diff --git a/stable_diffusion.ipynb b/stable_diffusion.ipynb
index f7edf67..63226bd 100644
--- a/stable_diffusion.ipynb
+++ b/stable_diffusion.ipynb
@@ -363,8 +363,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "models--CompVis--stable-diffusion-v1-4\tmodels--google--ddpm-church-256\r\n",
-      "models--google--ddpm-celebahq-256\r\n"
+      "models--CompVis--stable-diffusion-v1-4\tmodels--google--ddpm-church-256\n",
+      "models--google--ddpm-celebahq-256\n"
      ]
     }
    ],
@@ -1323,9 +1323,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1384,7 +1382,11 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
+    "tags": []
    },
    "outputs": [
     {
@@ -1613,8 +1615,8 @@
     }
    ],
    "source": [
-    "db_pipe = StableDiffusionPipeline.from_pretrained(\"pcuenq/jh_dreambooth_1000\", torch_dtype=torch.float16)\n",
-    "db_pipe = db_pipe.to(\"cuda\")"
+    "pipe = StableDiffusionPipeline.from_pretrained(\"pcuenq/jh_dreambooth_1000\", torch_dtype=torch.float16)\n",
+    "pipe = pipe.to(\"cuda\")"
    ]
   },
   {
@@ -1652,7 +1654,7 @@
     "torch.manual_seed(1000)\n",
     "\n",
     "prompt = \"Painting of sks person in the style of Paul Signac\"\n",
-    "images = db_pipe(prompt, num_images_per_prompt=4).images\n",
+    "images = pipe(prompt, num_images_per_prompt=4).images\n",
     "image_grid(images, 1, 4)"
    ]
   },
@@ -1806,6 +1808,15 @@
     "First, we need the text encoder and the tokenizer. These come from the text portion of a standard CLIP model, so we'll use the weights released by Open AI."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del pipe"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 25,
@@ -1814,8 +1825,8 @@
    "source": [
     "from transformers import CLIPTextModel, CLIPTokenizer\n",
     "\n",
-    "tokenizer = CLIPTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\")\n",
-    "text_encoder = CLIPTextModel.from_pretrained(\"openai/clip-vit-large-patch14\")"
+    "tokenizer = CLIPTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", torch_dtype=torch.float16)\n",
+    "text_encoder = CLIPTextModel.from_pretrained(\"openai/clip-vit-large-patch14\", torch_dtype=torch.float16)"
    ]
   },
   {
@@ -1833,8 +1844,8 @@
    "source": [
     "from diffusers import AutoencoderKL, UNet2DConditionModel\n",
     "\n",
-    "vae = AutoencoderKL.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"vae\")\n",
-    "unet = UNet2DConditionModel.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"unet\")"
+    "vae = AutoencoderKL.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"vae\", torch_dtype=torch.float16)\n",
+    "unet = UNet2DConditionModel.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"unet\", torch_dtype=torch.float16)"
    ]
   },
   {
@@ -2059,7 +2070,7 @@
     }
    ],
    "source": [
-    "text_embeddings = text_encoder(text_input.input_ids.to(\"cuda\"))[0]\n",
+    "text_embeddings = text_encoder(text_input.input_ids.to(\"cuda\"))[0].half()\n",
     "text_embeddings.shape"
    ]
   },
@@ -2093,7 +2104,7 @@
     "uncond_input = tokenizer(\n",
     "    [\"\"] * batch_size, padding=\"max_length\", max_length=max_length, return_tensors=\"pt\"\n",
     ")\n",
-    "uncond_embeddings = text_encoder(uncond_input.input_ids.to(\"cuda\"))[0]\n",
+    "uncond_embeddings = text_encoder(uncond_input.input_ids.to(\"cuda\"))[0].half()\n",
     "uncond_embeddings.shape"
    ]
   },
@@ -2147,7 +2158,7 @@
    "source": [
     "torch.manual_seed(100)\n",
     "latents = torch.randn((batch_size, unet.in_channels, height // 8, width // 8))\n",
-    "latents = latents.to(\"cuda\")\n",
+    "latents = latents.to(\"cuda\").half()\n",
     "latents.shape"
    ]
   },
@@ -2356,8 +2367,7 @@
      "height": 529
     },
     "id": "AAVZStIokTVv",
-    "outputId": "7af6a1ea-f20a-4445-d756-8bb0dd6a0747",
-    "scrolled": false
+    "outputId": "7af6a1ea-f20a-4445-d756-8bb0dd6a0747"
    },
    "outputs": [
     {
@@ -2405,7 +2415,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.10"
+   "version": "3.10.6"
   },
   "toc": {
    "base_numbering": 1,
@@ -16198,5 +16208,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }