diff --git a/preprocess.py b/preprocess.py
index 8d5230d..e3b1543 100644
--- a/preprocess.py
+++ b/preprocess.py
@@ -12,7 +12,7 @@
 from tqdm import tqdm
 from PIL import Image
 from decord import VideoReader, cpu
-from transformers import Blip2Processor, Blip2ForConditionalGeneration
+
 
 decord.bridge.set_bridge('torch')
 
@@ -22,6 +22,7 @@ def __init__(
             config_name,
             config_save_name,
             video_directory,
+            low_ram_model,
             random_start_frame,
             clip_frame_data,
             max_frames,
@@ -35,6 +36,7 @@ def __init__(
         # Paramaters for parsing videos
         self.prompt_amount = prompt_amount
         self.video_directory = video_directory
+        self.low_ram_model = low_ram_model
         self.random_start_frame = random_start_frame
         self.clip_frame_data = clip_frame_data
         self.max_frames = max_frames
@@ -84,15 +86,23 @@ def build_video_data(self, frame_index: int, prompt: str):
     # Load BLIP2 for processing
     def load_blip(self):
         print("Loading BLIP2")
+        if self.low_ram_model:
+            from lavis.models import load_model_and_preprocess
+            model, self.processor, _ = load_model_and_preprocess(
+            name="blip2_opt", model_type="caption_coco_opt2.7b", is_eval=True, device=self.device
+            )
+            model.to(self.device)
+            self.blip_model = model
+        else:
+            from transformers import Blip2Processor, Blip2ForConditionalGeneration
+            processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
+            model = Blip2ForConditionalGeneration.from_pretrained(
+                "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
+            )
+            model.to(self.device)
 
-        processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
-        model = Blip2ForConditionalGeneration.from_pretrained(
-            "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
-        )
-        model.to(self.device)
-
-        self.processor = processor
-        self.blip_model = model
+            self.processor = processor
+            self.blip_model = model
 
     # Process the frames to get the length and image.
     # The limit parameter ensures we don't get near the max frame length.
@@ -115,6 +125,10 @@ def get_frame_range(self, derterministic):
         return range(self.prompt_amount) if self.random_start_frame else derterministic
 
     def process_blip(self, image: Image):
+      if self.low_ram_model:
+        input_img = self.processor["eval"](image).unsqueeze(0).to(self.device)
+        generated_text = self.blip_model.generate({"image": input_img})
+      else:
         inputs = self.processor(images=image, return_tensors="pt").to(self.device, torch.float16)
         generated_ids = self.blip_model.generate(
                 **inputs, 
@@ -126,7 +140,7 @@ def process_blip(self, image: Image):
             generated_ids, 
             skip_special_tokens=True)[0].strip()
         
-        return generated_text
+      return generated_text
     
     def get_out_paths(self, prompt, frame_number):
         out_name= f"{prompt}_{str(frame_number)}"
@@ -234,6 +248,7 @@ def process_videos(self):
     parser.add_argument('--config_name', help="The name of the configuration.", type=str, default='My Config')
     parser.add_argument('--config_save_name', help="The name of the config file that's saved.", type=str, default='my_config')
     parser.add_argument('--video_directory', help="The directory where your videos are located.", type=str, default='./videos')
+    parser.add_argument('--low_ram_model', help="The directory where your videos are located.", type=bool, default=False)
     parser.add_argument(
         '--random_start_frame', 
         help="Use random start frame when processing videos. Good for long videos where frames have different scenes and meanings.",