diff --git a/misinformation/summary.py b/misinformation/summary.py
index 60fd9cb..cca71af 100644
--- a/misinformation/summary.py
+++ b/misinformation/summary.py
@@ -16,32 +16,6 @@ class SummaryDetector(AnalysisMethod):
         device=summary_device,
     )
 
-    def load_model_base():
-        summary_model, summary_vis_processors, _ = load_model_and_preprocess(
-            name="blip_caption",
-            model_type="base_coco",
-            is_eval=True,
-            device=SummaryDetector.summary_device,
-        )
-        return summary_model, summary_vis_processors
-
-    def load_model_large():
-        summary_model, summary_vis_processors, _ = load_model_and_preprocess(
-            name="blip_caption",
-            model_type="large_coco",
-            is_eval=True,
-            device=SummaryDetector.summary_device,
-        )
-        return summary_model, summary_vis_processors
-
-    def load_model(model_type):
-        select_model = {
-            "base": SummaryDetector.load_model_base,
-            "large": SummaryDetector.load_model_large,
-        }
-        summary_model, summary_vis_processors = select_model[model_type]()
-        return summary_model, summary_vis_processors
-
     def analyse_image(self, summary_model=None, summary_vis_processors=None):
 
         if summary_model is None and summary_vis_processors is None:
diff --git a/misinformation/utils.py b/misinformation/utils.py
index 36c7690..4bb792c 100644
--- a/misinformation/utils.py
+++ b/misinformation/utils.py
@@ -2,6 +2,8 @@ import glob
 import os
 from pandas import DataFrame
 import pooch
+import torch
+from lavis.models import load_model_and_preprocess
 
 
 class DownloadResource:
@@ -106,3 +108,34 @@ if __name__ == "__main__":
     outdict = append_data_to_dict(mydict)
     df = dump_df(outdict)
     print(df.head(10))
+
+
+def load_model_base():
+    summary_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    summary_model, summary_vis_processors, _ = load_model_and_preprocess(
+        name="blip_caption",
+        model_type="base_coco",
+        is_eval=True,
+        device=summary_device,
+    )
+    return summary_model, summary_vis_processors
+
+
+def load_model_large():
+    summary_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    summary_model, summary_vis_processors, _ = load_model_and_preprocess(
+        name="blip_caption",
+        model_type="large_coco",
+        is_eval=True,
+        device=summary_device,
+    )
+    return summary_model, summary_vis_processors
+
+
+def load_model(model_type):
+    select_model = {
+        "base": load_model_base,
+        "large": load_model_large,
+    }
+    summary_model, summary_vis_processors = select_model[model_type]()
+    return summary_model, summary_vis_processors
diff --git a/notebooks/image_summary.ipynb b/notebooks/image_summary.ipynb
index 7b5b69b..051138e 100644
--- a/notebooks/image_summary.ipynb
+++ b/notebooks/image_summary.ipynb
@@ -70,13 +70,21 @@
     "## Create captions for images and directly write to csv"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here you can choose between two models: \"base\" or \"large\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "summary_model, summary_vis_processors = sm.SummaryDetector.load_model(\"base\")"
+    "summary_model, summary_vis_processors = mutils.load_model(\"base\")\n",
+    "# summary_model, summary_vis_processors = mutils.load_model(\"large\")"
    ]
   },
   {