diff --git a/moondream.py b/moondream.py
index aa106b0..efdef0a 100644
--- a/moondream.py
+++ b/moondream.py
@@ -1,48 +1,65 @@
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from PIL import Image
 import torch
+import codecs
+import numpy
+import gc
 
 class Moondream:
     def __init__(self):
-        self.model_id = "vikhyatk/moondream2"
-        self.revision = "2024-08-26"
-        
         self.model = None
         self.tokenizer = None
     
-    def load_model(self):
-        if self.model is None:
-            self.model = AutoModelForCausalLM.from_pretrained(
-                self.model_id,
-                trust_remote_code=True,
-                revision=self.revision,
-                torch_dtype=torch.float16
-            )
-            self.tokenizer = AutoTokenizer.from_pretrained(
-                self.model_id,
-                revision=self.revision
-            )
-    
     @classmethod
     def INPUT_TYPES(s):
         return {
             "required": {
                 "image": ("IMAGE",),
+                "prompt": ("STRING", {"multiline": True, "default": "Please provide a detailed description of this image."},),
+                "separator": ("STRING", {"multiline": False, "default": r"\n"},),
             },
         }
 
     RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("Image Description",)
-
-    FUNCTION = "describe_image"
+    RETURN_NAMES = ("Description",)
 
+    FUNCTION = "interrogate"
+    OUTPUT_NODE = False
     CATEGORY = "image/utils"
 
-    def describe_image(self, image):
-        self.load_model()
+    def interrogate(self, image:torch.Tensor, prompt:str, separator:str):
+        model_id = "vikhyatk/moondream2"
+        revision = "2024-08-26"
+
+        if (self.model == None) or (self.tokenizer == None):
+            try:
+                self.model = AutoModelForCausalLM.from_pretrained(
+                    model_id, 
+                    trust_remote_code=True,
+                    revision=revision
+                ).to('cuda')
+                self.tokenizer = AutoTokenizer.from_pretrained(model_id)
+            except RuntimeError:
+                raise ValueError(f"Error loading model {model_id} revision {revision}")
         
-        pil_image = Image.fromarray((image[0] * 255).numpy().astype('uint8'))
+        descriptions = ""
+        prompts = list(filter(lambda x: x!="", [s.lstrip() for s in prompt.splitlines()])) # make a prompt list and remove unnecessary whitechars and empty lines
+        if len(prompts) == 0:
+            prompts = [""]
+
+        try:
+            for im in image:
+                i = 255. * im.cpu().numpy()
+                img = Image.fromarray(numpy.clip(i, 0, 255).astype(numpy.uint8))
+                enc_image = self.model.encode_image(img)
+
+                descr = ""
+                sep = codecs.decode(separator, 'unicode_escape')
+                for p in prompts:
+                    answer = self.model.answer_question(enc_image, p, self.tokenizer, temperature=None, do_sample=None)
+                    descr += f"{answer}{sep}"
+                descriptions += f"{descr[0:-len(sep)]}\n"
+        except RuntimeError:
+            raise ValueError(f"Error loading model {model_id} revision {revision}")
         
-        with torch.inference_mode():
-            enc_image = self.model.encode_image(pil_image)
-            return (self.model.answer_question(enc_image, "Describe this image.", self.tokenizer),)
+        return(descriptions[0:-1],)