Wfxz2SKAyQxONx7Ny5Ez6fD59++ilPWZJIRjOFZfnUrVD0RRfuLwOLA4u0uL8ABoMBFRUVsNvtPN1KWhFfnJH6oZafAclg019fvxRuyYhDCrdkrNJf4R4Zgx0lEolE0m+jRQq3RCKRjCD6I95SuCUSiaTIkMItkUgkRYYUbolEIikypHBLJBJJkSGFWyKRSIoMKdwSiURSZEjhlkgkkiJDCrdEIpEUGVK4JRKJpMiQwi2RSCRFhhRuiUQiKTKkcEskEkmRIYVbIpFIigwp3BKJRFJkSOGWSCSSIkMKt0QikRQZUrglEsmYpVjH0UnhlkgkY5aROt/085DCLZFIJEWGFG6JRCIpMgzDvQCJRCKRHKa/PndpcUskEskI4HgCpdLilkgkkhHA8QRKpcUtkZwAxZpGJumbYvt7SuGWSCSSIkNhxZrIKJFIJGMUaXFLJBJJkSGFWyKRSIoMKdwSiURSZEjhlkgkkiJDCrdEIpEUGVK4JRKJpMiQwi2RSCRFhhRuiUQiKTKkcEskEkmR8f8BZd2aBtC3DBYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 400x200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 125: 100%|███████████| 6/6 [00:02<00:00,  2.48it/s, loss=0.0163]\n",
+      "Epoch 126: 100%|█████████████| 6/6 [00:02<00:00,  2.45it/s, loss=0.02]\n",
+      "Epoch 127: 100%|███████████| 6/6 [00:02<00:00,  2.36it/s, loss=0.0159]\n",
+      "Epoch 128: 100%|███████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.0201]\n",
+      "Epoch 129: 100%|███████████| 6/6 [00:02<00:00,  2.40it/s, loss=0.0148]\n",
+      "Epoch 130: 100%|███████████| 6/6 [00:02<00:00,  2.36it/s, loss=0.0153]\n",
+      "Epoch 131: 100%|███████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.0175]\n",
+      "Epoch 132: 100%|███████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.0178]\n",
+      "Epoch 133: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.018]\n",
+      "Epoch 134: 100%|███████████| 6/6 [00:02<00:00,  2.29it/s, loss=0.0179]\n",
+      "Epoch 135: 100%|███████████| 6/6 [00:02<00:00,  2.22it/s, loss=0.0153]\n",
+      "Epoch 136: 100%|███████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0173]\n",
+      "Epoch 137: 100%|███████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0164]\n",
+      "Epoch 138: 100%|███████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0165]\n",
+      "Epoch 139: 100%|███████████| 6/6 [00:03<00:00,  1.97it/s, loss=0.0146]\n",
+      "Epoch 140: 100%|███████████| 6/6 [00:02<00:00,  2.02it/s, loss=0.0188]\n",
+      "Epoch 141: 100%|███████████| 6/6 [00:02<00:00,  2.08it/s, loss=0.0194]\n",
+      "Epoch 142: 100%|███████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0143]\n",
+      "Epoch 143: 100%|███████████| 6/6 [00:03<00:00,  2.00it/s, loss=0.0154]\n",
+      "Epoch 144: 100%|████████████| 6/6 [00:02<00:00,  2.12it/s, loss=0.012]\n",
+      "Epoch 145: 100%|███████████| 6/6 [00:02<00:00,  2.00it/s, loss=0.0198]\n",
+      "Epoch 146: 100%|███████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0224]\n",
+      "Epoch 147: 100%|███████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0152]\n",
+      "Epoch 148: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.018]\n",
+      "Epoch 149: 100%|████████████| 6/6 [00:02<00:00,  2.09it/s, loss=0.018]\n",
+      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:32<00:00, 30.67it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 400x200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train completed, total time: 599.0462603569031.\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_epochs = 150\n",
+    "val_interval = 25\n",
+    "epoch_loss_list = []\n",
+    "val_epoch_loss_list = []\n",
+    "\n",
+    "scaler = GradScaler()\n",
+    "total_start = time.time()\n",
+    "for epoch in range(n_epochs):\n",
+    "    model.train()\n",
+    "    epoch_loss = 0\n",
+    "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
+    "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
+    "    for step, batch in progress_bar:\n",
+    "        images = batch[\"image\"].to(device)\n",
+    "        masks = batch[\"mask\"].to(device)\n",
+    "\n",
+    "        optimizer.zero_grad(set_to_none=True)\n",
+    "\n",
+    "        with autocast(enabled=True):\n",
+    "\n",
+    "            # Generate random noise\n",
+    "            noise = torch.randn_like(images).to(device)\n",
+    "\n",
+    "            # Create timesteps\n",
+    "            timesteps = torch.randint(\n",
+    "                0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
+    "            ).long()\n",
+    "\n",
+    "            images_noised = scheduler.add_noise(images, noise=noise, timesteps=timesteps)\n",
+    "\n",
+    "            # Get controlnet output\n",
+    "            down_block_res_samples, mid_block_res_sample = controlnet(\n",
+    "                x=images_noised, timesteps=timesteps, controlnet_cond=masks\n",
+    "            )\n",
+    "            # Get model prediction\n",
+    "            noise_pred = model(\n",
+    "                x=images_noised,\n",
+    "                timesteps=timesteps,\n",
+    "                down_block_additional_residuals=down_block_res_samples,\n",
+    "                mid_block_additional_residual=mid_block_res_sample,\n",
+    "            )\n",
+    "\n",
+    "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+    "\n",
+    "        scaler.scale(loss).backward()\n",
+    "        scaler.step(optimizer)\n",
+    "        scaler.update()\n",
+    "\n",
+    "        epoch_loss += loss.item()\n",
+    "\n",
+    "        progress_bar.set_postfix({\"loss\": epoch_loss / (step + 1)})\n",
+    "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
+    "\n",
+    "    if (epoch + 1) % val_interval == 0:\n",
+    "        model.eval()\n",
+    "        val_epoch_loss = 0\n",
+    "        for step, batch in enumerate(val_loader):\n",
+    "            images = batch[\"image\"].to(device)\n",
+    "            masks = batch[\"mask\"].to(device)\n",
+    "\n",
+    "            with torch.no_grad():\n",
+    "                with autocast(enabled=True):\n",
+    "                    noise = torch.randn_like(images).to(device)\n",
+    "                    timesteps = torch.randint(\n",
+    "                        0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
+    "                    ).long()\n",
+    "                    noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
+    "                    val_loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+    "\n",
+    "            val_epoch_loss += val_loss.item()\n",
+    "            progress_bar.set_postfix({\"val_loss\": val_epoch_loss / (step + 1)})\n",
+    "            break\n",
+    "        val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
+    "\n",
+    "        # Sampling image during training with controlnet conditioning\n",
+    "        progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110)\n",
+    "        progress_bar_sampling.set_description(\"sampling...\")\n",
+    "        sample = torch.randn((1, 1, 64, 64)).to(device)\n",
+    "        for t in progress_bar_sampling:\n",
+    "            with torch.no_grad():\n",
+    "                with autocast(enabled=True):\n",
+    "                    down_block_res_samples, mid_block_res_sample = controlnet(\n",
+    "                        x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=masks[0, None, ...]\n",
+    "                    )\n",
+    "                    noise_pred = model(\n",
+    "                        sample,\n",
+    "                        timesteps=torch.Tensor((t,)).to(device),\n",
+    "                        down_block_additional_residuals=down_block_res_samples,\n",
+    "                        mid_block_additional_residual=mid_block_res_sample,\n",
+    "                    )\n",
+    "                    sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n",
+    "\n",
+    "        plt.subplots(1, 2, figsize=(4, 2))\n",
+    "        plt.subplot(1, 2, 1)\n",
+    "        plt.imshow(masks[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "        plt.axis(\"off\")\n",
+    "        plt.title(\"Conditioning mask\")\n",
+    "        plt.subplot(1, 2, 2)\n",
+    "        plt.imshow(sample[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "        plt.axis(\"off\")\n",
+    "        plt.title(\"Sample image\")\n",
+    "        plt.tight_layout()\n",
+    "        plt.axis(\"off\")\n",
+    "        plt.show()\n",
+    "\n",
+    "total_time = time.time() - total_start\n",
+    "print(f\"train completed, total time: {total_time}.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b005e3bd-54b9-44bc-964d-ca0c9585a139",
+   "metadata": {},
+   "source": [
+    "## Sample with ControlNet conditioning\n",
+    "First we'll provide a few different masks from the validation data as conditioning. The samples should respect the shape of the conditioning mask, but don't need to have the same content as the corresponding validation image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "262a5129-9445-4ecc-a37a-a97c59386747",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:37<00:00, 27.00it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x800 with 24 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110, position=0, leave=True)\n",
+    "progress_bar_sampling.set_description(\"sampling...\")\n",
+    "num_samples = 8\n",
+    "sample = torch.randn((num_samples, 1, 64, 64)).to(device)\n",
+    "\n",
+    "val_batch = first(val_loader)\n",
+    "val_images = val_batch[\"image\"].to(device)\n",
+    "val_masks = val_batch[\"mask\"].to(device)\n",
+    "for t in progress_bar_sampling:\n",
+    "    with torch.no_grad():\n",
+    "        with autocast(enabled=True):\n",
+    "            down_block_res_samples, mid_block_res_sample = controlnet(\n",
+    "                x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=val_masks[:num_samples, ...]\n",
+    "            )\n",
+    "            noise_pred = model(\n",
+    "                sample,\n",
+    "                timesteps=torch.Tensor((t,)).to(device),\n",
+    "                down_block_additional_residuals=down_block_res_samples,\n",
+    "                mid_block_additional_residual=mid_block_res_sample,\n",
+    "            )\n",
+    "            sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n",
+    "\n",
+    "plt.subplots(num_samples, 3, figsize=(6, 8))\n",
+    "for k in range(num_samples):\n",
+    "    plt.subplot(num_samples, 3, k * 3 + 1)\n",
+    "    plt.imshow(val_masks[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "    plt.axis(\"off\")\n",
+    "    if k == 0:\n",
+    "        plt.title(\"Conditioning mask\")\n",
+    "    plt.subplot(num_samples, 3, k * 3 + 2)\n",
+    "    plt.imshow(val_images[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "    plt.axis(\"off\")\n",
+    "    if k == 0:\n",
+    "        plt.title(\"Actual val image\")\n",
+    "    plt.subplot(num_samples, 3, k * 3 + 3)\n",
+    "    plt.imshow(sample[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "    plt.axis(\"off\")\n",
+    "    if k == 0:\n",
+    "        plt.title(\"Sampled image\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1ca8274-d85c-4dcc-9c16-08ac2b6ce0fd",
+   "metadata": {},
+   "source": [
+    "What happens if we invent some masks? Let's try a circle, and a square"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "393fca6c-2446-4822-8aad-44403761b40e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:16<00:00, 61.51it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 400x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xx, yy = np.mgrid[:64, :64]\n",
+    "circle = ((xx - 32) ** 2 + (yy - 32) ** 2) < 30**2\n",
+    "\n",
+    "square = np.zeros((64, 64))\n",
+    "square[10:50, 10:50] = 1\n",
+    "\n",
+    "mask = np.concatenate((circle[None, None, ...], square[None, None, ...]), axis=0)\n",
+    "mask = torch.from_numpy(mask.astype(np.float32)).to(device)\n",
+    "\n",
+    "\n",
+    "progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110, position=0, leave=True)\n",
+    "progress_bar_sampling.set_description(\"sampling...\")\n",
+    "num_samples = 2\n",
+    "sample = torch.randn((num_samples, 1, 64, 64)).to(device)\n",
+    "\n",
+    "for t in progress_bar_sampling:\n",
+    "    with torch.no_grad():\n",
+    "        with autocast(enabled=True):\n",
+    "            down_block_res_samples, mid_block_res_sample = controlnet(\n",
+    "                x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=mask\n",
+    "            )\n",
+    "            noise_pred = model(\n",
+    "                sample,\n",
+    "                timesteps=torch.Tensor((t,)).to(device),\n",
+    "                down_block_additional_residuals=down_block_res_samples,\n",
+    "                mid_block_additional_residual=mid_block_res_sample,\n",
+    "            )\n",
+    "            sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n",
+    "\n",
+    "plt.subplots(num_samples, 2, figsize=(4, 4))\n",
+    "for k in range(num_samples):\n",
+    "    plt.subplot(num_samples, 2, k * 2 + 1)\n",
+    "    plt.imshow(mask[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "    plt.axis(\"off\")\n",
+    "    if k == 0:\n",
+    "        plt.title(\"Conditioning mask\")\n",
+    "    plt.subplot(num_samples, 2, k * 2 + 2)\n",
+    "    plt.imshow(sample[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "    plt.axis(\"off\")\n",
+    "    if k == 0:\n",
+    "        plt.title(\"Sampled image\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "py:percent,ipynb"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4f1513a79f82193cb81c96943579af15c6a44d6347609348bde584197ab7b1ab"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorials/generative/2d_controlnet/2d_controlnet.py b/tutorials/generative/2d_controlnet/2d_controlnet.py
new file mode 100644
index 00000000..da08911f
--- /dev/null
+++ b/tutorials/generative/2d_controlnet/2d_controlnet.py
@@ -0,0 +1,524 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: py:percent,ipynb
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.14.1
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# %%
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# %% [markdown]
+# # Using ControlNet to control image generation
+#
+# This tutorial illustrates how to use MONAI Generative Models to train a ControlNet [1]. ControlNets are hypernetworks that allow for supplying extra conditioning to ready-trained diffusion models. In this example, we will walk through training a ControlNet that allows us to specify a whole-brain mask that the sampled image must respect.
+#
+#
+#
+# In summary, the tutorial will cover the following:
+# 1. Loading and preprocessing a dataset (we extract the brain MRI dataset 2D slices from 3D volumes from the BraTS dataset)
+# 2. Training a 2D diffusion model
+# 3. Freeze the diffusion model and train a ControlNet
+# 3. Conditional sampling with the ControlNet
+#
+# [1] - Zhang et al. [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543)
+
+# %%
+# !python -c "import monai" || pip install -q "monai-weekly[tqdm]"
+# !python -c "import matplotlib" || pip install -q matplotlib
+# %matplotlib inline
+
+# %% [markdown]
+# ## Setup environment
+
+# %% jupyter={"outputs_hidden": false}
+import os
+import tempfile
+import time
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import torch.nn.functional as F
+from monai import transforms
+from monai.apps import DecathlonDataset
+from monai.config import print_config
+from monai.data import DataLoader
+from monai.utils import first, set_determinism
+from torch.cuda.amp import GradScaler, autocast
+from tqdm import tqdm
+
+
+from generative.inferers import DiffusionInferer
+from generative.networks.nets import DiffusionModelUNet, ControlNet
+from generative.networks.schedulers import DDPMScheduler
+
+print_config()
+
+
+# %% [markdown]
+# ### Setup data directory
+
+# %% jupyter={"outputs_hidden": false}
+directory = os.environ.get("MONAI_DATA_DIRECTORY")
+root_dir = tempfile.mkdtemp() if directory is None else directory
+
+# %% [markdown]
+# ### Set deterministic training for reproducibility
+
+# %% jupyter={"outputs_hidden": false}
+set_determinism(42)
+
+# %% [markdown] tags=[]
+# ## Setup BRATS dataset
+#
+# We now download the BraTS dataset and extract the 2D slices from the 3D volumes.
+#
+
+# %% [markdown]
+# ### Specify transforms
+# We create a rough brain mask by thresholding the image.
+
+# %%
+channel = 0
+assert channel in [0, 1, 2, 3], "Choose a valid channel"
+
+train_transforms = transforms.Compose(
+    [
+        transforms.LoadImaged(keys=["image"]),
+        transforms.EnsureChannelFirstd(keys=["image"]),
+        transforms.Lambdad(keys=["image"], func=lambda x: x[channel, :, :, :]),
+        transforms.AddChanneld(keys=["image"]),
+        transforms.EnsureTyped(keys=["image"]),
+        transforms.Orientationd(keys=["image"], axcodes="RAS"),
+        transforms.Spacingd(keys=["image"], pixdim=(3.0, 3.0, 2.0), mode="bilinear"),
+        transforms.CenterSpatialCropd(keys=["image"], roi_size=(64, 64, 44)),
+        transforms.ScaleIntensityRangePercentilesd(keys="image", lower=0, upper=99.5, b_min=0, b_max=1),
+        transforms.RandSpatialCropd(keys=["image"], roi_size=(64, 64, 1), random_size=False),
+        transforms.Lambdad(keys=["image"], func=lambda x: x.squeeze(-1)),
+        transforms.CopyItemsd(keys=["image"], times=1, names=["mask"]),
+        transforms.Lambdad(keys=["mask"], func=lambda x: torch.where(x > 0.1, 1, 0)),
+        transforms.FillHolesd(keys=["mask"]),
+        transforms.CastToTyped(keys=["mask"], dtype=np.float32),
+    ]
+)
+
+# %% [markdown]
+# ### Load training and validation datasets
+
+# %% jupyter={"outputs_hidden": false}
+train_ds = DecathlonDataset(
+    root_dir=root_dir,
+    task="Task01_BrainTumour",
+    section="training",
+    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise
+    num_workers=4,
+    download=True,
+    seed=0,
+    transform=train_transforms,
+)
+print(f"Length of training data: {len(train_ds)}")
+print(f'Train image shape {train_ds[0]["image"].shape}')
+
+val_ds = DecathlonDataset(
+    root_dir=root_dir,
+    task="Task01_BrainTumour",
+    section="validation",
+    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise
+    num_workers=4,
+    download=False,
+    seed=0,
+    transform=train_transforms,
+)
+print(f"Length of val data: {len(val_ds)}")
+print(f'Validation image shape {val_ds[0]["image"].shape}')
+
+# %%
+train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4, drop_last=True, persistent_workers=True)
+val_loader = DataLoader(val_ds, batch_size=64, shuffle=False, num_workers=4, drop_last=True, persistent_workers=True)
+
+# %% [markdown]
+# ### Visualise the images and masks
+
+# %%
+check_data = first(train_loader)
+print(f"Batch shape: {check_data['image'].shape}")
+image_visualisation = torch.cat(
+    (
+        torch.cat(
+            [
+                check_data["image"][0, 0],
+                check_data["image"][1, 0],
+                check_data["image"][2, 0],
+                check_data["image"][3, 0],
+            ],
+            dim=1,
+        ),
+        torch.cat(
+            [check_data["mask"][0, 0], check_data["mask"][1, 0], check_data["mask"][2, 0], check_data["mask"][3, 0]],
+            dim=1,
+        ),
+    ),
+    dim=0,
+)
+plt.figure(figsize=(6, 3))
+plt.imshow(image_visualisation, vmin=0, vmax=1, cmap="gray")
+plt.axis("off")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Train the Diffusion model
+# In general, a ControlNet can be trained in combination with a pre-trained, frozen diffusion model. In this case we will quickly train the diffusion model first.
+
+# %% [markdown]
+# ### Define network, scheduler, optimizer, and inferer
+
+# %% tags=[]
+device = torch.device("cuda")
+
+model = DiffusionModelUNet(
+    spatial_dims=2,
+    in_channels=1,
+    out_channels=1,
+    num_channels=(128, 256, 256),
+    attention_levels=(False, True, True),
+    num_res_blocks=1,
+    num_head_channels=256,
+)
+model.to(device)
+
+scheduler = DDPMScheduler(num_train_timesteps=1000)
+
+optimizer = torch.optim.Adam(params=model.parameters(), lr=2.5e-5)
+
+inferer = DiffusionInferer(scheduler)
+
+
+# %% [markdown]
+# ### Run training
+#
+
+# %% tags=[]
+n_epochs = 150
+val_interval = 25
+epoch_loss_list = []
+val_epoch_loss_list = []
+
+scaler = GradScaler()
+total_start = time.time()
+for epoch in range(n_epochs):
+    model.train()
+    epoch_loss = 0
+    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)
+    progress_bar.set_description(f"Epoch {epoch}")
+    for step, batch in progress_bar:
+        images = batch["image"].to(device)
+        optimizer.zero_grad(set_to_none=True)
+
+        with autocast(enabled=True):
+            # Generate random noise
+            noise = torch.randn_like(images).to(device)
+
+            # Create timesteps
+            timesteps = torch.randint(
+                0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device
+            ).long()
+
+            # Get model prediction
+            noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)
+
+            loss = F.mse_loss(noise_pred.float(), noise.float())
+
+        scaler.scale(loss).backward()
+        scaler.step(optimizer)
+        scaler.update()
+
+        epoch_loss += loss.item()
+
+        progress_bar.set_postfix({"loss": epoch_loss / (step + 1)})
+    epoch_loss_list.append(epoch_loss / (step + 1))
+
+    if (epoch + 1) % val_interval == 0:
+        model.eval()
+        val_epoch_loss = 0
+        for step, batch in enumerate(val_loader):
+            images = batch["image"].to(device)
+            with torch.no_grad():
+                with autocast(enabled=True):
+                    noise = torch.randn_like(images).to(device)
+                    timesteps = torch.randint(
+                        0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device
+                    ).long()
+                    noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)
+                    val_loss = F.mse_loss(noise_pred.float(), noise.float())
+
+            val_epoch_loss += val_loss.item()
+            progress_bar.set_postfix({"val_loss": val_epoch_loss / (step + 1)})
+        val_epoch_loss_list.append(val_epoch_loss / (step + 1))
+
+        # Sampling image during training
+        noise = torch.randn((1, 1, 64, 64))
+        noise = noise.to(device)
+        scheduler.set_timesteps(num_inference_steps=1000)
+        with autocast(enabled=True):
+            image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler)
+
+        plt.figure(figsize=(2, 2))
+        plt.imshow(image[0, 0].cpu(), vmin=0, vmax=1, cmap="gray")
+        plt.tight_layout()
+        plt.axis("off")
+        plt.show()
+
+total_time = time.time() - total_start
+print(f"train completed, total time: {total_time}.")
+
+# %% [markdown]
+# ## Train the ControlNet
+
+# %% [markdown]
+# ### Set up models
+
+# %%
+# Create control net
+controlnet = ControlNet(
+    spatial_dims=2,
+    in_channels=1,
+    num_channels=(128, 256, 256),
+    attention_levels=(False, True, True),
+    num_res_blocks=1,
+    num_head_channels=256,
+    conditioning_embedding_num_channels=(16,),
+)
+# Copy weights from the DM to the controlnet
+controlnet.load_state_dict(model.state_dict(), strict=False)
+controlnet = controlnet.to(device)
+# Now, we freeze the parameters of the diffusion model.
+for p in model.parameters():
+    p.requires_grad = False
+optimizer = torch.optim.Adam(params=controlnet.parameters(), lr=2.5e-5)
+
+
+# %% [markdown] tags=[]
+# ### Run ControlNet training
+
+# %% tags=[]
+n_epochs = 150
+val_interval = 25
+epoch_loss_list = []
+val_epoch_loss_list = []
+
+scaler = GradScaler()
+total_start = time.time()
+for epoch in range(n_epochs):
+    model.train()
+    epoch_loss = 0
+    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)
+    progress_bar.set_description(f"Epoch {epoch}")
+    for step, batch in progress_bar:
+        images = batch["image"].to(device)
+        masks = batch["mask"].to(device)
+
+        optimizer.zero_grad(set_to_none=True)
+
+        with autocast(enabled=True):
+
+            # Generate random noise
+            noise = torch.randn_like(images).to(device)
+
+            # Create timesteps
+            timesteps = torch.randint(
+                0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device
+            ).long()
+
+            images_noised = scheduler.add_noise(images, noise=noise, timesteps=timesteps)
+
+            # Get controlnet output
+            down_block_res_samples, mid_block_res_sample = controlnet(
+                x=images_noised, timesteps=timesteps, controlnet_cond=masks
+            )
+            # Get model prediction
+            noise_pred = model(
+                x=images_noised,
+                timesteps=timesteps,
+                down_block_additional_residuals=down_block_res_samples,
+                mid_block_additional_residual=mid_block_res_sample,
+            )
+
+            loss = F.mse_loss(noise_pred.float(), noise.float())
+
+        scaler.scale(loss).backward()
+        scaler.step(optimizer)
+        scaler.update()
+
+        epoch_loss += loss.item()
+
+        progress_bar.set_postfix({"loss": epoch_loss / (step + 1)})
+    epoch_loss_list.append(epoch_loss / (step + 1))
+
+    if (epoch + 1) % val_interval == 0:
+        model.eval()
+        val_epoch_loss = 0
+        for step, batch in enumerate(val_loader):
+            images = batch["image"].to(device)
+            masks = batch["mask"].to(device)
+
+            with torch.no_grad():
+                with autocast(enabled=True):
+                    noise = torch.randn_like(images).to(device)
+                    timesteps = torch.randint(
+                        0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device
+                    ).long()
+                    noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)
+                    val_loss = F.mse_loss(noise_pred.float(), noise.float())
+
+            val_epoch_loss += val_loss.item()
+            progress_bar.set_postfix({"val_loss": val_epoch_loss / (step + 1)})
+            break
+        val_epoch_loss_list.append(val_epoch_loss / (step + 1))
+
+        # Sampling image during training with controlnet conditioning
+        progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110)
+        progress_bar_sampling.set_description("sampling...")
+        sample = torch.randn((1, 1, 64, 64)).to(device)
+        for t in progress_bar_sampling:
+            with torch.no_grad():
+                with autocast(enabled=True):
+                    down_block_res_samples, mid_block_res_sample = controlnet(
+                        x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=masks[0, None, ...]
+                    )
+                    noise_pred = model(
+                        sample,
+                        timesteps=torch.Tensor((t,)).to(device),
+                        down_block_additional_residuals=down_block_res_samples,
+                        mid_block_additional_residual=mid_block_res_sample,
+                    )
+                    sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)
+
+        plt.subplots(1, 2, figsize=(4, 2))
+        plt.subplot(1, 2, 1)
+        plt.imshow(masks[0, 0].cpu(), vmin=0, vmax=1, cmap="gray")
+        plt.axis("off")
+        plt.title("Conditioning mask")
+        plt.subplot(1, 2, 2)
+        plt.imshow(sample[0, 0].cpu(), vmin=0, vmax=1, cmap="gray")
+        plt.axis("off")
+        plt.title("Sample image")
+        plt.tight_layout()
+        plt.axis("off")
+        plt.show()
+
+total_time = time.time() - total_start
+print(f"train completed, total time: {total_time}.")
+
+# %% [markdown]
+# ## Sample with ControlNet conditioning
+# First we'll provide a few different masks from the validation data as conditioning. The samples should respect the shape of the conditioning mask, but don't need to have the same content as the corresponding validation image.
+
+# %%
+progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110, position=0, leave=True)
+progress_bar_sampling.set_description("sampling...")
+num_samples = 8
+sample = torch.randn((num_samples, 1, 64, 64)).to(device)
+
+val_batch = first(val_loader)
+val_images = val_batch["image"].to(device)
+val_masks = val_batch["mask"].to(device)
+for t in progress_bar_sampling:
+    with torch.no_grad():
+        with autocast(enabled=True):
+            down_block_res_samples, mid_block_res_sample = controlnet(
+                x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=val_masks[:num_samples, ...]
+            )
+            noise_pred = model(
+                sample,
+                timesteps=torch.Tensor((t,)).to(device),
+                down_block_additional_residuals=down_block_res_samples,
+                mid_block_additional_residual=mid_block_res_sample,
+            )
+            sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)
+
+plt.subplots(num_samples, 3, figsize=(6, 8))
+for k in range(num_samples):
+    plt.subplot(num_samples, 3, k * 3 + 1)
+    plt.imshow(val_masks[k, 0, ...].cpu(), vmin=0, vmax=1, cmap="gray")
+    plt.axis("off")
+    if k == 0:
+        plt.title("Conditioning mask")
+    plt.subplot(num_samples, 3, k * 3 + 2)
+    plt.imshow(val_images[k, 0, ...].cpu(), vmin=0, vmax=1, cmap="gray")
+    plt.axis("off")
+    if k == 0:
+        plt.title("Actual val image")
+    plt.subplot(num_samples, 3, k * 3 + 3)
+    plt.imshow(sample[k, 0, ...].cpu(), vmin=0, vmax=1, cmap="gray")
+    plt.axis("off")
+    if k == 0:
+        plt.title("Sampled image")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# What happens if we invent some masks? Let's try a circle, and a square
+
+# %%
+xx, yy = np.mgrid[:64, :64]
+circle = ((xx - 32) ** 2 + (yy - 32) ** 2) < 30**2
+
+square = np.zeros((64, 64))
+square[10:50, 10:50] = 1
+
+mask = np.concatenate((circle[None, None, ...], square[None, None, ...]), axis=0)
+mask = torch.from_numpy(mask.astype(np.float32)).to(device)
+
+
+progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110, position=0, leave=True)
+progress_bar_sampling.set_description("sampling...")
+num_samples = 2
+sample = torch.randn((num_samples, 1, 64, 64)).to(device)
+
+for t in progress_bar_sampling:
+    with torch.no_grad():
+        with autocast(enabled=True):
+            down_block_res_samples, mid_block_res_sample = controlnet(
+                x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=mask
+            )
+            noise_pred = model(
+                sample,
+                timesteps=torch.Tensor((t,)).to(device),
+                down_block_additional_residuals=down_block_res_samples,
+                mid_block_additional_residual=mid_block_res_sample,
+            )
+            sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)
+
+plt.subplots(num_samples, 2, figsize=(4, 4))
+for k in range(num_samples):
+    plt.subplot(num_samples, 2, k * 2 + 1)
+    plt.imshow(mask[k, 0, ...].cpu(), vmin=0, vmax=1, cmap="gray")
+    plt.axis("off")
+    if k == 0:
+        plt.title("Conditioning mask")
+    plt.subplot(num_samples, 2, k * 2 + 2)
+    plt.imshow(sample[k, 0, ...].cpu(), vmin=0, vmax=1, cmap="gray")
+    plt.axis("off")
+    if k == 0:
+        plt.title("Sampled image")
+plt.tight_layout()
+plt.show()