From d5c3ca4456f436e99564e7afdbeb9c67c3e9653a Mon Sep 17 00:00:00 2001
From: OeslleLucena <oeslle.lucena@gmail.com>
Date: Thu, 6 Apr 2023 17:59:38 +0100
Subject: [PATCH 01/13] [WIP] reformat using pl

---
 ...fusion_v2_super_resolution-lightning.ipynb | 1638 +++++++++++++++++
 1 file changed, 1638 insertions(+)
 create mode 100644 tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb

diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
new file mode 100644
index 00000000..a03c168e
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+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
@@ -0,0 +1,1638 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bc11fdc9",
+   "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": "95c08725",
+   "metadata": {},
+   "source": [
+    "# Super-resolution using Stable Diffusion v2 Upscalers\n",
+    "\n",
+    "Tutorial to illustrate the super-resolution task on medical images using Latent Diffusion Models (LDMs) [1]. For that, we will use an autoencoder to obtain a latent representation of the high-resolution images. Then, we train a diffusion model to infer this latent representation when conditioned on a low-resolution image.\n",
+    "\n",
+    "To improve the performance of our models, we will use a method called \"noise conditioning augmentation\" (introduced in [2] and used in Stable Diffusion v2.0 and Imagen Video [3]). During the training, we add noise to the low-resolution images using a random signal-to-noise ratio, and we condition the diffusion models on the amount of noise added. At sampling time, we use a fixed signal-to-noise ratio, representing a small amount of augmentation that aids in removing artefacts in the samples.\n",
+    "\n",
+    "\n",
+    "[1] - Rombach et al. \"High-Resolution Image Synthesis with Latent Diffusion Models\" https://arxiv.org/abs/2112.10752\n",
+    "\n",
+    "[2] - Ho et al. \"Cascaded diffusion models for high fidelity image generation\" https://arxiv.org/abs/2106.15282\n",
+    "\n",
+    "[3] - Ho et al. \"High Definition Video Generation with Diffusion Models\" https://arxiv.org/abs/2210.02303"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b839bf2d",
+   "metadata": {},
+   "source": [
+    "## Set up environment using Colab\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "77f7e633",
+   "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": "214066de",
+   "metadata": {},
+   "source": [
+    "## Set up imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "de71fe08",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ol18/miniconda3/envs/monai_generative/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-04-06 16:56:02,588 - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:\n",
+      "    PyTorch 1.13.1+cu117 with CUDA 1107 (you have 1.12.1)\n",
+      "    Python  3.8.16 (you have 3.8.16)\n",
+      "  Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)\n",
+      "  Memory-efficient attention, SwiGLU, sparse and more won't be available.\n",
+      "  Set XFORMERS_MORE_DETAILS=1 for more details\n",
+      "2023-04-06 16:56:04,614 - Created a temporary directory at /tmp/tmp0zwlum7_\n",
+      "2023-04-06 16:56:04,617 - Writing /tmp/tmp0zwlum7_/_remote_module_non_scriptable.py\n",
+      "MONAI version: 1.2.dev2304\n",
+      "Numpy version: 1.23.5\n",
+      "Pytorch version: 1.12.1\n",
+      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
+      "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
+      "MONAI __file__: /home/ol18/miniconda3/envs/monai_generative/lib/python3.8/site-packages/monai_weekly-1.2.dev2304-py3.8.egg/monai/__init__.py\n",
+      "\n",
+      "Optional dependencies:\n",
+      "Pytorch Ignite version: 0.4.10\n",
+      "ITK version: 5.3.0\n",
+      "Nibabel version: 5.1.0\n",
+      "scikit-image version: 0.20.0\n",
+      "Pillow version: 9.4.0\n",
+      "Tensorboard version: 2.12.1\n",
+      "gdown version: 4.7.1\n",
+      "TorchVision version: 0.13.1\n",
+      "tqdm version: 4.65.0\n",
+      "lmdb version: 1.4.0\n",
+      "psutil version: 5.9.4\n",
+      "pandas version: 2.0.0\n",
+      "einops version: 0.6.0\n",
+      "transformers version: 4.21.3\n",
+      "mlflow version: 2.2.2\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 shutil\n",
+    "import tempfile\n",
+    "\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 MedNISTDataset\n",
+    "from monai.config import print_config\n",
+    "from monai.data import CacheDataset, DataLoader\n",
+    "from monai.utils import first, set_determinism\n",
+    "from torch import nn\n",
+    "from torch.cuda.amp import GradScaler, autocast\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from generative.losses import PatchAdversarialLoss, PerceptualLoss\n",
+    "from generative.networks.nets import AutoencoderKL, DiffusionModelUNet, PatchDiscriminator\n",
+    "from generative.networks.schedulers import DDPMScheduler\n",
+    "\n",
+    "import pytorch_lightning as pl\n",
+    "from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint\n",
+    "\n",
+    "print_config()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9f0a17bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for reproducibility purposes set a seed\n",
+    "set_determinism(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0dde922",
+   "metadata": {},
+   "source": [
+    "## Setup a data directory and download dataset\n",
+    "Specify a MONAI_DATA_DIRECTORY variable, where the data will be downloaded. If not specified a temporary directory will be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ded618a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/tmp/tmpqpsolhvx\n"
+     ]
+    }
+   ],
+   "source": [
+    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
+    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
+    "print(root_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d80e045b",
+   "metadata": {},
+   "source": [
+    "## Define the LightningModule for AutoEncoder (transforms, network, loaders, etc)\n",
+    "The LightningModule contains a refactoring of your training code. The following module is a refactoring of the code in 2d_stable_diffusion_v2_super_resolution-lightning\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d5d1caff",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "class AutoEnconder(pl.LightningModule):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.data_dir = root_dir\n",
+    "        self.autoencoderkl = AutoencoderKL(spatial_dims=2,\n",
+    "                                           in_channels=1,\n",
+    "                                           out_channels=1,\n",
+    "                                           num_channels=(256, 512, 512),\n",
+    "                                           latent_channels=3,\n",
+    "                                           num_res_blocks=2,\n",
+    "                                           norm_num_groups=32,\n",
+    "                                           attention_levels=(False, False, True))\n",
+    "        self.discriminator = PatchDiscriminator(spatial_dims=2, in_channels=1,\n",
+    "                                                num_layers_d=3, num_channels=64)\n",
+    "        self.perceptual_loss = PerceptualLoss(spatial_dims=2, network_type=\"alex\")\n",
+    "        self.perceptual_weight = 0.002\n",
+    "        self.autoencoder_warm_up_n_epochs = 10\n",
+    "        self.automatic_optimization = False\n",
+    "        self.adv_loss = PatchAdversarialLoss(criterion=\"least_squares\")\n",
+    "        self.adv_weight = 0.005\n",
+    "        self.kl_weight = 1e-6\n",
+    "        \n",
+    "    def forward(self, z):\n",
+    "        return self.autoencoderkl(z)\n",
+    "\n",
+    "    def prepare_data(self):\n",
+    "        image_size = 64\n",
+    "        train_transforms = transforms.Compose(\n",
+    "            [\n",
+    "                transforms.LoadImaged(keys=[\"image\"]),\n",
+    "                transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
+    "                transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0,\n",
+    "                                                b_max=1.0, clip=True),\n",
+    "                transforms.RandAffined(\n",
+    "                    keys=[\"image\"],\n",
+    "                    rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n",
+    "                    translate_range=[(-1, 1), (-1, 1)],\n",
+    "                    scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n",
+    "                    spatial_size=[image_size, image_size],\n",
+    "                    padding_mode=\"zeros\",\n",
+    "                    prob=0.5,\n",
+    "                ),\n",
+    "                transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n",
+    "                transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n",
+    "            ]\n",
+    "        )\n",
+    "        \n",
+    "        val_transforms = transforms.Compose(\n",
+    "            [\n",
+    "                transforms.LoadImaged(keys=[\"image\"]),\n",
+    "                transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
+    "                transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, \n",
+    "                                                b_max=1.0, clip=True),\n",
+    "                transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n",
+    "                transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n",
+    "            ]\n",
+    "        )\n",
+    "        \n",
+    "        train_data = MedNISTDataset(root_dir=self.data_dir, section=\"training\", download=True, seed=0)\n",
+    "        train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
+    "        val_data = MedNISTDataset(root_dir=self.data_dir, section=\"validation\", download=True, seed=0)\n",
+    "        val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
+    "        \n",
+    "        self.train_ds = CacheDataset(data=train_datalist, transform=train_transforms)\n",
+    "        self.val_ds = CacheDataset(data=val_datalist, transform=val_transforms)\n",
+    "        \n",
+    "    def train_dataloader(self):\n",
+    "        return DataLoader(self.train_ds, batch_size=16, shuffle=True,\n",
+    "                          num_workers=4, persistent_workers=True)\n",
+    "        \n",
+    "    def val_dataloader(self):\n",
+    "        return DataLoader(self.val_ds, batch_size=16, shuffle=False,\n",
+    "                          num_workers=4)\n",
+    "                          \n",
+    "    def _compute_loss_generator(self, images, reconstruction, z_mu, z_sigma):\n",
+    "        recons_loss = F.l1_loss(reconstruction.float(), images.float())\n",
+    "        p_loss = self.perceptual_loss(reconstruction.float(), images.float())\n",
+    "        kl_loss = 0.5 * torch.sum(z_mu.pow(2) + z_sigma.pow(2) - torch.log(z_sigma.pow(2)) - 1, dim=[1, 2, 3])\n",
+    "        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]\n",
+    "        loss_g = recons_loss + (self.kl_weight * kl_loss) + (self.perceptual_weight * p_loss)\n",
+    "        return loss_g,recons_loss\n",
+    "    \n",
+    "    def _compute_loss_discriminator(self, reconstruction):\n",
+    "        logits_fake = discriminator(reconstruction.contiguous().detach())[-1]\n",
+    "        loss_d_fake = self.adv_loss(logits_fake, target_is_real=False, for_discriminator=True)\n",
+    "        logits_real = discriminator(images.contiguous().detach())[-1]\n",
+    "        loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)\n",
+    "        discriminator_loss = (loss_d_fake + loss_d_real) * 0.5\n",
+    "        loss_d = self.adv_weight * discriminator_loss\n",
+    "        return loss_d, discriminator_loss\n",
+    "        \n",
+    "    def training_step(self, batch, batch_idx):\n",
+    "        optimizer_g, optimizer_d = self.optimizers()\n",
+    "        images = batch[\"image\"]\n",
+    "        reconstruction, z_mu, z_sigma = self.forward(images)\n",
+    "        loss_g, recons_loss = self._compute_loss_generator(images, reconstruction, z_mu, z_sigma)\n",
+    "        self.log(\"recons_loss\", recons_loss, prog_bar=True)\n",
+    "\n",
+    "        if self.current_epoch > self.autoencoder_warm_up_n_epochs:\n",
+    "            logits_fake = discriminator(reconstruction.contiguous().float())[-1]\n",
+    "            generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)\n",
+    "            loss_g += adv_weight * generator_loss\n",
+    "                          \n",
+    "\n",
+    "        self.log(\"loss_g\", loss_g, prog_bar=True)\n",
+    "        self.manual_backward(loss_g)\n",
+    "        optimizer_g.step()\n",
+    "        optimizer_g.zero_grad()\n",
+    "        self.untoggle_optimizer(optimizer_g)\n",
+    "\n",
+    "        if self.current_epoch > self.autoencoder_warm_up_n_epochs:\n",
+    "            loss_d, discriminator_loss = self._compute_loss_discriminator(reconstruction)\n",
+    "            self.log(\"loss_d\", loss_d, prog_bar=True)\n",
+    "            self.manual_backward(loss_d)\n",
+    "            optimizer_d.step()\n",
+    "            optimizer_d.zero_grad()\n",
+    "            self.untoggle_optimizer(optimizer_d)\n",
+    "\n",
+    "                          \n",
+    "        if self.current_epoch > self.autoencoder_warm_up_n_epochs:\n",
+    "            gen_epoch_loss += generator_loss.item()\n",
+    "            disc_epoch_loss += discriminator_loss.item()\n",
+    "            self.log(\"gen_loss\", gen_loss, prog_bar=True)\n",
+    "            self.log(\"disc_loss\", disc_loss, prog_bar=True)\n",
+    "                          \n",
+    "                                    \n",
+    "    def validation_step(self, batch, batch_idx):\n",
+    "        images = batch[\"image\"]\n",
+    "        reconstruction, z_mu, z_sigma = self.autoencoderkl(images)\n",
+    "        recons_loss = F.l1_loss(images.float(), reconstruction.float())\n",
+    "        self.log(\"loss_d\", recons_loss, prog_bar=True)\n",
+    "                              \n",
+    "\n",
+    "    def configure_optimizers(self):\n",
+    "        optimizer_g = torch.optim.Adam(self.autoencoderkl.parameters(), lr=5e-5)\n",
+    "        optimizer_d = torch.optim.Adam(self.discriminator.parameters(), lr=1e-4)\n",
+    "        return [optimizer_g, optimizer_d], []\n",
+    "                          \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c16de505",
+   "metadata": {},
+   "source": [
+    "## Train Autoencoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "fd8ccebf",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n",
+      "Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-04-06 17:32:20,399 - GPU available: True (cuda), used: True\n",
+      "2023-04-06 17:32:20,401 - TPU available: False, using: 0 TPU cores\n",
+      "2023-04-06 17:32:20,402 - IPU available: False, using: 0 IPUs\n",
+      "2023-04-06 17:32:20,403 - HPU available: False, using: 0 HPUs\n",
+      "2023-04-06 17:32:20,576 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-04-06 17:32:20,578 - INFO - File exists: /tmp/tmpqpsolhvx/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-04-06 17:32:20,579 - INFO - Non-empty folder exists in /tmp/tmpqpsolhvx/MedNIST, skipped extracting.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
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+      "2023-04-06 17:33:06,469 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset:  93%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 908/972 [00:02<00:00, 458.87it/s]\u001b[A\n",
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+      "Checkpoint directory /tmp/tmpqpsolhvx exists and is not empty.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-04-06 17:33:27,505 - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
+      "2023-04-06 17:33:27,519 - \n",
+      "  | Name            | Type                 | Params\n",
+      "---------------------------------------------------------\n",
+      "0 | autoencoderkl   | AutoencoderKL        | 75.1 M\n",
+      "1 | discriminator   | PatchDiscriminator   | 2.8 M \n",
+      "2 | perceptual_loss | PerceptualLoss       | 2.5 M \n",
+      "3 | adv_loss        | PatchAdversarialLoss | 0     \n",
+      "---------------------------------------------------------\n",
+      "77.8 M    Trainable params\n",
+      "2.5 M     Non-trainable params\n",
+      "80.3 M    Total params\n",
+      "321.225   Total estimated model params size (MB)\n",
+      "Sanity Checking: 0it [00:00, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Your `val_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1:   2%|█▊                                                                                  | 11/500 [00:07<05:36,  1.45it/s, v_num=7, recons_loss=0.0389, loss_g=0.0403]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Detected KeyboardInterrupt, attempting graceful shutdown...\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_epochs = 75\n",
+    "val_interval = 10\n",
+    "\n",
+    "                          \n",
+    "# initialise the LightningModule\n",
+    "net = AutoEnconder()\n",
+    "\n",
+    "# set up checkpoints\n",
+    "\n",
+    "checkpoint_callback = ModelCheckpoint(dirpath=root_dir, filename=\"best_metric_model\")\n",
+    "\n",
+    "                         \n",
+    "# initialise Lightning's trainer.\n",
+    "trainer = pl.Trainer(devices=1,\n",
+    "                     max_epochs=n_epochs,\n",
+    "                     check_val_every_n_epoch=val_interval,\n",
+    "                     callbacks=checkpoint_callback,\n",
+    "                     default_root_dir=root_dir)\n",
+    "\n",
+    "# train\n",
+    "trainer.fit(net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7108b87",
+   "metadata": {},
+   "source": [
+    "## Rescaling factor\n",
+    "\n",
+    "As mentioned in Rombach et al. [1] Section 4.3.2 and D.1, the signal-to-noise ratio (induced by the scale of the latent space) became crucial in image-to-image translation models (such as the ones used for super-resolution). For this reason, we will compute the component-wise standard deviation to be used as scaling factor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "ccb6ba9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scaling factor set to 0.8888660669326782\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_loader = net.train_dataloader()\n",
+    "check_data = first(train_loader)\n",
+    "with torch.no_grad():\n",
+    "    with autocast(enabled=True):\n",
+    "        z = net.autoencoderkl.encode_stage_2_inputs(check_data[\"image\"].to(net.device))\n",
+    "\n",
+    "print(f\"Scaling factor set to {1/torch.std(z)}\")\n",
+    "scale_factor = 1 / torch.std(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce628878",
+   "metadata": {},
+   "source": [
+    "## Define the LightningModule for AutoEncoder (transforms, network, loaders, etc)\n",
+    "The LightningModule contains a refactoring of your training code. The following module is a refactoring of the code in 2d_stable_diffusion_v2_super_resolution-lightning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b4e1df0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DiffusionUNET(pl.LightningModule):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.data_dir = root\n",
+    "        self.unet = DiffusionModelUNet(\n",
+    "                                spatial_dims=2,\n",
+    "                                in_channels=4,\n",
+    "                                out_channels=3,\n",
+    "                                num_res_blocks=2,\n",
+    "                                num_channels=(256, 256, 512, 1024),\n",
+    "                                attention_levels=(False, False, True, True),\n",
+    "                                num_head_channels=(0, 0, 64, 64),\n",
+    "                            )\n",
+    "        self.max_noise_level = 350\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.unet(x)\n",
+    "    \n",
+    "    \n",
+    "    def prepare_data(self):\n",
+    "        image_size = 64\n",
+    "        train_transforms = transforms.Compose(\n",
+    "            [\n",
+    "                transforms.LoadImaged(keys=[\"image\"]),\n",
+    "                transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
+    "                transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0,\n",
+    "                                                b_max=1.0, clip=True),\n",
+    "                transforms.RandAffined(\n",
+    "                    keys=[\"image\"],\n",
+    "                    rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n",
+    "                    translate_range=[(-1, 1), (-1, 1)],\n",
+    "                    scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n",
+    "                    spatial_size=[image_size, image_size],\n",
+    "                    padding_mode=\"zeros\",\n",
+    "                    prob=0.5,\n",
+    "                ),\n",
+    "                transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n",
+    "                transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n",
+    "            ]\n",
+    "        )\n",
+    "        \n",
+    "        val_transforms = transforms.Compose(\n",
+    "            [\n",
+    "                transforms.LoadImaged(keys=[\"image\"]),\n",
+    "                transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
+    "                transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, \n",
+    "                                                b_max=1.0, clip=True),\n",
+    "                transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n",
+    "                transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n",
+    "            ]\n",
+    "        )\n",
+    "        \n",
+    "        train_data = MedNISTDataset(root_dir=self.data_dir, section=\"training\", download=True, seed=0)\n",
+    "        train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
+    "        val_data = MedNISTDataset(root_dir=self.data_dir, section=\"validation\", download=True, seed=0)\n",
+    "        val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
+    "        \n",
+    "        self.train_ds = CacheDataset(data=train_datalist, transform=train_transforms)\n",
+    "        self.val_ds = CacheDataset(data=val_datalist, transform=val_transforms)\n",
+    "        \n",
+    "    def train_dataloader(self):\n",
+    "        return DataLoader(self.train_ds, batch_size=16, shuffle=True,\n",
+    "                          num_workers=4, persistent_workers=True)\n",
+    "        \n",
+    "    def val_dataloader(self):\n",
+    "        return DataLoader(self.val_ds, batch_size=16, shuffle=True,\n",
+    "                          num_workers=4)\n",
+    "\n",
+    "    def training_step(self, batch, batch_idx):\n",
+    "        images = batch[\"image\"]\n",
+    "        low_res_image = batch[\"low_res_image\"]\n",
+    "        optimizer = self.optimizers()\n",
+    "        scheduler, low_res_scheduler = self.schedulers()\n",
+    "        \n",
+    "        latent = net.autoencoderkl.encode_stage_2_inputs(images) * scale_factor\n",
+    "\n",
+    "        # Noise augmentation\n",
+    "        noise = torch.randn_like(latent)\n",
+    "        low_res_noise = torch.randn_like(low_res_image)\n",
+    "        timesteps = torch.randint(0, scheduler.num_train_timesteps, (latent.shape[0],),\n",
+    "                                  device=latent.device).long()\n",
+    "        low_res_timesteps = torch.randint(\n",
+    "            0, max_noise_level, (low_res_image.shape[0],), device=low_res_image.device\n",
+    "        ).long()\n",
+    "\n",
+    "        noisy_latent = scheduler.add_noise(original_samples=latent, noise=noise, timesteps=timesteps)\n",
+    "        noisy_low_res_image = scheduler.add_noise(\n",
+    "            original_samples=low_res_image, noise=low_res_noise, timesteps=low_res_timesteps\n",
+    "        )\n",
+    "\n",
+    "        latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n",
+    "\n",
+    "        noise_pred = self.forward(x=latent_model_input, timesteps=timesteps, class_labels=low_res_timesteps)\n",
+    "        loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+    "        \n",
+    "       \n",
+    "        self.log(\"loss\", loss, prog_bar=True)\n",
+    "        optimizer.zero_grad()\n",
+    "        self.manual_backward(loss)\n",
+    "        optimizer.step()\n",
+    "        scheduler.step()\n",
+    "        low_res_scheduler.step()\n",
+    "\n",
+    "        \n",
+    "    def validation_step(self, batch, batch_idx):\n",
+    "        \n",
+    "        images = batch[\"image\"].to(device)\n",
+    "        low_res_image = batch[\"low_res_image\"].to(device)\n",
+    "        latent = net.autoencoderkl.encode_stage_2_inputs(images) * scale_factor\n",
+    "        \n",
+    "        # Noise augmentation\n",
+    "        noise = torch.randn_like(latent).to(device)\n",
+    "        low_res_noise = torch.randn_like(low_res_image).to(device)\n",
+    "        timesteps = torch.randint(\n",
+    "            0, scheduler.num_train_timesteps, (latent.shape[0],), device=latent.device\n",
+    "        ).long()\n",
+    "        low_res_timesteps = torch.randint(\n",
+    "            0, max_noise_level, (low_res_image.shape[0],), device=low_res_image.device\n",
+    "        ).long()\n",
+    "\n",
+    "        noisy_latent = scheduler.add_noise(original_samples=latent, noise=noise, timesteps=timesteps)\n",
+    "        noisy_low_res_image = scheduler.add_noise(\n",
+    "            original_samples=low_res_image, noise=low_res_noise, timesteps=low_res_timesteps\n",
+    "        )\n",
+    "\n",
+    "        latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n",
+    "        noise_pred = self.forward(x=latent_model_input, timesteps=timesteps, class_labels=low_res_timesteps)\n",
+    "        loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+    "\n",
+    "        val_loss += loss.item()\n",
+    "    def configure_optimizers(self):\n",
+    "        optimizer = torch.optim.Adam(unet.parameters(), lr=5e-5)\n",
+    "        scheduler = DDPMScheduler(num_train_timesteps=1000, \n",
+    "                                   beta_schedule=\"linear\", beta_start=0.0015, beta_end=0.0195)\n",
+    "        low_res_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule=\"linear\",\n",
+    "                                          beta_start=0.0015, beta_end=0.0195)\n",
+    "\n",
+    "        return [optimizer], [scheduler, low_res_scheduler]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b386a0c2",
+   "metadata": {},
+   "source": [
+    "## Train Diffusion Model\n",
+    "\n",
+    "In order to train the diffusion model to perform super-resolution, we will need to concatenate the latent representation of the high-resolution with the low-resolution image. For this, we create a Diffusion model with `in_channels=4`. Since only the outputted latent representation is interesting, we set `out_channels=3`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fb22b1a",
+   "metadata": {},
+   "source": [
+    "As mentioned, we will use the conditioned augmentation (introduced in [2] section 3 and used on Stable Diffusion Upscalers and Imagen Video [3] Section 2.5) as it has been shown critical for cascaded diffusion models, as well for super-resolution tasks. For this, we apply Gaussian noise augmentation to the low-resolution images. We will use a scheduler `low_res_scheduler` to add this noise, with the `t` step defining the signal-to-noise ratio and use the `t` value to condition the diffusion model (inputted using `class_labels` argument)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25d9d3e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa959db4",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "optimizer = torch.optim.Adam(unet.parameters(), lr=5e-5)\n",
+    "\n",
+    "scaler_diffusion = GradScaler()\n",
+    "\n",
+    "n_epochs = 200\n",
+    "val_interval = 20\n",
+    "epoch_loss_list = []\n",
+    "val_epoch_loss_list = []\n",
+    "\n",
+    "for epoch in range(n_epochs):\n",
+    "    unet.train()\n",
+    "    autoencoderkl.eval()\n",
+    "    epoch_loss = 0\n",
+    "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=110)\n",
+    "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
+    "    for step, batch in progress_bar:\n",
+    "        images = batch[\"image\"].to(device)\n",
+    "        low_res_image = batch[\"low_res_image\"].to(device)\n",
+    "        optimizer.zero_grad(set_to_none=True)\n",
+    "\n",
+    "        with autocast(enabled=True):\n",
+    "            with torch.no_grad():\n",
+    "                latent = autoencoderkl.encode_stage_2_inputs(images) * scale_factor\n",
+    "\n",
+    "            # Noise augmentation\n",
+    "            noise = torch.randn_like(latent).to(device)\n",
+    "            low_res_noise = torch.randn_like(low_res_image).to(device)\n",
+    "            timesteps = torch.randint(0, scheduler.num_train_timesteps, (latent.shape[0],), device=latent.device).long()\n",
+    "            low_res_timesteps = torch.randint(\n",
+    "                0, max_noise_level, (low_res_image.shape[0],), device=low_res_image.device\n",
+    "            ).long()\n",
+    "\n",
+    "            noisy_latent = scheduler.add_noise(original_samples=latent, noise=noise, timesteps=timesteps)\n",
+    "            noisy_low_res_image = scheduler.add_noise(\n",
+    "                original_samples=low_res_image, noise=low_res_noise, timesteps=low_res_timesteps\n",
+    "            )\n",
+    "\n",
+    "            latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n",
+    "\n",
+    "            noise_pred = unet(x=latent_model_input, timesteps=timesteps, class_labels=low_res_timesteps)\n",
+    "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+    "\n",
+    "        scaler_diffusion.scale(loss).backward()\n",
+    "        scaler_diffusion.step(optimizer)\n",
+    "        scaler_diffusion.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",
+    "        unet.eval()\n",
+    "        val_loss = 0\n",
+    "        for val_step, batch in enumerate(val_loader, start=1):\n",
+    "            images = batch[\"image\"].to(device)\n",
+    "            low_res_image = batch[\"low_res_image\"].to(device)\n",
+    "\n",
+    "            with torch.no_grad():\n",
+    "                with autocast(enabled=True):\n",
+    "                    latent = autoencoderkl.encode_stage_2_inputs(images) * scale_factor\n",
+    "                    # Noise augmentation\n",
+    "                    noise = torch.randn_like(latent).to(device)\n",
+    "                    low_res_noise = torch.randn_like(low_res_image).to(device)\n",
+    "                    timesteps = torch.randint(\n",
+    "                        0, scheduler.num_train_timesteps, (latent.shape[0],), device=latent.device\n",
+    "                    ).long()\n",
+    "                    low_res_timesteps = torch.randint(\n",
+    "                        0, max_noise_level, (low_res_image.shape[0],), device=low_res_image.device\n",
+    "                    ).long()\n",
+    "\n",
+    "                    noisy_latent = scheduler.add_noise(original_samples=latent, noise=noise, timesteps=timesteps)\n",
+    "                    noisy_low_res_image = scheduler.add_noise(\n",
+    "                        original_samples=low_res_image, noise=low_res_noise, timesteps=low_res_timesteps\n",
+    "                    )\n",
+    "\n",
+    "                    latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n",
+    "                    noise_pred = unet(x=latent_model_input, timesteps=timesteps, class_labels=low_res_timesteps)\n",
+    "                    loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+    "\n",
+    "            val_loss += loss.item()\n",
+    "        val_loss /= val_step\n",
+    "        val_epoch_loss_list.append(val_loss)\n",
+    "        print(f\"Epoch {epoch} val loss: {val_loss:.4f}\")\n",
+    "\n",
+    "        # Sampling image during training\n",
+    "        sampling_image = low_res_image[0].unsqueeze(0)\n",
+    "        latents = torch.randn((1, 3, 16, 16)).to(device)\n",
+    "        low_res_noise = torch.randn((1, 1, 16, 16)).to(device)\n",
+    "        noise_level = 20\n",
+    "        noise_level = torch.Tensor((noise_level,)).long().to(device)\n",
+    "        noisy_low_res_image = scheduler.add_noise(\n",
+    "            original_samples=sampling_image,\n",
+    "            noise=low_res_noise,\n",
+    "            timesteps=torch.Tensor((noise_level,)).long().to(device),\n",
+    "        )\n",
+    "\n",
+    "        scheduler.set_timesteps(num_inference_steps=1000)\n",
+    "        for t in tqdm(scheduler.timesteps, ncols=110):\n",
+    "            with torch.no_grad():\n",
+    "                with autocast(enabled=True):\n",
+    "                    latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)\n",
+    "                    noise_pred = unet(\n",
+    "                        x=latent_model_input, timesteps=torch.Tensor((t,)).to(device), class_labels=noise_level\n",
+    "                    )\n",
+    "                latents, _ = scheduler.step(noise_pred, t, latents)\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            decoded = autoencoderkl.decode_stage_2_outputs(latents / scale_factor)\n",
+    "\n",
+    "        low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode=\"bicubic\")\n",
+    "        plt.figure(figsize=(2, 2))\n",
+    "        plt.style.use(\"default\")\n",
+    "        plt.imshow(\n",
+    "            torch.cat([images[0, 0].cpu(), low_res_bicubic[0, 0].cpu(), decoded[0, 0].cpu()], dim=1),\n",
+    "            vmin=0,\n",
+    "            vmax=1,\n",
+    "            cmap=\"gray\",\n",
+    "        )\n",
+    "        plt.tight_layout()\n",
+    "        plt.axis(\"off\")\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30f24595",
+   "metadata": {},
+   "source": [
+    "### Plotting sampling example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "155be091",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sampling image during training\n",
+    "unet.eval()\n",
+    "num_samples = 3\n",
+    "validation_batch = first(val_loader)\n",
+    "\n",
+    "images = validation_batch[\"image\"].to(device)\n",
+    "sampling_image = validation_batch[\"low_res_image\"].to(device)[:num_samples]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aaf61020",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "latents = torch.randn((num_samples, 3, 16, 16)).to(device)\n",
+    "low_res_noise = torch.randn((num_samples, 1, 16, 16)).to(device)\n",
+    "noise_level = 10\n",
+    "noise_level = torch.Tensor((noise_level,)).long().to(device)\n",
+    "noisy_low_res_image = scheduler.add_noise(\n",
+    "    original_samples=sampling_image, noise=low_res_noise, timesteps=torch.Tensor((noise_level,)).long().to(device)\n",
+    ")\n",
+    "scheduler.set_timesteps(num_inference_steps=1000)\n",
+    "for t in tqdm(scheduler.timesteps, ncols=110):\n",
+    "    with torch.no_grad():\n",
+    "        with autocast(enabled=True):\n",
+    "            latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)\n",
+    "            noise_pred = unet(x=latent_model_input, timesteps=torch.Tensor((t,)).to(device), class_labels=noise_level)\n",
+    "\n",
+    "        # 2. compute previous image: x_t -> x_t-1\n",
+    "        latents, _ = scheduler.step(noise_pred, t, latents)\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    decoded = autoencoderkl.decode_stage_2_outputs(latents / scale_factor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32e16e69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode=\"bicubic\")\n",
+    "fig, axs = plt.subplots(num_samples, 3, figsize=(8, 8))\n",
+    "axs[0, 0].set_title(\"Original image\")\n",
+    "axs[0, 1].set_title(\"Low-resolution Image\")\n",
+    "axs[0, 2].set_title(\"Outputted image\")\n",
+    "for i in range(0, num_samples):\n",
+    "    axs[i, 0].imshow(images[i, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "    axs[i, 0].axis(\"off\")\n",
+    "    axs[i, 1].imshow(low_res_bicubic[i, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "    axs[i, 1].axis(\"off\")\n",
+    "    axs[i, 2].imshow(decoded[i, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "    axs[i, 2].axis(\"off\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fa52acc",
+   "metadata": {},
+   "source": [
+    "### Clean-up data directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a6f6d5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if directory is None:\n",
+    "    shutil.rmtree(root_dir)"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "formats": "ipynb,py:percent"
+  },
+  "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": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 5501053ac4141c9f88eda8d5ad5080c9ea1a259d Mon Sep 17 00:00:00 2001
From: OeslleLucena <oeslle.lucena@gmail.com>
Date: Fri, 7 Apr 2023 18:58:49 +0100
Subject: [PATCH 02/13] [WIP] full draft done

---
 ...fusion_v2_super_resolution-lightning.ipynb | 1421 +++++------------
 ...diffusion_v2_super_resolution-lightning.py |  479 ++++++
 2 files changed, 917 insertions(+), 983 deletions(-)
 create mode 100644 tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py

diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
index a03c168e..c722a653 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "77f7e633",
    "metadata": {},
    "outputs": [],
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "de71fe08",
    "metadata": {},
    "outputs": [
@@ -84,14 +84,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-06 16:56:02,588 - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:\n",
+      "2023-04-07 18:33:20,193 - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:\n",
       "    PyTorch 1.13.1+cu117 with CUDA 1107 (you have 1.12.1)\n",
       "    Python  3.8.16 (you have 3.8.16)\n",
       "  Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)\n",
       "  Memory-efficient attention, SwiGLU, sparse and more won't be available.\n",
       "  Set XFORMERS_MORE_DETAILS=1 for more details\n",
-      "2023-04-06 16:56:04,614 - Created a temporary directory at /tmp/tmp0zwlum7_\n",
-      "2023-04-06 16:56:04,617 - Writing /tmp/tmp0zwlum7_/_remote_module_non_scriptable.py\n",
+      "2023-04-07 18:33:21,929 - Created a temporary directory at /tmp/tmpq2wlch6q\n",
+      "2023-04-07 18:33:21,932 - Writing /tmp/tmpq2wlch6q/_remote_module_non_scriptable.py\n",
       "MONAI version: 1.2.dev2304\n",
       "Numpy version: 1.23.5\n",
       "Pytorch version: 1.12.1\n",
@@ -153,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "9f0a17bc",
    "metadata": {},
    "outputs": [],
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "ded618a7",
    "metadata": {},
    "outputs": [
@@ -181,7 +181,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/tmpqpsolhvx\n"
+      "/tmp/tmp1sbvw3_e\n"
      ]
     }
    ],
@@ -191,18 +191,70 @@
     "print(root_dir)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "46bafb78",
+   "metadata": {},
+   "source": [
+    "## Setup utils functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4f8eff03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_train_transforms():\n",
+    "    image_size = 64\n",
+    "    train_transforms = transforms.Compose(\n",
+    "    [\n",
+    "        transforms.LoadImaged(keys=[\"image\"]),\n",
+    "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
+    "        transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0,\n",
+    "                                        b_max=1.0, clip=True),\n",
+    "        transforms.RandAffined(\n",
+    "            keys=[\"image\"],\n",
+    "            rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n",
+    "            translate_range=[(-1, 1), (-1, 1)],\n",
+    "            scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n",
+    "            spatial_size=[image_size, image_size],\n",
+    "            padding_mode=\"zeros\",\n",
+    "            prob=0.5,\n",
+    "        ),\n",
+    "        transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n",
+    "        transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n",
+    "    ]\n",
+    "    )\n",
+    "    return train_transforms\n",
+    "\n",
+    "def get_val_transforms():\n",
+    "    val_transforms = transforms.Compose(\n",
+    "        [\n",
+    "            transforms.LoadImaged(keys=[\"image\"]),\n",
+    "            transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
+    "            transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, \n",
+    "                                            b_max=1.0, clip=True),\n",
+    "            transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n",
+    "            transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n",
+    "        ]\n",
+    "    )\n",
+    "    return val_transforms\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "d80e045b",
    "metadata": {},
    "source": [
     "## Define the LightningModule for AutoEncoder (transforms, network, loaders, etc)\n",
-    "The LightningModule contains a refactoring of your training code. The following module is a refactoring of the code in 2d_stable_diffusion_v2_super_resolution-lightning\n"
+    "The LightningModule contains a refactoring of your training code. The following module is a reformatiing of the code in 2d_stable_diffusion_v2_super_resolution.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 7,
    "id": "d5d1caff",
    "metadata": {
     "scrolled": false
@@ -235,37 +287,8 @@
     "        return self.autoencoderkl(z)\n",
     "\n",
     "    def prepare_data(self):\n",
-    "        image_size = 64\n",
-    "        train_transforms = transforms.Compose(\n",
-    "            [\n",
-    "                transforms.LoadImaged(keys=[\"image\"]),\n",
-    "                transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
-    "                transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0,\n",
-    "                                                b_max=1.0, clip=True),\n",
-    "                transforms.RandAffined(\n",
-    "                    keys=[\"image\"],\n",
-    "                    rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n",
-    "                    translate_range=[(-1, 1), (-1, 1)],\n",
-    "                    scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n",
-    "                    spatial_size=[image_size, image_size],\n",
-    "                    padding_mode=\"zeros\",\n",
-    "                    prob=0.5,\n",
-    "                ),\n",
-    "                transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n",
-    "                transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n",
-    "            ]\n",
-    "        )\n",
-    "        \n",
-    "        val_transforms = transforms.Compose(\n",
-    "            [\n",
-    "                transforms.LoadImaged(keys=[\"image\"]),\n",
-    "                transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
-    "                transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, \n",
-    "                                                b_max=1.0, clip=True),\n",
-    "                transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n",
-    "                transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n",
-    "            ]\n",
-    "        )\n",
+    "        train_transforms = get_train_transforms()\n",
+    "        val_transforms = get_val_transforms()\n",
     "        \n",
     "        train_data = MedNISTDataset(root_dir=self.data_dir, section=\"training\", download=True, seed=0)\n",
     "        train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
@@ -305,7 +328,7 @@
     "        images = batch[\"image\"]\n",
     "        reconstruction, z_mu, z_sigma = self.forward(images)\n",
     "        loss_g, recons_loss = self._compute_loss_generator(images, reconstruction, z_mu, z_sigma)\n",
-    "        self.log(\"recons_loss\", recons_loss, prog_bar=True)\n",
+    "        self.log(\"recons_loss\", recons_loss, batch_size=16, prog_bar=True)\n",
     "\n",
     "        if self.current_epoch > self.autoencoder_warm_up_n_epochs:\n",
     "            logits_fake = discriminator(reconstruction.contiguous().float())[-1]\n",
@@ -313,7 +336,7 @@
     "            loss_g += adv_weight * generator_loss\n",
     "                          \n",
     "\n",
-    "        self.log(\"loss_g\", loss_g, prog_bar=True)\n",
+    "        self.log(\"loss_g\", loss_g, batch_size=16, prog_bar=True)\n",
     "        self.manual_backward(loss_g)\n",
     "        optimizer_g.step()\n",
     "        optimizer_g.zero_grad()\n",
@@ -321,7 +344,7 @@
     "\n",
     "        if self.current_epoch > self.autoencoder_warm_up_n_epochs:\n",
     "            loss_d, discriminator_loss = self._compute_loss_discriminator(reconstruction)\n",
-    "            self.log(\"loss_d\", loss_d, prog_bar=True)\n",
+    "            self.log(\"train_loss_d\", loss_d, batch_size=16, prog_bar=True)\n",
     "            self.manual_backward(loss_d)\n",
     "            optimizer_d.step()\n",
     "            optimizer_d.zero_grad()\n",
@@ -331,15 +354,15 @@
     "        if self.current_epoch > self.autoencoder_warm_up_n_epochs:\n",
     "            gen_epoch_loss += generator_loss.item()\n",
     "            disc_epoch_loss += discriminator_loss.item()\n",
-    "            self.log(\"gen_loss\", gen_loss, prog_bar=True)\n",
-    "            self.log(\"disc_loss\", disc_loss, prog_bar=True)\n",
+    "            self.log(\"gen_loss\", gen_loss, batch_size=16, prog_bar=True)\n",
+    "            self.log(\"disc_loss\", disc_loss, batch_size=16, prog_bar=True)\n",
     "                          \n",
     "                                    \n",
     "    def validation_step(self, batch, batch_idx):\n",
     "        images = batch[\"image\"]\n",
     "        reconstruction, z_mu, z_sigma = self.autoencoderkl(images)\n",
     "        recons_loss = F.l1_loss(images.float(), reconstruction.float())\n",
-    "        self.log(\"loss_d\", recons_loss, prog_bar=True)\n",
+    "        self.log(\"val_loss_d\", recons_loss, prog_bar=True)\n",
     "                              \n",
     "\n",
     "    def configure_optimizers(self):\n",
@@ -359,8 +382,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "fd8ccebf",
+   "execution_count": 8,
+   "id": "9d903aaa",
    "metadata": {
     "scrolled": true
    },
@@ -377,733 +400,86 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-06 17:32:20,399 - GPU available: True (cuda), used: True\n",
-      "2023-04-06 17:32:20,401 - TPU available: False, using: 0 TPU cores\n",
-      "2023-04-06 17:32:20,402 - IPU available: False, using: 0 IPUs\n",
-      "2023-04-06 17:32:20,403 - HPU available: False, using: 0 HPUs\n",
-      "2023-04-06 17:32:20,576 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-04-06 17:32:20,578 - INFO - File exists: /tmp/tmpqpsolhvx/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-04-06 17:32:20,579 - INFO - Non-empty folder exists in /tmp/tmpqpsolhvx/MedNIST, skipped extracting.\n"
-     ]
-    },
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+      "2023-04-07 18:33:24,966 - GPU available: True (cuda), used: True\n",
+      "2023-04-07 18:33:24,967 - TPU available: False, using: 0 TPU cores\n",
+      "2023-04-07 18:33:24,968 - IPU available: False, using: 0 IPUs\n",
+      "2023-04-07 18:33:24,968 - HPU available: False, using: 0 HPUs\n"
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+      "MedNIST.tar.gz: 59.0MB [00:02, 23.0MB/s]                                                                                                                                       "
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-      "Loading dataset:  79%|█████████████████████████████████████████████████████████████████████████████████████████████                         | 767/972 [00:01<00:00, 425.25it/s]\u001b[A\n",
-      "Loading dataset:  83%|██████████████████████████████████████████████████████████████████████████████████████████████████▎                   | 810/972 [00:01<00:00, 423.99it/s]\u001b[A\n",
-      "Loading dataset:  88%|███████████████████████████████████████████████████████████████████████████████████████████████████████▊              | 855/972 [00:02<00:00, 430.57it/s]\u001b[A\n"
+      "2023-04-07 18:34:35,829 - Missing logger folder: /tmp/tmp1sbvw3_e/lightning_logs\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset:  93%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████▏       | 908/972 [00:02<00:00, 458.87it/s]\u001b[A\n",
-      "Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 972/972 [00:02<00:00, 413.47it/s]\u001b[A\n",
-      "Checkpoint directory /tmp/tmpqpsolhvx exists and is not empty.\n"
+      "Checkpoint directory /tmp/tmp1sbvw3_e exists and is not empty.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-06 17:33:27,505 - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
-      "2023-04-06 17:33:27,519 - \n",
+      "2023-04-07 18:34:37,274 - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
+      "2023-04-07 18:34:37,290 - \n",
       "  | Name            | Type                 | Params\n",
       "---------------------------------------------------------\n",
       "0 | autoencoderkl   | AutoencoderKL        | 75.1 M\n",
@@ -1115,38 +491,117 @@
       "2.5 M     Non-trainable params\n",
       "80.3 M    Total params\n",
       "321.225   Total estimated model params size (MB)\n",
-      "Sanity Checking: 0it [00:00, ?it/s]"
+      "Sanity Checking DataLoader 0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00,  1.30it/s]"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Your `val_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n"
+      "Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 16. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|███████████████████████████████████████████████████████████████████████████████████| 500/500 [05:34<00:00,  1.50it/s, v_num=0, recons_loss=0.0421, loss_g=0.0443]\n",
+      "Validation: 0it [00:00, ?it/s]\u001b[A\n",
+      "Validation:   0%|                                                                                                                                       | 0/61 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   0%|                                                                                                                          | 0/61 [00:00<?, ?it/s]\u001b[A\n",
+      "Validation DataLoader 0:   2%|█▊                                                                                                                | 1/61 [00:00<00:02, 23.92it/s]\u001b[A\n",
+      "Validation DataLoader 0:   3%|███▋                                                                                                              | 2/61 [00:00<00:03, 17.60it/s]\u001b[A\n",
+      "Validation DataLoader 0:   5%|█████▌                                                                                                            | 3/61 [00:00<00:03, 18.64it/s]\u001b[A\n",
+      "Validation DataLoader 0:   7%|███████▍                                                                                                          | 4/61 [00:00<00:04, 11.49it/s]\u001b[A\n",
+      "Validation DataLoader 0:   8%|█████████▎                                                                                                        | 5/61 [00:00<00:06,  8.88it/s]\u001b[A\n",
+      "Validation DataLoader 0:  10%|███████████▏                                                                                                      | 6/61 [00:00<00:07,  7.71it/s]\u001b[A\n",
+      "Validation DataLoader 0:  11%|█████████████                                                                                                     | 7/61 [00:00<00:07,  7.04it/s]\u001b[A\n",
+      "Validation DataLoader 0:  13%|██████████████▉                                                                                                   | 8/61 [00:01<00:08,  6.62it/s]\u001b[A\n",
+      "Validation DataLoader 0:  15%|████████████████▊                                                                                                 | 9/61 [00:01<00:08,  6.32it/s]\u001b[A\n",
+      "Validation DataLoader 0:  16%|██████████████████▌                                                                                              | 10/61 [00:01<00:08,  6.10it/s]\u001b[A\n",
+      "Validation DataLoader 0:  18%|████████████████████▍                                                                                            | 11/61 [00:01<00:08,  5.94it/s]\u001b[A\n",
+      "Validation DataLoader 0:  20%|██████████████████████▏                                                                                          | 12/61 [00:02<00:08,  5.80it/s]\u001b[A\n",
+      "Validation DataLoader 0:  21%|████████████████████████                                                                                         | 13/61 [00:02<00:08,  5.69it/s]\u001b[A\n",
+      "Validation DataLoader 0:  23%|█████████████████████████▉                                                                                       | 14/61 [00:02<00:08,  5.60it/s]\u001b[A\n",
+      "Validation DataLoader 0:  25%|███████████████████████████▊                                                                                     | 15/61 [00:02<00:08,  5.53it/s]\u001b[A\n",
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+      "Validation DataLoader 0:  30%|█████████████████████████████████▎                                                                               | 18/61 [00:03<00:08,  5.36it/s]\u001b[A\n",
+      "Validation DataLoader 0:  31%|███████████████████████████████████▏                                                                             | 19/61 [00:03<00:07,  5.32it/s]\u001b[A\n",
+      "Validation DataLoader 0:  33%|█████████████████████████████████████                                                                            | 20/61 [00:03<00:07,  5.28it/s]\u001b[A\n",
+      "Validation DataLoader 0:  34%|██████████████████████████████████████▉                                                                          | 21/61 [00:04<00:07,  5.24it/s]\u001b[A\n",
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+      "Validation DataLoader 0:  66%|██████████████████████████████████████████████████████████████████████████                                       | 40/61 [00:08<00:04,  4.94it/s]\u001b[A\n",
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      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1:   2%|█▊                                                                                  | 11/500 [00:07<05:36,  1.45it/s, v_num=7, recons_loss=0.0389, loss_g=0.0403]"
+      "Validation DataLoader 0:  70%|███████████████████████████████████████████████████████████████████████████████▋                                 | 43/61 [00:08<00:03,  4.92it/s]\u001b[A\n",
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+      "Validation DataLoader 0:  93%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▌       | 57/61 [00:11<00:00,  4.85it/s]\u001b[A\n",
+      "Validation DataLoader 0:  95%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▍     | 58/61 [00:11<00:00,  4.85it/s]\u001b[A\n",
+      "Validation DataLoader 0:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎   | 59/61 [00:12<00:00,  4.84it/s]\u001b[A\n",
+      "Validation DataLoader 0:  98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 60/61 [00:12<00:00,  4.84it/s]\u001b[A\n",
+      "Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:12<00:00,  4.84it/s]\u001b[A"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Detected KeyboardInterrupt, attempting graceful shutdown...\n"
+      "Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 12. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0: 100%|████████████████████████████████████████████████████████████████| 500/500 [05:47<00:00,  1.44it/s, v_num=0, recons_loss=0.0421, loss_g=0.0443, val_loss_d=0.0415]\n",
+      "Epoch 0: 100%|████████████████████████████████████████████████████████████████| 500/500 [05:47<00:00,  1.44it/s, v_num=0, recons_loss=0.0421, loss_g=0.0443, val_loss_d=0.0415]\u001b[A2023-04-07 18:40:29,834 - `Trainer.fit` stopped: `max_epochs=1` reached.\n",
+      "Epoch 0: 100%|████████████████████████████████████████████████████████████████| 500/500 [05:49<00:00,  1.43it/s, v_num=0, recons_loss=0.0421, loss_g=0.0443, val_loss_d=0.0415]\n"
      ]
     }
    ],
    "source": [
-    "n_epochs = 75\n",
-    "val_interval = 10\n",
+    "n_epochs = 1\n",
+    "val_interval = 1\n",
     "\n",
     "                          \n",
     "# initialise the LightningModule\n",
-    "net = AutoEnconder()\n",
+    "ae_net = AutoEnconder()\n",
     "\n",
     "# set up checkpoints\n",
     "\n",
@@ -1161,7 +616,7 @@
     "                     default_root_dir=root_dir)\n",
     "\n",
     "# train\n",
-    "trainer.fit(net)"
+    "trainer.fit(ae_net)"
    ]
   },
   {
@@ -1176,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 9,
    "id": "ccb6ba9f",
    "metadata": {},
    "outputs": [
@@ -1184,41 +639,38 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Scaling factor set to 0.8888660669326782\n"
+      "Scaling factor set to 0.48439180850982666\n"
      ]
     }
    ],
    "source": [
-    "train_loader = net.train_dataloader()\n",
+    "train_loader = ae_net.train_dataloader()\n",
     "check_data = first(train_loader)\n",
-    "with torch.no_grad():\n",
-    "    with autocast(enabled=True):\n",
-    "        z = net.autoencoderkl.encode_stage_2_inputs(check_data[\"image\"].to(net.device))\n",
-    "\n",
+    "z = ae_net.autoencoderkl.train(mode=False).encode_stage_2_inputs(check_data[\"image\"].to(ae_net.device))\n",
     "print(f\"Scaling factor set to {1/torch.std(z)}\")\n",
     "scale_factor = 1 / torch.std(z)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ce628878",
+   "id": "3baa2b0f",
    "metadata": {},
    "source": [
-    "## Define the LightningModule for AutoEncoder (transforms, network, loaders, etc)\n",
-    "The LightningModule contains a refactoring of your training code. The following module is a refactoring of the code in 2d_stable_diffusion_v2_super_resolution-lightning"
+    "## Define the LightningModule for DiffusionModelUnet (transforms, network, loaders, etc)\n",
+    "The LightningModule contains a refactoring of your training code. The following module is a reformatiing of the code in 2d_stable_diffusion_v2_super_resolution."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "0b4e1df0",
+   "execution_count": 10,
+   "id": "731034ec",
    "metadata": {},
    "outputs": [],
    "source": [
     "class DiffusionUNET(pl.LightningModule):\n",
     "    def __init__(self):\n",
     "        super().__init__()\n",
-    "        self.data_dir = root\n",
+    "        self.data_dir = root_dir\n",
     "        self.unet = DiffusionModelUNet(\n",
     "                                spatial_dims=2,\n",
     "                                in_channels=4,\n",
@@ -1229,47 +681,28 @@
     "                                num_head_channels=(0, 0, 64, 64),\n",
     "                            )\n",
     "        self.max_noise_level = 350\n",
+    "        self.scheduler = DDPMScheduler(num_train_timesteps=1000, \n",
+    "                          beta_schedule=\"linear\",\n",
+    "                          beta_start=0.0015,\n",
+    "                          beta_end=0.0195)\n",
+    "        self.z = ae_net.autoencoderkl.train(mode=False)\n",
+    "\n",
     "\n",
-    "    def forward(self, x):\n",
-    "        return self.unet(x)\n",
+    "    def forward(self, x, timesteps, low_res_timesteps):\n",
+    "        return self.unet(x=x, \n",
+    "                         timesteps=timesteps,\n",
+    "                         class_labels=low_res_timesteps)\n",
     "    \n",
     "    \n",
     "    def prepare_data(self):\n",
-    "        image_size = 64\n",
-    "        train_transforms = transforms.Compose(\n",
-    "            [\n",
-    "                transforms.LoadImaged(keys=[\"image\"]),\n",
-    "                transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
-    "                transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0,\n",
-    "                                                b_max=1.0, clip=True),\n",
-    "                transforms.RandAffined(\n",
-    "                    keys=[\"image\"],\n",
-    "                    rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],\n",
-    "                    translate_range=[(-1, 1), (-1, 1)],\n",
-    "                    scale_range=[(-0.05, 0.05), (-0.05, 0.05)],\n",
-    "                    spatial_size=[image_size, image_size],\n",
-    "                    padding_mode=\"zeros\",\n",
-    "                    prob=0.5,\n",
-    "                ),\n",
-    "                transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n",
-    "                transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n",
-    "            ]\n",
-    "        )\n",
-    "        \n",
-    "        val_transforms = transforms.Compose(\n",
-    "            [\n",
-    "                transforms.LoadImaged(keys=[\"image\"]),\n",
-    "                transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
-    "                transforms.ScaleIntensityRanged(keys=[\"image\"], a_min=0.0, a_max=255.0, b_min=0.0, \n",
-    "                                                b_max=1.0, clip=True),\n",
-    "                transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"low_res_image\"]),\n",
-    "                transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n",
-    "            ]\n",
-    "        )\n",
+    "        train_transforms = get_train_transforms()\n",
+    "        val_transforms = get_val_transforms()\n",
     "        \n",
-    "        train_data = MedNISTDataset(root_dir=self.data_dir, section=\"training\", download=True, seed=0)\n",
+    "        train_data = MedNISTDataset(root_dir=self.data_dir, section=\"training\",\n",
+    "                                    download=True, seed=0)\n",
     "        train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
-    "        val_data = MedNISTDataset(root_dir=self.data_dir, section=\"validation\", download=True, seed=0)\n",
+    "        val_data = MedNISTDataset(root_dir=self.data_dir, section=\"validation\",\n",
+    "                                  download=True, seed=0)\n",
     "        val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
     "        \n",
     "        self.train_ds = CacheDataset(data=train_datalist, transform=train_transforms)\n",
@@ -1282,77 +715,50 @@
     "    def val_dataloader(self):\n",
     "        return DataLoader(self.val_ds, batch_size=16, shuffle=True,\n",
     "                          num_workers=4)\n",
-    "\n",
-    "    def training_step(self, batch, batch_idx):\n",
+    "    \n",
+    "    def _calculate_loss(self, batch, batch_idx):\n",
     "        images = batch[\"image\"]\n",
-    "        low_res_image = batch[\"low_res_image\"]\n",
+    "        low_res_image = batch[\"low_res_image\"]       \n",
+    "        latent = self.z.encode_stage_2_inputs(images) * scale_factor\n",
+    "        latent =  latent.detach() # avoid adding this to graph.\n",
     "        optimizer = self.optimizers()\n",
-    "        scheduler, low_res_scheduler = self.schedulers()\n",
     "        \n",
-    "        latent = net.autoencoderkl.encode_stage_2_inputs(images) * scale_factor\n",
-    "\n",
     "        # Noise augmentation\n",
     "        noise = torch.randn_like(latent)\n",
     "        low_res_noise = torch.randn_like(low_res_image)\n",
-    "        timesteps = torch.randint(0, scheduler.num_train_timesteps, (latent.shape[0],),\n",
+    "        timesteps = torch.randint(0, self.scheduler.num_train_timesteps, (latent.shape[0],),\n",
     "                                  device=latent.device).long()\n",
     "        low_res_timesteps = torch.randint(\n",
-    "            0, max_noise_level, (low_res_image.shape[0],), device=low_res_image.device\n",
+    "            0, self.max_noise_level, (low_res_image.shape[0],), device=latent.device\n",
     "        ).long()\n",
     "\n",
-    "        noisy_latent = scheduler.add_noise(original_samples=latent, noise=noise, timesteps=timesteps)\n",
-    "        noisy_low_res_image = scheduler.add_noise(\n",
-    "            original_samples=low_res_image, noise=low_res_noise, timesteps=low_res_timesteps\n",
+    "        noisy_latent = self.scheduler.add_noise(original_samples=latent, \n",
+    "                                           noise=noise, timesteps=timesteps)\n",
+    "        noisy_low_res_image = self.scheduler.add_noise(\n",
+    "            original_samples=low_res_image, noise=low_res_noise, \n",
+    "            timesteps=low_res_timesteps\n",
     "        )\n",
     "\n",
     "        latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n",
     "\n",
-    "        noise_pred = self.forward(x=latent_model_input, timesteps=timesteps, class_labels=low_res_timesteps)\n",
+    "        noise_pred = self.forward(latent_model_input, timesteps, low_res_timesteps)\n",
     "        loss = F.mse_loss(noise_pred.float(), noise.float())\n",
-    "        \n",
-    "       \n",
-    "        self.log(\"loss\", loss, prog_bar=True)\n",
-    "        optimizer.zero_grad()\n",
-    "        self.manual_backward(loss)\n",
-    "        optimizer.step()\n",
-    "        scheduler.step()\n",
-    "        low_res_scheduler.step()\n",
+    "        return loss\n",
+    "\n",
+    "    def training_step(self, batch, batch_idx):\n",
+    "        loss = self._calculate_loss(batch, batch_idx)\n",
+    "        self.log(\"train_loss\", loss, batch_size=16, prog_bar=True)\n",
+    "        return loss\n",
     "\n",
     "        \n",
     "    def validation_step(self, batch, batch_idx):\n",
-    "        \n",
-    "        images = batch[\"image\"].to(device)\n",
-    "        low_res_image = batch[\"low_res_image\"].to(device)\n",
-    "        latent = net.autoencoderkl.encode_stage_2_inputs(images) * scale_factor\n",
-    "        \n",
-    "        # Noise augmentation\n",
-    "        noise = torch.randn_like(latent).to(device)\n",
-    "        low_res_noise = torch.randn_like(low_res_image).to(device)\n",
-    "        timesteps = torch.randint(\n",
-    "            0, scheduler.num_train_timesteps, (latent.shape[0],), device=latent.device\n",
-    "        ).long()\n",
-    "        low_res_timesteps = torch.randint(\n",
-    "            0, max_noise_level, (low_res_image.shape[0],), device=low_res_image.device\n",
-    "        ).long()\n",
-    "\n",
-    "        noisy_latent = scheduler.add_noise(original_samples=latent, noise=noise, timesteps=timesteps)\n",
-    "        noisy_low_res_image = scheduler.add_noise(\n",
-    "            original_samples=low_res_image, noise=low_res_noise, timesteps=low_res_timesteps\n",
-    "        )\n",
-    "\n",
-    "        latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n",
-    "        noise_pred = self.forward(x=latent_model_input, timesteps=timesteps, class_labels=low_res_timesteps)\n",
-    "        loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+    "        loss = self._calculate_loss(batch, batch_idx)\n",
+    "        self.log(\"val_loss\", loss, batch_size=16, prog_bar=True)\n",
+    "        return loss\n",
     "\n",
-    "        val_loss += loss.item()\n",
     "    def configure_optimizers(self):\n",
-    "        optimizer = torch.optim.Adam(unet.parameters(), lr=5e-5)\n",
-    "        scheduler = DDPMScheduler(num_train_timesteps=1000, \n",
-    "                                   beta_schedule=\"linear\", beta_start=0.0015, beta_end=0.0195)\n",
-    "        low_res_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule=\"linear\",\n",
-    "                                          beta_start=0.0015, beta_end=0.0195)\n",
-    "\n",
-    "        return [optimizer], [scheduler, low_res_scheduler]\n"
+    "        optimizer = torch.optim.Adam(self.unet.parameters(), lr=5e-5)\n",
+    "        return optimizer\n"
    ]
   },
   {
@@ -1362,158 +768,185 @@
    "source": [
     "## Train Diffusion Model\n",
     "\n",
-    "In order to train the diffusion model to perform super-resolution, we will need to concatenate the latent representation of the high-resolution with the low-resolution image. For this, we create a Diffusion model with `in_channels=4`. Since only the outputted latent representation is interesting, we set `out_channels=3`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8fb22b1a",
-   "metadata": {},
-   "source": [
-    "As mentioned, we will use the conditioned augmentation (introduced in [2] section 3 and used on Stable Diffusion Upscalers and Imagen Video [3] Section 2.5) as it has been shown critical for cascaded diffusion models, as well for super-resolution tasks. For this, we apply Gaussian noise augmentation to the low-resolution images. We will use a scheduler `low_res_scheduler` to add this noise, with the `t` step defining the signal-to-noise ratio and use the `t` value to condition the diffusion model (inputted using `class_labels` argument)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "25d9d3e3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n"
+    "In order to train the diffusion model to perform super-resolution, we will need to concatenate the latent representation of the high-resolution with the low-resolution image. For this, we create a Diffusion model with `in_channels=4`. Since only the outputted latent representation is interesting, we set `out_channels=3`.\n",
+    "\n",
+    "As mentioned, we will use the conditioned augmentation (introduced in [2] section 3 and used on Stable Diffusion Upscalers and Imagen Video [3] Section 2.5) as it has been shown critical for cascaded diffusion models, as well for super-resolution tasks. For this, we apply Gaussian noise augmentation to the low-resolution images. We will use a scheduler low_res_scheduler to add this noise, with the t step defining the signal-to-noise ratio and use the t value to condition the diffusion model (inputted using class_labels argument)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "aa959db4",
+   "execution_count": 11,
+   "id": "936bbb9c",
    "metadata": {
-    "lines_to_next_cell": 2
+    "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-04-07 18:40:37,435 - GPU available: True (cuda), used: True\n",
+      "2023-04-07 18:40:37,436 - TPU available: False, using: 0 TPU cores\n",
+      "2023-04-07 18:40:37,436 - IPU available: False, using: 0 IPUs\n",
+      "2023-04-07 18:40:37,437 - HPU available: False, using: 0 HPUs\n",
+      "2023-04-07 18:40:37,539 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-04-07 18:40:37,540 - INFO - File exists: /tmp/tmp1sbvw3_e/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-04-07 18:40:37,541 - INFO - Non-empty folder exists in /tmp/tmp1sbvw3_e/MedNIST, skipped extracting.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:40<00:00, 1155.54it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-04-07 18:41:19,165 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-04-07 18:41:19,165 - INFO - File exists: /tmp/tmp1sbvw3_e/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-04-07 18:41:19,166 - INFO - Non-empty folder exists in /tmp/tmp1sbvw3_e/MedNIST, skipped extracting.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:04<00:00, 1226.18it/s]\n",
+      "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7991/7991 [00:11<00:00, 724.62it/s]\n",
+      "Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 972/972 [00:02<00:00, 432.39it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-04-07 18:41:38,130 - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
+      "2023-04-07 18:41:38,161 - \n",
+      "  | Name      | Type               | Params\n",
+      "-------------------------------------------------\n",
+      "0 | unet      | DiffusionModelUNet | 266 M \n",
+      "1 | scheduler | DDPMScheduler      | 0     \n",
+      "2 | z         | AutoencoderKL      | 75.1 M\n",
+      "-------------------------------------------------\n",
+      "342 M     Trainable params\n",
+      "0         Non-trainable params\n",
+      "342 M     Total params\n",
+      "1,368.189 Total estimated model params size (MB)\n",
+      "Sanity Checking: 0it [00:00, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Your `val_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 500/500 [02:22<00:00,  3.51it/s, v_num=1, train_loss=0.0418]\n",
+      "Validation: 0it [00:00, ?it/s]\u001b[A\n",
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+      "Validation DataLoader 0:  62%|██████████████████████████████████████████████████████████████████████▍                                          | 38/61 [00:04<00:02,  8.43it/s]\u001b[A\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation DataLoader 0:  70%|███████████████████████████████████████████████████████████████████████████████▋                                 | 43/61 [00:05<00:02,  8.42it/s]\u001b[A\n",
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+      "Validation DataLoader 0:  89%|████████████████████████████████████████████████████████████████████████████████████████████████████             | 54/61 [00:06<00:00,  8.40it/s]\u001b[A\n",
+      "Validation DataLoader 0:  90%|█████████████████████████████████████████████████████████████████████████████████████████████████████▉           | 55/61 [00:06<00:00,  8.40it/s]\u001b[A\n",
+      "Validation DataLoader 0:  92%|███████████████████████████████████████████████████████████████████████████████████████████████████████▋         | 56/61 [00:06<00:00,  8.40it/s]\u001b[A\n",
+      "Validation DataLoader 0:  93%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▌       | 57/61 [00:06<00:00,  8.40it/s]\u001b[A\n",
+      "Validation DataLoader 0:  95%|███████████████████████████████████████████████████████████████████████████████████████████████████████████▍     | 58/61 [00:06<00:00,  8.40it/s]\u001b[A\n",
+      "Validation DataLoader 0:  97%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▎   | 59/61 [00:07<00:00,  8.40it/s]\u001b[A\n",
+      "Validation DataLoader 0:  98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 60/61 [00:07<00:00,  8.40it/s]\u001b[A\n",
+      "Epoch 2: 100%|███████████████████████████████████████████████████████████████████████████████████| 500/500 [02:30<00:00,  3.31it/s, v_num=1, train_loss=0.0418, val_loss=0.113]\u001b[A\n",
+      "Epoch 2: 100%|███████████████████████████████████████████████████████████████████████████████████| 500/500 [02:39<00:00,  3.13it/s, v_num=1, train_loss=0.0418, val_loss=0.113]\u001b[A2023-04-07 18:48:59,747 - `Trainer.fit` stopped: `max_epochs=3` reached.\n",
+      "Epoch 2: 100%|███████████████████████████████████████████████████████████████████████████████████| 500/500 [02:39<00:00,  3.13it/s, v_num=1, train_loss=0.0418, val_loss=0.113]\n"
+     ]
+    }
+   ],
    "source": [
-    "optimizer = torch.optim.Adam(unet.parameters(), lr=5e-5)\n",
-    "\n",
-    "scaler_diffusion = GradScaler()\n",
-    "\n",
-    "n_epochs = 200\n",
-    "val_interval = 20\n",
-    "epoch_loss_list = []\n",
-    "val_epoch_loss_list = []\n",
-    "\n",
-    "for epoch in range(n_epochs):\n",
-    "    unet.train()\n",
-    "    autoencoderkl.eval()\n",
-    "    epoch_loss = 0\n",
-    "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=110)\n",
-    "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
-    "    for step, batch in progress_bar:\n",
-    "        images = batch[\"image\"].to(device)\n",
-    "        low_res_image = batch[\"low_res_image\"].to(device)\n",
-    "        optimizer.zero_grad(set_to_none=True)\n",
-    "\n",
-    "        with autocast(enabled=True):\n",
-    "            with torch.no_grad():\n",
-    "                latent = autoencoderkl.encode_stage_2_inputs(images) * scale_factor\n",
-    "\n",
-    "            # Noise augmentation\n",
-    "            noise = torch.randn_like(latent).to(device)\n",
-    "            low_res_noise = torch.randn_like(low_res_image).to(device)\n",
-    "            timesteps = torch.randint(0, scheduler.num_train_timesteps, (latent.shape[0],), device=latent.device).long()\n",
-    "            low_res_timesteps = torch.randint(\n",
-    "                0, max_noise_level, (low_res_image.shape[0],), device=low_res_image.device\n",
-    "            ).long()\n",
-    "\n",
-    "            noisy_latent = scheduler.add_noise(original_samples=latent, noise=noise, timesteps=timesteps)\n",
-    "            noisy_low_res_image = scheduler.add_noise(\n",
-    "                original_samples=low_res_image, noise=low_res_noise, timesteps=low_res_timesteps\n",
-    "            )\n",
-    "\n",
-    "            latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n",
-    "\n",
-    "            noise_pred = unet(x=latent_model_input, timesteps=timesteps, class_labels=low_res_timesteps)\n",
-    "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
-    "\n",
-    "        scaler_diffusion.scale(loss).backward()\n",
-    "        scaler_diffusion.step(optimizer)\n",
-    "        scaler_diffusion.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",
-    "        unet.eval()\n",
-    "        val_loss = 0\n",
-    "        for val_step, batch in enumerate(val_loader, start=1):\n",
-    "            images = batch[\"image\"].to(device)\n",
-    "            low_res_image = batch[\"low_res_image\"].to(device)\n",
-    "\n",
-    "            with torch.no_grad():\n",
-    "                with autocast(enabled=True):\n",
-    "                    latent = autoencoderkl.encode_stage_2_inputs(images) * scale_factor\n",
-    "                    # Noise augmentation\n",
-    "                    noise = torch.randn_like(latent).to(device)\n",
-    "                    low_res_noise = torch.randn_like(low_res_image).to(device)\n",
-    "                    timesteps = torch.randint(\n",
-    "                        0, scheduler.num_train_timesteps, (latent.shape[0],), device=latent.device\n",
-    "                    ).long()\n",
-    "                    low_res_timesteps = torch.randint(\n",
-    "                        0, max_noise_level, (low_res_image.shape[0],), device=low_res_image.device\n",
-    "                    ).long()\n",
+    "n_epochs = 3\n",
+    "val_interval = 3\n",
     "\n",
-    "                    noisy_latent = scheduler.add_noise(original_samples=latent, noise=noise, timesteps=timesteps)\n",
-    "                    noisy_low_res_image = scheduler.add_noise(\n",
-    "                        original_samples=low_res_image, noise=low_res_noise, timesteps=low_res_timesteps\n",
-    "                    )\n",
-    "\n",
-    "                    latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n",
-    "                    noise_pred = unet(x=latent_model_input, timesteps=timesteps, class_labels=low_res_timesteps)\n",
-    "                    loss = F.mse_loss(noise_pred.float(), noise.float())\n",
-    "\n",
-    "            val_loss += loss.item()\n",
-    "        val_loss /= val_step\n",
-    "        val_epoch_loss_list.append(val_loss)\n",
-    "        print(f\"Epoch {epoch} val loss: {val_loss:.4f}\")\n",
+    "                          \n",
+    "# initialise the LightningModule\n",
+    "d_net = DiffusionUNET()\n",
     "\n",
-    "        # Sampling image during training\n",
-    "        sampling_image = low_res_image[0].unsqueeze(0)\n",
-    "        latents = torch.randn((1, 3, 16, 16)).to(device)\n",
-    "        low_res_noise = torch.randn((1, 1, 16, 16)).to(device)\n",
-    "        noise_level = 20\n",
-    "        noise_level = torch.Tensor((noise_level,)).long().to(device)\n",
-    "        noisy_low_res_image = scheduler.add_noise(\n",
-    "            original_samples=sampling_image,\n",
-    "            noise=low_res_noise,\n",
-    "            timesteps=torch.Tensor((noise_level,)).long().to(device),\n",
-    "        )\n",
+    "# set up checkpoints\n",
     "\n",
-    "        scheduler.set_timesteps(num_inference_steps=1000)\n",
-    "        for t in tqdm(scheduler.timesteps, ncols=110):\n",
-    "            with torch.no_grad():\n",
-    "                with autocast(enabled=True):\n",
-    "                    latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)\n",
-    "                    noise_pred = unet(\n",
-    "                        x=latent_model_input, timesteps=torch.Tensor((t,)).to(device), class_labels=noise_level\n",
-    "                    )\n",
-    "                latents, _ = scheduler.step(noise_pred, t, latents)\n",
+    "checkpoint_callback = ModelCheckpoint(dirpath=root_dir, filename=\"best_metric_model_dunet\")\n",
     "\n",
-    "        with torch.no_grad():\n",
-    "            decoded = autoencoderkl.decode_stage_2_outputs(latents / scale_factor)\n",
+    "                         \n",
+    "# initialise Lightning's trainer.\n",
+    "trainer = pl.Trainer(devices=1,\n",
+    "                     max_epochs=n_epochs,\n",
+    "                     check_val_every_n_epoch=val_interval,\n",
+    "                     callbacks=checkpoint_callback,\n",
+    "                     default_root_dir=root_dir)\n",
     "\n",
-    "        low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode=\"bicubic\")\n",
-    "        plt.figure(figsize=(2, 2))\n",
-    "        plt.style.use(\"default\")\n",
-    "        plt.imshow(\n",
-    "            torch.cat([images[0, 0].cpu(), low_res_bicubic[0, 0].cpu(), decoded[0, 0].cpu()], dim=1),\n",
-    "            vmin=0,\n",
-    "            vmax=1,\n",
-    "            cmap=\"gray\",\n",
-    "        )\n",
-    "        plt.tight_layout()\n",
-    "        plt.axis(\"off\")\n",
-    "        plt.show()"
+    "# train\n",
+    "trainer.fit(d_net)"
    ]
   },
   {
@@ -1526,54 +959,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "155be091",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Sampling image during training\n",
-    "unet.eval()\n",
     "num_samples = 3\n",
-    "validation_batch = first(val_loader)\n",
-    "\n",
-    "images = validation_batch[\"image\"].to(device)\n",
-    "sampling_image = validation_batch[\"low_res_image\"].to(device)[:num_samples]"
+    "val_loader = d_net.val_dataloader()\n",
+    "check_data = first(val_loader)\n",
+    "images = check_data[\"image\"]\n",
+    "sampling_image = check_data[\"low_res_image\"][:num_samples]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "aaf61020",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [07:46<00:00,  2.14it/s]\n"
+     ]
+    }
+   ],
    "source": [
-    "latents = torch.randn((num_samples, 3, 16, 16)).to(device)\n",
-    "low_res_noise = torch.randn((num_samples, 1, 16, 16)).to(device)\n",
+    "latents = torch.randn((num_samples, 3, 16, 16)).to(images.device)\n",
+    "low_res_noise = torch.randn((num_samples, 1, 16, 16)).to(images.device)\n",
     "noise_level = 10\n",
-    "noise_level = torch.Tensor((noise_level,)).long().to(device)\n",
-    "noisy_low_res_image = scheduler.add_noise(\n",
-    "    original_samples=sampling_image, noise=low_res_noise, timesteps=torch.Tensor((noise_level,)).long().to(device)\n",
-    ")\n",
+    "noise_level = torch.Tensor((noise_level,)).long().to(images.device)\n",
+    "scheduler = d_net.scheduler\n",
+    "noisy_low_res_image = scheduler.add_noise(original_samples=sampling_image, \n",
+    "                                          noise=low_res_noise,\n",
+    "                                          timesteps=torch.Tensor((noise_level,)).long())\n",
+    "\n",
     "scheduler.set_timesteps(num_inference_steps=1000)\n",
     "for t in tqdm(scheduler.timesteps, ncols=110):\n",
     "    with torch.no_grad():\n",
     "        with autocast(enabled=True):\n",
     "            latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)\n",
-    "            noise_pred = unet(x=latent_model_input, timesteps=torch.Tensor((t,)).to(device), class_labels=noise_level)\n",
+    "            noise_pred = d_net.forward(x=latent_model_input,\n",
+    "                                       timesteps=torch.Tensor((t,)),\n",
+    "                                       low_res_timesteps=noise_level)\n",
     "\n",
     "        # 2. compute previous image: x_t -> x_t-1\n",
     "        latents, _ = scheduler.step(noise_pred, t, latents)\n",
     "\n",
     "with torch.no_grad():\n",
-    "    decoded = autoencoderkl.decode_stage_2_outputs(latents / scale_factor)"
+    "    decoded = ae_net.autoencoderkl.decode_stage_2_outputs(latents / scale_factor)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "32e16e69",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode=\"bicubic\")\n",
     "fig, axs = plt.subplots(num_samples, 3, figsize=(8, 8))\n",
@@ -1600,7 +1055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "3a6f6d5a",
    "metadata": {},
    "outputs": [],
diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
new file mode 100644
index 00000000..0449491c
--- /dev/null
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
@@ -0,0 +1,479 @@
+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: -all
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.14.5
+#   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]
+# # Super-resolution using Stable Diffusion v2 Upscalers
+#
+# Tutorial to illustrate the super-resolution task on medical images using Latent Diffusion Models (LDMs) [1]. For that, we will use an autoencoder to obtain a latent representation of the high-resolution images. Then, we train a diffusion model to infer this latent representation when conditioned on a low-resolution image.
+#
+# To improve the performance of our models, we will use a method called "noise conditioning augmentation" (introduced in [2] and used in Stable Diffusion v2.0 and Imagen Video [3]). During the training, we add noise to the low-resolution images using a random signal-to-noise ratio, and we condition the diffusion models on the amount of noise added. At sampling time, we use a fixed signal-to-noise ratio, representing a small amount of augmentation that aids in removing artefacts in the samples.
+#
+#
+# [1] - Rombach et al. "High-Resolution Image Synthesis with Latent Diffusion Models" https://arxiv.org/abs/2112.10752
+#
+# [2] - Ho et al. "Cascaded diffusion models for high fidelity image generation" https://arxiv.org/abs/2106.15282
+#
+# [3] - Ho et al. "High Definition Video Generation with Diffusion Models" https://arxiv.org/abs/2210.02303
+
+# %% [markdown]
+# ## Set up environment using Colab
+#
+
+# %%
+# !python -c "import monai" || pip install -q "monai-weekly[tqdm]"
+# !python -c "import matplotlib" || pip install -q matplotlib
+# %matplotlib inline
+
+# %% [markdown]
+# ## Set up imports
+
+# %%
+import os
+import shutil
+import tempfile
+
+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 MedNISTDataset
+from monai.config import print_config
+from monai.data import CacheDataset, DataLoader
+from monai.utils import first, set_determinism
+from torch import nn
+from torch.cuda.amp import GradScaler, autocast
+from tqdm import tqdm
+
+from generative.losses import PatchAdversarialLoss, PerceptualLoss
+from generative.networks.nets import AutoencoderKL, DiffusionModelUNet, PatchDiscriminator
+from generative.networks.schedulers import DDPMScheduler
+
+import pytorch_lightning as pl
+from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
+
+print_config()
+
+# %%
+# for reproducibility purposes set a seed
+set_determinism(42)
+
+# %% [markdown]
+# ## Setup a data directory and download dataset
+# Specify a MONAI_DATA_DIRECTORY variable, where the data will be downloaded. If not specified a temporary directory will be used.
+
+# %%
+directory = os.environ.get("MONAI_DATA_DIRECTORY")
+root_dir = tempfile.mkdtemp() if directory is None else directory
+print(root_dir)
+
+
+# %% [markdown]
+# ## Setup utils functions
+
+# %%
+def get_train_transforms():
+    image_size = 64
+    train_transforms = transforms.Compose(
+    [
+        transforms.LoadImaged(keys=["image"]),
+        transforms.EnsureChannelFirstd(keys=["image"]),
+        transforms.ScaleIntensityRanged(keys=["image"], a_min=0.0, a_max=255.0, b_min=0.0,
+                                        b_max=1.0, clip=True),
+        transforms.RandAffined(
+            keys=["image"],
+            rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],
+            translate_range=[(-1, 1), (-1, 1)],
+            scale_range=[(-0.05, 0.05), (-0.05, 0.05)],
+            spatial_size=[image_size, image_size],
+            padding_mode="zeros",
+            prob=0.5,
+        ),
+        transforms.CopyItemsd(keys=["image"], times=1, names=["low_res_image"]),
+        transforms.Resized(keys=["low_res_image"], spatial_size=(16, 16)),
+    ]
+    )
+    return train_transforms
+
+def get_val_transforms():
+    val_transforms = transforms.Compose(
+        [
+            transforms.LoadImaged(keys=["image"]),
+            transforms.EnsureChannelFirstd(keys=["image"]),
+            transforms.ScaleIntensityRanged(keys=["image"], a_min=0.0, a_max=255.0, b_min=0.0, 
+                                            b_max=1.0, clip=True),
+            transforms.CopyItemsd(keys=["image"], times=1, names=["low_res_image"]),
+            transforms.Resized(keys=["low_res_image"], spatial_size=(16, 16)),
+        ]
+    )
+    return val_transforms
+
+
+
+# %% [markdown]
+# ## Define the LightningModule for AutoEncoder (transforms, network, loaders, etc)
+# The LightningModule contains a refactoring of your training code. The following module is a reformatiing of the code in 2d_stable_diffusion_v2_super_resolution.
+#
+
+# %%
+class AutoEnconder(pl.LightningModule):
+    def __init__(self):
+        super().__init__()
+        self.data_dir = root_dir
+        self.autoencoderkl = AutoencoderKL(spatial_dims=2,
+                                           in_channels=1,
+                                           out_channels=1,
+                                           num_channels=(256, 512, 512),
+                                           latent_channels=3,
+                                           num_res_blocks=2,
+                                           norm_num_groups=32,
+                                           attention_levels=(False, False, True))
+        self.discriminator = PatchDiscriminator(spatial_dims=2, in_channels=1,
+                                                num_layers_d=3, num_channels=64)
+        self.perceptual_loss = PerceptualLoss(spatial_dims=2, network_type="alex")
+        self.perceptual_weight = 0.002
+        self.autoencoder_warm_up_n_epochs = 10
+        self.automatic_optimization = False
+        self.adv_loss = PatchAdversarialLoss(criterion="least_squares")
+        self.adv_weight = 0.005
+        self.kl_weight = 1e-6
+        
+    def forward(self, z):
+        return self.autoencoderkl(z)
+
+    def prepare_data(self):
+        train_transforms = get_train_transforms()
+        val_transforms = get_val_transforms()
+        
+        train_data = MedNISTDataset(root_dir=self.data_dir, section="training", download=True, seed=0)
+        train_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "HeadCT"]
+        val_data = MedNISTDataset(root_dir=self.data_dir, section="validation", download=True, seed=0)
+        val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "HeadCT"]
+        
+        self.train_ds = CacheDataset(data=train_datalist, transform=train_transforms)
+        self.val_ds = CacheDataset(data=val_datalist, transform=val_transforms)
+        
+    def train_dataloader(self):
+        return DataLoader(self.train_ds, batch_size=16, shuffle=True,
+                          num_workers=4, persistent_workers=True)
+        
+    def val_dataloader(self):
+        return DataLoader(self.val_ds, batch_size=16, shuffle=False,
+                          num_workers=4)
+                          
+    def _compute_loss_generator(self, images, reconstruction, z_mu, z_sigma):
+        recons_loss = F.l1_loss(reconstruction.float(), images.float())
+        p_loss = self.perceptual_loss(reconstruction.float(), images.float())
+        kl_loss = 0.5 * torch.sum(z_mu.pow(2) + z_sigma.pow(2) - torch.log(z_sigma.pow(2)) - 1, dim=[1, 2, 3])
+        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
+        loss_g = recons_loss + (self.kl_weight * kl_loss) + (self.perceptual_weight * p_loss)
+        return loss_g,recons_loss
+    
+    def _compute_loss_discriminator(self, reconstruction):
+        logits_fake = discriminator(reconstruction.contiguous().detach())[-1]
+        loss_d_fake = self.adv_loss(logits_fake, target_is_real=False, for_discriminator=True)
+        logits_real = discriminator(images.contiguous().detach())[-1]
+        loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)
+        discriminator_loss = (loss_d_fake + loss_d_real) * 0.5
+        loss_d = self.adv_weight * discriminator_loss
+        return loss_d, discriminator_loss
+        
+    def training_step(self, batch, batch_idx):
+        optimizer_g, optimizer_d = self.optimizers()
+        images = batch["image"]
+        reconstruction, z_mu, z_sigma = self.forward(images)
+        loss_g, recons_loss = self._compute_loss_generator(images, reconstruction, z_mu, z_sigma)
+        self.log("recons_loss", recons_loss, batch_size=16, prog_bar=True)
+
+        if self.current_epoch > self.autoencoder_warm_up_n_epochs:
+            logits_fake = discriminator(reconstruction.contiguous().float())[-1]
+            generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)
+            loss_g += adv_weight * generator_loss
+                          
+
+        self.log("loss_g", loss_g, batch_size=16, prog_bar=True)
+        self.manual_backward(loss_g)
+        optimizer_g.step()
+        optimizer_g.zero_grad()
+        self.untoggle_optimizer(optimizer_g)
+
+        if self.current_epoch > self.autoencoder_warm_up_n_epochs:
+            loss_d, discriminator_loss = self._compute_loss_discriminator(reconstruction)
+            self.log("train_loss_d", loss_d, batch_size=16, prog_bar=True)
+            self.manual_backward(loss_d)
+            optimizer_d.step()
+            optimizer_d.zero_grad()
+            self.untoggle_optimizer(optimizer_d)
+
+                          
+        if self.current_epoch > self.autoencoder_warm_up_n_epochs:
+            gen_epoch_loss += generator_loss.item()
+            disc_epoch_loss += discriminator_loss.item()
+            self.log("gen_loss", gen_loss, batch_size=16, prog_bar=True)
+            self.log("disc_loss", disc_loss, batch_size=16, prog_bar=True)
+                          
+                                    
+    def validation_step(self, batch, batch_idx):
+        images = batch["image"]
+        reconstruction, z_mu, z_sigma = self.autoencoderkl(images)
+        recons_loss = F.l1_loss(images.float(), reconstruction.float())
+        self.log("val_loss_d", recons_loss, prog_bar=True)
+                              
+
+    def configure_optimizers(self):
+        optimizer_g = torch.optim.Adam(self.autoencoderkl.parameters(), lr=5e-5)
+        optimizer_d = torch.optim.Adam(self.discriminator.parameters(), lr=1e-4)
+        return [optimizer_g, optimizer_d], []
+                          
+
+
+# %% [markdown]
+# ## Train Autoencoder
+
+# %%
+n_epochs = 1
+val_interval = 1
+
+                          
+# initialise the LightningModule
+ae_net = AutoEnconder()
+
+# set up checkpoints
+
+checkpoint_callback = ModelCheckpoint(dirpath=root_dir, filename="best_metric_model")
+
+                         
+# initialise Lightning's trainer.
+trainer = pl.Trainer(devices=1,
+                     max_epochs=n_epochs,
+                     check_val_every_n_epoch=val_interval,
+                     callbacks=checkpoint_callback,
+                     default_root_dir=root_dir)
+
+# train
+trainer.fit(ae_net)
+
+# %% [markdown]
+# ## Rescaling factor
+#
+# As mentioned in Rombach et al. [1] Section 4.3.2 and D.1, the signal-to-noise ratio (induced by the scale of the latent space) became crucial in image-to-image translation models (such as the ones used for super-resolution). For this reason, we will compute the component-wise standard deviation to be used as scaling factor.
+
+# %%
+train_loader = ae_net.train_dataloader()
+check_data = first(train_loader)
+z = ae_net.autoencoderkl.train(mode=False).encode_stage_2_inputs(check_data["image"].to(ae_net.device))
+print(f"Scaling factor set to {1/torch.std(z)}")
+scale_factor = 1 / torch.std(z)
+
+
+# %% [markdown]
+# ## Define the LightningModule for DiffusionModelUnet (transforms, network, loaders, etc)
+# The LightningModule contains a refactoring of your training code. The following module is a reformatiing of the code in 2d_stable_diffusion_v2_super_resolution.
+
+# %%
+class DiffusionUNET(pl.LightningModule):
+    def __init__(self):
+        super().__init__()
+        self.data_dir = root_dir
+        self.unet = DiffusionModelUNet(
+                                spatial_dims=2,
+                                in_channels=4,
+                                out_channels=3,
+                                num_res_blocks=2,
+                                num_channels=(256, 256, 512, 1024),
+                                attention_levels=(False, False, True, True),
+                                num_head_channels=(0, 0, 64, 64),
+                            )
+        self.max_noise_level = 350
+        self.scheduler = DDPMScheduler(num_train_timesteps=1000, 
+                          beta_schedule="linear",
+                          beta_start=0.0015,
+                          beta_end=0.0195)
+        self.z = ae_net.autoencoderkl.train(mode=False)
+
+
+    def forward(self, x, timesteps, low_res_timesteps):
+        return self.unet(x=x, 
+                         timesteps=timesteps,
+                         class_labels=low_res_timesteps)
+    
+    
+    def prepare_data(self):
+        train_transforms = get_train_transforms()
+        val_transforms = get_val_transforms()
+        
+        train_data = MedNISTDataset(root_dir=self.data_dir, section="training",
+                                    download=True, seed=0)
+        train_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "HeadCT"]
+        val_data = MedNISTDataset(root_dir=self.data_dir, section="validation",
+                                  download=True, seed=0)
+        val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "HeadCT"]
+        
+        self.train_ds = CacheDataset(data=train_datalist, transform=train_transforms)
+        self.val_ds = CacheDataset(data=val_datalist, transform=val_transforms)
+        
+    def train_dataloader(self):
+        return DataLoader(self.train_ds, batch_size=16, shuffle=True,
+                          num_workers=4, persistent_workers=True)
+        
+    def val_dataloader(self):
+        return DataLoader(self.val_ds, batch_size=16, shuffle=True,
+                          num_workers=4)
+    
+    def _calculate_loss(self, batch, batch_idx):
+        images = batch["image"]
+        low_res_image = batch["low_res_image"]       
+        latent = self.z.encode_stage_2_inputs(images) * scale_factor
+        latent =  latent.detach() # avoid adding this to graph.
+        optimizer = self.optimizers()
+        
+        # Noise augmentation
+        noise = torch.randn_like(latent)
+        low_res_noise = torch.randn_like(low_res_image)
+        timesteps = torch.randint(0, self.scheduler.num_train_timesteps, (latent.shape[0],),
+                                  device=latent.device).long()
+        low_res_timesteps = torch.randint(
+            0, self.max_noise_level, (low_res_image.shape[0],), device=latent.device
+        ).long()
+
+        noisy_latent = self.scheduler.add_noise(original_samples=latent, 
+                                           noise=noise, timesteps=timesteps)
+        noisy_low_res_image = self.scheduler.add_noise(
+            original_samples=low_res_image, noise=low_res_noise, 
+            timesteps=low_res_timesteps
+        )
+
+        latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)
+
+        noise_pred = self.forward(latent_model_input, timesteps, low_res_timesteps)
+        loss = F.mse_loss(noise_pred.float(), noise.float())
+        return loss
+
+    def training_step(self, batch, batch_idx):
+        loss = self._calculate_loss(batch, batch_idx)
+        self.log("train_loss", loss, batch_size=16, prog_bar=True)
+        return loss
+
+        
+    def validation_step(self, batch, batch_idx):
+        loss = self._calculate_loss(batch, batch_idx)
+        self.log("val_loss", loss, batch_size=16, prog_bar=True)
+        return loss
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.Adam(self.unet.parameters(), lr=5e-5)
+        return optimizer
+
+
+# %% [markdown]
+# ## Train Diffusion Model
+#
+# In order to train the diffusion model to perform super-resolution, we will need to concatenate the latent representation of the high-resolution with the low-resolution image. For this, we create a Diffusion model with `in_channels=4`. Since only the outputted latent representation is interesting, we set `out_channels=3`.
+#
+# As mentioned, we will use the conditioned augmentation (introduced in [2] section 3 and used on Stable Diffusion Upscalers and Imagen Video [3] Section 2.5) as it has been shown critical for cascaded diffusion models, as well for super-resolution tasks. For this, we apply Gaussian noise augmentation to the low-resolution images. We will use a scheduler low_res_scheduler to add this noise, with the t step defining the signal-to-noise ratio and use the t value to condition the diffusion model (inputted using class_labels argument).
+
+# %%
+n_epochs = 3
+val_interval = 3
+
+                          
+# initialise the LightningModule
+d_net = DiffusionUNET()
+
+# set up checkpoints
+
+checkpoint_callback = ModelCheckpoint(dirpath=root_dir, filename="best_metric_model_dunet")
+
+                         
+# initialise Lightning's trainer.
+trainer = pl.Trainer(devices=1,
+                     max_epochs=n_epochs,
+                     check_val_every_n_epoch=val_interval,
+                     callbacks=checkpoint_callback,
+                     default_root_dir=root_dir)
+
+# train
+trainer.fit(d_net)
+
+# %% [markdown]
+# ### Plotting sampling example
+
+# %%
+# Sampling image during training
+num_samples = 3
+val_loader = d_net.val_dataloader()
+check_data = first(val_loader)
+images = check_data["image"]
+sampling_image = check_data["low_res_image"][:num_samples]
+
+# %%
+latents = torch.randn((num_samples, 3, 16, 16)).to(images.device)
+low_res_noise = torch.randn((num_samples, 1, 16, 16)).to(images.device)
+noise_level = 10
+noise_level = torch.Tensor((noise_level,)).long().to(images.device)
+scheduler = d_net.scheduler
+noisy_low_res_image = scheduler.add_noise(original_samples=sampling_image, 
+                                          noise=low_res_noise,
+                                          timesteps=torch.Tensor((noise_level,)).long())
+
+scheduler.set_timesteps(num_inference_steps=1000)
+for t in tqdm(scheduler.timesteps, ncols=110):
+    with torch.no_grad():
+        with autocast(enabled=True):
+            latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)
+            noise_pred = d_net.forward(x=latent_model_input,
+                                       timesteps=torch.Tensor((t,)),
+                                       low_res_timesteps=noise_level)
+
+        # 2. compute previous image: x_t -> x_t-1
+        latents, _ = scheduler.step(noise_pred, t, latents)
+
+with torch.no_grad():
+    decoded = ae_net.autoencoderkl.decode_stage_2_outputs(latents / scale_factor)
+
+# %%
+low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode="bicubic")
+fig, axs = plt.subplots(num_samples, 3, figsize=(8, 8))
+axs[0, 0].set_title("Original image")
+axs[0, 1].set_title("Low-resolution Image")
+axs[0, 2].set_title("Outputted image")
+for i in range(0, num_samples):
+    axs[i, 0].imshow(images[i, 0].cpu(), vmin=0, vmax=1, cmap="gray")
+    axs[i, 0].axis("off")
+    axs[i, 1].imshow(low_res_bicubic[i, 0].cpu(), vmin=0, vmax=1, cmap="gray")
+    axs[i, 1].axis("off")
+    axs[i, 2].imshow(decoded[i, 0].cpu(), vmin=0, vmax=1, cmap="gray")
+    axs[i, 2].axis("off")
+plt.tight_layout()
+
+# %% [markdown]
+# ### Clean-up data directory
+
+# %%
+if directory is None:
+    shutil.rmtree(root_dir)

From ed276ada2c6916dd4a4570b2b5fd278ee16f30fa Mon Sep 17 00:00:00 2001
From: OeslleLucena <oeslle.lucena@gmail.com>
Date: Mon, 10 Apr 2023 18:43:57 +0100
Subject: [PATCH 03/13] [WIP] 2nd draft done

---
 ...fusion_v2_super_resolution-lightning.ipynb | 958 +++++++++++++-----
 ...diffusion_v2_super_resolution-lightning.py |   8 +-
 2 files changed, 733 insertions(+), 233 deletions(-)

diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
index c722a653..5a56755e 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
@@ -84,14 +84,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-07 18:33:20,193 - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:\n",
+      "2023-04-10 18:37:53,570 - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:\n",
       "    PyTorch 1.13.1+cu117 with CUDA 1107 (you have 1.12.1)\n",
       "    Python  3.8.16 (you have 3.8.16)\n",
       "  Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)\n",
       "  Memory-efficient attention, SwiGLU, sparse and more won't be available.\n",
-      "  Set XFORMERS_MORE_DETAILS=1 for more details\n",
-      "2023-04-07 18:33:21,929 - Created a temporary directory at /tmp/tmpq2wlch6q\n",
-      "2023-04-07 18:33:21,932 - Writing /tmp/tmpq2wlch6q/_remote_module_non_scriptable.py\n",
+      "  Set XFORMERS_MORE_DETAILS=1 for more details\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 804: forward compatibility was attempted on non supported HW (Triggered internally at  /opt/conda/conda-bld/pytorch_1659484810403/work/c10/cuda/CUDAFunctions.cpp:109.)\n",
+      "User provided device_type of 'cuda', but CUDA is not available. Disabling\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-04-10 18:37:54,723 - Created a temporary directory at /tmp/tmpk_8ni2di\n",
+      "2023-04-10 18:37:54,724 - Writing /tmp/tmpk_8ni2di/_remote_module_non_scriptable.py\n",
       "MONAI version: 1.2.dev2304\n",
       "Numpy version: 1.23.5\n",
       "Pytorch version: 1.12.1\n",
@@ -181,7 +195,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/tmp1sbvw3_e\n"
+      "/tmp/tmpa48mavq5\n"
      ]
     }
    ],
@@ -240,7 +254,8 @@
     "            transforms.Resized(keys=[\"low_res_image\"], spatial_size=(16, 16)),\n",
     "        ]\n",
     "    )\n",
-    "    return val_transforms\n"
+    "    return val_transforms\n",
+    "\n"
    ]
   },
   {
@@ -400,24 +415,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-07 18:33:24,966 - GPU available: True (cuda), used: True\n",
-      "2023-04-07 18:33:24,967 - TPU available: False, using: 0 TPU cores\n",
-      "2023-04-07 18:33:24,968 - IPU available: False, using: 0 IPUs\n",
-      "2023-04-07 18:33:24,968 - HPU available: False, using: 0 HPUs\n"
+      "2023-04-10 18:37:58,137 - GPU available: False, used: False\n",
+      "2023-04-10 18:37:58,138 - TPU available: False, using: 0 TPU cores\n",
+      "2023-04-10 18:37:58,140 - IPU available: False, using: 0 IPUs\n",
+      "2023-04-10 18:37:58,141 - HPU available: False, using: 0 HPUs\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MedNIST.tar.gz: 59.0MB [00:02, 23.0MB/s]                                                                                                                                       "
+      "Can't initialize NVML\n",
+      "MedNIST.tar.gz: 59.0MB [00:02, 23.5MB/s]                                                                                                                                       "
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-07 18:33:27,673 - INFO - Downloaded: /tmp/tmp1sbvw3_e/MedNIST.tar.gz\n"
+      "2023-04-10 18:38:00,799 - INFO - Downloaded: /tmp/tmpa48mavq5/MedNIST.tar.gz\n"
      ]
     },
     {
@@ -431,55 +447,55 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-07 18:33:27,791 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-04-07 18:33:27,792 - INFO - Writing into directory: /tmp/tmp1sbvw3_e.\n"
+      "2023-04-10 18:38:00,916 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-04-10 18:38:00,918 - INFO - Writing into directory: /tmp/tmpa48mavq5.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:40<00:00, 1177.19it/s]\n"
+      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:45<00:00, 1047.66it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-07 18:34:17,104 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-04-07 18:34:17,105 - INFO - File exists: /tmp/tmp1sbvw3_e/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-04-07 18:34:17,107 - INFO - Non-empty folder exists in /tmp/tmp1sbvw3_e/MedNIST, skipped extracting.\n"
+      "2023-04-10 18:38:55,238 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-04-10 18:38:55,240 - INFO - File exists: /tmp/tmpa48mavq5/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-04-10 18:38:55,242 - INFO - Non-empty folder exists in /tmp/tmpa48mavq5/MedNIST, skipped extracting.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:04<00:00, 1228.05it/s]\n",
-      "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7991/7991 [00:11<00:00, 715.18it/s]\n",
-      "Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 972/972 [00:02<00:00, 442.12it/s]\n"
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:05<00:00, 1061.53it/s]\n",
+      "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7991/7991 [00:12<00:00, 637.95it/s]\n",
+      "Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 972/972 [00:02<00:00, 414.79it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-07 18:34:35,829 - Missing logger folder: /tmp/tmp1sbvw3_e/lightning_logs\n"
+      "2023-04-10 18:39:16,217 - Missing logger folder: /tmp/tmpa48mavq5/lightning_logs\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Checkpoint directory /tmp/tmp1sbvw3_e exists and is not empty.\n"
+      "\n",
+      "Checkpoint directory /tmp/tmpa48mavq5 exists and is not empty.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-07 18:34:37,274 - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
-      "2023-04-07 18:34:37,290 - \n",
+      "2023-04-10 18:39:16,255 - \n",
       "  | Name            | Type                 | Params\n",
       "---------------------------------------------------------\n",
       "0 | autoencoderkl   | AutoencoderKL        | 75.1 M\n",
@@ -491,7 +507,7 @@
       "2.5 M     Non-trainable params\n",
       "80.3 M    Total params\n",
       "321.225   Total estimated model params size (MB)\n",
-      "Sanity Checking DataLoader 0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00,  1.30it/s]"
+      "Sanity Checking DataLoader 0:  50%|███████████████████████████████████████████████████████                                                       | 1/2 [00:04<00:04,  4.34s/it]"
      ]
     },
     {
@@ -505,99 +521,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 0: 100%|███████████████████████████████████████████████████████████████████████████████████| 500/500 [05:34<00:00,  1.50it/s, v_num=0, recons_loss=0.0421, loss_g=0.0443]\n",
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+      "Epoch 0:   2%|█▎                                                                                   | 8/500 [01:59<2:02:20, 14.92s/it, v_num=0, recons_loss=0.212, loss_g=0.215]"
      ]
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      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 12. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|████████████████████████████████████████████████████████████████| 500/500 [05:47<00:00,  1.44it/s, v_num=0, recons_loss=0.0421, loss_g=0.0443, val_loss_d=0.0415]\n",
-      "Epoch 0: 100%|████████████████████████████████████████████████████████████████| 500/500 [05:47<00:00,  1.44it/s, v_num=0, recons_loss=0.0421, loss_g=0.0443, val_loss_d=0.0415]\u001b[A2023-04-07 18:40:29,834 - `Trainer.fit` stopped: `max_epochs=1` reached.\n",
-      "Epoch 0: 100%|████████████████████████████████████████████████████████████████| 500/500 [05:49<00:00,  1.43it/s, v_num=0, recons_loss=0.0421, loss_g=0.0443, val_loss_d=0.0415]\n"
+      "Detected KeyboardInterrupt, attempting graceful shutdown...\n"
      ]
     }
    ],
    "source": [
-    "n_epochs = 1\n",
-    "val_interval = 1\n",
+    "n_epochs = 75\n",
+    "val_interval = 10\n",
     "\n",
     "                          \n",
     "# initialise the LightningModule\n",
@@ -639,7 +576,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Scaling factor set to 0.48439180850982666\n"
+      "Scaling factor set to 0.3829643428325653\n"
      ]
     }
    ],
@@ -664,7 +601,9 @@
    "cell_type": "code",
    "execution_count": 10,
    "id": "731034ec",
-   "metadata": {},
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
    "outputs": [],
    "source": [
     "class DiffusionUNET(pl.LightningModule):\n",
@@ -758,7 +697,7 @@
     "\n",
     "    def configure_optimizers(self):\n",
     "        optimizer = torch.optim.Adam(self.unet.parameters(), lr=5e-5)\n",
-    "        return optimizer\n"
+    "        return optimizer"
    ]
   },
   {
@@ -775,7 +714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "936bbb9c",
    "metadata": {
     "scrolled": true
@@ -785,46 +724,698 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-07 18:40:37,435 - GPU available: True (cuda), used: True\n",
-      "2023-04-07 18:40:37,436 - TPU available: False, using: 0 TPU cores\n",
-      "2023-04-07 18:40:37,436 - IPU available: False, using: 0 IPUs\n",
-      "2023-04-07 18:40:37,437 - HPU available: False, using: 0 HPUs\n",
-      "2023-04-07 18:40:37,539 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-04-07 18:40:37,540 - INFO - File exists: /tmp/tmp1sbvw3_e/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-04-07 18:40:37,541 - INFO - Non-empty folder exists in /tmp/tmp1sbvw3_e/MedNIST, skipped extracting.\n"
+      "2023-04-10 18:41:46,760 - GPU available: False, used: False\n",
+      "2023-04-10 18:41:46,761 - TPU available: False, using: 0 TPU cores\n",
+      "2023-04-10 18:41:46,762 - IPU available: False, using: 0 IPUs\n",
+      "2023-04-10 18:41:46,762 - HPU available: False, using: 0 HPUs\n",
+      "2023-04-10 18:41:46,869 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-04-10 18:41:46,869 - INFO - File exists: /tmp/tmpa48mavq5/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-04-10 18:41:46,870 - INFO - Non-empty folder exists in /tmp/tmpa48mavq5/MedNIST, skipped extracting.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation DataLoader 0:  70%|███████████████████████████████████████████████████████████████████████████████▋                                 | 43/61 [00:05<00:02,  8.42it/s]\u001b[A\n",
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-      "Validation DataLoader 0:  98%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 60/61 [00:07<00:00,  8.40it/s]\u001b[A\n",
-      "Epoch 2: 100%|███████████████████████████████████████████████████████████████████████████████████| 500/500 [02:30<00:00,  3.31it/s, v_num=1, train_loss=0.0418, val_loss=0.113]\u001b[A\n",
-      "Epoch 2: 100%|███████████████████████████████████████████████████████████████████████████████████| 500/500 [02:39<00:00,  3.13it/s, v_num=1, train_loss=0.0418, val_loss=0.113]\u001b[A2023-04-07 18:48:59,747 - `Trainer.fit` stopped: `max_epochs=3` reached.\n",
-      "Epoch 2: 100%|███████████████████████████████████████████████████████████████████████████████████| 500/500 [02:39<00:00,  3.13it/s, v_num=1, train_loss=0.0418, val_loss=0.113]\n"
+      "Epoch 0:   0%|▍                                                                                                   | 2/500 [00:16<1:08:40,  8.27s/it, v_num=1, train_loss=0.980]"
      ]
     }
    ],
    "source": [
-    "n_epochs = 3\n",
-    "val_interval = 3\n",
+    "n_epochs = 200\n",
+    "val_interval = 20\n",
     "\n",
     "                          \n",
     "# initialise the LightningModule\n",
@@ -959,7 +1478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "155be091",
    "metadata": {},
    "outputs": [],
@@ -974,18 +1493,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "aaf61020",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [07:46<00:00,  2.14it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "latents = torch.randn((num_samples, 3, 16, 16)).to(images.device)\n",
     "low_res_noise = torch.randn((num_samples, 1, 16, 16)).to(images.device)\n",
@@ -1014,21 +1525,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "32e16e69",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 9 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode=\"bicubic\")\n",
     "fig, axs = plt.subplots(num_samples, 3, figsize=(8, 8))\n",
@@ -1055,7 +1555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "3a6f6d5a",
    "metadata": {},
    "outputs": [],
diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
index 0449491c..14d36417 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
@@ -256,8 +256,8 @@ def configure_optimizers(self):
 # ## Train Autoencoder
 
 # %%
-n_epochs = 1
-val_interval = 1
+n_epochs = 75
+val_interval = 10
 
                           
 # initialise the LightningModule
@@ -398,8 +398,8 @@ def configure_optimizers(self):
 # As mentioned, we will use the conditioned augmentation (introduced in [2] section 3 and used on Stable Diffusion Upscalers and Imagen Video [3] Section 2.5) as it has been shown critical for cascaded diffusion models, as well for super-resolution tasks. For this, we apply Gaussian noise augmentation to the low-resolution images. We will use a scheduler low_res_scheduler to add this noise, with the t step defining the signal-to-noise ratio and use the t value to condition the diffusion model (inputted using class_labels argument).
 
 # %%
-n_epochs = 3
-val_interval = 3
+n_epochs = 200
+val_interval = 20
 
                           
 # initialise the LightningModule

From aea030200153f8865cd03bf764d6d026f522d7a6 Mon Sep 17 00:00:00 2001
From: OeslleLucena <oeslle.lucena@gmail.com>
Date: Wed, 12 Apr 2023 15:13:23 +0100
Subject: [PATCH 04/13] Tutorial with a few images

---
 ...fusion_v2_super_resolution-lightning.ipynb | 1883 +++++++++--------
 ...diffusion_v2_super_resolution-lightning.py |  285 ++-
 2 files changed, 1176 insertions(+), 992 deletions(-)

diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
index 5a56755e..1b8251da 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
@@ -72,40 +72,18 @@
    "id": "de71fe08",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ol18/miniconda3/envs/monai_generative/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-10 18:37:53,570 - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:\n",
+      "2023-04-12 14:09:21,976 - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:\n",
       "    PyTorch 1.13.1+cu117 with CUDA 1107 (you have 1.12.1)\n",
       "    Python  3.8.16 (you have 3.8.16)\n",
       "  Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)\n",
       "  Memory-efficient attention, SwiGLU, sparse and more won't be available.\n",
-      "  Set XFORMERS_MORE_DETAILS=1 for more details\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "CUDA initialization: Unexpected error from cudaGetDeviceCount(). Did you run some cuda functions before calling NumCudaDevices() that might have already set an error? Error 804: forward compatibility was attempted on non supported HW (Triggered internally at  /opt/conda/conda-bld/pytorch_1659484810403/work/c10/cuda/CUDAFunctions.cpp:109.)\n",
-      "User provided device_type of 'cuda', but CUDA is not available. Disabling\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-04-10 18:37:54,723 - Created a temporary directory at /tmp/tmpk_8ni2di\n",
-      "2023-04-10 18:37:54,724 - Writing /tmp/tmpk_8ni2di/_remote_module_non_scriptable.py\n",
+      "  Set XFORMERS_MORE_DETAILS=1 for more details\n",
+      "2023-04-12 14:09:23,860 - Created a temporary directory at /tmp/tmpo49u4ma3\n",
+      "2023-04-12 14:09:23,864 - Writing /tmp/tmpo49u4ma3/_remote_module_non_scriptable.py\n",
       "MONAI version: 1.2.dev2304\n",
       "Numpy version: 1.23.5\n",
       "Pytorch version: 1.12.1\n",
@@ -141,6 +119,7 @@
     "import os\n",
     "import shutil\n",
     "import tempfile\n",
+    "from pathlib import Path\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -149,11 +128,11 @@
     "from monai import transforms\n",
     "from monai.apps import MedNISTDataset\n",
     "from monai.config import print_config\n",
-    "from monai.data import CacheDataset, DataLoader\n",
+    "from monai.data import CacheDataset, ThreadDataLoader\n",
     "from monai.utils import first, set_determinism\n",
-    "from torch import nn\n",
     "from torch.cuda.amp import GradScaler, autocast\n",
-    "from tqdm import tqdm\n",
+    "from torch import nn\n",
+    "from tqdm.notebook import tqdm\n",
     "\n",
     "from generative.losses import PatchAdversarialLoss, PerceptualLoss\n",
     "from generative.networks.nets import AutoencoderKL, DiffusionModelUNet, PatchDiscriminator\n",
@@ -195,7 +174,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/tmpa48mavq5\n"
+      "/tmp/tmpizuu167z\n"
      ]
     }
    ],
@@ -205,6 +184,74 @@
     "print(root_dir)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "298d964a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MedNIST.tar.gz: 59.0MB [00:11, 5.17MB/s]                                                                                                                               "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-04-12 14:09:35,992 - INFO - Downloaded: /tmp/tmpizuu167z/MedNIST.tar.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-04-12 14:09:36,099 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-04-12 14:09:36,100 - INFO - Writing into directory: /tmp/tmpizuu167z.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:35<00:00, 1335.82it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-04-12 14:10:20,302 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-04-12 14:10:20,303 - INFO - File exists: /tmp/tmpizuu167z/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-04-12 14:10:20,304 - INFO - Non-empty folder exists in /tmp/tmpizuu167z/MedNIST, skipped extracting.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:04<00:00, 1332.80it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_data = MedNISTDataset(root_dir=root_dir, section=\"training\",\n",
+    "                                download=True, seed=0)\n",
+    "train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
+    "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\",\n",
+    "                              download=True, seed=0)\n",
+    "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "46bafb78",
@@ -215,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "4f8eff03",
    "metadata": {},
    "outputs": [],
@@ -255,6 +302,14 @@
     "        ]\n",
     "    )\n",
     "    return val_transforms\n",
+    "\n",
+    "    \n",
+    "def get_datasets():\n",
+    "    train_transforms = get_train_transforms()\n",
+    "    val_transforms = get_val_transforms()\n",
+    "    train_ds = CacheDataset(data=train_datalist[:320], transform=train_transforms)\n",
+    "    val_ds = CacheDataset(data=val_datalist[:32], transform=val_transforms)\n",
+    "    return train_ds, val_ds\n",
     "\n"
    ]
   },
@@ -269,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "d5d1caff",
    "metadata": {
     "scrolled": false
@@ -302,23 +357,14 @@
     "        return self.autoencoderkl(z)\n",
     "\n",
     "    def prepare_data(self):\n",
-    "        train_transforms = get_train_transforms()\n",
-    "        val_transforms = get_val_transforms()\n",
-    "        \n",
-    "        train_data = MedNISTDataset(root_dir=self.data_dir, section=\"training\", download=True, seed=0)\n",
-    "        train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
-    "        val_data = MedNISTDataset(root_dir=self.data_dir, section=\"validation\", download=True, seed=0)\n",
-    "        val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
-    "        \n",
-    "        self.train_ds = CacheDataset(data=train_datalist, transform=train_transforms)\n",
-    "        self.val_ds = CacheDataset(data=val_datalist, transform=val_transforms)\n",
-    "        \n",
+    "        self.train_ds, self.val_ds = get_datasets()\n",
+    "                \n",
     "    def train_dataloader(self):\n",
-    "        return DataLoader(self.train_ds, batch_size=16, shuffle=True,\n",
+    "        return ThreadDataLoader(self.train_ds, batch_size=16, shuffle=True,\n",
     "                          num_workers=4, persistent_workers=True)\n",
     "        \n",
     "    def val_dataloader(self):\n",
-    "        return DataLoader(self.val_ds, batch_size=16, shuffle=False,\n",
+    "        return ThreadDataLoader(self.val_ds, batch_size=16, shuffle=False,\n",
     "                          num_workers=4)\n",
     "                          \n",
     "    def _compute_loss_generator(self, images, reconstruction, z_mu, z_sigma):\n",
@@ -329,11 +375,11 @@
     "        loss_g = recons_loss + (self.kl_weight * kl_loss) + (self.perceptual_weight * p_loss)\n",
     "        return loss_g,recons_loss\n",
     "    \n",
-    "    def _compute_loss_discriminator(self, reconstruction):\n",
-    "        logits_fake = discriminator(reconstruction.contiguous().detach())[-1]\n",
+    "    def _compute_loss_discriminator(self, images, reconstruction):\n",
+    "        logits_fake = self.discriminator(reconstruction.contiguous().detach())[-1]\n",
     "        loss_d_fake = self.adv_loss(logits_fake, target_is_real=False, for_discriminator=True)\n",
-    "        logits_real = discriminator(images.contiguous().detach())[-1]\n",
-    "        loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)\n",
+    "        logits_real = self.discriminator(images.contiguous().detach())[-1]\n",
+    "        loss_d_real = self.adv_loss(logits_real, target_is_real=True, for_discriminator=True)\n",
     "        discriminator_loss = (loss_d_fake + loss_d_real) * 0.5\n",
     "        loss_d = self.adv_weight * discriminator_loss\n",
     "        return loss_d, discriminator_loss\n",
@@ -346,9 +392,11 @@
     "        self.log(\"recons_loss\", recons_loss, batch_size=16, prog_bar=True)\n",
     "\n",
     "        if self.current_epoch > self.autoencoder_warm_up_n_epochs:\n",
-    "            logits_fake = discriminator(reconstruction.contiguous().float())[-1]\n",
-    "            generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)\n",
-    "            loss_g += adv_weight * generator_loss\n",
+    "            logits_fake = self.discriminator(reconstruction.contiguous().float())[-1]\n",
+    "            generator_loss = self.adv_loss(logits_fake, target_is_real=True, for_discriminator=False)\n",
+    "            loss_g += self.adv_weight * generator_loss\n",
+    "            self.log(\"gen_loss\", generator_loss, batch_size=16, prog_bar=True)\n",
+    "            \n",
     "                          \n",
     "\n",
     "        self.log(\"loss_g\", loss_g, batch_size=16, prog_bar=True)\n",
@@ -358,7 +406,8 @@
     "        self.untoggle_optimizer(optimizer_g)\n",
     "\n",
     "        if self.current_epoch > self.autoencoder_warm_up_n_epochs:\n",
-    "            loss_d, discriminator_loss = self._compute_loss_discriminator(reconstruction)\n",
+    "            loss_d, discriminator_loss = self._compute_loss_discriminator(images, reconstruction)\n",
+    "            self.log(\"disc_loss\", loss_d, batch_size=16, prog_bar=True)\n",
     "            self.log(\"train_loss_d\", loss_d, batch_size=16, prog_bar=True)\n",
     "            self.manual_backward(loss_d)\n",
     "            optimizer_d.step()\n",
@@ -366,19 +415,26 @@
     "            self.untoggle_optimizer(optimizer_d)\n",
     "\n",
     "                          \n",
-    "        if self.current_epoch > self.autoencoder_warm_up_n_epochs:\n",
-    "            gen_epoch_loss += generator_loss.item()\n",
-    "            disc_epoch_loss += discriminator_loss.item()\n",
-    "            self.log(\"gen_loss\", gen_loss, batch_size=16, prog_bar=True)\n",
-    "            self.log(\"disc_loss\", disc_loss, batch_size=16, prog_bar=True)\n",
-    "                          \n",
     "                                    \n",
     "    def validation_step(self, batch, batch_idx):\n",
     "        images = batch[\"image\"]\n",
     "        reconstruction, z_mu, z_sigma = self.autoencoderkl(images)\n",
     "        recons_loss = F.l1_loss(images.float(), reconstruction.float())\n",
-    "        self.log(\"val_loss_d\", recons_loss, prog_bar=True)\n",
-    "                              \n",
+    "        self.log(\"val_loss_d\", recons_loss, batch_size=1, prog_bar=True)\n",
+    "        self.images = images\n",
+    "        self.reconstruction = reconstruction\n",
+    "\n",
+    "\n",
+    "    def on_validation_epoch_end(self):\n",
+    "        # ploting reconstruction\n",
+    "        plt.figure(figsize=(2, 2))\n",
+    "        plt.imshow(torch.cat([self.images[0, 0].cpu(), \n",
+    "                              self.reconstruction[0, 0].cpu()],\n",
+    "                             dim=1), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "        plt.tight_layout()\n",
+    "        plt.axis(\"off\")\n",
+    "        plt.show()\n",
+    "        \n",
     "\n",
     "    def configure_optimizers(self):\n",
     "        optimizer_g = torch.optim.Adam(self.autoencoderkl.parameters(), lr=5e-5)\n",
@@ -397,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "9d903aaa",
    "metadata": {
     "scrolled": true
@@ -415,125 +471,253 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-10 18:37:58,137 - GPU available: False, used: False\n",
-      "2023-04-10 18:37:58,138 - TPU available: False, using: 0 TPU cores\n",
-      "2023-04-10 18:37:58,140 - IPU available: False, using: 0 IPUs\n",
-      "2023-04-10 18:37:58,141 - HPU available: False, using: 0 HPUs\n"
+      "2023-04-12 14:10:27,910 - GPU available: True (cuda), used: True\n",
+      "2023-04-12 14:10:27,911 - TPU available: False, using: 0 TPU cores\n",
+      "2023-04-12 14:10:27,912 - IPU available: False, using: 0 IPUs\n",
+      "2023-04-12 14:10:27,913 - HPU available: False, using: 0 HPUs\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Can't initialize NVML\n",
-      "MedNIST.tar.gz: 59.0MB [00:02, 23.5MB/s]                                                                                                                                       "
+      "Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 320/320 [00:00<00:00, 892.62it/s]\n",
+      "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:00<00:00, 413.74it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-10 18:38:00,799 - INFO - Downloaded: /tmp/tmpa48mavq5/MedNIST.tar.gz\n"
+      "2023-04-12 14:10:28,377 - Missing logger folder: /tmp/tmpizuu167z/lightning_logs\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "\n",
+      "Checkpoint directory /tmp/tmpizuu167z exists and is not empty.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-10 18:38:00,916 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-04-10 18:38:00,918 - INFO - Writing into directory: /tmp/tmpa48mavq5.\n"
+      "2023-04-12 14:10:29,835 - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
+      "2023-04-12 14:10:29,850 - \n",
+      "  | Name            | Type                 | Params\n",
+      "---------------------------------------------------------\n",
+      "0 | autoencoderkl   | AutoencoderKL        | 75.1 M\n",
+      "1 | discriminator   | PatchDiscriminator   | 2.8 M \n",
+      "2 | perceptual_loss | PerceptualLoss       | 2.5 M \n",
+      "3 | adv_loss        | PatchAdversarialLoss | 0     \n",
+      "---------------------------------------------------------\n",
+      "77.8 M    Trainable params\n",
+      "2.5 M     Non-trainable params\n",
+      "80.3 M    Total params\n",
+      "321.225   Total estimated model params size (MB)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:45<00:00, 1047.66it/s]\n"
+      "The number of training batches (20) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-04-10 18:38:55,238 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-04-10 18:38:55,240 - INFO - File exists: /tmp/tmpa48mavq5/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-04-10 18:38:55,242 - INFO - Non-empty folder exists in /tmp/tmpa48mavq5/MedNIST, skipped extracting.\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7a5ce61b3dd41b386312a100272db42",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:05<00:00, 1061.53it/s]\n",
-      "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7991/7991 [00:12<00:00, 637.95it/s]\n",
-      "Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 972/972 [00:02<00:00, 414.79it/s]"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-04-10 18:39:16,217 - Missing logger folder: /tmp/tmpa48mavq5/lightning_logs\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Checkpoint directory /tmp/tmpa48mavq5 exists and is not empty.\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-04-10 18:39:16,255 - \n",
-      "  | Name            | Type                 | Params\n",
-      "---------------------------------------------------------\n",
-      "0 | autoencoderkl   | AutoencoderKL        | 75.1 M\n",
-      "1 | discriminator   | PatchDiscriminator   | 2.8 M \n",
-      "2 | perceptual_loss | PerceptualLoss       | 2.5 M \n",
-      "3 | adv_loss        | PatchAdversarialLoss | 0     \n",
-      "---------------------------------------------------------\n",
-      "77.8 M    Trainable params\n",
-      "2.5 M     Non-trainable params\n",
-      "80.3 M    Total params\n",
-      "321.225   Total estimated model params size (MB)\n",
-      "Sanity Checking DataLoader 0:  50%|███████████████████████████████████████████████████████                                                       | 1/2 [00:04<00:04,  4.34s/it]"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 16. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0:   2%|█▎                                                                                   | 8/500 [01:59<2:02:20, 14.92s/it, v_num=0, recons_loss=0.212, loss_g=0.215]"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Detected KeyboardInterrupt, attempting graceful shutdown...\n"
+      "2023-04-12 14:28:55,430 - `Trainer.fit` stopped: `max_epochs=75` reached.\n"
      ]
     }
    ],
    "source": [
-    "n_epochs = 75\n",
+    "n_epochs = 75 \n",
     "val_interval = 10\n",
     "\n",
     "                          \n",
@@ -549,6 +733,7 @@
     "trainer = pl.Trainer(devices=1,\n",
     "                     max_epochs=n_epochs,\n",
     "                     check_val_every_n_epoch=val_interval,\n",
+    "                     num_sanity_val_steps=0,\n",
     "                     callbacks=checkpoint_callback,\n",
     "                     default_root_dir=root_dir)\n",
     "\n",
@@ -568,7 +753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "ccb6ba9f",
    "metadata": {},
    "outputs": [
@@ -576,16 +761,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Scaling factor set to 0.3829643428325653\n"
+      "Scaling factor set to 0.5467963814735413\n"
      ]
     }
    ],
    "source": [
-    "train_loader = ae_net.train_dataloader()\n",
-    "check_data = first(train_loader)\n",
-    "z = ae_net.autoencoderkl.train(mode=False).encode_stage_2_inputs(check_data[\"image\"].to(ae_net.device))\n",
-    "print(f\"Scaling factor set to {1/torch.std(z)}\")\n",
-    "scale_factor = 1 / torch.std(z)"
+    "def get_scaler_factor():\n",
+    "    ae_net.eval()\n",
+    "    device = torch.device(\"cuda:0\")\n",
+    "    ae_net.to(device)\n",
+    "\n",
+    "    train_loader = ae_net.train_dataloader()\n",
+    "    check_data = first(train_loader)\n",
+    "    z = ae_net.autoencoderkl.encode_stage_2_inputs(check_data[\"image\"].to(ae_net.device))\n",
+    "    print(f\"Scaling factor set to {1/torch.std(z)}\")\n",
+    "    scale_factor = 1 / torch.std(z)\n",
+    "    return scale_factor\n",
+    "\n",
+    "scale_factor = get_scaler_factor()"
    ]
   },
   {
@@ -599,7 +792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "731034ec",
    "metadata": {
     "lines_to_next_cell": 2
@@ -624,7 +817,7 @@
     "                          beta_schedule=\"linear\",\n",
     "                          beta_start=0.0015,\n",
     "                          beta_end=0.0195)\n",
-    "        self.z = ae_net.autoencoderkl.train(mode=False)\n",
+    "        self.z = ae_net.autoencoderkl.eval()\n",
     "\n",
     "\n",
     "    def forward(self, x, timesteps, low_res_timesteps):\n",
@@ -632,68 +825,106 @@
     "                         timesteps=timesteps,\n",
     "                         class_labels=low_res_timesteps)\n",
     "    \n",
-    "    \n",
+    "           \n",
     "    def prepare_data(self):\n",
-    "        train_transforms = get_train_transforms()\n",
-    "        val_transforms = get_val_transforms()\n",
-    "        \n",
-    "        train_data = MedNISTDataset(root_dir=self.data_dir, section=\"training\",\n",
-    "                                    download=True, seed=0)\n",
-    "        train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
-    "        val_data = MedNISTDataset(root_dir=self.data_dir, section=\"validation\",\n",
-    "                                  download=True, seed=0)\n",
-    "        val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
-    "        \n",
-    "        self.train_ds = CacheDataset(data=train_datalist, transform=train_transforms)\n",
-    "        self.val_ds = CacheDataset(data=val_datalist, transform=val_transforms)\n",
+    "        self.train_ds, self.val_ds = get_datasets()\n",
     "        \n",
     "    def train_dataloader(self):\n",
     "        return DataLoader(self.train_ds, batch_size=16, shuffle=True,\n",
     "                          num_workers=4, persistent_workers=True)\n",
     "        \n",
     "    def val_dataloader(self):\n",
-    "        return DataLoader(self.val_ds, batch_size=16, shuffle=True,\n",
+    "        return DataLoader(self.val_ds, batch_size=16, shuffle=False,\n",
     "                          num_workers=4)\n",
     "    \n",
-    "    def _calculate_loss(self, batch, batch_idx):\n",
+    "    def _calculate_loss(self, batch, batch_idx, plt_image=False):\n",
     "        images = batch[\"image\"]\n",
-    "        low_res_image = batch[\"low_res_image\"]       \n",
-    "        latent = self.z.encode_stage_2_inputs(images) * scale_factor\n",
-    "        latent =  latent.detach() # avoid adding this to graph.\n",
-    "        optimizer = self.optimizers()\n",
+    "        low_res_image = batch[\"low_res_image\"]  \n",
+    "        with autocast(enabled=True):\n",
+    "            with torch.no_grad():\n",
+    "                latent = self.z.encode_stage_2_inputs(images) * scale_factor\n",
+    "#             latent =  latent.detach() # avoid adding this to graph.\n",
     "        \n",
-    "        # Noise augmentation\n",
-    "        noise = torch.randn_like(latent)\n",
-    "        low_res_noise = torch.randn_like(low_res_image)\n",
-    "        timesteps = torch.randint(0, self.scheduler.num_train_timesteps, (latent.shape[0],),\n",
-    "                                  device=latent.device).long()\n",
-    "        low_res_timesteps = torch.randint(\n",
-    "            0, self.max_noise_level, (low_res_image.shape[0],), device=latent.device\n",
-    "        ).long()\n",
     "\n",
-    "        noisy_latent = self.scheduler.add_noise(original_samples=latent, \n",
-    "                                           noise=noise, timesteps=timesteps)\n",
-    "        noisy_low_res_image = self.scheduler.add_noise(\n",
-    "            original_samples=low_res_image, noise=low_res_noise, \n",
-    "            timesteps=low_res_timesteps\n",
-    "        )\n",
+    "            # Noise augmentation\n",
+    "            noise = torch.randn_like(latent)\n",
+    "            low_res_noise = torch.randn_like(low_res_image)\n",
+    "            timesteps = torch.randint(0, self.scheduler.num_train_timesteps, (latent.shape[0],),\n",
+    "                                      device=latent.device).long()\n",
+    "            low_res_timesteps = torch.randint(\n",
+    "                0, self.max_noise_level, (low_res_image.shape[0],), device=latent.device\n",
+    "            ).long()\n",
+    "\n",
+    "            noisy_latent = self.scheduler.add_noise(original_samples=latent, \n",
+    "                                               noise=noise, timesteps=timesteps)\n",
+    "            noisy_low_res_image = self.scheduler.add_noise(\n",
+    "                original_samples=low_res_image, noise=low_res_noise, \n",
+    "                timesteps=low_res_timesteps\n",
+    "            )\n",
     "\n",
-    "        latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n",
+    "            latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)\n",
     "\n",
-    "        noise_pred = self.forward(latent_model_input, timesteps, low_res_timesteps)\n",
-    "        loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+    "            noise_pred = self.forward(latent_model_input, timesteps, low_res_timesteps)\n",
+    "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+    "        \n",
+    "        if plt_image:\n",
+    "            # Sampling image during training\n",
+    "            sampling_image = low_res_image[0].unsqueeze(0)\n",
+    "            latents = torch.randn((1, 3, 16, 16)).to(sampling_image.device)\n",
+    "            low_res_noise = torch.randn((1, 1, 16, 16)).to(sampling_image.device)\n",
+    "            noise_level = 20\n",
+    "            noise_level = torch.Tensor((noise_level,)).long().to(sampling_image.device)\n",
+    "            \n",
+    "            noisy_low_res_image = self.scheduler.add_noise(\n",
+    "                original_samples=sampling_image,\n",
+    "                noise=low_res_noise,\n",
+    "                timesteps=noise_level,\n",
+    "            )\n",
+    "            self.scheduler.set_timesteps(num_inference_steps=1000)\n",
+    "            for t in tqdm(self.scheduler.timesteps, ncols=110):\n",
+    "                with autocast(enabled=True):\n",
+    "                    with torch.no_grad():\n",
+    "                        latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)\n",
+    "                        noise_pred = self.forward(latent_model_input, \n",
+    "                                                  torch.Tensor((t,)).to(sampling_image.device)\n",
+    "                                                  , noise_level)\n",
+    "                    latents, _ = self.scheduler.step(noise_pred, t, latents)\n",
+    "            with torch.no_grad():\n",
+    "                decoded = self.z.decode_stage_2_outputs(latents / scale_factor)\n",
+    "            low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode=\"bicubic\")\n",
+    "            # plot images\n",
+    "            \n",
+    "            self.images = images\n",
+    "            self.low_res_bicubic = low_res_bicubic\n",
+    "            self.decoded = decoded\n",
+    "            \n",
     "        return loss\n",
+    "    \n",
+    "    def _plot_image(self, images, low_res_bicubic, decoded):\n",
+    "        plt.figure(figsize=(2, 2))\n",
+    "        plt.style.use(\"default\")\n",
+    "        plt.imshow(\n",
+    "            torch.cat([images[0, 0].cpu(), low_res_bicubic[0, 0].cpu(), decoded[0, 0].cpu()], dim=1),\n",
+    "            vmin=0,\n",
+    "            vmax=1,\n",
+    "            cmap=\"gray\",\n",
+    "        )\n",
+    "        plt.tight_layout()\n",
+    "        plt.axis(\"off\")\n",
+    "        plt.show()\n",
     "\n",
     "    def training_step(self, batch, batch_idx):\n",
     "        loss = self._calculate_loss(batch, batch_idx)\n",
     "        self.log(\"train_loss\", loss, batch_size=16, prog_bar=True)\n",
     "        return loss\n",
-    "\n",
     "        \n",
     "    def validation_step(self, batch, batch_idx):\n",
-    "        loss = self._calculate_loss(batch, batch_idx)\n",
+    "        loss = self._calculate_loss(batch, batch_idx, plt_image=True)\n",
     "        self.log(\"val_loss\", loss, batch_size=16, prog_bar=True)\n",
     "        return loss\n",
+    "    \n",
+    "    def on_validation_epoch_end(self):\n",
+    "        self._plot_image(self.images, self.low_res_bicubic, self.decoded)\n",
     "\n",
     "    def configure_optimizers(self):\n",
     "        optimizer = torch.optim.Adam(self.unet.parameters(), lr=5e-5)\n",
@@ -714,733 +945,587 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "936bbb9c",
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-10 18:41:46,760 - GPU available: False, used: False\n",
-      "2023-04-10 18:41:46,761 - TPU available: False, using: 0 TPU cores\n",
-      "2023-04-10 18:41:46,762 - IPU available: False, using: 0 IPUs\n",
-      "2023-04-10 18:41:46,762 - HPU available: False, using: 0 HPUs\n",
-      "2023-04-10 18:41:46,869 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-04-10 18:41:46,869 - INFO - File exists: /tmp/tmpa48mavq5/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-04-10 18:41:46,870 - INFO - Non-empty folder exists in /tmp/tmpa48mavq5/MedNIST, skipped extracting.\n"
+      "2023-04-12 14:29:00,910 - GPU available: True (cuda), used: True\n",
+      "2023-04-12 14:29:00,911 - TPU available: False, using: 0 TPU cores\n",
+      "2023-04-12 14:29:00,912 - IPU available: False, using: 0 IPUs\n",
+      "2023-04-12 14:29:00,912 - HPU available: False, using: 0 HPUs\n"
      ]
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+      "2023-04-12 14:29:01,593 - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
+      "2023-04-12 14:29:01,624 - \n",
+      "  | Name      | Type               | Params\n",
+      "-------------------------------------------------\n",
+      "0 | unet      | DiffusionModelUNet | 266 M \n",
+      "1 | scheduler | DDPMScheduler      | 0     \n",
+      "2 | z         | AutoencoderKL      | 75.1 M\n",
+      "-------------------------------------------------\n",
+      "342 M     Trainable params\n",
+      "0         Non-trainable params\n",
+      "342 M     Total params\n",
+      "1,368.189 Total estimated model params size (MB)\n"
      ]
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-      "Loading dataset:  75%|█████████████████████████████████████████████████████████████████████████████████████                            | 35506/47164 [00:33<00:10, 1134.94it/s]\u001b[A\n",
-      "Loading dataset:  76%|█████████████████████████████████████████████████████████████████████████████████████▎                           | 35622/47164 [00:34<00:10, 1121.34it/s]\u001b[A\n",
-      "Loading dataset:  76%|█████████████████████████████████████████████████████████████████████████████████████▋                           | 35756/47164 [00:34<00:09, 1180.96it/s]\u001b[A\n",
-      "Loading dataset:  76%|██████████████████████████████████████████████████████████████████████████████████████                           | 35896/47164 [00:34<00:09, 1243.42it/s]\u001b[A\n",
-      "Loading dataset:  76%|██████████████████████████████████████████████████████████████████████████████████████▎                          | 36023/47164 [00:34<00:08, 1250.89it/s]\u001b[A\n",
-      "Loading dataset:  77%|██████████████████████████████████████████████████████████████████████████████████████▋                          | 36176/47164 [00:34<00:08, 1331.38it/s]\u001b[A\n",
-      "Loading dataset:  77%|██████████████████████████████████████████████████████████████████████████████████████▉                          | 36310/47164 [00:34<00:08, 1294.63it/s]\u001b[A\n",
-      "Loading dataset:  77%|███████████████████████████████████████████████████████████████████████████████████████▎                         | 36441/47164 [00:34<00:08, 1213.64it/s]\u001b[A\n"
-     ]
+     "data": {
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+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset:  78%|███████████████████████████████████████████████████████████████████████████████████████▋                         | 36587/47164 [00:34<00:08, 1279.91it/s]\u001b[A\n",
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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset:  89%|███████████████████████████████████████████████████████████████████████████████████████████████████████▎            | 5248/5895 [00:04<00:00, 989.63it/s]\u001b[A\n",
-      "Loading dataset:  91%|████████████████████████████████████████████████████████████████████████████████████████████████████████▋          | 5365/5895 [00:05<00:00, 1035.91it/s]\u001b[A\n",
-      "Loading dataset:  93%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▋        | 5470/5895 [00:05<00:00, 1025.43it/s]\u001b[A\n",
-      "Loading dataset:  95%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████▋      | 5574/5895 [00:05<00:00, 962.79it/s]\u001b[A\n",
-      "Loading dataset:  96%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████▌    | 5672/5895 [00:05<00:00, 966.62it/s]\u001b[A\n",
-      "Loading dataset:  98%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋  | 5775/5895 [00:05<00:00, 984.30it/s]\u001b[A\n",
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:05<00:00, 1060.59it/s]\u001b[A\n",
-      "\n",
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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-04-10 18:42:51,417 - \n",
-      "  | Name      | Type               | Params\n",
-      "-------------------------------------------------\n",
-      "0 | unet      | DiffusionModelUNet | 266 M \n",
-      "1 | scheduler | DDPMScheduler      | 0     \n",
-      "2 | z         | AutoencoderKL      | 75.1 M\n",
-      "-------------------------------------------------\n",
-      "342 M     Trainable params\n",
-      "0         Non-trainable params\n",
-      "342 M     Total params\n",
-      "1,368.189 Total estimated model params size (MB)\n",
-      "Sanity Checking: 0it [00:00, ?it/s]"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Your `val_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a2dbfb54bcc8427682275ec8ca7717ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d3152112c4747c3a14274f8f9169661",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a42ac2f90cb461fb9f0c82d544fb8d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8e20b5bfbc64b28a910c9d11ef6851c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6d737f986a64c22895476a68c2b3e87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1743b884fbf4ae4bebf051ea1f5bf23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e79ff41ccf54a45885ea18dd5bfe401",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3dd47e394441481da5c2e8b8af54a451",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c23581c04d474a69a45d37fce99ec3f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96313db64cb54ee991281b16c1276823",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "009e46d5a9094efb96c33cbed7b09d93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d28bcf839d3f438bb525f98f15c31092",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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I0dryXUVUJaLqDL0/MjICj8fDxkVjPYwd2TUTjVutVlbEp7TCzMwMFhcXkclk0Gq1oCgKcrkcKpUKo3jSQ+Qi6XykS2gCaVGpA4fceSaTgWEYGBgY6LkJk4xfdHJlrVaL5eQEYa9pgTaXOZ1ibpAlEPLfNR9D901ujIIBc9dOpzGaP6N5HhwchMvlgtvthizLjOErlQrzOsDjxgYCFKXcaJMTm5F2pPNTCxyRB1VcXC4XK4dS5cwMxM/ENdPNkUWjUaTTaTgcDjQaDdjtdgSDQezu7rKUDAGNAhcyAgHw2A3QBJG75hdPEAQEAgEEg0FWxD+sEauQPCgUCiyXZ7PZAAA2m62tU4VYhZjZHCGamw148PAazNy/uF9ViPcStEkJnBTxDw8Ps7Hm83msrKwgm82yxDVteGJCYjR+/Lz75dmdxklzBbSzeKFQQD6f31dHH2RHBiLvLhKJBO7fv4+RkREGjNOnTyMWiyGfz0PX9SdcC+ktSumQe6Qbph1OC0+NBLQo1ExAE9GrkY4iMOXzecRiMcRiMfj9fjgcDthsNthsNiiK0tZN02kOaIPwC9Gpd5I2lc1mY2kRCir4zWhmFjOg+ccPSBZJkgSHw4FsNoutrS1WwaJ0DTEc7wGouiUIApMd/EbiNTLlN4vFIrLZLCqVClRVxebmJtLp9BO6uNfUDXBMGpEAlcvl8M477zBWMQwDwWCQuTa+dYoaS/nWe2IaytVVq1WmD8mNG4aBUqnEUhOBQABWqxWZTAa5XO5QY+eDn1QqhY2NDWxsbLBJtdvtcDgcrMRF8oDGa2Yy2lC0uXhNS8cQaPjgpFqtMkCYI/BOxifPKfih/Gur1YLT6WSdTjRnFoulreGiVquxZDYfvJi7zPmsBX2/VCqx+S6Xy0gkEm1FCLMn6GktDrVyXYzXLzdu3MDHH3/Mdp/NZkMwGES5XEa5XGapEtJDxBD0r8ViaduhdF7DMJgeGRwchMPhwJe+9CX2nbm5OdYFYtZlnYzP7dXrdezu7mJtbQ0rKyuIx+OoVCqwWq2MvcgN8QEIHc8HJnRNXnPxkSTVbL1eL+x2O4A9hqFF7VZXblu0/16/1WqhVCphfX0d+Xwe2WwWu7u7EAQBU1NTmJqaYoxObWCUnaCqCg9CYkBefvDpK/Nn1GtJXedm/Ur33Isdi2sWRZGV4dLpNN58802Ew2GcOnWK1UD9fj8DY6PRYFoSAKrVKmvhonwVH81ZLBbY7XYIgsCiNpfLhdOnT7PdfefOnUPrE0qkE/tWq1UGxmAwCACsYkDBER/90gYhBjdrJNJx5DWom8Xr9cLn88FqtaJQKCCRSDD5Qvdr3kz87zwjFotFLC4u4oUXXmDno8cZzp07h+XlZWSzWSZ9+HPwTxWSTKF7ovnhNS/PluTVcrkcNjc3n8ghdgrW9rNjYUTSEMBeIvv27dv42c9+hvn5eQCAz+eDKIrMZfO1VEVRGCD58hm9pudeqKhOjDExMcEeK7h//z7ef//9Q2kSvipCwVOpVMLOzg7m5+dx9+5d3L9/n1Uo3G43a1bw+XxwOp2MyXlGJIbk9RUFXNSbGAwGMTQ0BLvdjlKphGg0irW1Nezu7rIkcy8NHBRkzc3N4dGjRyw1VCwW0Wq1EAgE4HK5mEs1N2qY9Sp5KX6O+ICMT+cMDAxAVVUsLy9jdXWVnfNpqirAMdWa+c4N0ny3bt1CNBrFG2+8gZdffhlXrlzBW2+9hUePHkGWZZw7d46lFqxWK8tDVavVtiw/5dw0TUMgEICu6xgbG8MXv/hFWK1W5HI5/PWvf0UqlXqiGnCQ8ZNO4y4UCnj48CGy2SxisRjGx8cxPDzMmluppZ5a6Dslf4HHVQySKBRIeDweDA4OwufzsSBrfX0da2tryGQybQ+VddpY5oS5YRhYW1vD3//+d+b2qRzpcrlw9uxZLC0toVKpsDwl8Ni9UyrHYrEwd03foWoZz9CUbnK73Ugmk7h58yZisVjbPHQb+352LK6ZdgpFYsQOkUgEP//5z/H666/jm9/8Jl5//XV8+OGHSKVSUBQF6+vraDabbGFlWWaLQJpQ0zQ0m01UKhUkEglMT0/jpZdeYm37d+/exY0bN5DL5WC323vuvuGNd1OVSgXRaBSJRAK5XA7FYhGGYTD2APZSF+SCKNgh9iM2oEpMs9lkFRuXy8UkSLFYRCKRwNbWFqLRKGKxGKrVKmPYXsYsCI+fhpybm0M4HMb58+ehKApLxF++fBnLy8vMO9HGMOs+vp2Mzk9pMXpNAZrX60Wr1cK//vUvfPTRR211a36jHAaMx9L0wFdNiFkotbK+vo7f/e53+Oc//4mf/OQn+MpXvsIivOXlZczNzSGTyUCWZZYHI5GsaRoslr0HmXK5HCYmJlh3T7PZxMLCAt544w1sbGzAbrejVqsduieRjCaNosl8Pr83Qf+NMiuVCgYGBlgNnVrpiYEpsqdFITYkbUhVDkEQWDD08OFDPHz4EPF4nMkW/hnnbgvJ5/1IV6fTaTx48AAOh4OBxefzweVyYXZ2FrVaDVtbW2yj8jKIfszsTnqc9KHFYsHAwAB8Ph+Wl5dx8+ZNpj+JYfnxHWr+Wz0eddDDU+YdYC7XAYDdbsdrr72G7373u5ienmbuYGVlBbu7u6w9ioIHanoIBoMYHx/HmTNnYLfbsbu7i+vXr+P3v/89otEoA2+vnTfdxg+gjdk0TcPAwAB75NXtdsPj8SAcDmN0dJQFYRR512o1FvQAYKUwAletVkM8Hsf29jZWV1fx6aefYmtri7EvHQPs/wcFyMzSwul0YmxsDBMTE3j++efxwgsvIBwOQxAERCIRfPDBB0gmk0wv0nGkbckIVJReI0b0er0YGRlBMpnEX/7yF8zNzTF51SnfSSm5WCx24Pwf2zMr5onqtKuLxSL++Mc/4vbt2/jBD36AV199FV6vFxcvXkSr1UI+n0c0Gm0LUtxuN4LBIJuMTz75BG+++Sb+9re/oVwus4WjVrSjjp8YGNhjjVQqhXQ6jUQiAafTiaGhIQjC4zo4XwYD2nsFqT5NrrpUKmFzcxPz8/N48OABVldXWeub+THawxrlE5eWllgfJTWOeDweKIqCyclJAHtPSfKSioxPZPN5X1VVWWdSJpPBW2+9hfn5edaU8rSRctv8Hxcj9mJ8akBVVVy4cAHf+973cPXqVYRCobZnm+m7RPfJZBLXr1/Hn//8Z9y/f59NEnWYKIrS818V6HWslMSl51ko9XLmzBmMj49jdHQUoVAINpuNLR65ZeBxAERBQCqVwtLSEhYXF7G8vIx4PM7molt9msZijmbNn9OGoIjb7/fjzJkzOHv2LGPzQCAAi8XCSnJUi6aMBO9a6Y8LeL1e+P1+2O12LC4u4saNG7h37x4KhQKbe97MY+uVET9zINJOJH1TLpcRDAZx+fJl9gxxIBBgbBSNRnHnzh08ePAAqVSKuUy+QYGv+x628WG/sfLpF8ptEktT8OF2u1kkrWlaWxKegEydNvToAV+V4AMB4OnYhIDK3zvpdQr4nE4nRkdHcfbsWYyOjiIQCDyhcYlBqdl2cHAQmqax6Pjtt99mD7916lLvFKg0m//jB+w7GV2KIk2qHdPO4ovrpKlIy5iL8VTElySJVWKO6p67jZesk26jqoXD4WBlOyqtUSMF/aWEbq7sOMZoBjIPCr7GTRtpeHgYIyMjGBoawvDwMPt7PjTPtMGj0Shu3bqF9957D9lslmVGzHPSrZrVarVOLhC71SIp4OBTMHw9mI41uxH6TrVafeqouZexk1ygwISv4fJNtfxTiKVSCeVymX2fNg/VfXu9di+g7RQg0vzwjEksTC1dFGFTkh7YW4t8Po+trS3s7OygUCgw70Dn6HQ9uib/+sQB8SA7bO7puI8/6NzA4yYASjERE/N/oo7ydARa/sF7AkSv1RO6ZifJwW/mTrVdHozm4/hx8J6IPx/lKfnHDfhgzAz4TuP7zKPm47CjguhZgZDOzefLOrFZp+ecgcd/du8o1+b/NYOvm3XamGbm4suz5vPziW+zBzOD8KgkcKKAeNKt00TzCWH+h69lHzW10WkcPAOaE9Fm1jKPvxOQO32vUydQL6DbL8LvZn0gHoPxIOvmBvnvHdc1zaAH9m+/4r/biU3Nbhl4/Oy3Gfz7MW0v75utD8QjWi8LclQA7sduT3OubtYNoN3O00krPu24+kA8JnvW+vQge1qNZgZ3L8za6zgoF9uL9YH4ObBOIOsGlP2Ys1tku19Q003vdbvG0+hDoA/Ez4XtV/LrFVwHRdrdkvfdzm8eV6dk/2Gs/59Cfk7sMPpwP9AdxHwHachuzNzt936w8v/YukXE+32vW1BhTjXx75nP1eka1DPQDZC9ArHPiJ8DO4h1ejmuU2qm12tQ0MHnSfczc6qnF+sz4ufEemGc/d4zH7tfuqVTErvb+Tq5dL4c2qv1XGvuW9+epfVdc99OhPWB2LcTYX0g9u1EWB+IfTsR1gdi306E9YHYtxNhfSD27URYH4h9OxHWB2LfToT9HywjdaQGGjYWAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "712d910c6b8a4d529c7b936c29f8c70a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb338a4be9044e6b899b95847c926ba8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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wOp2o1+vQdR2VSoVpOL6QgV4UR3U4HFAUhYXgyGzhx4Zv6354bCLSYAuCwJL4ZOiePHkSt2/fRj6fR6vVgqIoKBaLqNVqbIDIHuI7T6uXV1Nkz1Dowe12wzRN5PN5WJYFn8/XMQXWC/wgNxoNNpiUN3Y47hct0KKxq0d7gSyvljuFXkg6NRoNFjd1Op0dq3bsbezWdj71R+PJx0ZJzdI48qqX3klSUjSDn1fec+fz8RQVoXiqfRyeahyROkiIx+PI5XLwer0wTRMejwfRaJQl0WnQSA3woRZ+RdEg0uoldc1PnsPhQCQSQTQaZXWBBwUNGE1CuVxmYQi32w0AcLvdTOrbJ5OfHLpfJ/vQ7gBQqIQvjthPjfHEo/OoPWQz67qOSCSCfD4PAEwwkMahRcA7H/y9aPEDYNqg0Wi09Y1X7+StU9UR9aub6dILj01E3l1PpVK4du0aJicnGTGmpqaQSCRY8psfAN4JIbFP6pFIShuHaOKpkIA8NyomKJVKB+o87yU2m02USiUW6giHw/B6vaxESlGUtmqaTmNAC4SXXp1qJ2lRud1uuFwuFvoyDOMh9Ub35j11/pkEy7KQSqVQKpWwuLiI6elpZuqQ9uFTkPz9iXz8iw+U07Ps2Reyqz0eD1qtFgutEaEP6rA8ttdMXpJlWSgWi3jvvfeYVLEsC9FolKk2vnSKCkv50ns+eW6aJgzDYBkMUjuUKyWbMRKJQBAE5PN5FIvFg3Wec36y2Sy2t7exvb2NXC6HVqsFj8cDr9cLVVVZsQIf7rBLMlpQNPG8LUbX0OTxzolhGG2Fp70m0p6xoEWQz+exu7uLVqvFaiep0NjuDdvBB7QlSWLFGXxpHZGTJyzZ+3xBC/9+oLk48BUdwEf53333XXz88ces4W63G9FolFV1UKiE7CGSEPROhjNNGt3XsixWnjUyMgKv14uXXnqJnXPjxg3m+HQLdfDgY3uNRgOZTAZ3797FxsYGkskkcyhIepEzwReZ0vUUDrHbU/aJIQPf5/MhFArB4/EAACqVCorFIqt66adim7cNLctCpVLBxsYGy4OT2iQVyy9mPkhN0QByVviiB5KEfN+AB8KHStZoKwSR3Z7a7AeHoppFUWSeWS6Xw9tvv42ZmRmMj49DEASMjo4iHA4zMpqmyWxJADAMg5VwUVaFViOFEqhyWJZlVjE9NTWFVquFer2Oy5cvHyjsAYCpGpK+hmEwMkajUQD3VZvD4WDOER9PowXCV6rwbSBJQhOnKAo8Hg9CoRCGh4chCALK5TJSqRQzX6i//UwgHx80TRPxeBzZbJaNFZGHJ5E9pMSn8Ehi8jFHAplNvHqmOSKzgK61t7EfHEqKj+JsJK7/9a9/4ec//zl+9rOfYWlpCcPDwxBFEZqmAUBbLpVUAJ8+421Cy7LgdrvZHhaSGHNzcwiFQlAUBZcvX8YHH3xwILuEP5ecJ9M0WZUQlcl7PB6YpolAIMCkY7lcZsWs1BcaB/6efPyOr02MRqMYHR1FtVpFIpFAPB7H3bt3WX2fPShsd3rsoLjgvXv3sLu7i7m5ObjdbpY+pciCXRra03m0AEhbEeySlNpD8V7Kwx/UU+ZxKERsNu9vCyAPFwAuXbqEeDyOt956C+fPn8fp06fxzjvv4LPPPoMsy1hYWGABbUEQEAwGAYDFtWjl1mo1aJoGVVURiUSg6zqmp6fxxS9+EYIgoFgs4i9/+Quy2WxbuKEf8ANG7S6Xy7h58yYKhQISiQRmZ2cxNjbGilvJQyyXyywzRPfqNHk0yZIkwev1IhgMsqpncrK2trZw9+5d5PP5tk1l+00o70m3Wi3cu3cPly5dgqIomJ+fZ3uDJEnC7du3WdUQH3YiE8Juw5NdSdEN0lREWDJXdnZ2cPXqVVQqFaYV+FRhvzgU1Ux2BMXiyKZYX1/HL37xC7z55pv45je/iTfffBNXrlxBNpuFoijY2tpiKb5yucxsGgDMJlRVFc1mE7VaDalUCsePH8fZs2dZ2f7KygreffddFItFVvh5UJC9CNwnfjweRyqVQrFYRKVSgWVZzIYF7m/W4rMSNAGkvigCQJNOGRu/389MkEqlglQqhZ2dHcTjcSQSCRiGwUI6/Y49kZXK6a5cuQLLsvDaa6/h1KlTCAaDOHHiBEzTxPb2NjRNY9KfzxrRgiIyklQmT57MJZLsfr8foiji6tWruHXrVpvH38nL3w+HUvRA2z75bYgUWtna2sIf/vAH/POf/8RPfvITfPnLX2YG8Z07d3Djxg3k83nIssxidqQGVFVlqaZisYi5uTlW3dNsNrG2toa33noL29vbbOPPQWsSCTShZLSTzUPhjFqtBp/Px3LotGuPz1hQtRAfZ+TVMoVryBm6efMmbt68iWQyySINvFru5nTZPWv6TBufPvzwQxZofv755+F0OhGLxSCKItLpNAul8U4K2b+SJDFpTm0gh4ccNL/fj9HRURSLRaysrCCXy7UFwqlNByHkoW2e4lcn/5k/7vF48MYbb+A73/kOjh8/Dqfz/p7kjY0NZDIZVgxAzgNtjIpGo5idncX8/Dw8Hg8ymQwuXryIP/7xj4jH44y8/VbedGs/gDbJpqoqfD4f2/IaCAQQDAYxMzODWCzGnDDyvClXS3YjSRAiV71eRzKZxO7uLjY3N3Hr1i3s7Oww6UvXAJ1/UKCbnUjqn86hnZFjY2NYXl7GyZMnWTEHmTOZTAbFYpHtMCQbX1XVtg1ctMAAsP088/PzUFUVly5dwjvvvMOIyIfyyEYmE2Y/PLFdfJ1AAzo9PY0f/OAH+MY3vsEcjlarhVKphHg8zqQK7YGORqNsgK9fv463334bf/vb31CtVtvikI8Sv+rURv5vyiD4fD4MDQ1hdHQUJ06cwOLiIubn5xGLxVgunfe+yZinYg5KR966dQurq6v45JNPsLm5yUrf+IoXu23IB5jt08WT0x5iocrzyclJzM/PY35+HnNzc/D7/XA4HG0ee6VSYc4jnwHjVXYgEMD4+DhCoRCuX7+Ov/71r/jss89Y5TYfhaA+U7ZqPzx1ItKAu1wuLC0t4Xvf+x7OnTuHiYmJtr3NdC4NbjqdxsWLF/GnP/0J165dY7YMrVZFUdq81sNoKyX4TdNsC73Mz89jdnYWsVgMExMTcLvdbPL4MAgtkkajAV3Xkc1m8emnn+L27du4c+cOkskkG4tu+WlCNzXdLTfNB6kpdknOy/z8PJ555hlMTU1BkiQWBSD1ShKN39NCpohlWVhdXcWlS5ewsbHBnBS+QMJezX0kiUh2EIUNqtUqotEolpeX2R7iSCQCy7KQzWYRj8dx+fJlfPLJJ8hms0xl8gUKfN73oIUPvdpKapoWB2US6McB/H4/AoEA86RJrdGEEJE1TUOtVmNbDygLRIFnPlzSiYR8vK8X7NeSqiZPl/9pl2g0ioWFBczNzbEfPCBnkV5UdaTrOjKZDLa2trC2tobNzU2Uy+W2qm9+fmkhHGkiAmCeZut/uWOSauQx0gCSMQ08SMfxOWoyrikTQ0b1YaGTGuS/I0fE5XLB6/WytB3lyqmQolKpsLgjv83gUeKe/ZKRt835ZxExaeM/xUZpcVEq0+FwMPtQ13XkcjkWRSCvm/cD7GNG19OGs/3wf/lZOl798lsF+E1YBFqlvNrlVTbFt2gFP6rX3Al24pEBTo4JtYO8Yiqq5XchapqGarXKzqfFQxN+GG3sRE570QSvssn2MwwD5XIZTqcTiUSCFZjYzSI+zMNnkOyk5yUj/94PnioR98sB81F+HvY9HPz3vBo8LLXcDbw04esmaVGRPUnf8dXQ5LTQPQ4iDe0RiU4quNs1nSQvb8cBD7JA5O3zUo3/bG+LXSXvF3bqhSP1Q52Pkho6zOv3uzefCuskzTrtcwYe/OzeYbWjFzqpyV7XdrLv+PPtkpWH3ZM/aBCbx5Ei4lFHt4ns9OKlkd1Ge9JttBNiP4L0Uu38993sQfq7k+TtFwMiHgJ4knWSQk+LhPb2AL1/R4c/bpeE9uu7gb9/N3u1HwyI+Jjox157UgTsRbL9pKLdwejkbNjt0l4k6/Zdv30fEPGQ8KSlXT+SphOB9lOZRLBuqcNu19jRibQHGZMBET9HOKjq60dK9WMf9rINO7Wvl8feDYP/xfc5QjcC2MMnhEe1TztJv0cJwB/kmoFE/JzDrgZ7TXy37+wqvZsq78dG7BXu6YUBET9HsDsX/PFO53ZCN8nJf7dfWGa/tvXTDjsGRPwcYb9QTL/X2wsV+GPdvOtu6LUIev1wlR0DIn4OQHnubhLR/rmbB2x/8Rup+F8866WKO9md3Y61Wi1WtLIf+q6+GWCAJ4mB1zzAkcCAiAMcCQyIOMCRwICIAxwJDIg4wJHAgIgDHAkMiDjAkcCAiAMcCQyIOMCRwH8BfG1SlC8T+1IAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 0:   0%|▍                                                                                                   | 2/500 [00:16<1:08:40,  8.27s/it, v_num=1, train_loss=0.980]"
+      "2023-04-12 15:10:07,913 - `Trainer.fit` stopped: `max_epochs=200` reached.\n"
      ]
     }
    ],
@@ -1461,6 +1546,7 @@
     "trainer = pl.Trainer(devices=1,\n",
     "                     max_epochs=n_epochs,\n",
     "                     check_val_every_n_epoch=val_interval,\n",
+    "                     num_sanity_val_steps=0,\n",
     "                     callbacks=checkpoint_callback,\n",
     "                     default_root_dir=root_dir)\n",
     "\n",
@@ -1478,57 +1564,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "155be091",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2d9060b0533142ceac3699efbb441dc5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|                                                                                | 0/1000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Sampling image during training\n",
     "num_samples = 3\n",
-    "val_loader = d_net.val_dataloader()\n",
-    "check_data = first(val_loader)\n",
-    "images = check_data[\"image\"]\n",
-    "sampling_image = check_data[\"low_res_image\"][:num_samples]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aaf61020",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "latents = torch.randn((num_samples, 3, 16, 16)).to(images.device)\n",
-    "low_res_noise = torch.randn((num_samples, 1, 16, 16)).to(images.device)\n",
-    "noise_level = 10\n",
-    "noise_level = torch.Tensor((noise_level,)).long().to(images.device)\n",
-    "scheduler = d_net.scheduler\n",
-    "noisy_low_res_image = scheduler.add_noise(original_samples=sampling_image, \n",
-    "                                          noise=low_res_noise,\n",
-    "                                          timesteps=torch.Tensor((noise_level,)).long())\n",
+    "def get_images_to_plot():\n",
+    "    d_net.eval()\n",
+    "    device = torch.device(\"cuda:0\")\n",
+    "    d_net.to(device)\n",
     "\n",
-    "scheduler.set_timesteps(num_inference_steps=1000)\n",
-    "for t in tqdm(scheduler.timesteps, ncols=110):\n",
-    "    with torch.no_grad():\n",
+    "    \n",
+    "    val_loader = d_net.val_dataloader()\n",
+    "    check_data = first(val_loader)\n",
+    "    images = check_data[\"image\"].to(d_net.device)\n",
+    "\n",
+    "    sampling_image = check_data[\"low_res_image\"][:num_samples].to(d_net.device)\n",
+    "    latents = torch.randn((num_samples, 3, 16, 16)).to(d_net.device)\n",
+    "    low_res_noise = torch.randn((num_samples, 1, 16, 16)).to(d_net.device)\n",
+    "    noise_level = 10\n",
+    "    noise_level = torch.Tensor((noise_level,)).long().to(d_net.device)\n",
+    "    scheduler = d_net.scheduler\n",
+    "    noisy_low_res_image = scheduler.add_noise(original_samples=sampling_image, \n",
+    "                                              noise=low_res_noise,\n",
+    "                                              timesteps=torch.Tensor((noise_level,)).long())\n",
+    "\n",
+    "    scheduler.set_timesteps(num_inference_steps=1000)\n",
+    "    for t in tqdm(scheduler.timesteps, ncols=110):\n",
     "        with autocast(enabled=True):\n",
-    "            latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)\n",
-    "            noise_pred = d_net.forward(x=latent_model_input,\n",
-    "                                       timesteps=torch.Tensor((t,)),\n",
-    "                                       low_res_timesteps=noise_level)\n",
+    "            with torch.no_grad():\n",
+    "                latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)\n",
+    "                noise_pred = d_net.forward(x=latent_model_input,\n",
+    "                                           timesteps=torch.Tensor((t,)).to(d_net.device),\n",
+    "                                           low_res_timesteps=noise_level)\n",
+    "            # 2. compute previous image: x_t -> x_t-1\n",
+    "            latents, _ = scheduler.step(noise_pred, t, latents)\n",
     "\n",
-    "        # 2. compute previous image: x_t -> x_t-1\n",
-    "        latents, _ = scheduler.step(noise_pred, t, latents)\n",
+    "    with torch.no_grad():\n",
+    "        decoded = ae_net.autoencoderkl.decode_stage_2_outputs(latents / scale_factor)\n",
+    "    return sampling_image, images, decoded\n",
     "\n",
-    "with torch.no_grad():\n",
-    "    decoded = ae_net.autoencoderkl.decode_stage_2_outputs(latents / scale_factor)"
+    "sampling_image, images, decoded = get_images_to_plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "32e16e69",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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T28uRzNJpVL0nmiZP+x3kKZk+1fucS2Pn99cxohIbPXaU4lf9ifZlb+empSNZjbZNLg212h6th45dLy3UMm0rPZYvz0mbFH9N/fr1S8pUFhbJEvS3eo+0Ht6Pq6zSp5k0q/eRXragbaX9TO9JyTNO6YpypahOJbKhEjmSlpemqIyeJdrnVJrm5av6PH/HO96R2JH0UPuNvndof/a/1XGgY05lQ94eO3ZsUjZy5MjwvL5e2rf1eajlvs4q/9RxEckHdSzr9Ws6ZS8L9ylUzervmfaNp556qtq+5pprkrKrrroqsdVv+GPl3gX0GqI+qUT9WUGeBAAAAAAATYWPBgAAAAAACOGjAQAAAAAAQrp8TINqsaJ0dKoRi3RcqgnTY3ldvpnZr371q2r73//935OyX/ziF4n9wQ9+MLH//ve/V9vHHXdcUvbJT34ysU855ZTE9tew6667JmWaKkzxevrnnnsu3NcvQW+WtuUPf/jDpExTjv7+979PbK/jU02/6hYjTbZquXPLynu9ek5jvcUWW7RarwceeCAp0zRqqlPcaKONqm3VOndU+kFoG5HWM0oxalYfq+T7qva3XCyF92Paz1XLqumh/VjV8aU+QW0df1EdVVPt+/ayZcuSMh2LEaprzv3WxwxpGkbVl6vt207bVdtd77fXqmtaaY1r0vbwx4riaHJ0xRiFUqI02s2kvY6tz45cTIp/Pvzud79Lyq699trE1vgAj16PPqO1v/o04qrx19gCjS301zh06NCkbNNNN03srbfeOrG978ulMVYfW5IKVP2g9wUas6BxCZrafvDgwS0ex6z+3U/T0V933XXV9sMPP5yUaTvrWPd9J/cO2lXTKzPTAAAAAAAAIXw0AAAAAABACB8NAAAAAAAQ0uVjGqJctaoVzf22teO0hC4P7uMWHn300aTstNNOS2zVMX7+85+vti+++OKkTPWDhx9+eGJ7bd5nPvOZpOzUU09N7A9/+MOJ7TW5uoaDojpNr0387W9/m5Spvvess85KbL//Zz/72aRs8803T+w//OEPie1zRKuGT/XYqnH0++uaFrvttltia+7pe++9t9r+zne+k5Q99NBDia06ctWWQtdB+1AUP6D+RPOvqz/xY0a1yXoexZdHOnyzes2tj1Pw2lyz+r6pOuhIF6v9WH2g10EvWLAgKVP/EcUq5bS52naRdlmvX6/Xt20uh7zGKvm4hdmzZ7daZlbfVl67rbpuJXpOrW80M8Yrp/kuyU1fUq9c7KSPJ9B99dlZcu81dkLjFP2aRxrToH1bfZtH/dGDDz6Y2P5ZaZauc6DHjcayWeo3dW0sXUtJ14/w70Z6PzVeQO+DX/9Kn+933313Yj/99NOJ7de80PUvND5L+4avVyPjPtdfS2JFWKcBAAAAAACaCh8NAAAAAAAQwkcDAAAAAACEdPmYhs5CNcpeE3j77bcnZRprcOihhya2j3nwazaYmZ155pmJvcMOOyS21zdrnVRXPHfu3MT2uYlVV6v6ZdVLeu2l6nfHjBmT2H/7298S2+t/L7jggqTsl7/8ZWJrfMiiRYtaPI5Zuh6CWX07z5gxo9r2+s6W0LUYvvnNb1bbV111VVKm8Q+qq/b57LWdVeOY0zdDc1HdaKTnVB2sz3tuVr8Ww8qVK6ttHZs6niItr9ZR9cga4+Bt7U+5tWv8NWqZrjHidb9mqX9R36P9XLXMXiddGsPhr1HbWWMaNEbMt6Wus7F06dLEVt239z/PPPNMUqZtpVpu3856j/R+5/oo/IvSeIiS/RuJtYhiK3OxE2r7/SM9vFm9P/L9O7c+gh7bX4O+G6it67f4dwUdj7m1Fnx81oQJE5KyKVOmJLauteD9iMYUPfvss4mtcRi33nprta3vTer3o/bwzwCz+r6g98G3ey6mIfIDuf4alUd9ri0w0wAAAAAAACF8NAAAAAAAQAhaiVbQKSkvydFpbZ2q/tWvfpXYf/nLX6rt9773vUnZj3/848S+6aabWj2vyoLe9773JbamLItQiYDi06bqNJrKhi699NLE9ilY77jjjqTs8ssvT2xNb+qnYHUaNCf5iHjyyScT+ytf+Upi++lKlVaorTIGX+6nW83ql6SHjqUkvZxOLasdTVOrPEclbCoP8PXQsZiTr3jJzbJly5IylQ3p9XrfpXXWFI6aanDWrFktHses/vpUhuBlRZoWVafLI2mF3hOVaOg98teoY9FLIc3qr99LHLRM5UiRRKVEIrc+EMkfSlOdRnKdRshJNHx57v5EkiOVFJWgx9Uxpvg+mDtvJFfS513uvNEzOycn9FIgfY/Sdn/iiScS2/ugmTNnJmVq61iPfLf6EJUr+XbOvSuUSIz0njUyjkpkb6Uw0wAAAAAAACF8NAAAAAAAQAgfDQAAAAAAEEJMQyuoztin9FJdnqbdUj2z17/+/Oc/T8oOOuigxNb0rVtssUW1rXo51eJpmjGvEdTlzhcuXJjYqoX+3//932pbUxVqbIVfRt7M7MILL6y2L7rooqRM9XR+XzOzAw88sNrWFGuvv/56Yqv20N8zvb7zzjsvsa+44orE9ulc+/fvn5Tl9Nteg6110riLSPsMXQvVmEaa01z6vBJts/Yh9S9+LOuY0HGu49qPEf2taoo11bIfU1on9U2KP29Ou6tjxNdTfZEe64UXXkhsH/MQpVQ1S1NHmqXjXq9X75FqjH17lMYwrOsxDiU6brVz4ygiOnZpLEWzzlsSO6G27qt9PRdrEJ1H2zmKw9D3jCjWQn2KxhxFY11TPGssgY5f7wv0PNp2Wu7rrNen+2p7RPEBpfEvJb+NzlsSG6TnKR1zzDQAAAAAAEAIHw0AAAAAABDCRwMAAAAAAIR0q7VR3Dd27Nj2rkuXQnVu/vo1f7lq4DVXv9cC61oDqpUdOHBgYvul1PW4qmnUPOQ+h7vuq9pnzY3utYlbbrllUqbXqxq5n/3sZ9W2xjtoXnnN2e5z0CuaA9rnjTdL8zjffPPNSdnFF1+c2Bp34jXIqpvW+JZRo0Ylttda6vXpuFGdZkeheu1msC74hEa0yyW5r1XTr+NJ+7W3tS+qj9A1D7ytv9W86Np3fb00dkDXHtD4gKjvauyO+ipva5leg16vr3NO96t+zvtX1URrDIfmcve2+gTVbqsu2PeH0j7YUTEN7eETzOpj3ta3GI0Sv9CR+D5Y2uei/XNrEfjxqf5G7ej5rv5Hx5j6K18P3VfRuAR/rFzsSEkcQmkMS0TUj3IxclGMVS5WT2NLFGYaAAAAAAAghI8GAAAAAAAI4aMBAAAAAABCWKehFVS3/8gjj1TbU6dOTcpU4+fXdDBL13XQ/N5+fQCz+liDBx98sNXzDB06NLE1v7CPcVAd8WabbZbYuq6B1wJrTINq4PSaPv7xj1fb73nPe5IyvV6NS/DaxMcffzwpe+ihhxJ75syZrdZL20Z14rrWhr9+bcdo3Q09r/abklza0HxKNadtLcuh911tHzOlsQIaDxHZ6hMayXOf07r6Y+fiMKJj6fjS+DHVLvvf6r4aE6Zj0/tijfnSfdWPeTuX5z5q93Vd09+RtNd6Cs3UmivNvL+NrC0RrQ/RzOuPnmnRGMqdV8d9bk0jP+bUp0TrULRkR3XMxTxERMcqOY5Z2fof0W/1/pX2X2YaAAAAAAAghI8GAAAAAAAIIeVqK6gkwMsAdIpc0Wlwn3ZM23H27NmJrdP+PpWhpiLU8+i005AhQ6ptvc259GZevqTTaD61qVm9XMtLoXwdzOplDCpB8nIDTYmo6Wj1Hvl0p5rqTX+rbefrpekjVT6i6Rd92lhNealpYlVO0lG8XVOutifRdHEubV/J9LhOtftyPU5OFuXrlTuPpkf040LHU5Ri1SweI+oTtF5eeqDyTZWCappYX65jUX1eJBfIyQFK5Eo5miXJyZFLrbi2NJJyNdq3o9pF6ciUq43Ik0rqEMlmSmRQJcfNUXre6FnaiJQrR3SPlOge5uRJUb/KnVd9qkf9nr5X6jupwkwDAAAAAACE8NEAAAAAAAAhfDQAAAAAAEAIKVdbQeMWvH4upyMeMWJEYvv9FyxYkJQNHz48sVWL5jW6qoFT7b2Wey2wpiRTHZvW48knn6y2I32yWf01zZkzp9pWXbRqkidNmpTY48ePr7ZVt6f6Zb1+X09tiyVLliS2xo74/bWOGjuh99enhovSsZp1XkwDlFOidS3V4/p+kdNER9pXTUuo41x9k99fz6P9PEq5mksTq3gdbW58ab38NeTiiyJbz6ttF8Up5LTakc69JB1iS/a6RlfU+Cslx2pPzXszz1tCM31byW+jVKil5/HjN5ditZmptaPYgtx5S2J2SvqGvmfpebz/1Xc/TROfg5kGAAAAAAAI4aMBAAAAAABC+GgAAAAAAIAQYhpaIdKeq15M9WTRMuuqJ1MNstpe79vIsuN6PaqJUy2wjxfQvL6KtoeukeDReAjd119/Lte9XpPXOKp+OYphUPR6VPsc/Vb12WprW/prUl1mTifu65XTjkI5JbrnnC42WmuhEXKa2dy49+T6nz+2+ji/vkpL5X48atxWbo0DP97UP2qMw6uvvprYOnY97ZlDvtRXl5xrXaaRuIRmauu7ypoHJTTLZ+SOVVrnkvUDSnxoLj6gJB4i9y4RnUfx/qmRNS1yRHEape08bNiwalvjSDVWNFuvor0BAAAAAOBtBx8NAAAAAAAQwkcDAAAAAACEENMAncq8efMS2+uoc7mINW7B6/yiuAqzWOud0ydHdk47qzmRI811Li6hRJ8OjVOiKW6m/rgEHSPNzGXvbe23GkugsTv+tzr2cn3Vl+vYi9ah0N9qLEUjcQc5GomXaKbe/O1Ee+bmb6QebS0zK6tzI/EBUVvl6hjFHDXyzMrRzDUtGhmfnbW2RtS2uTr696rc2j7ZehTtDQAAAAAAbzv4aAAAAAAAgBDkSdCpvPbaa4ntZQ2anlVTQGqaWD99pzINPY9KE/x0ZS4FpMocvNwiNz0byZFyaeHUjlL7Ik/qWJopd+iqaRp9vbTvlfZdTy5tbJTSMWd7cimMIzpLztKev13fKUlR2V7nzdHMtL9ru28zj12agrRZUrycH4iOFb0LtPex2lqWK9f3CvXP8+fPr7YXLlyYlOkyADl4swAAAAAAgBA+GgAAAAAAIISPBgAAAAAACCGmAToVjQ/wekFN25j7rdcs5+ISSpakz+kFWztOS2h6s0h3m9NgR6leOyvlJ6y/RGMkl6a45LeRDljTtZbY6gN033VhzLSnrr1ZdFZq4mamWG0kHqIkfWlbj9MSeuwoNXHu2M263kbuZ4n+v9Fje9QvlKTFje5BKY2kDM75Qd8f9B1EU9fnYKYBAAAAAABC+GgAAAAAAIAQPhoAAAAAACCEmAboVDQuwbN8+fLE1tiCMWPGJHbv3r2r7VdffTU8T79+/RK7RDetsQZex6j7qsaxb9++rZZrHfv06ZPYuvZESe57WHcoyQvekZTogktihnJ2NL50TOjY9DnIdd8S/XEj46mReKPcvc9p17sCzdS5exrRgJccqz3jEta2Trn9G8n5357+piv6tkbaqnTfyA/mKHnea9ylL1cfmYsdVZhpAAAAAACAED4aAAAAAAAghI8GAAAAAAAIIaYBOhXNEdy/f/9qW+MOhg4dmti77bZbYk+cOLHa1viAwYMHJ7bqmb02OLcuQ2Trb1WHqNfr91d9sh7rlVdeSeyXX3651eNGa0nAukVXiU+JNNON6PRzazxE8Uaqz/VxTWZxTENuHZR1gUjn3BX04mZxv8np9turz3VVukI9c/eoJP6jo3xXM8/bzDo3Uq9G4j8iH5o7Tw5mGgAAAAAAIISPBgAAAAAACFn352dhnUYlAz79l6ZNnTNnTmIvWbIksb3cQJdKV1T6U0IjU8jRVK9KplQ+UTJd2chy9gAt0V5Sg0bGk/bzKCWrlyq1tG8zU3Z2VDrknLRrXaNEbtWe0pfOkgQ2sw8267wl+7ZnHduL0jr5a2xmutYSH5L7beQH1GeWvgvxZgEAAAAAACF8NAAAAAAAQAgfDQAAAAAAEEJMA3QquoS51wAOGjSo1TKz+jSjXten6Vr1PKpn7ig0TsGnRi1Jk6bk4iEA1kdK0nBqWUncT6nGvVkacSWnc/Z+rbP045ru2V9fe8ZatZemv5HUl82sR46SepWktu2ofpQ7T9Q2jcQDlNajEfyxc+dpJIanJKbqtddea/NxzZhpAAAAAACADHw0AAAAAABACB8NAAAAAAAQ0q3WFRPnAgAAAABAl4GZBgAAAAAACOGjAQAAAAAAQvhoAAAAAACAED4aAAAAAAAghI8GAAAAAAAI4aMBAAAAAABC+GgAAAAAAIAQPhoAAAAAACCEjwYAAAAAAAjhowEAAAAAAEL4aAAAAAAAgBA+GgAAAAAAIISPBgAAAAAACOGjAQAAAAAAQnq0dcexY8e2Zz0AoB2ZP39+04+JTwBYd2kPn2BmNnDgwMTu3bt3q/t269YtsWu1Wpv3VUp+q/vmjr22+ypRHUt/28w6R/VqpM6l9Wiv85RcXyN9spltFVF6P/3+b775ZlL2xhtvJPaLL74YHpuZBgAAAAAACOGjAQAAAAAAQtosTwIAAACIUDlSjx5tf81opvTH2xtssEG4r5ZH+5bUo5lynFw9onM1ItcpoZkysBxRO5ect5F2zdHMtmzkvP5YWqZypRzMNAAAAAAAQAgfDQAAAAAAEII8CQAAAJqCyiq83YiMJCfXeOutt9q8v8qRonpF15OjmdKXkmOVZtdpL0lVjmbJhhqpc+63jdyjEtlbR2XDUlavXt3mfc2YaQAAAAAAgAx8NAAAAAAAQAgfDQAAAAAAEEJMAwAAALQ7kZY+t38utqCZWvxG4gM6MiagWUQpOdf2OO1NI/EvnkbuX2l/js7TzLS4JSlXS+83Mw0AAAAAABDCRwMAAAAAAITw0QAAAAAAACHENAAAAEC7U6Lbbmn/jiKKaSiJcWhmDEdUx+g4ZmVrWDRCI3r5Um19s9aWyNW5pM92VH/N1akkHoKYBgAAAAAAaCp8NAAAAAAAQAgfDQAAAAAAEEJMAwAAAHQ4zdSilxyrdO2FRo61tvs28ttG1lpopqa/pJ1L412ifUviQXLnbdZ6EM0k1zYbbND6fEAj7WzGTAMAAAAAAGTgowEAAAAAAEKQJwEAQB2NSBw6a9oeuh7NSo3ZzPNqClKlJJ3p2tapPX/byHmUXLrWZp4rIjpve9ahJH1pybHaU44Vlev9zI0FhZkGAAAAAAAI4aMBAAAAAABC+GgAAAAAAIAQYhoAANYhSlIeru1xGqVZdYR1j5J4gBJtdi6dZ4n2vJF0rbljN3LcRuI/mqm9b9Z5SrT2uTq2l+a/NE1syXkbaauIzvShzDQAAAAAAEAIHw0AAAAAABDCRwMAAAAAAIQQ09DF6N69e2J73Vtpft1oqXRdZlzP++abb1bbq1evbrVOLR0rWsK8o3I6A6wvdFZ+9vZap6GZenJYvynRvDeTaA2AzoobWhfWTWlmDEczNf/NjPco6ZOd5dsaiSvKwUwDAAAAAACE8NEAAAAAAAAhfDQAAAAAAEAIMQ1djNdffz2xvTYtF4eg+JgHH6OgZXoetfU8PXv2DOsRnVftHj3oggCerhL3U6ILXldyjMO6RTNz87fXeSNKxlCuvCteX2k9usp6GGt7ntJ9G4l/6ap+kpkGAAAAAAAI4aMBAAAAAABC0IZ0MZqZVi1Kfar79urVK7F9mlWVFK1atSqxS6ZU1UaeBOsjzZzi7ipSgmZJC9pTfoVMat2iVL7TUefpqH5U8lt9nkdylq5yfbljRSnlS46V81Wlx46O1Sxyx+2osUHKVQAAAAAAaCp8NAAAAAAAQAgfDQAAAAAAEIKgvIuhGv9IC5xL0eU1kJomNaf5i1Kuqh2lgs2lidX4CIB1kdzYVNuPNx17JXbuPEqkA9axWhIjFV1frl45PxalnS6xO0p7Ds0javfcPWmvFJ25/tqseKZSzXuzrqn0vH6sN3JPcueJfF1pnUvqVJIKteT6mxnL1pExKsw0AAAAAABACB8NAAAAAAAQwkcDAAAAAACEENPQxYh0iqX5dCPdtOLXZTBLtcAah5CLh3jttdeqbV3jQfft3bt3WC+AdZGcxt+PNx17Oma03NslsQNmcXxAaexSdN6ozqU55CNfVGLn/Bi0D83SWzeiW8/p4ztK897M30Y0chwd57kYq0inH/kMs9gflcSKlMQ/tCftFf/QTBptG2YaAAAAAAAghI8GAAAAAAAI4aMBAAAAAABCiGnoYqie2ev8cjnJVWvoj/XGG28kZao5Hjx4cGJ7nZvWKRenEK0PobpirQfA+kDJGgc5otiD0rVbovVXdI0YtXV/T+R7zNovDqOj1mmAttMV27lUx12yBkAj2vP2imlQmnncRtaHiO6DlkX+Rs+bexdqZC2NRuJdomM1c62QjjqvGTMNAAAAAACQgY8GAAAAAAAIQZ7UxSlJm6pTdF5eoDIh3Xf58uWtljeynH1OpoE8CboKpakVPdrPc+lKI9mhyoKi35bsm/tt7lj+mnJpl3Vce3lkTt4YHUtlliXpabWsRBbViBzg7Y5vO+1TOXlOI9KfZqYd7ag6RH2umVKmRtKZRuWl7yjReXJS5kja1Ij8KJcuO9q3mf23EdrT/zDTAAAAAAAAIXw0AAAAAABACB8NAAAAAAAQQkxDF2PDDTdMbK/hfe2115Iy1dpp3MKAAQOqbU2p2q9fv8TeYYcdErt///4tbreExkPMnTu3xW0zsxUrViR2nz59wmMDNJNIc6r6U9XQqn7e76/6Wx3HWu77vR+nZvXjTceqt7WsV69eiR3FLWgdczENJahvivyYxilo+SuvvFJtq/9Q+6WXXkrsl19+udXjRqmi1SZGoe3oOCrRsUfjMxdHEx27mbERzYwHiDTxpTFWjaSJbVa8Vu6eqI9pVrpa9dVdJTamkb4S7VvaN5rpv5hpAAAAAACAED4aAAAAAAAghI8GAAAAAAAIIaahFVS35/WwgwYNSspUs7ls2bJWj6XHVU2uxiXsuOOO1fYWW2yRlG222WaJPW3atMQeN25ci9tm9dpn1SL27t3b1hbfVs8++2xSNmvWrMS+5JJLEvvmm2+utl944YWkTOMftNzXeciQIeG+eh+8Nlz3VS276shVG97WOkLnE2k99b6//vrrie11o7n4AC33cQsjRoxIykaNGpXYw4YNS+zhw4e3uK3HNav3J9HaLTomlEb0uL4tV61alZS9+uqria3lPmZq/vz5Sdm8efMSe8GCBYntfZH6OL2/kVa7PTXC8P9oRMcdUapxj+IDSnLzNxKXoJSsl1Cy1oCWR+uztITfX/2cPu/02en9kcYlRLFNur/6ZiW3nksJ0f0tWfMhFysT3e9mxtmU+jJmGgAAAAAAIISPBgAAAAAACOGjAQAAAAAAQohpaAXVz3k9/cKFC8N9R48endhex6Yavw9/+MOJ/YUvfCGxp06dWm2rTlrzm0frNqhuTX9borXP5Tf38RIbb7xxUqaxFe9617sSe+bMmdX2eeedl5RdddVViT1w4MDEXrlyZbW9aNGipExjHF588cXE9tekusy+ffsmtuonva3tqLpx6FxKdNE57afv93rfde0FXSdl5MiR1fbEiROTMh0j6k+8rWU6JkpiGnQc6zj3di4Xv7aV1xT7cWpW7z/92gpmZosXL662tZ1zdfZ+Tn2enlcp0Yg3qhNenylZP6CrnLdkvYRGrik3bqJ99X2gpL9qnEKk8dfz6G/9eXV8avynxm+NHTu21TJ9Rj/88MOJ7d/D9Pr0eqK1QvS3jTwjOms9iBJK4mhagpkGAAAAAAAI4aMBAAAAAABCkCe1gsoL/LSbptFUeYGmDPTlZ599dlLmU6q2hJ9S12l7Ta8YodN1UZrQHJH0wCxORalSH023OH78+Gr7e9/7XlJ26qmnJvZPfvKTxL744otbrENLddSUsz69m9ZZbU0F59tW21m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",
+      "text/plain": [
+       "<Figure size 800x800 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode=\"bicubic\")\n",
     "fig, axs = plt.subplots(num_samples, 3, figsize=(8, 8))\n",
@@ -1540,7 +1651,7 @@
     "    axs[i, 0].axis(\"off\")\n",
     "    axs[i, 1].imshow(low_res_bicubic[i, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
     "    axs[i, 1].axis(\"off\")\n",
-    "    axs[i, 2].imshow(decoded[i, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "    axs[i, 2].imshow(decoded[i, 0].cpu().detach().numpy(), vmin=0, vmax=1, cmap=\"gray\")\n",
     "    axs[i, 2].axis(\"off\")\n",
     "plt.tight_layout()"
    ]
@@ -1555,7 +1666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "3a6f6d5a",
    "metadata": {},
    "outputs": [],
diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
index 14d36417..084404bc 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
@@ -56,6 +56,7 @@
 import os
 import shutil
 import tempfile
+from pathlib import Path
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -64,11 +65,11 @@
 from monai import transforms
 from monai.apps import MedNISTDataset
 from monai.config import print_config
-from monai.data import CacheDataset, DataLoader
+from monai.data import CacheDataset, ThreadDataLoader
 from monai.utils import first, set_determinism
-from torch import nn
 from torch.cuda.amp import GradScaler, autocast
-from tqdm import tqdm
+from torch import nn
+from tqdm.notebook import tqdm
 
 from generative.losses import PatchAdversarialLoss, PerceptualLoss
 from generative.networks.nets import AutoencoderKL, DiffusionModelUNet, PatchDiscriminator
@@ -92,6 +93,14 @@
 root_dir = tempfile.mkdtemp() if directory is None else directory
 print(root_dir)
 
+# %%
+train_data = MedNISTDataset(root_dir=root_dir, section="training",
+                                download=True, seed=0)
+train_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "HeadCT"]
+val_data = MedNISTDataset(root_dir=root_dir, section="validation",
+                              download=True, seed=0)
+val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "HeadCT"]
+
 
 # %% [markdown]
 # ## Setup utils functions
@@ -133,6 +142,14 @@ def get_val_transforms():
     )
     return val_transforms
 
+    
+def get_datasets():
+    train_transforms = get_train_transforms()
+    val_transforms = get_val_transforms()
+    train_ds = CacheDataset(data=train_datalist[:320], transform=train_transforms)
+    val_ds = CacheDataset(data=val_datalist[:32], transform=val_transforms)
+    return train_ds, val_ds
+
 
 
 # %% [markdown]
@@ -167,23 +184,14 @@ def forward(self, z):
         return self.autoencoderkl(z)
 
     def prepare_data(self):
-        train_transforms = get_train_transforms()
-        val_transforms = get_val_transforms()
-        
-        train_data = MedNISTDataset(root_dir=self.data_dir, section="training", download=True, seed=0)
-        train_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "HeadCT"]
-        val_data = MedNISTDataset(root_dir=self.data_dir, section="validation", download=True, seed=0)
-        val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "HeadCT"]
-        
-        self.train_ds = CacheDataset(data=train_datalist, transform=train_transforms)
-        self.val_ds = CacheDataset(data=val_datalist, transform=val_transforms)
-        
+        self.train_ds, self.val_ds = get_datasets()
+                
     def train_dataloader(self):
-        return DataLoader(self.train_ds, batch_size=16, shuffle=True,
+        return ThreadDataLoader(self.train_ds, batch_size=16, shuffle=True,
                           num_workers=4, persistent_workers=True)
         
     def val_dataloader(self):
-        return DataLoader(self.val_ds, batch_size=16, shuffle=False,
+        return ThreadDataLoader(self.val_ds, batch_size=16, shuffle=False,
                           num_workers=4)
                           
     def _compute_loss_generator(self, images, reconstruction, z_mu, z_sigma):
@@ -194,11 +202,11 @@ def _compute_loss_generator(self, images, reconstruction, z_mu, z_sigma):
         loss_g = recons_loss + (self.kl_weight * kl_loss) + (self.perceptual_weight * p_loss)
         return loss_g,recons_loss
     
-    def _compute_loss_discriminator(self, reconstruction):
-        logits_fake = discriminator(reconstruction.contiguous().detach())[-1]
+    def _compute_loss_discriminator(self, images, reconstruction):
+        logits_fake = self.discriminator(reconstruction.contiguous().detach())[-1]
         loss_d_fake = self.adv_loss(logits_fake, target_is_real=False, for_discriminator=True)
-        logits_real = discriminator(images.contiguous().detach())[-1]
-        loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)
+        logits_real = self.discriminator(images.contiguous().detach())[-1]
+        loss_d_real = self.adv_loss(logits_real, target_is_real=True, for_discriminator=True)
         discriminator_loss = (loss_d_fake + loss_d_real) * 0.5
         loss_d = self.adv_weight * discriminator_loss
         return loss_d, discriminator_loss
@@ -211,9 +219,11 @@ def training_step(self, batch, batch_idx):
         self.log("recons_loss", recons_loss, batch_size=16, prog_bar=True)
 
         if self.current_epoch > self.autoencoder_warm_up_n_epochs:
-            logits_fake = discriminator(reconstruction.contiguous().float())[-1]
-            generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)
-            loss_g += adv_weight * generator_loss
+            logits_fake = self.discriminator(reconstruction.contiguous().float())[-1]
+            generator_loss = self.adv_loss(logits_fake, target_is_real=True, for_discriminator=False)
+            loss_g += self.adv_weight * generator_loss
+            self.log("gen_loss", generator_loss, batch_size=16, prog_bar=True)
+            
                           
 
         self.log("loss_g", loss_g, batch_size=16, prog_bar=True)
@@ -223,7 +233,8 @@ def training_step(self, batch, batch_idx):
         self.untoggle_optimizer(optimizer_g)
 
         if self.current_epoch > self.autoencoder_warm_up_n_epochs:
-            loss_d, discriminator_loss = self._compute_loss_discriminator(reconstruction)
+            loss_d, discriminator_loss = self._compute_loss_discriminator(images, reconstruction)
+            self.log("disc_loss", loss_d, batch_size=16, prog_bar=True)
             self.log("train_loss_d", loss_d, batch_size=16, prog_bar=True)
             self.manual_backward(loss_d)
             optimizer_d.step()
@@ -231,19 +242,26 @@ def training_step(self, batch, batch_idx):
             self.untoggle_optimizer(optimizer_d)
 
                           
-        if self.current_epoch > self.autoencoder_warm_up_n_epochs:
-            gen_epoch_loss += generator_loss.item()
-            disc_epoch_loss += discriminator_loss.item()
-            self.log("gen_loss", gen_loss, batch_size=16, prog_bar=True)
-            self.log("disc_loss", disc_loss, batch_size=16, prog_bar=True)
-                          
                                     
     def validation_step(self, batch, batch_idx):
         images = batch["image"]
         reconstruction, z_mu, z_sigma = self.autoencoderkl(images)
         recons_loss = F.l1_loss(images.float(), reconstruction.float())
-        self.log("val_loss_d", recons_loss, prog_bar=True)
-                              
+        self.log("val_loss_d", recons_loss, batch_size=1, prog_bar=True)
+        self.images = images
+        self.reconstruction = reconstruction
+
+
+    def on_validation_epoch_end(self):
+        # ploting reconstruction
+        plt.figure(figsize=(2, 2))
+        plt.imshow(torch.cat([self.images[0, 0].cpu(), 
+                              self.reconstruction[0, 0].cpu()],
+                             dim=1), vmin=0, vmax=1, cmap="gray")
+        plt.tight_layout()
+        plt.axis("off")
+        plt.show()
+        
 
     def configure_optimizers(self):
         optimizer_g = torch.optim.Adam(self.autoencoderkl.parameters(), lr=5e-5)
@@ -256,7 +274,7 @@ def configure_optimizers(self):
 # ## Train Autoencoder
 
 # %%
-n_epochs = 75
+n_epochs = 75 
 val_interval = 10
 
                           
@@ -272,23 +290,33 @@ def configure_optimizers(self):
 trainer = pl.Trainer(devices=1,
                      max_epochs=n_epochs,
                      check_val_every_n_epoch=val_interval,
+                     num_sanity_val_steps=0,
                      callbacks=checkpoint_callback,
                      default_root_dir=root_dir)
 
 # train
 trainer.fit(ae_net)
 
+
 # %% [markdown]
 # ## Rescaling factor
 #
 # As mentioned in Rombach et al. [1] Section 4.3.2 and D.1, the signal-to-noise ratio (induced by the scale of the latent space) became crucial in image-to-image translation models (such as the ones used for super-resolution). For this reason, we will compute the component-wise standard deviation to be used as scaling factor.
 
 # %%
-train_loader = ae_net.train_dataloader()
-check_data = first(train_loader)
-z = ae_net.autoencoderkl.train(mode=False).encode_stage_2_inputs(check_data["image"].to(ae_net.device))
-print(f"Scaling factor set to {1/torch.std(z)}")
-scale_factor = 1 / torch.std(z)
+def get_scaler_factor():
+    ae_net.eval()
+    device = torch.device("cuda:0")
+    ae_net.to(device)
+
+    train_loader = ae_net.train_dataloader()
+    check_data = first(train_loader)
+    z = ae_net.autoencoderkl.encode_stage_2_inputs(check_data["image"].to(ae_net.device))
+    print(f"Scaling factor set to {1/torch.std(z)}")
+    scale_factor = 1 / torch.std(z)
+    return scale_factor
+
+scale_factor = get_scaler_factor()
 
 
 # %% [markdown]
@@ -314,7 +342,7 @@ def __init__(self):
                           beta_schedule="linear",
                           beta_start=0.0015,
                           beta_end=0.0195)
-        self.z = ae_net.autoencoderkl.train(mode=False)
+        self.z = ae_net.autoencoderkl.eval()
 
 
     def forward(self, x, timesteps, low_res_timesteps):
@@ -322,68 +350,106 @@ def forward(self, x, timesteps, low_res_timesteps):
                          timesteps=timesteps,
                          class_labels=low_res_timesteps)
     
-    
+           
     def prepare_data(self):
-        train_transforms = get_train_transforms()
-        val_transforms = get_val_transforms()
-        
-        train_data = MedNISTDataset(root_dir=self.data_dir, section="training",
-                                    download=True, seed=0)
-        train_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "HeadCT"]
-        val_data = MedNISTDataset(root_dir=self.data_dir, section="validation",
-                                  download=True, seed=0)
-        val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "HeadCT"]
-        
-        self.train_ds = CacheDataset(data=train_datalist, transform=train_transforms)
-        self.val_ds = CacheDataset(data=val_datalist, transform=val_transforms)
+        self.train_ds, self.val_ds = get_datasets()
         
     def train_dataloader(self):
         return DataLoader(self.train_ds, batch_size=16, shuffle=True,
                           num_workers=4, persistent_workers=True)
         
     def val_dataloader(self):
-        return DataLoader(self.val_ds, batch_size=16, shuffle=True,
+        return DataLoader(self.val_ds, batch_size=16, shuffle=False,
                           num_workers=4)
     
-    def _calculate_loss(self, batch, batch_idx):
+    def _calculate_loss(self, batch, batch_idx, plt_image=False):
         images = batch["image"]
-        low_res_image = batch["low_res_image"]       
-        latent = self.z.encode_stage_2_inputs(images) * scale_factor
-        latent =  latent.detach() # avoid adding this to graph.
-        optimizer = self.optimizers()
+        low_res_image = batch["low_res_image"]  
+        with autocast(enabled=True):
+            with torch.no_grad():
+                latent = self.z.encode_stage_2_inputs(images) * scale_factor
+#             latent =  latent.detach() # avoid adding this to graph.
         
-        # Noise augmentation
-        noise = torch.randn_like(latent)
-        low_res_noise = torch.randn_like(low_res_image)
-        timesteps = torch.randint(0, self.scheduler.num_train_timesteps, (latent.shape[0],),
-                                  device=latent.device).long()
-        low_res_timesteps = torch.randint(
-            0, self.max_noise_level, (low_res_image.shape[0],), device=latent.device
-        ).long()
-
-        noisy_latent = self.scheduler.add_noise(original_samples=latent, 
-                                           noise=noise, timesteps=timesteps)
-        noisy_low_res_image = self.scheduler.add_noise(
-            original_samples=low_res_image, noise=low_res_noise, 
-            timesteps=low_res_timesteps
-        )
-
-        latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)
 
-        noise_pred = self.forward(latent_model_input, timesteps, low_res_timesteps)
-        loss = F.mse_loss(noise_pred.float(), noise.float())
+            # Noise augmentation
+            noise = torch.randn_like(latent)
+            low_res_noise = torch.randn_like(low_res_image)
+            timesteps = torch.randint(0, self.scheduler.num_train_timesteps, (latent.shape[0],),
+                                      device=latent.device).long()
+            low_res_timesteps = torch.randint(
+                0, self.max_noise_level, (low_res_image.shape[0],), device=latent.device
+            ).long()
+
+            noisy_latent = self.scheduler.add_noise(original_samples=latent, 
+                                               noise=noise, timesteps=timesteps)
+            noisy_low_res_image = self.scheduler.add_noise(
+                original_samples=low_res_image, noise=low_res_noise, 
+                timesteps=low_res_timesteps
+            )
+
+            latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)
+
+            noise_pred = self.forward(latent_model_input, timesteps, low_res_timesteps)
+            loss = F.mse_loss(noise_pred.float(), noise.float())
+        
+        if plt_image:
+            # Sampling image during training
+            sampling_image = low_res_image[0].unsqueeze(0)
+            latents = torch.randn((1, 3, 16, 16)).to(sampling_image.device)
+            low_res_noise = torch.randn((1, 1, 16, 16)).to(sampling_image.device)
+            noise_level = 20
+            noise_level = torch.Tensor((noise_level,)).long().to(sampling_image.device)
+            
+            noisy_low_res_image = self.scheduler.add_noise(
+                original_samples=sampling_image,
+                noise=low_res_noise,
+                timesteps=noise_level,
+            )
+            self.scheduler.set_timesteps(num_inference_steps=1000)
+            for t in tqdm(self.scheduler.timesteps, ncols=110):
+                with autocast(enabled=True):
+                    with torch.no_grad():
+                        latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)
+                        noise_pred = self.forward(latent_model_input, 
+                                                  torch.Tensor((t,)).to(sampling_image.device)
+                                                  , noise_level)
+                    latents, _ = self.scheduler.step(noise_pred, t, latents)
+            with torch.no_grad():
+                decoded = self.z.decode_stage_2_outputs(latents / scale_factor)
+            low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode="bicubic")
+            # plot images
+            
+            self.images = images
+            self.low_res_bicubic = low_res_bicubic
+            self.decoded = decoded
+            
         return loss
+    
+    def _plot_image(self, images, low_res_bicubic, decoded):
+        plt.figure(figsize=(2, 2))
+        plt.style.use("default")
+        plt.imshow(
+            torch.cat([images[0, 0].cpu(), low_res_bicubic[0, 0].cpu(), decoded[0, 0].cpu()], dim=1),
+            vmin=0,
+            vmax=1,
+            cmap="gray",
+        )
+        plt.tight_layout()
+        plt.axis("off")
+        plt.show()
 
     def training_step(self, batch, batch_idx):
         loss = self._calculate_loss(batch, batch_idx)
         self.log("train_loss", loss, batch_size=16, prog_bar=True)
         return loss
-
         
     def validation_step(self, batch, batch_idx):
-        loss = self._calculate_loss(batch, batch_idx)
+        loss = self._calculate_loss(batch, batch_idx, plt_image=True)
         self.log("val_loss", loss, batch_size=16, prog_bar=True)
         return loss
+    
+    def on_validation_epoch_end(self):
+        self._plot_image(self.images, self.low_res_bicubic, self.decoded)
 
     def configure_optimizers(self):
         optimizer = torch.optim.Adam(self.unet.parameters(), lr=5e-5)
@@ -414,6 +480,7 @@ def configure_optimizers(self):
 trainer = pl.Trainer(devices=1,
                      max_epochs=n_epochs,
                      check_val_every_n_epoch=val_interval,
+                     num_sanity_val_steps=0,
                      callbacks=checkpoint_callback,
                      default_root_dir=root_dir)
 
@@ -424,37 +491,43 @@ def configure_optimizers(self):
 # ### Plotting sampling example
 
 # %%
-# Sampling image during training
 num_samples = 3
-val_loader = d_net.val_dataloader()
-check_data = first(val_loader)
-images = check_data["image"]
-sampling_image = check_data["low_res_image"][:num_samples]
+def get_images_to_plot():
+    d_net.eval()
+    device = torch.device("cuda:0")
+    d_net.to(device)
 
-# %%
-latents = torch.randn((num_samples, 3, 16, 16)).to(images.device)
-low_res_noise = torch.randn((num_samples, 1, 16, 16)).to(images.device)
-noise_level = 10
-noise_level = torch.Tensor((noise_level,)).long().to(images.device)
-scheduler = d_net.scheduler
-noisy_low_res_image = scheduler.add_noise(original_samples=sampling_image, 
-                                          noise=low_res_noise,
-                                          timesteps=torch.Tensor((noise_level,)).long())
-
-scheduler.set_timesteps(num_inference_steps=1000)
-for t in tqdm(scheduler.timesteps, ncols=110):
-    with torch.no_grad():
+    
+    val_loader = d_net.val_dataloader()
+    check_data = first(val_loader)
+    images = check_data["image"].to(d_net.device)
+
+    sampling_image = check_data["low_res_image"][:num_samples].to(d_net.device)
+    latents = torch.randn((num_samples, 3, 16, 16)).to(d_net.device)
+    low_res_noise = torch.randn((num_samples, 1, 16, 16)).to(d_net.device)
+    noise_level = 10
+    noise_level = torch.Tensor((noise_level,)).long().to(d_net.device)
+    scheduler = d_net.scheduler
+    noisy_low_res_image = scheduler.add_noise(original_samples=sampling_image, 
+                                              noise=low_res_noise,
+                                              timesteps=torch.Tensor((noise_level,)).long())
+
+    scheduler.set_timesteps(num_inference_steps=1000)
+    for t in tqdm(scheduler.timesteps, ncols=110):
         with autocast(enabled=True):
-            latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)
-            noise_pred = d_net.forward(x=latent_model_input,
-                                       timesteps=torch.Tensor((t,)),
-                                       low_res_timesteps=noise_level)
+            with torch.no_grad():
+                latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)
+                noise_pred = d_net.forward(x=latent_model_input,
+                                           timesteps=torch.Tensor((t,)).to(d_net.device),
+                                           low_res_timesteps=noise_level)
+            # 2. compute previous image: x_t -> x_t-1
+            latents, _ = scheduler.step(noise_pred, t, latents)
 
-        # 2. compute previous image: x_t -> x_t-1
-        latents, _ = scheduler.step(noise_pred, t, latents)
+    with torch.no_grad():
+        decoded = ae_net.autoencoderkl.decode_stage_2_outputs(latents / scale_factor)
+    return sampling_image, images, decoded
 
-with torch.no_grad():
-    decoded = ae_net.autoencoderkl.decode_stage_2_outputs(latents / scale_factor)
+sampling_image, images, decoded = get_images_to_plot()
 
 # %%
 low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode="bicubic")
@@ -467,7 +540,7 @@ def configure_optimizers(self):
     axs[i, 0].axis("off")
     axs[i, 1].imshow(low_res_bicubic[i, 0].cpu(), vmin=0, vmax=1, cmap="gray")
     axs[i, 1].axis("off")
-    axs[i, 2].imshow(decoded[i, 0].cpu(), vmin=0, vmax=1, cmap="gray")
+    axs[i, 2].imshow(decoded[i, 0].cpu().detach().numpy(), vmin=0, vmax=1, cmap="gray")
     axs[i, 2].axis("off")
 plt.tight_layout()
 

From 72158473b10f8885a80a701c6f7ab512cdf23869 Mon Sep 17 00:00:00 2001
From: OeslleLucena <oeslle.lucena@gmail.com>
Date: Fri, 12 May 2023 18:28:02 +0100
Subject: [PATCH 05/13] revised tutorial

---
 ...fusion_v2_super_resolution-lightning.ipynb | 164 ++++++------
 ...diffusion_v2_super_resolution-lightning.py | 246 +++++++++---------
 2 files changed, 199 insertions(+), 211 deletions(-)

diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
index 1b8251da..029889d1 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
@@ -54,6 +54,7 @@
    "outputs": [],
    "source": [
     "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n",
+    "!python -c \"import pytorch_lightning\" || pip install pytorch-lightning\n",
     "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
     "%matplotlib inline"
    ]
@@ -76,14 +77,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-12 14:09:21,976 - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:\n",
+      "2023-05-12 17:22:32,886 - WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:\n",
       "    PyTorch 1.13.1+cu117 with CUDA 1107 (you have 1.12.1)\n",
       "    Python  3.8.16 (you have 3.8.16)\n",
       "  Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)\n",
       "  Memory-efficient attention, SwiGLU, sparse and more won't be available.\n",
       "  Set XFORMERS_MORE_DETAILS=1 for more details\n",
-      "2023-04-12 14:09:23,860 - Created a temporary directory at /tmp/tmpo49u4ma3\n",
-      "2023-04-12 14:09:23,864 - Writing /tmp/tmpo49u4ma3/_remote_module_non_scriptable.py\n",
+      "2023-05-12 17:22:35,069 - Created a temporary directory at /tmp/tmph4_v9gin\n",
+      "2023-05-12 17:22:35,071 - Writing /tmp/tmph4_v9gin/_remote_module_non_scriptable.py\n",
       "MONAI version: 1.2.dev2304\n",
       "Numpy version: 1.23.5\n",
       "Pytorch version: 1.12.1\n",
@@ -174,7 +175,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/tmpizuu167z\n"
+      "/tmp/tmphf3tvpfi\n"
      ]
     }
    ],
@@ -194,14 +195,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MedNIST.tar.gz: 59.0MB [00:11, 5.17MB/s]                                                                                                                               "
+      "MedNIST.tar.gz: 59.0MB [00:01, 36.4MB/s]                                                                                                                               "
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-12 14:09:35,992 - INFO - Downloaded: /tmp/tmpizuu167z/MedNIST.tar.gz\n"
+      "2023-05-12 17:22:36,915 - INFO - Downloaded: /tmp/tmphf3tvpfi/MedNIST.tar.gz\n"
      ]
     },
     {
@@ -215,31 +216,32 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-12 14:09:36,099 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-04-12 14:09:36,100 - INFO - Writing into directory: /tmp/tmpizuu167z.\n"
+      "2023-05-12 17:22:37,020 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-05-12 17:22:37,022 - INFO - Writing into directory: /tmp/tmphf3tvpfi.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:35<00:00, 1335.82it/s]\n"
+      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:35<00:00, 1323.43it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-12 14:10:20,302 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-04-12 14:10:20,303 - INFO - File exists: /tmp/tmpizuu167z/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-04-12 14:10:20,304 - INFO - Non-empty folder exists in /tmp/tmpizuu167z/MedNIST, skipped extracting.\n"
+      "2023-05-12 17:23:21,457 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-05-12 17:23:21,459 - INFO - File exists: /tmp/tmphf3tvpfi/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-05-12 17:23:21,460 - INFO - Non-empty folder exists in /tmp/tmphf3tvpfi/MedNIST, skipped extracting.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:04<00:00, 1332.80it/s]\n"
+      "\n",
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:04<00:00, 1319.58it/s]\n"
      ]
     }
    ],
@@ -319,7 +321,7 @@
    "metadata": {},
    "source": [
     "## Define the LightningModule for AutoEncoder (transforms, network, loaders, etc)\n",
-    "The LightningModule contains a refactoring of your training code. The following module is a reformatiing of the code in 2d_stable_diffusion_v2_super_resolution.\n"
+    "The LightningModule contains a refactoring of your training code. The following module is a reformating of the code in 2d_stable_diffusion_v2_super_resolution.\n"
    ]
   },
   {
@@ -471,25 +473,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-12 14:10:27,910 - GPU available: True (cuda), used: True\n",
-      "2023-04-12 14:10:27,911 - TPU available: False, using: 0 TPU cores\n",
-      "2023-04-12 14:10:27,912 - IPU available: False, using: 0 IPUs\n",
-      "2023-04-12 14:10:27,913 - HPU available: False, using: 0 HPUs\n"
+      "2023-05-12 17:23:29,608 - GPU available: True (cuda), used: True\n",
+      "2023-05-12 17:23:29,609 - TPU available: False, using: 0 TPU cores\n",
+      "2023-05-12 17:23:29,610 - IPU available: False, using: 0 IPUs\n",
+      "2023-05-12 17:23:29,611 - HPU available: False, using: 0 HPUs\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 320/320 [00:00<00:00, 892.62it/s]\n",
-      "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:00<00:00, 413.74it/s]"
+      "Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 320/320 [00:00<00:00, 858.75it/s]\n",
+      "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:00<00:00, 284.05it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-12 14:10:28,377 - Missing logger folder: /tmp/tmpizuu167z/lightning_logs\n"
+      "2023-05-12 17:23:30,118 - Missing logger folder: /tmp/tmphf3tvpfi/lightning_logs\n"
      ]
     },
     {
@@ -497,15 +499,15 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Checkpoint directory /tmp/tmpizuu167z exists and is not empty.\n"
+      "Checkpoint directory /tmp/tmphf3tvpfi exists and is not empty.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-12 14:10:29,835 - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
-      "2023-04-12 14:10:29,850 - \n",
+      "2023-05-12 17:23:31,637 - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
+      "2023-05-12 17:23:31,652 - \n",
       "  | Name            | Type                 | Params\n",
       "---------------------------------------------------------\n",
       "0 | autoencoderkl   | AutoencoderKL        | 75.1 M\n",
@@ -529,7 +531,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a7a5ce61b3dd41b386312a100272db42",
+       "model_id": "e345b86c597145adad868d37e0416041",
        "version_major": 2,
        "version_minor": 0
       },
@@ -556,7 +558,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -580,7 +582,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -604,7 +606,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -628,7 +630,7 @@
     },
     {
      "data": {
-      "image/png": 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NjQ2kUinhglAqlGtAGgcCGvmInOuj8eJ0EfWJH+fXkFuUyWTgdDqRTCbR19cHh8OBcDiMyclJ9PX1IZlMttSIT5xHpA64XC6USiXhBAcCAUQikToaZmFhAalUSlAmiqLA4/HA5XIhl8uJ2a3rOpxOp8gBkxDtQOksmgD0QgiI1AYafGoXvRAidu/du4c9e/YIymdgYAB79+7F7du3kUqlOvIT5TyzLKR5iEUYGxsTKTsemJAfCGwHGQRg2fXgBR9yO2Q+kYhtuq+cJy+Xy1AUBfl8XmRqiK90OBwAgL1796K/vx/3798XyoL7wF9p1AzUZyNUVRVZj/3794tGFgoFfPjhh7h79y7Gx8fhcDjE7KeIjROmlKKTfR5OWdDLoONERBM4ge28MWlXuqZareL+/fv4xje+Aa/XKzIr+/btg9/vRyKRaAuI8uDzwE3mDUm8Xi9GRkbE3/TyiaqSzTpNMl5RLY+//Ex5nQuftAQgOk7XUiIgnU5DVVW43W5sbm7C7/dDURQMDAxg3759uHr1qtDIcpV3p2xD14BI2of7YltbW/D5fHjqqacEb+hwOHDu3DmUSiU4HA5MTk4im81idXUVhUIBExMTWF9fRywWE6aJijY1TYPH44HNZkOpVBKVOACEr8SLHUhzkFNOLgNxnYZhIJfLQVVVbGxswO/3Q9d11Go19Pf3w+v1djS7m5kpM2Dqug5d18U5ZFqpyqhQKIg+yDwdXw7AiXQ5BcdX4xE4KG9PE5PcGg78fD4vOGBN00Sw5PP5EAwGMTo6CrfbjVQq1bCvnUjXNWKxWISmaUgkEggEAhgZGRGmhwb12LFjuHLlCqxWK9LpNKrVKkKhUF1ZkaZpMAxDFD+Qhsxms1BVFS6XS9yTTDXxiJVKRdAcuq5D0zTxUuVqZ4/HA6/Xi3fffRcTExOiHxMTEwiFQo9oFBpkmbCWP8s+IgcSTVa32w1N08Q5DocDPp8PPp9P8IQ8QJOfBWyXYvHv5HNJ+5Fmp9/URg5GrhlzuZxgQzRNQzAYhM/ng6qqGB4eRiAQQDqdrqOM6LlmQVIz6TqPSGpaVVVR3eF2ux8+7P++SSaTEbOMagvJJBQKBbhcLqG17Ha7ACJlDjhXaDb7rFarINSJryMA8iJYAna1WkU6nRYErqIo6OvrQzAYFL5oKzHLMPDfcjvJR+UmzOl0ilI1AiCvGOfPkYWbcF7EyseE3BFOJXE6h4T6S3wt+YjpdBrFYhEOh0NUxcuk/06pnK4CkVdZkwNtt9uFmeT+49TUFK5fvy58OZqZwPZAytoI2J7FRGLzVBOwPRk0TYOmaWIAy+UyrFarqMEjsjuXyyGdTiOVSmF2dhYDAwNwuVyIRCIYGhoSBHs7wv0y/re80InTN5yMpxx5OBwWrAGlMAuFQl3/+bN4BE0ia0PZt27UfgIwvy+nwwiIgUBAlNvJ1exfuWlWFEX4aUSZFAoF0VDqpN1uxzPPPIPFxUVhmsnppTwype44VcM1IJ3PKRnuPwH1fiNfDEXahtbA5PN5kWelCUDauxuVN/L31M5yuSwWa5F4PJ66IgZFUepoHA4Q/ptbCK6hZJKdzjdrqxmASEHwCBt4mBIlRsPsuk5B2fWiBxo8q9WKfD6PRCKBTCaDYDAoOmWxWBAKheD3+wVRy9evAKjTkPSZR2M8JSXPSAITryShBVVEhHNg2+12BINB6Lou/Ee+VKBdkakb3h4udJxyulxsNhtcLhd8Pp+YxNRfIq65RiQQciDy4IXzq3wc5b5xH49PaHJzKDDkfqaqqo/4wvTcTrViVzMrBAACS6lUQiwWw9raWl2ER4Dy+Xx1pfw043haTjZFJNzPkwleOalPg8cJYKouoZy01+tFMBg05eS+rMj3o8g2l8shFoshl8uJ7yjL5Ha7oes6vF4vXC6XWKHIo2JiAHiqjsaKjw9fQkpg5tqNZ1ZkzpEYCL6kgM7jv0mavbNm0lUgkrbjHVxbW8Ps7KwoV+KzirQiFV/ySmXqFBcaYO5D8moS+o5eCgUERAFRWRmvTuYUEAVGAJDL5eo0UitpReby43RuNpvF4uIi4vG4+E5RFPHiNU2DzWari0pp0hGgqDKJ95tAxrUl/zHTYvwzWSa73S58fAIhAZGAbXYPuZ/tSNdNM5UUEWe4urqKTz75BMePH8fU1BSA7UYPDAxgfX1d+HHAdm4Z2M4akMjLGqmjPP1HAKXCAD6g/HrKXZMprFQqIrNDRZ+d+oeycK1OwjV2KpXCnTt38ODBA4yOjtaNIddSwHYJG+XU5RwznzDcNNM4cuFmnbeV0zxcE/KF+iRUmMufJbsFnViWrmpEMnk0O+12O+LxOK5evYp79+7VDVixWITH46nbfYDMMpknon80TRORLvluQL3vyE0SzXzuIvBqZl4gYBiGCFr27t0rsgepVApra2uP+HA7ERmQfALFYjHMzc2J9dd0PqdbSPNT9ErtB7ZpILI0Zpq3kWXhnzmQaRMEWkxG2pADMR6PI5lMChdIThVyE9+OdL36JpPJ1PF1qqpidXUVv/vd7+oGm3y0oaEheDweZDIZxONxbG1tiYIAt9stzCaBlABKs5ZMOlELAOq4SaKGisWioEM8Ho9YmE4Byrlz54RrUa1Wce/ePWxtbdXNbvklm/lCZoEJ1zb04unlLS4u4uOPP8a1a9fqruOmlTSgXFnDwUrjwflS7lPyjI1MaNO7IyXA70NLCTRNE0mEUqlU51LILhW/d7uuTVeBSPwh+TXAdu3hF198gT/+8Y/ib4pmCRShUAiRSAThcBh2u12UYVGFNq87LJfLYrD4C6FBpkF3OBwiIqddEXRdF1QORcs2mw2Dg4NC+ymKIta6dOLncPNEf/Pv5LGi/ly/fh3vvfcebt++Lb73+XzCEvDNBUiov/TDGQd6Hh8TTnvx1Cf5lzRpSbOSpqWJ7/F4RBZoeXlZFITwNhHgd8Ipdj3Fx18GAbNSqWBpaQlvv/029u7di5MnT4rCU0odra+vi2JX4GGWIZPJoFwuw+12i0GhCFOOtDkgKbtDmQHSmvl8XvxNdE6pVMKBAwfg9/thGIYour169SrW1tbaTt43Ax2NjfyZXlo8Hsff//532Gw2fP/73xdFsuFwGLlcDqlUStQEcoaAa0gac7IAslnmhDZ3X7hG5Gbe5XKJXTZ8Pp9YKVmtVjE3N4c7d+7U7VUk+5ucGG9Hup7io5lGDSPNk81mcfv2bfz6178WS0kpWubmla4hpxyAuKfFYhF+CoGKojfuh5F5pbUyFHzQvemeAMQSTk4bffTRR7h161bdxGglrfwhmbfj5p4m6gcffADgoel79tlnoaoqBgYGxM5dnCng/eb8IbBdaUTtkicoN5nyBOKaUNM06Lou9tMBgGg0itu3byMajQpty/nenVJfjwWIMo1Dvl2xWMStW7fw1ltvIZvN4vz584LCiUQiWFpawvz8vNBUVqu1biZyk0d+Is1KOY1FFAdVsVBJGS34qdVqojIoHA6L+1erVbz33nuIx+Ntz2YSsxchA5D3gSYYEe189WKpVMKxY8egaZoooyNQ0TIKbhWoKMQsIub+IrCdlaJ28GwMvSuXyyV2fwiHw6JwZHZ2Fp9//jk2NzfrfE1+r0Z9byZdp2/o4TxFRv4KAeRf//qXKGg9ffq0WD9L6zHm5+eRSCSwZ88eRCIR1Go1pNPputQbj+ZoHxwAddqBgJTL5QR3SAPl8XgwMjKCqampuvK1mzdv4tq1a8hmsx1nVpqNCYHELHKmtmcyGczOzgoOs1Kp4MyZM1BVFfv374eiPNyab319HclkUvCiBF6afMC268L9NtnNkDU0+YjEIvj9fvT39yMcDgMAHjx4gKtXr+L27duiyNiswILux3+3kseyVICCBfK5ePqOqmuuXLkiVoqdO3cOo6OjOHnyJA4ePIiLFy9icXFRFAAkk0lR/kUEdTKZrGP5OXVDg+pyucRg12o1sXVJMBjEvn37MDU1JSqDSCv9/ve/RzQaFUWh1I+dCveXZM1I/hX3rYvFIh48eIBcLodEIoFSqYQXX3wRNpsNU1NT8Hq9WF5exurqKjY3N+uCN9KSfJkpj7CpnzxzRZOVNCFldUKhEIaHh0XhbjQaxZUrV3Dp0iWsra0Jy0IuU7NERFvjZLR5Vatt6cisUdkUzUK+5FFRFASDQcTjcQGeYDCIs2fP4kc/+hGOHz8uKIPl5WWsr68jm82KTSSpmFZRFMRiMQQCARiGIfZkIY6NiieIH6MKm2q1isHBQRw+fBiHDh1Cf3+/4DsTiQQuX76Mn//85wCAra0toXFbEdsymMwoHK4ROUD4cUqpEUAVRcEzzzyDV155BS+//DKGh4fF86LRKJaWlhCNRpFKpcQuDTQGZHFkcpvelbwEV1VVETgODw9jampKLG9NJBJ499138Ze//AWXL19GOp0WwOfLL8xIbMMwnuz+iMTV6bqOfD6PWq0mMgQ8gspkMohEIshkMgJY1WoVfX19ePHFF/Hmm2/i4MGDdZ2iDATt1EDFDKFQCADETg+0ws1isQgOkgZrc3MTmqbh7NmzOHjwILxer9BKhUIBn332GV5//XWxZsPtdgv+rtH+iyQymcvHRNYSHIhk2ugeBAiiY0hj9ff346WXXsL3vvc9PPvss3Xb0kWjUayurmJ9fV1sRUImmptNbi0oYubEORXlDg0NYXJyUmR6kskkLly4gIsXL+LatWvCqgDb9YpyFoj33zCM3bdjbDuiaRp+8IMf4I033sD09LQAFy03pUwEdW5rawv5fF4ELrSEQFVVZDIZuFwu+P1+jI6OYv/+/RgeHobD4RCBQjQaxW9/+1v84Q9/wObmppgYVEXUznqVZhqxGRBln1Hm38iE5vN5OBwOTExM4PTp0zh37hyOHTsGn88nnpNOpxGLxRCPx8W68FwuJ4IYviqSLAU9w+l0IhQKYXBwEKOjoyJCvnv3Li5cuIC//vWvwnelBAPtvCGPj6x5a7X2/hffrgMiNT4SieCnP/0p3nzzTei6LiJJMt1kXmZnZ3H//n0BWAAiNUj7bA8ODopqaCpP29jYwKeffoq33noLc3NziEajgryVsyWtpJEJ5H0CHgUlv1bWnvQ3+cWUBtV1HVNTUzh+/DhOnz6Nw4cP163BNgxDZKj49iTkSxIAiZ7x+XwIh8MYHBwUmRMA+Oyzz/D222/jk08+wcLCwiN77FDk3aj2kbshX0uNCED4SHa7HSdOnMBrr72GEydOIBKJCDNAGiufzyOZTApTRoNks9nERuc2mw2FQkEUcl6+fBl/+tOf8I9//ANLS0sAthcU8cJerjmaiRkQzYa10Xc8aJA5PtJiFKQBEMt09+7di8OHD+PEiRM4cuRInYYkoS1LyBUCIPZDNNv2+M6dO/j000/x0Ucf4cqVK0gkEkKbUnBCbZOpGjPX5GsLRGo80RpOpxODg4M4cuQITp48iWeeeUbsH02+Gy0D4EEBsB0hAg/9mS+++AKffvqpyO1Go9G6HCsVFlCuWi4GaCRm5DD1pdlxEjmY4X0g4alTAiftLHvgwAEcOnQIR44cwdTUFCKRSLvDDeBhAcPCwgJmZ2dx7do13Lx5EysrK9jc3KzL4hDFZbbsQKZruMvxtQUiRb6UCaAi2qGhIbGh+YEDBxCJRNDf3w+/3y80H2VUyJTTjvnLy8u4desWbty4gZWVFbFCjYpjaQCpjpFHtO1oRKD5LmDNRDZnZhNAJuzpx2azwePxIBQKYXJyEpOTk5iYmMDIyAgikYjIrVPgSOOTSqUQi8VEEoH22lleXkYqlaqriOKZskaarxGR/bUGYrVaFf+gh8wk+YCKosDv92NkZATDw8MYGxtDJBJBIBAQu8/Svi203d3s7CxWVlbEnoK8QpuqWyhyNCsebZVvbkaTdEKK8wCGa0XuKpDwtB2dRz5fJBIRyQDKXNFEpX/5sb6+jpWVFaysrIg9F4mN4Gt+eB5ZruQ2+8z7Qd99bYFIHaO9FvmOA5TikitwKKdN5pr2byGQ0f8doV1n5X9pQZkaWvAFoCEtIYusORu9LN7HVlqlUZTNXzInp+laTgNRXSH9kMXge9vwZbpU60kakPxlOSvDn9+OL7zr/s9KO0LNIX7PYnm4CN74P3FdLpcFiAzDEEAk/46X0BNJSylCVVXh9XpF/pny2UQeU6WQxWIRa0RIQzeTRqZZ7pMsrbQlBxcPYvh3ZnQPP07X8WCOzLwZ1UKmmO5hdp7cBjNfmGv3doC4a/87abX6cINPi8UicqoUVJC5IEqB73BFmo0GgsyXw+GoW2gEQBRO0ECSpqTUIRVLtCK0zaQT+odfYwZmOkZgkukfEj5heG0mr7zhqT7u/3G/lAI8DqZGEXIjEPLr25FdB0TeIbN6O26y+I77nFjl58nb+Mr+npm2I5pD5slaSSPz3Ox8Lo1eHDfL8vWNOEq+OwT1RT6HB2kUDZuBvtHzefvMfsufm8muA+LXUZq9qFYvwgyMzc4xI41l2srsXnwCy9womWC5D2bH+P1kX7iRn9uO9ID4GKUd6oefy6XZdbLpa6ZJ+b259uSAlp/Fjzczr7IFkAMys341kh4QuyDNgpFW0ghMjSgRMzNIZtWsHWaReyMgyma5HTFrU6M+NJMeELsonQw8FxlknZi0Vs9rpi2bAb2ddjSaPDsJ1HpA7JK0egnt+o/tBAVm5/Fnt2PWzc5rZyK0usbMPLcjPSA+BjEDpBlI5GNmQDADnNl9ze7ViFrZqdZqJN24Vw+Ij0E6pWzMfsvSDvkt0y38mY0i6Eb3MruuVUT/ZaQHxC5KO2a1mWak4+2Aph3/rdHfnYCnEU3T6p6dat4eELsozWiVdoHUzPdrdh1QT9Y3MuHyec3u3Q4HSu3dSZDGpatbjvSkXhoFD620hJzGa2QS5fMa0TftHGv2LP5dJ2DtRPP2gPiYZSfZFrNzzOiWZqaRg7RTU9xMW3cinWjJHhC7JF/WNMnSDBBm58qfucY0u1eziLqb0u79ekDssjSKMrv1gpul45q1o5XJ7XZbOwV1L1jpkrRDdXTqK/LIsxGl0izFZsZL7sQH3KlP2Yn0gNglaVSB0kw6cf5b5Z7NwNZJpqVZ0cSXuaZH3zxBacf/aiSNNJ3ZvdulcZpFyl8mF97Os9tpi5n0gNgFMYz6DUMbaYx2c8A7kWaalJ/TSZZlJ1kTHhjRDhPtSNtrVnrSk8cpvai5J7tCekDsya6QHhB7siukB8Se7ArpAbEnu0J6QOzJrpAeEHuyK6QHxJ7sCukBsSe7Qv4HhjZFeewz1j8AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -652,7 +654,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -676,7 +678,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -700,7 +702,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -712,7 +714,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-12 14:28:55,430 - `Trainer.fit` stopped: `max_epochs=75` reached.\n"
+      "2023-05-12 17:41:32,347 - `Trainer.fit` stopped: `max_epochs=75` reached.\n"
      ]
     }
    ],
@@ -761,7 +763,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Scaling factor set to 0.5467963814735413\n"
+      "Scaling factor set to 0.5302040576934814\n"
      ]
     }
    ],
@@ -787,7 +789,7 @@
    "metadata": {},
    "source": [
     "## Define the LightningModule for DiffusionModelUnet (transforms, network, loaders, etc)\n",
-    "The LightningModule contains a refactoring of your training code. The following module is a reformatiing of the code in 2d_stable_diffusion_v2_super_resolution."
+    "The LightningModule contains a refactoring of your training code. The following module is a reformating of the code in 2d_stable_diffusion_v2_super_resolution."
    ]
   },
   {
@@ -830,11 +832,11 @@
     "        self.train_ds, self.val_ds = get_datasets()\n",
     "        \n",
     "    def train_dataloader(self):\n",
-    "        return DataLoader(self.train_ds, batch_size=16, shuffle=True,\n",
+    "        return ThreadDataLoader(self.train_ds, batch_size=16, shuffle=True,\n",
     "                          num_workers=4, persistent_workers=True)\n",
     "        \n",
     "    def val_dataloader(self):\n",
-    "        return DataLoader(self.val_ds, batch_size=16, shuffle=False,\n",
+    "        return ThreadDataLoader(self.val_ds, batch_size=16, shuffle=False,\n",
     "                          num_workers=4)\n",
     "    \n",
     "    def _calculate_loss(self, batch, batch_idx, plt_image=False):\n",
@@ -843,9 +845,7 @@
     "        with autocast(enabled=True):\n",
     "            with torch.no_grad():\n",
     "                latent = self.z.encode_stage_2_inputs(images) * scale_factor\n",
-    "#             latent =  latent.detach() # avoid adding this to graph.\n",
     "        \n",
-    "\n",
     "            # Noise augmentation\n",
     "            noise = torch.randn_like(latent)\n",
     "            low_res_noise = torch.randn_like(low_res_image)\n",
@@ -948,33 +948,33 @@
    "execution_count": 12,
    "id": "936bbb9c",
    "metadata": {
-    "scrolled": false
+    "scrolled": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-12 14:29:00,910 - GPU available: True (cuda), used: True\n",
-      "2023-04-12 14:29:00,911 - TPU available: False, using: 0 TPU cores\n",
-      "2023-04-12 14:29:00,912 - IPU available: False, using: 0 IPUs\n",
-      "2023-04-12 14:29:00,912 - HPU available: False, using: 0 HPUs\n"
+      "2023-05-12 17:41:38,503 - GPU available: True (cuda), used: True\n",
+      "2023-05-12 17:41:38,503 - TPU available: False, using: 0 TPU cores\n",
+      "2023-05-12 17:41:38,504 - IPU available: False, using: 0 IPUs\n",
+      "2023-05-12 17:41:38,504 - HPU available: False, using: 0 HPUs\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 320/320 [00:00<00:00, 881.71it/s]\n",
-      "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:00<00:00, 432.94it/s]\n"
+      "Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 320/320 [00:00<00:00, 773.44it/s]\n",
+      "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 32/32 [00:00<00:00, 495.25it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-12 14:29:01,593 - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
-      "2023-04-12 14:29:01,624 - \n",
+      "2023-05-12 17:41:39,194 - LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
+      "2023-05-12 17:41:39,226 - \n",
       "  | Name      | Type               | Params\n",
       "-------------------------------------------------\n",
       "0 | unet      | DiffusionModelUNet | 266 M \n",
@@ -990,7 +990,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "982fa79bf5f14929a0c3fd70493f04f2",
+       "model_id": "71e4d5d2e391477aac58bfb05ecefb85",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1018,7 +1018,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "853963627ed142b78baae63ae84ed458",
+       "model_id": "be43a3c9e09a4405a067d9fc887e151a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1032,7 +1032,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "debc57241c794afc9c724d59047c9c6b",
+       "model_id": "bc6fe02c06e848a48a3cff1662788638",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1045,7 +1045,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -1070,7 +1070,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e68a0826dfad4a2b933c1793ae5a5961",
+       "model_id": "5aa3448de1094d0f82f2808b8131bb9e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1084,7 +1084,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f7b5d62453ce4440986f2a966f993691",
+       "model_id": "d3db0817d9674e5f9fb7027521b9e032",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1097,7 +1097,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -1122,7 +1122,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "82a45e1e568f4af68ed20bf170a32569",
+       "model_id": "9b9fad73a311409082600b576378c2b7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1136,7 +1136,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f7ebd03b89314e0f92c65c1095b6a4e4",
+       "model_id": "4fee43bf923247019574f67cc2aa7c0d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1149,7 +1149,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -1174,7 +1174,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a2dbfb54bcc8427682275ec8ca7717ee",
+       "model_id": "323be58fabcb4ab4a9a128d9f6cc9b52",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1188,7 +1188,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8d3152112c4747c3a14274f8f9169661",
+       "model_id": "b80e5eb10ac04eb6a3f90ac7415ab681",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1201,7 +1201,7 @@
     },
     {
      "data": {
-      "image/png": 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XaQFpYQE0LQ4Ptlbaj/cN6ZloKyKhA4EAI75J+3fSInxel9yQQqGAdDrN/D9VVRmHyWekqtUq6wtZFuILyY0gS0AbjtaG31S1Wo3lnO2fHVQODUSabEEQWBKfVP3p06extLSEbDbLzEMul2MTRE4yb96pPaImaBdTHpUqcDRNg2mayGazsCwLXq+3bRVzO6H7UkRLZozyxkTm0qaxmx57gSxvlluRz7S4tVqN8aaUG7ZX7bTqI28eScitcLvdUFUVmqah0WiwtCG/icnc8lkQ0nA0BxSU0L3JDaDr+fmibBZpcrrGPu5u5MgIbQDY2NhAJpOBx+NhyfRoNIrt7W1GyRDQyIchoYEB71Tu0G4jc80vnsPhQCQSQTQaZXWB+xWaaHIPCoUC4/I0TQMAaJrGtD5tCr5Okfcd2/mH9DmvPe31i91khVpRRcBuxsbv97PrXC4XvF4v42qJrKex8u4FP5+0DgBYZoh/5qkgUiJ+vx+jo6NdB4nt5NBA5HdqMpnEnTt3MDIywoAxNjaGeDyOfD6PSqXSpCn4IIQGSCaHQEoJeZpA8mPI3FAxQT6f35eJ4B3+er2OfD6PeDyOeDzO6vU0TYOmaVAUpamaptUc0ILy2qxV7SRtKk3T4HK5GPXFZzqof9Q232c7GA3DQC6XA7AbIJIWpFw3gCZqpVqtsrGQNubHQSDli2d5bUrCVz0NDg6yghB7P7uVQ0fNtFMsy0Iul8Mrr7zCtIplWYhGo8y08aVTVFjKl97zGQByiCmDQTvQsixGT4iiiEgkAkEQkM1m2YJ0PXgu+Emn01hfX8f6+joymQwajQbcbjc8Hg9UVWXFCgQu3oG3R5S8tqENRN8hX5oPTnjHnzZ2pwiUz3xUq1UsLi4im80y80zBBtVTut1uAGD3Ib+Q5kCWZbhcLrbpeS6xldDYiRPN5/NN2n2/LhJwRAfseQf6xo0buH37NvMTNU1DNBqFruvQdZ35NOQPkYagZxogLRrP/lN5Vn9/PzweDz7ykY+wa+7du8cCH/sithKe26vVatje3sbq6iqWl5eRSCRYQEHai4IJAiNPd5D54v0kPkLlAxyqlA4GgwwgxWIRuVyOcXGtItBWwCSXYn5+Hjdv3gQADAwMYHR0FJqmMcAAu0Q6+Y28VqdCEwJnq1IzUgTkx1OwWalUsLCwgLt376JarTaZ8P3KkZhmqvZwOp3IZDJ46aWXMDExgaGhIQiCgIGBAYTDYQZG0zSZLwns7lQq4aKsCpl2yqy43W44HA7IsswqpsfGxtBoNFCtVnHr1q19mwPa8aR9DcNgYIxGowB2S6IcDgcLjmiSeV+LNLidayTA0OIoigK3241gMIhQKARBEFAoFJBMJpn7QuPtZjH5wtu//vWveOqpp/CpT30KPp8PbrcbiUSiiX6i/vE5Zop8+YwLKQleI5IZJ23udDoRj8dZIQsAFnHzQO8WlEeS4qOBUtBw8+ZNfOc738G3v/1tnDlzBqFQCKIoolQqAUBTLpWIVj59xvuElmVB0zQW0dGunZqaQjAYhKIouHXrFl5//fV97UT+Wr42j6qEBEHAzs4O3G43TNOE3+9n2rFQKDDtQmOheeDbJBNNIKTaxGg0ioGBAei6jng8jo2NDayurrKK6U5ahfcdyVrUajWsrq7iV7/6FXw+H2ZmZpr6QXNGbpA90OK5RHtwRd9TVRV9fX3w+XyQZRmpVArpdBqJRIJdQ5U93eb7eTkSINbrdVZsSYN99dVXsbGxgRdffBEf+9jHcPHiRfzxj3/Ew4cPIcsynnzySWYmBEFAIBAA8M7Pa9COpbymqqqIRCKoVCoYHx/Hhz/8YQiCgFwuhz/96U9Ip9PvMi17Cb/YPCe3sLCAnZ0dxONxTE5OYnBwkBW30rmUQqHAMkPUFt8eLQi5KJIkwePxIBAIoL+/H6FQiAVZa2trWF1dRTabbTpUZjfHBBL7vYgCu3fvHn75y1/i+eefh9/vh9frZf4bFSYLgvCuIlae9+RP+tn9RSpbSyQSiMViKJVKcDgcjHcl63AQORLTTJ0kLo4Gsri4iO9+97t44YUX8PnPfx4vvPACZmdnkU6noSgK1tbWWIqvUChAluUmrkuWZaiqinq9jnK5jGQyiZmZGTz99NMsSnvrrbdw48YN5HI5lk/dr/BmqFwuY2NjA8lkErlcDsViEZZlMR8W2C3LJ0BQsMPzcXwKrF6vs4xNX18fc0GKxSKSySQ2NzexsbGBeDwOwzBaRqjUR37O+dcU6JmmiYWFBczOzuKTn/wkBgYGUK/XUSwWWVBSq9UYOQ+gSVOSS0SKgH9QarJWqyGVSiGTycCyLLjd7qYCFTsH2a0cSdEDHcqhUB8Ao1bW1tbws5/9DH//+9/xzW9+Ex//+MeZc/zf//4X9+7dQzabhSzLjLMjk0bl7rIsI5fLYWpqilX31Ot1zM/P48UXX8T6+jrcbjejJg4iNGnk1Ofz+d0J+n+Ct1wuw+v1shw6+UukgcnPohpF0oa8WSa6hoKhhYUFLCwsIJFIMKaBN8vdLiQtPlW3v/nmmxgZGcGpU6cQDAabAELUDp88IPeB14BEKdFa8LWOlMsmP57fsDwBv58xHNnhKZ5P5N/zf3e73Xjuuefw/PPPY2Zmhg14eXkZ29vbrBiAggc6GBWNRjE5OYknnngCbrcb29vbuH79On7+859jY2ODgfcwpCrvF9HCqaoKr9fLjrz6/X4EAgFMTExgdHSUBWGkEarVapPfSOeJCVzVapWZtZWVFdy/fx+bm5tM+9J3gM4/KMAL/3cKchRFwenTp3H16lWcOHGiKfgg4p4qzO00EwGPgEjX8Hwvfcfn8yGXy+H27duIx+NNPCj1zTRNJBKJPef/yM6stKMb+L8Xi0X84he/wM2bN/HVr34Vn/nMZxAMBnHu3Dk0Gg12zJG0Cp2BjkajjOq5e/cuXnrpJfz5z3+Gruts4ezVxgftP2lgYNc9SKfTyGQySCaT8Pl8GBgYYHlwn8/HfEDaBHw6jrg5MtWlUgmPHj3C3Nwc3n77baysrDSV8beby26Fxm8YBlZWVhj3efbsWfj9fgiCwM7GUKDIp/nIbFOxBKU66XwOFbgEg0GEw2EAwMLCAjszZE9F7mcs7/lv35imiYcPH+IHP/gBXn75ZXz5y1/GlStXMDw8DK/Xi5mZGZbxIIYf2D1iev36dfz617/GnTt3WIaFUlKKohxJ8p2PSomnI3+PXtMClkolpNPpJs6Od9gJnLTA6XQaDx48wNLSEtbW1tgxS+JPDwLAVpmXen337M/S0hI0TcPg4CBCoRBjKDRNY9krvgCCxkeAc7lcTMtRHYGqqjhx4gSi0Sj+8Y9/4OHDh00EuT3y7pZSe2znmlsJz6tROkzXdUSjUVy4cIGdIY5EIkwbbWxs4NatW3j77beRTqeZyeQLFHg64iCsfru+kqagog5aHPpxgL6+Pvj9fhZJq6raRMKTX1UqlVAul9nRA8oC6brONH0nv7AVtdLuc2qDAkiXy4UzZ87gmWeewczMDAv+KKC0LIvxu9RHPqFAxzuodG98fByRSARvvPEGXn75ZWZ22wWJ3Zrm9xyIAFikSblj0mo8M08+FZUu8eXrFCXSZNHEHdY8t+svSSu/jX6YiX5Lhtc25I8Vi0XGO9o5u277wV9v75cdqPSaHpIkYWhoCBcvXsT58+dZ8S9tHN43pMNhdGSBtB398oUsy5idncUf/vAHPHr0qG1akuRYA7Hd7qaAg99dfD6YvstrPvJ3BEGAYRgHjpq76TstGAUm5DpQVExFtfwpRNImdD0Bg/y3bu/dCoj2OWz1HninmknTNIyMjGBiYoJxo5SF4XP/PPUEgOX3k8kklpaW8J///IcdU211rMHOcVLmpZO8p0DcSzpFh+/F9/dqG0BTlEm0B5lBoq+IFiHQ8gfvyZckCuUg47IDrpPp5s01+aE8cU21kEQv8T9TR6cZ0+k0lpeXkc1m2a8/8McnOuXzuwXisfqhzsOC6HGBkNom7ctHyby0OucMvPOze0clnVwGvq90LR/EAGAm1+FwsOwIWRh7JoVy+eQr8wBsdc/98ockxwqIx13aBRKtHnY/bb8k9UGEB53dfLcy2fxrew6b3vNar9sxHGSMPSAegfAL1EpTPC4Q2ttrB7ZO925Fs7Qytbzpb5WsOGhqj6QHxENKNwv+uP3WTu0fxm9ulS1r97pdX7rlEXtAPCJ53P4pSSutt5c2s0srcLb6215pxcP4hHbp/Qu097HsBTq7mewEmv34ffZ22vnO3fSRpAfE95nsV/Pslzjf67NuAGaP2LuRHhDfh3JQM3gU0W6397azBXtJz0d8HwkflXfiDtt9dy9p1fZePmC7bA7/3W6kB8T3gfB0Sadr+OdOAGknnTIzfNvdtLcfEAI9IL4vhLIirchp+ryVtCKu6XW7zzq1t5/+0nO3Rze6zjX3pCePU3rBSk+OhfSA2JNjIT0g9uRYSA+IPTkW0gNiT46F9IDYk2MhPSD25FhID4g9ORbSA2JPjoX8H2sdFeh9Ah8eAAAAAElFTkSuQmCC",
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nPR8EeyYiLbYkSayIT27jxIkT+PDDD1EqldDtduHz+VCpVNBoNGDbNotVePdOxyO5ht/9REpRFJk7L5VKsG0bIyMjfeu0/cC7UHI33W6X1Y0F4WHTAm0aZ+LgbJDldTreIvPfoXmT7EFkcnbtOMfIH4uPP2njUNmNDAORybIs5qJps/OtWnyrmzOuc5KKuqREUezJqokHOyUfj30TtAEglUqhWCwiGAzCsiwEAgEkEgnk83kmyRDRKHEh8IvDi65EQl70pknH43EkEgnWF7hTOF1OrVZjWp7f7wcA+P1+dnFpU/AXj48deSLyFpJPMGh+zv7F3cge1GYXDAYxMjKCUCiEfD7fkwDSBifrDoBZL56AlCjyWblb7EiNLCSS5/N5JuPwSdag1SHCnm8n5dmfzWZx7do1dDodVrwnlxkMBnsaQGli1D1Dpp6sJK8n8rIPNRLQd2VZRrvdRrVa3VF8QlaFNka1WkU6nUY6nUa9XmeZIZW1nA2vzjWguNGZjfLrQwSkBE1RFBb8k6bnvID9rAuNPxQKsda0EydOIBKJsHOJoghVVVnDA32Hb+jlwwlaa0mS2DrzTb/0GoUUmqZBkiTXRGWn2LcY0bIsVCoVvPnmm3jhhRcwMjKCTqeDRCLBXBvfOkWdLLSbyDLRIvOCM5GOLq6u66z2GY/HIUkSSqUSKpXKjsZOxLIsC4VCAaurq0gkEpicnEQikWBdKlSfpeSKxuSUOMhaO0V1/p4QiqX55KTZbLJyp9MdEvhzEKEAIBQKIRwOw+PxYH5+HqFQCDdv3mQaLcW7VJLkLRdZZACPrDcPGhNPYFoTajThhfbdYF8EbX4Qly5dwte+9jV84QtfYLFcIpFAKpViQjXJMrZts5iDOlFIB6O4hiclNWkGAgFIkoTPfvaz7DM3b95kEpBzbG7gL2a73WYuLRaLYXZ2FjMzMwiFQsx6qarKpA+eWPSXNgud003MFgQBPp+Pdb8EAgEAQL1eR6VSgWEYjyQO/Hn4cxGoGVcQBIRCISSTSfj9fiwsLMCyLNazSdoiXyTgrSRtIKci4CbFkNHh+xKdlabt1t+JPRORMk0qwxWLRbzxxhuYmZnB5OQkJEnC+Pg4NE2DYRgwDAOWZbFYEngoLdDOIrJSzEeLFggEIAgCI2goFMLhw4dZ9eDKlSs73o1ERAr0m80m7t+/j3v37iGRSAAAawag5IjPLGmDUGLg1BrpwlBI4vP5WG9fLBaDJEmo1WrIZrNIp9OoVqtsvlu5OJ7ovAVtNBpQFAXRaBSTk5PI5XJotVqsusKPk/5Ss6sz/iV4vV7m5om4VBUj78T3ZfJj3An2xSJSdkam+/Lly/jxj3+MH/3oRzh58iRisRg8Hg90XQeAnloqdTjz5TM+JrRtG36/n3UWk8WYm5tDNBqFz+fDlStX8M477+woPuE/SxaDWucXFhYgSRLK5TLLSMPhMLOOtVoNpmky8vLrwB+TrAuRkHoTE4kExsfHYRgG0uk0UqkU7t+/j3w+zxpH3ObCWxwiTrFYZO1blmWxvs5ms4l8Po96vc7Wk+I//lh8WMRXR2iN+klJRGRd19n5+Wyb/n4sgjaBkhPKcAHgrbfeQiqVwuuvv47Pf/7zOH36NP70pz/h7t27kGUZx44dY7tUkiQWZJP+RYvRaDSg6zpUVUU8HodpmpiensYzzzwDSZJQqVTw5z//GYVCwbUDZCvwi0TjrtVquH37NsrlMtLpNOuqpuZWcoW1Wg31er1vsZ+SLpKyvF4vgsEgIpEIxsbGEIvFWJL14MED3L9/H6VSqeemMqf0Q68RiTqdDjY2NvDgwQMkEgmW9KTTaaysrGB1dRWdToclRoqi9IQYZMGpi4g2jVMLJaJSTElJC1VsarUas6JOMg6KfXHNlIlRDEJx39LSEn7yk5/gtddew8svv4zXXnsNV69eRaFQgM/nw4MHD5ggWqvVIMsyuwi2bbNGzk6ng0ajgWw2i/n5eZw5c4YlO++++y4uXbqESqWCQCCwq344Pl5sNBpIpVKsQ6Ver7OLQFUKn8/HdjtVesjdEUn4BlOq2FDM2e12Ua/Xkc1msb6+jlQqhXQ6zcTordrZeMmp2+0im83i7bffZjFttVpFJpNBLpdjlQ/KegVBwOjoKILBIBPpybLTHEjq4evkdF46FiVAa2tryGazzE079U/ixyDYl6YHuu2TMioALP548OABfvOb3+Dvf/87fvCDH+D8+fMscF5eXsbNmzdRKpVYWYpua6QLSA2rlUoFc3NzrLun0+lgcXERr7/+OlZXVxEIBNBqtXbck0ighaM2+Gq1+nCB/r+tv9FoYGRkhMkXdC8In3lTrMTrjLxbpqQik8mg0Wjg9u3buH37NjKZDNMvnRKX25oTKci1/uMf/0AikcDLL78MWZZZLE4Wj8pyVBIkF03HcFZaeK/CZ8QAWLwuiiLS6TSLQ7dz59thz7Vmkm94d8pPwuv1olar4a233sLVq1fx6quv4utf/zrm5+fx5JNPYn5+Hvfu3UM+n2ftUeSeKLuemZnB7Owsjhw5gkAggHw+j4sXL+K3v/0tUqkU07IG7bzZCkR8usipVAr1eh2bm5sIh8OIRCKYmZnB1NQUNE1jWhpVS/i40ev1Mg1SEB7eKpHJZHD37l2srKzggw8+wPr6Omq12iPu2E2+cbo9+k61WsUf//hHdLtdnDt3Du12m92ySuQnD1KtVnvcLV9L55MWWgveSlJ5VpZlNn5d13s6gfqNeTvs2z0r/XQv/vV6vY7f/e53uHz5Mr7zne/gy1/+MqLRKE6dOoVut4tqtYpUKsWsCt0DnUgkmI51/fp1vPHGG/jLX/4CwzB67jbbbcGdHycREXgYHhQKBRSLRWSzWYyOjmJ8fByCILB7mmkT8rVWsiB00chV67qOtbU1LCws4NatW1hZWWEVDedttDycQT+vX9L5qtUqLl68iHw+j3g8zm6oJ1dKfYrkgp2dTmQl+WMTCfn3qM5//fp1rK+vs2vF90juxiIK3QG/tR+/j8iLpoqi4OTJk/jmN7+Js2fPIplM9tzbTJ+lCeZyOVy8eBG///3vce3aNSaOk47l8/l6stb9GCsJ7pZl9UgvR44cwezsLKampphux1eA+M5v4D9t9oVCAXfu3MGHH36I5eVlZDIZthb96tM0Fj7mcloa2gzkRSYmJpBMJqFpGsbGxjA7OwtVVZHP55FOp9mtCABYZcvNPZMKQi1lFOMuLi7i8uXLSKfTj4jwvDFot9us/3E7fOxEpN1P8YlhGEgkEnjqqafYPcTxeJxZo1QqhStXruDWrVsoFArodrtQVbWnQYGv++608WGrsfJ6G++a6McBqKpBmbSqqj0iPBGZJA669YCqQIZhMEu/VVzYb3z011miCwQCiEQimJ2dxdNPP43z588jEomgUqngxo0bPXEdzZPkGyIWZfl09x9Vlu7evYv33nsPa2trPbd08NeXj7cPLBEBsEyTJkwDpYyRFpXEWKDXdVB8QyUz0sL26p77jZfgXHRKRPjGA/7nOaiRol6v92Sn/cp4uxkT/zqtD5X2NE3D0aNHcf78eVb+W19fx507d5hUxOuJfK+ioiisjq2qKsrlMm7evInFxUV2Jya/8Z3xK12/QYn4ifwsHe9++U4R/iYsAkk6vNvlXTZVYihh2W3W7AYn8chyUGJC46CsmBpR+bsQdV2HYRg9NV5KYnaaXPUTl/l/89a4Uqng7t27aLfbWF1dxRNPPIFoNApN09BqtVCr1XrIRwkL9QPQL0Touo6FhQXcuHEDxWKR/RiAW9y6W3ysRHQTaHmQu3a6V+c9HM72JFr4/XLL/UCuj8pefAMCySR89knxIsVYvPSyG2tIYyA4dTs+BidXW6/XkU6n2XvT09PMclN4QGSkTaKqKvx+P8rlMhYWFrC2tsbud6EixE51wu1woH6oc7cXZ7++v92xaZPwWTIPt/ucgf80lO7HGNzGxD/nhXXSDW3bZo0Vm5ubrETp/L1GsuSNRgPpdBqrq6tIp9NoNBostOgX+riJ2Tu5HgeKiAcdW9V/nQ8+gXBWKPYCN5fIv0dEIctMMR7Fz5VKhVWN+OoQWW9d15HP55HL5VCtVnt+0YE8AD/vvbpkwpCI+wCeZG4Jzl5J6JRF+ITA+TmyatT0IMsyzpw5g0OHDqFSqWB1dRXlcpndN0NtaZIkoVgsYn19HaVSicWBfHtXvw2wVeY86JyHRNwj+gn52732UYCPQYmMa2truHXrFiYnJ6FpGpaXl1EqldhtudSwYpom+8liXtrZqonBSU63zwyJ+DHjoySbUzN0O5/zOZGRavq1Wg1Hjx4FAHY/Dn2PGiXo51CchQG+99F5LicZd4shEf9L4EbAfsSg18ilN5tN1uFDv/+oaRoURUGj0cCDBw9QKpXY593CAP48zvNuFSMOLeL/MAa98OSmycVSmx79HuPIyAgajQbK5XLPbb1u7niQEGTQMbphSMT/Arh13mwFPpt1fpesXrlcZjeb8e87v0vn54+zE1c8tIj/Y3ATr93eo+fO9/lEhrJhN3ff70b57eJDt/HR+QbBkIj/BdgqG3WSzk1W4T/Lt/Tz5OI1wn5WrJ9muFXmPCTi/xBIShkkOeBFdedzErydWbDz827xYD9x3kle/ruUtQ+Cgbtvhhjio8Twf7Af4kBgSMQhDgSGRBziQGBIxCEOBIZEHOJAYEjEIQ4EhkQc4kBgSMQhDgSGRBziQOD/ACF9j6TBlDGkAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -1226,7 +1226,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4a42ac2f90cb461fb9f0c82d544fb8d2",
+       "model_id": "4b79fa6a38cd49f7bb65fc6ad327af73",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1240,7 +1240,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c8e20b5bfbc64b28a910c9d11ef6851c",
+       "model_id": "05dfeb5eed1b44bfa4d3986dd0f97e1f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1253,7 +1253,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -1278,7 +1278,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c6d737f986a64c22895476a68c2b3e87",
+       "model_id": "02c9a151f246464da9eebd015e6beb9d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1292,7 +1292,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b1743b884fbf4ae4bebf051ea1f5bf23",
+       "model_id": "233d0384d2c54aeca65d790b19565232",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1305,7 +1305,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -1330,7 +1330,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7e79ff41ccf54a45885ea18dd5bfe401",
+       "model_id": "cbaf0368cb304e6bbfa8ceac20f5a247",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1344,7 +1344,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3dd47e394441481da5c2e8b8af54a451",
+       "model_id": "5bbc10ac52ca4835a55e13b73e1fb263",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1357,7 +1357,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -1382,7 +1382,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c23581c04d474a69a45d37fce99ec3f4",
+       "model_id": "2a95535f104048b2ac1756ff1db454f8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1396,7 +1396,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "96313db64cb54ee991281b16c1276823",
+       "model_id": "319034a2b62d49cbb3e4ca25a30bc76a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1409,7 +1409,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -1434,7 +1434,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "009e46d5a9094efb96c33cbed7b09d93",
+       "model_id": "8959e5f59fb14792abea3737d90363ee",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1448,7 +1448,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d28bcf839d3f438bb525f98f15c31092",
+       "model_id": "e4c2571d928941b4850869d0d0a86ea9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1461,7 +1461,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -1486,7 +1486,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "712d910c6b8a4d529c7b936c29f8c70a",
+       "model_id": "40b8269d8e05404da709e1f212edc47c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1500,7 +1500,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eb338a4be9044e6b899b95847c926ba8",
+       "model_id": "371f42e9994942a489b04ef0d2b2e022",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1513,7 +1513,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 200x200 with 1 Axes>"
       ]
@@ -1525,7 +1525,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-04-12 15:10:07,913 - `Trainer.fit` stopped: `max_epochs=200` reached.\n"
+      "2023-05-12 18:22:06,851 - `Trainer.fit` stopped: `max_epochs=200` reached.\n"
      ]
     }
    ],
@@ -1571,7 +1571,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2d9060b0533142ceac3699efbb441dc5",
+       "model_id": "631635b665454a2884dc88d7d466c50b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1631,7 +1631,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x800 with 9 Axes>"
       ]
diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
index 084404bc..2a430db5 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
@@ -46,6 +46,7 @@
 
 # %%
 # !python -c "import monai" || pip install -q "monai-weekly[tqdm]"
+# !python -c "import pytorch_lightning" || pip install pytorch-lightning
 # !python -c "import matplotlib" || pip install -q matplotlib
 # %matplotlib inline
 
@@ -94,55 +95,53 @@
 print(root_dir)
 
 # %%
-train_data = MedNISTDataset(root_dir=root_dir, section="training",
-                                download=True, seed=0)
+train_data = MedNISTDataset(root_dir=root_dir, section="training", download=True, seed=0)
 train_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "HeadCT"]
-val_data = MedNISTDataset(root_dir=root_dir, section="validation",
-                              download=True, seed=0)
+val_data = MedNISTDataset(root_dir=root_dir, section="validation", download=True, seed=0)
 val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "HeadCT"]
 
 
 # %% [markdown]
 # ## Setup utils functions
 
+
 # %%
 def get_train_transforms():
     image_size = 64
     train_transforms = transforms.Compose(
-    [
-        transforms.LoadImaged(keys=["image"]),
-        transforms.EnsureChannelFirstd(keys=["image"]),
-        transforms.ScaleIntensityRanged(keys=["image"], a_min=0.0, a_max=255.0, b_min=0.0,
-                                        b_max=1.0, clip=True),
-        transforms.RandAffined(
-            keys=["image"],
-            rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],
-            translate_range=[(-1, 1), (-1, 1)],
-            scale_range=[(-0.05, 0.05), (-0.05, 0.05)],
-            spatial_size=[image_size, image_size],
-            padding_mode="zeros",
-            prob=0.5,
-        ),
-        transforms.CopyItemsd(keys=["image"], times=1, names=["low_res_image"]),
-        transforms.Resized(keys=["low_res_image"], spatial_size=(16, 16)),
-    ]
+        [
+            transforms.LoadImaged(keys=["image"]),
+            transforms.EnsureChannelFirstd(keys=["image"]),
+            transforms.ScaleIntensityRanged(keys=["image"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True),
+            transforms.RandAffined(
+                keys=["image"],
+                rotate_range=[(-np.pi / 36, np.pi / 36), (-np.pi / 36, np.pi / 36)],
+                translate_range=[(-1, 1), (-1, 1)],
+                scale_range=[(-0.05, 0.05), (-0.05, 0.05)],
+                spatial_size=[image_size, image_size],
+                padding_mode="zeros",
+                prob=0.5,
+            ),
+            transforms.CopyItemsd(keys=["image"], times=1, names=["low_res_image"]),
+            transforms.Resized(keys=["low_res_image"], spatial_size=(16, 16)),
+        ]
     )
     return train_transforms
 
+
 def get_val_transforms():
     val_transforms = transforms.Compose(
         [
             transforms.LoadImaged(keys=["image"]),
             transforms.EnsureChannelFirstd(keys=["image"]),
-            transforms.ScaleIntensityRanged(keys=["image"], a_min=0.0, a_max=255.0, b_min=0.0, 
-                                            b_max=1.0, clip=True),
+            transforms.ScaleIntensityRanged(keys=["image"], a_min=0.0, a_max=255.0, b_min=0.0, b_max=1.0, clip=True),
             transforms.CopyItemsd(keys=["image"], times=1, names=["low_res_image"]),
             transforms.Resized(keys=["low_res_image"], spatial_size=(16, 16)),
         ]
     )
     return val_transforms
 
-    
+
 def get_datasets():
     train_transforms = get_train_transforms()
     val_transforms = get_val_transforms()
@@ -151,27 +150,28 @@ def get_datasets():
     return train_ds, val_ds
 
 
-
 # %% [markdown]
 # ## Define the LightningModule for AutoEncoder (transforms, network, loaders, etc)
-# The LightningModule contains a refactoring of your training code. The following module is a reformatiing of the code in 2d_stable_diffusion_v2_super_resolution.
+# The LightningModule contains a refactoring of your training code. The following module is a reformating of the code in 2d_stable_diffusion_v2_super_resolution.
 #
 
+
 # %%
 class AutoEnconder(pl.LightningModule):
     def __init__(self):
         super().__init__()
         self.data_dir = root_dir
-        self.autoencoderkl = AutoencoderKL(spatial_dims=2,
-                                           in_channels=1,
-                                           out_channels=1,
-                                           num_channels=(256, 512, 512),
-                                           latent_channels=3,
-                                           num_res_blocks=2,
-                                           norm_num_groups=32,
-                                           attention_levels=(False, False, True))
-        self.discriminator = PatchDiscriminator(spatial_dims=2, in_channels=1,
-                                                num_layers_d=3, num_channels=64)
+        self.autoencoderkl = AutoencoderKL(
+            spatial_dims=2,
+            in_channels=1,
+            out_channels=1,
+            num_channels=(256, 512, 512),
+            latent_channels=3,
+            num_res_blocks=2,
+            norm_num_groups=32,
+            attention_levels=(False, False, True),
+        )
+        self.discriminator = PatchDiscriminator(spatial_dims=2, in_channels=1, num_layers_d=3, num_channels=64)
         self.perceptual_loss = PerceptualLoss(spatial_dims=2, network_type="alex")
         self.perceptual_weight = 0.002
         self.autoencoder_warm_up_n_epochs = 10
@@ -179,29 +179,27 @@ def __init__(self):
         self.adv_loss = PatchAdversarialLoss(criterion="least_squares")
         self.adv_weight = 0.005
         self.kl_weight = 1e-6
-        
+
     def forward(self, z):
         return self.autoencoderkl(z)
 
     def prepare_data(self):
         self.train_ds, self.val_ds = get_datasets()
-                
+
     def train_dataloader(self):
-        return ThreadDataLoader(self.train_ds, batch_size=16, shuffle=True,
-                          num_workers=4, persistent_workers=True)
-        
+        return ThreadDataLoader(self.train_ds, batch_size=16, shuffle=True, num_workers=4, persistent_workers=True)
+
     def val_dataloader(self):
-        return ThreadDataLoader(self.val_ds, batch_size=16, shuffle=False,
-                          num_workers=4)
-                          
+        return ThreadDataLoader(self.val_ds, batch_size=16, shuffle=False, num_workers=4)
+
     def _compute_loss_generator(self, images, reconstruction, z_mu, z_sigma):
         recons_loss = F.l1_loss(reconstruction.float(), images.float())
         p_loss = self.perceptual_loss(reconstruction.float(), images.float())
         kl_loss = 0.5 * torch.sum(z_mu.pow(2) + z_sigma.pow(2) - torch.log(z_sigma.pow(2)) - 1, dim=[1, 2, 3])
         kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
         loss_g = recons_loss + (self.kl_weight * kl_loss) + (self.perceptual_weight * p_loss)
-        return loss_g,recons_loss
-    
+        return loss_g, recons_loss
+
     def _compute_loss_discriminator(self, images, reconstruction):
         logits_fake = self.discriminator(reconstruction.contiguous().detach())[-1]
         loss_d_fake = self.adv_loss(logits_fake, target_is_real=False, for_discriminator=True)
@@ -210,7 +208,7 @@ def _compute_loss_discriminator(self, images, reconstruction):
         discriminator_loss = (loss_d_fake + loss_d_real) * 0.5
         loss_d = self.adv_weight * discriminator_loss
         return loss_d, discriminator_loss
-        
+
     def training_step(self, batch, batch_idx):
         optimizer_g, optimizer_d = self.optimizers()
         images = batch["image"]
@@ -223,8 +221,6 @@ def training_step(self, batch, batch_idx):
             generator_loss = self.adv_loss(logits_fake, target_is_real=True, for_discriminator=False)
             loss_g += self.adv_weight * generator_loss
             self.log("gen_loss", generator_loss, batch_size=16, prog_bar=True)
-            
-                          
 
         self.log("loss_g", loss_g, batch_size=16, prog_bar=True)
         self.manual_backward(loss_g)
@@ -241,8 +237,6 @@ def training_step(self, batch, batch_idx):
             optimizer_d.zero_grad()
             self.untoggle_optimizer(optimizer_d)
 
-                          
-                                    
     def validation_step(self, batch, batch_idx):
         images = batch["image"]
         reconstruction, z_mu, z_sigma = self.autoencoderkl(images)
@@ -251,33 +245,30 @@ def validation_step(self, batch, batch_idx):
         self.images = images
         self.reconstruction = reconstruction
 
-
     def on_validation_epoch_end(self):
         # ploting reconstruction
         plt.figure(figsize=(2, 2))
-        plt.imshow(torch.cat([self.images[0, 0].cpu(), 
-                              self.reconstruction[0, 0].cpu()],
-                             dim=1), vmin=0, vmax=1, cmap="gray")
+        plt.imshow(
+            torch.cat([self.images[0, 0].cpu(), self.reconstruction[0, 0].cpu()], dim=1), vmin=0, vmax=1, cmap="gray"
+        )
         plt.tight_layout()
         plt.axis("off")
         plt.show()
-        
 
     def configure_optimizers(self):
         optimizer_g = torch.optim.Adam(self.autoencoderkl.parameters(), lr=5e-5)
         optimizer_d = torch.optim.Adam(self.discriminator.parameters(), lr=1e-4)
         return [optimizer_g, optimizer_d], []
-                          
 
 
 # %% [markdown]
 # ## Train Autoencoder
 
 # %%
-n_epochs = 75 
+n_epochs = 75
 val_interval = 10
 
-                          
+
 # initialise the LightningModule
 ae_net = AutoEnconder()
 
@@ -285,14 +276,16 @@ def configure_optimizers(self):
 
 checkpoint_callback = ModelCheckpoint(dirpath=root_dir, filename="best_metric_model")
 
-                         
+
 # initialise Lightning's trainer.
-trainer = pl.Trainer(devices=1,
-                     max_epochs=n_epochs,
-                     check_val_every_n_epoch=val_interval,
-                     num_sanity_val_steps=0,
-                     callbacks=checkpoint_callback,
-                     default_root_dir=root_dir)
+trainer = pl.Trainer(
+    devices=1,
+    max_epochs=n_epochs,
+    check_val_every_n_epoch=val_interval,
+    num_sanity_val_steps=0,
+    callbacks=checkpoint_callback,
+    default_root_dir=root_dir,
+)
 
 # train
 trainer.fit(ae_net)
@@ -303,6 +296,7 @@ def configure_optimizers(self):
 #
 # As mentioned in Rombach et al. [1] Section 4.3.2 and D.1, the signal-to-noise ratio (induced by the scale of the latent space) became crucial in image-to-image translation models (such as the ones used for super-resolution). For this reason, we will compute the component-wise standard deviation to be used as scaling factor.
 
+
 # %%
 def get_scaler_factor():
     ae_net.eval()
@@ -316,12 +310,14 @@ def get_scaler_factor():
     scale_factor = 1 / torch.std(z)
     return scale_factor
 
+
 scale_factor = get_scaler_factor()
 
 
 # %% [markdown]
 # ## Define the LightningModule for DiffusionModelUnet (transforms, network, loaders, etc)
-# The LightningModule contains a refactoring of your training code. The following module is a reformatiing of the code in 2d_stable_diffusion_v2_super_resolution.
+# The LightningModule contains a refactoring of your training code. The following module is a reformating of the code in 2d_stable_diffusion_v2_super_resolution.
+
 
 # %%
 class DiffusionUNET(pl.LightningModule):
@@ -329,69 +325,59 @@ def __init__(self):
         super().__init__()
         self.data_dir = root_dir
         self.unet = DiffusionModelUNet(
-                                spatial_dims=2,
-                                in_channels=4,
-                                out_channels=3,
-                                num_res_blocks=2,
-                                num_channels=(256, 256, 512, 1024),
-                                attention_levels=(False, False, True, True),
-                                num_head_channels=(0, 0, 64, 64),
-                            )
+            spatial_dims=2,
+            in_channels=4,
+            out_channels=3,
+            num_res_blocks=2,
+            num_channels=(256, 256, 512, 1024),
+            attention_levels=(False, False, True, True),
+            num_head_channels=(0, 0, 64, 64),
+        )
         self.max_noise_level = 350
-        self.scheduler = DDPMScheduler(num_train_timesteps=1000, 
-                          beta_schedule="linear",
-                          beta_start=0.0015,
-                          beta_end=0.0195)
+        self.scheduler = DDPMScheduler(
+            num_train_timesteps=1000, beta_schedule="linear", beta_start=0.0015, beta_end=0.0195
+        )
         self.z = ae_net.autoencoderkl.eval()
 
-
     def forward(self, x, timesteps, low_res_timesteps):
-        return self.unet(x=x, 
-                         timesteps=timesteps,
-                         class_labels=low_res_timesteps)
-    
-           
+        return self.unet(x=x, timesteps=timesteps, class_labels=low_res_timesteps)
+
     def prepare_data(self):
         self.train_ds, self.val_ds = get_datasets()
-        
+
     def train_dataloader(self):
-        return DataLoader(self.train_ds, batch_size=16, shuffle=True,
-                          num_workers=4, persistent_workers=True)
-        
+        return ThreadDataLoader(self.train_ds, batch_size=16, shuffle=True, num_workers=4, persistent_workers=True)
+
     def val_dataloader(self):
-        return DataLoader(self.val_ds, batch_size=16, shuffle=False,
-                          num_workers=4)
-    
+        return ThreadDataLoader(self.val_ds, batch_size=16, shuffle=False, num_workers=4)
+
     def _calculate_loss(self, batch, batch_idx, plt_image=False):
         images = batch["image"]
-        low_res_image = batch["low_res_image"]  
+        low_res_image = batch["low_res_image"]
         with autocast(enabled=True):
             with torch.no_grad():
                 latent = self.z.encode_stage_2_inputs(images) * scale_factor
-#             latent =  latent.detach() # avoid adding this to graph.
-        
 
             # Noise augmentation
             noise = torch.randn_like(latent)
             low_res_noise = torch.randn_like(low_res_image)
-            timesteps = torch.randint(0, self.scheduler.num_train_timesteps, (latent.shape[0],),
-                                      device=latent.device).long()
+            timesteps = torch.randint(
+                0, self.scheduler.num_train_timesteps, (latent.shape[0],), device=latent.device
+            ).long()
             low_res_timesteps = torch.randint(
                 0, self.max_noise_level, (low_res_image.shape[0],), device=latent.device
             ).long()
 
-            noisy_latent = self.scheduler.add_noise(original_samples=latent, 
-                                               noise=noise, timesteps=timesteps)
+            noisy_latent = self.scheduler.add_noise(original_samples=latent, noise=noise, timesteps=timesteps)
             noisy_low_res_image = self.scheduler.add_noise(
-                original_samples=low_res_image, noise=low_res_noise, 
-                timesteps=low_res_timesteps
+                original_samples=low_res_image, noise=low_res_noise, timesteps=low_res_timesteps
             )
 
             latent_model_input = torch.cat([noisy_latent, noisy_low_res_image], dim=1)
 
             noise_pred = self.forward(latent_model_input, timesteps, low_res_timesteps)
             loss = F.mse_loss(noise_pred.float(), noise.float())
-        
+
         if plt_image:
             # Sampling image during training
             sampling_image = low_res_image[0].unsqueeze(0)
@@ -399,32 +385,30 @@ def _calculate_loss(self, batch, batch_idx, plt_image=False):
             low_res_noise = torch.randn((1, 1, 16, 16)).to(sampling_image.device)
             noise_level = 20
             noise_level = torch.Tensor((noise_level,)).long().to(sampling_image.device)
-            
+
             noisy_low_res_image = self.scheduler.add_noise(
-                original_samples=sampling_image,
-                noise=low_res_noise,
-                timesteps=noise_level,
+                original_samples=sampling_image, noise=low_res_noise, timesteps=noise_level
             )
             self.scheduler.set_timesteps(num_inference_steps=1000)
             for t in tqdm(self.scheduler.timesteps, ncols=110):
                 with autocast(enabled=True):
                     with torch.no_grad():
                         latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)
-                        noise_pred = self.forward(latent_model_input, 
-                                                  torch.Tensor((t,)).to(sampling_image.device)
-                                                  , noise_level)
+                        noise_pred = self.forward(
+                            latent_model_input, torch.Tensor((t,)).to(sampling_image.device), noise_level
+                        )
                     latents, _ = self.scheduler.step(noise_pred, t, latents)
             with torch.no_grad():
                 decoded = self.z.decode_stage_2_outputs(latents / scale_factor)
             low_res_bicubic = nn.functional.interpolate(sampling_image, (64, 64), mode="bicubic")
             # plot images
-            
+
             self.images = images
             self.low_res_bicubic = low_res_bicubic
             self.decoded = decoded
-            
+
         return loss
-    
+
     def _plot_image(self, images, low_res_bicubic, decoded):
         plt.figure(figsize=(2, 2))
         plt.style.use("default")
@@ -442,12 +426,12 @@ def training_step(self, batch, batch_idx):
         loss = self._calculate_loss(batch, batch_idx)
         self.log("train_loss", loss, batch_size=16, prog_bar=True)
         return loss
-        
+
     def validation_step(self, batch, batch_idx):
         loss = self._calculate_loss(batch, batch_idx, plt_image=True)
         self.log("val_loss", loss, batch_size=16, prog_bar=True)
         return loss
-    
+
     def on_validation_epoch_end(self):
         self._plot_image(self.images, self.low_res_bicubic, self.decoded)
 
@@ -467,7 +451,7 @@ def configure_optimizers(self):
 n_epochs = 200
 val_interval = 20
 
-                          
+
 # initialise the LightningModule
 d_net = DiffusionUNET()
 
@@ -475,14 +459,16 @@ def configure_optimizers(self):
 
 checkpoint_callback = ModelCheckpoint(dirpath=root_dir, filename="best_metric_model_dunet")
 
-                         
+
 # initialise Lightning's trainer.
-trainer = pl.Trainer(devices=1,
-                     max_epochs=n_epochs,
-                     check_val_every_n_epoch=val_interval,
-                     num_sanity_val_steps=0,
-                     callbacks=checkpoint_callback,
-                     default_root_dir=root_dir)
+trainer = pl.Trainer(
+    devices=1,
+    max_epochs=n_epochs,
+    check_val_every_n_epoch=val_interval,
+    num_sanity_val_steps=0,
+    callbacks=checkpoint_callback,
+    default_root_dir=root_dir,
+)
 
 # train
 trainer.fit(d_net)
@@ -492,12 +478,13 @@ def configure_optimizers(self):
 
 # %%
 num_samples = 3
+
+
 def get_images_to_plot():
     d_net.eval()
     device = torch.device("cuda:0")
     d_net.to(device)
 
-    
     val_loader = d_net.val_dataloader()
     check_data = first(val_loader)
     images = check_data["image"].to(d_net.device)
@@ -508,18 +495,18 @@ def get_images_to_plot():
     noise_level = 10
     noise_level = torch.Tensor((noise_level,)).long().to(d_net.device)
     scheduler = d_net.scheduler
-    noisy_low_res_image = scheduler.add_noise(original_samples=sampling_image, 
-                                              noise=low_res_noise,
-                                              timesteps=torch.Tensor((noise_level,)).long())
+    noisy_low_res_image = scheduler.add_noise(
+        original_samples=sampling_image, noise=low_res_noise, timesteps=torch.Tensor((noise_level,)).long()
+    )
 
     scheduler.set_timesteps(num_inference_steps=1000)
     for t in tqdm(scheduler.timesteps, ncols=110):
         with autocast(enabled=True):
             with torch.no_grad():
                 latent_model_input = torch.cat([latents, noisy_low_res_image], dim=1)
-                noise_pred = d_net.forward(x=latent_model_input,
-                                           timesteps=torch.Tensor((t,)).to(d_net.device),
-                                           low_res_timesteps=noise_level)
+                noise_pred = d_net.forward(
+                    x=latent_model_input, timesteps=torch.Tensor((t,)).to(d_net.device), low_res_timesteps=noise_level
+                )
             # 2. compute previous image: x_t -> x_t-1
             latents, _ = scheduler.step(noise_pred, t, latents)
 
@@ -527,6 +514,7 @@ def get_images_to_plot():
         decoded = ae_net.autoencoderkl.decode_stage_2_outputs(latents / scale_factor)
     return sampling_image, images, decoded
 
+
 sampling_image, images, decoded = get_images_to_plot()
 
 # %%

From 5876ccd2bf918a64b72cc8fa3a3904ca063ec5d4 Mon Sep 17 00:00:00 2001
From: Oeslle Lucena <oeslle.lucena@gmail.com>
Date: Mon, 15 May 2023 11:48:04 +0100
Subject: [PATCH 06/13] Update
 tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb

Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>
Signed-off-by: Oeslle Lucena <oeslle.lucena@gmail.com>
---
 .../2d_stable_diffusion_v2_super_resolution-lightning.ipynb     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
index 029889d1..32b1d389 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
@@ -333,7 +333,7 @@
    },
    "outputs": [],
    "source": [
-    "class AutoEnconder(pl.LightningModule):\n",
+    "class AutoEncoder(pl.LightningModule):\n",
     "    def __init__(self):\n",
     "        super().__init__()\n",
     "        self.data_dir = root_dir\n",

From 156c9141fa2e5c8e0bfa72d3376c47597a38a24c Mon Sep 17 00:00:00 2001
From: Oeslle Lucena <oeslle.lucena@gmail.com>
Date: Mon, 15 May 2023 11:49:14 +0100
Subject: [PATCH 07/13] Update
 tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py

Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>
Signed-off-by: Oeslle Lucena <oeslle.lucena@gmail.com>
---
 .../2d_stable_diffusion_v2_super_resolution-lightning.py        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
index 2a430db5..5af91135 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
@@ -270,7 +270,7 @@ def configure_optimizers(self):
 
 
 # initialise the LightningModule
-ae_net = AutoEnconder()
+ae_net = AutoEncoder()
 
 # set up checkpoints
 

From 1e5e5323f86868d2d909a91a8456023582113c63 Mon Sep 17 00:00:00 2001
From: Oeslle Lucena <oeslle.lucena@gmail.com>
Date: Mon, 15 May 2023 11:49:28 +0100
Subject: [PATCH 08/13] Update
 tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py

Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>
Signed-off-by: Oeslle Lucena <oeslle.lucena@gmail.com>
---
 .../2d_stable_diffusion_v2_super_resolution-lightning.py        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
index 5af91135..ef18bc83 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
@@ -157,7 +157,7 @@ def get_datasets():
 
 
 # %%
-class AutoEnconder(pl.LightningModule):
+class AutoEncoder(pl.LightningModule):
     def __init__(self):
         super().__init__()
         self.data_dir = root_dir

From 41ce2e985b09e48e776b6f828430e7495a7f0c14 Mon Sep 17 00:00:00 2001
From: Oeslle Lucena <oeslle.lucena@gmail.com>
Date: Mon, 15 May 2023 13:15:07 +0100
Subject: [PATCH 09/13] Update
 tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py

Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>
Signed-off-by: Oeslle Lucena <oeslle.lucena@gmail.com>
---
 .../2d_stable_diffusion_v2_super_resolution-lightning.py        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
index ef18bc83..19c0984a 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
@@ -298,7 +298,7 @@ def configure_optimizers(self):
 
 
 # %%
-def get_scaler_factor():
+def get_scale_factor():
     ae_net.eval()
     device = torch.device("cuda:0")
     ae_net.to(device)

From 2c95ddeb2fe5adf9f165d90a91c0cee3dd30208a Mon Sep 17 00:00:00 2001
From: Oeslle Lucena <oeslle.lucena@gmail.com>
Date: Mon, 15 May 2023 13:15:26 +0100
Subject: [PATCH 10/13] Update
 tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb

Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>
Signed-off-by: Oeslle Lucena <oeslle.lucena@gmail.com>
---
 .../2d_stable_diffusion_v2_super_resolution-lightning.ipynb     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
index 32b1d389..e76ef085 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
@@ -724,7 +724,7 @@
     "\n",
     "                          \n",
     "# initialise the LightningModule\n",
-    "ae_net = AutoEnconder()\n",
+    "ae_net = AutoEncoder()\n",
     "\n",
     "# set up checkpoints\n",
     "\n",

From 1ccba15644eb44b75275d2b7d70278e701a56681 Mon Sep 17 00:00:00 2001
From: Oeslle Lucena <oeslle.lucena@gmail.com>
Date: Mon, 15 May 2023 13:16:00 +0100
Subject: [PATCH 11/13] Update
 tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb

Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>
Signed-off-by: Oeslle Lucena <oeslle.lucena@gmail.com>
---
 .../2d_stable_diffusion_v2_super_resolution-lightning.ipynb     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
index e76ef085..e3f57762 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
@@ -768,7 +768,7 @@
     }
    ],
    "source": [
-    "def get_scaler_factor():\n",
+    "def get_scale_factor():\n",
     "    ae_net.eval()\n",
     "    device = torch.device(\"cuda:0\")\n",
     "    ae_net.to(device)\n",

From ee5c8ee25e8076a4cac830dc20287e2f878ff9a4 Mon Sep 17 00:00:00 2001
From: Oeslle Lucena <oeslle.lucena@gmail.com>
Date: Mon, 15 May 2023 13:16:10 +0100
Subject: [PATCH 12/13] Update
 tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb

Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>
Signed-off-by: Oeslle Lucena <oeslle.lucena@gmail.com>
---
 .../2d_stable_diffusion_v2_super_resolution-lightning.ipynb     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
index e3f57762..dfc9d932 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.ipynb
@@ -780,7 +780,7 @@
     "    scale_factor = 1 / torch.std(z)\n",
     "    return scale_factor\n",
     "\n",
-    "scale_factor = get_scaler_factor()"
+    "scale_factor = get_scale_factor()"
    ]
   },
   {

From 5e8272050b4d02067b2250a3d66cca25320aaef1 Mon Sep 17 00:00:00 2001
From: Oeslle Lucena <oeslle.lucena@gmail.com>
Date: Mon, 15 May 2023 13:16:18 +0100
Subject: [PATCH 13/13] Update
 tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py

Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>
Signed-off-by: Oeslle Lucena <oeslle.lucena@gmail.com>
---
 .../2d_stable_diffusion_v2_super_resolution-lightning.py        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
index 19c0984a..34c8c8f0 100644
--- a/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
+++ b/tutorials/generative/2d_super_resolution/2d_stable_diffusion_v2_super_resolution-lightning.py
@@ -311,7 +311,7 @@ def get_scale_factor():
     return scale_factor
 
 
-scale_factor = get_scaler_factor()
+scale_factor = get_scale_factor()
 
 
 # %% [markdown]