diff --git a/examples/consistency_models.ipynb b/examples/consistency_models.ipynb
index 09ac6a5..2b1ed65 100644
--- a/examples/consistency_models.ipynb
+++ b/examples/consistency_models.ipynb
@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyMSVH2s2QGB6w2mhmEv6Y1D",
+      "authorship_tag": "ABX9TyNMuCZYN/WQPLvMBhitJgGv",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -65,7 +65,7 @@
     {
       "cell_type": "code",
       "source": [
-        "!pip install datasets wandb consistency==0.2.1"
+        "!pip install datasets wandb consistency==0.2.2"
       ],
       "metadata": {
         "id": "IpmvA2RSUctd"
@@ -89,7 +89,7 @@
       "source": [
         "DATASET_NAME = \"cifar10\"\n",
         "RESOLUTION = 32\n",
-        "BATCH_SIZE = 360\n",
+        "BATCH_SIZE = 300\n",
         "MAX_EPOCHS = 600\n",
         "LEARNING_RATE = 1e-4\n",
         "\n",
@@ -171,7 +171,7 @@
       "source": [
         "from diffusers import UNet2DModel\n",
         "from consistency import Consistency\n",
-        "from consistency.loss import LPIPSLoss\n",
+        "from consistency.loss import PerceptualLoss\n",
         "\n",
         "consistency = Consistency(\n",
         "    model=UNet2DModel(\n",
@@ -197,7 +197,11 @@
         "            \"UpBlock2D\",\n",
         "        ),\n",
         "    ),\n",
-        "    loss_fn=LPIPSLoss(net_type=\"squeeze\"),  # could use MSELoss here, but the sample quality is ⬇️\n",
+        "    # You could use multiple net types. \n",
+        "    # Recommended setting is \"squeeze\" + \"vgg\"\n",
+        "    # loss_fn=PerceptualLoss(net_type=(\"squeeze\", \"vgg\"))\n",
+        "    # See https://github.com/richzhang/PerceptualSimilarity\n",
+        "    loss_fn=PerceptualLoss(net_type=\"squeeze\"), \n",
         "    learning_rate=LEARNING_RATE,\n",
         "    samples_path=SAMPLES_PATH,\n",
         "    save_samples_every_n_epoch=1,\n",