diff --git a/nodes/mask_nodes.py b/nodes/mask_nodes.py
index 8b1d9f0..4019dd0 100644
--- a/nodes/mask_nodes.py
+++ b/nodes/mask_nodes.py
@@ -46,9 +46,8 @@ class BatchCLIPSeg:
 Segments an image or batch of images using CLIPSeg.
 """
 
-    def segment_image(self, images, text, threshold, binary_mask, combine_mask, use_cuda):        
+    def segment_image(self, images, text, threshold, binary_mask, combine_mask, use_cuda):
         from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
-        out = []
         height, width, _ = images[0].shape
         if use_cuda and torch.cuda.is_available():
             device = torch.device("cuda")
@@ -60,43 +59,38 @@ Segments an image or batch of images using CLIPSeg.
         model.to(device)
         images = images.to(device)
         processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
-        pbar = ProgressBar(images.shape[0])
         autocast_condition = (dtype != torch.float32) and not model_management.is_device_mps(device)
         with torch.autocast(model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
-            for image in images:
-                image = (image* 255).type(torch.uint8)
-                prompt = text
-                input_prc = processor(text=prompt, images=image, return_tensors="pt")
-                # Move the processed input to the device
-                for key in input_prc:
-                    input_prc[key] = input_prc[key].to(device)
-                
-                outputs = model(**input_prc)
-    
-                tensor = torch.sigmoid(outputs[0])
-                tensor_thresholded = torch.where(tensor > threshold, tensor, torch.tensor(0, dtype=torch.float))
-                tensor_normalized = (tensor_thresholded - tensor_thresholded.min()) / (tensor_thresholded.max() - tensor_thresholded.min())
-                tensor = tensor_normalized
 
-                # Resize the mask
-                if len(tensor.shape) == 3:
-                    tensor = tensor.unsqueeze(0)
-                resized_tensor = F.interpolate(tensor, size=(height, width), mode='nearest')
+            images = [Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) for image in images ]
+            prompt = [text] * len(images)
+            input_prc = processor(text=prompt, images=images, return_tensors="pt")
+            # Move the processed input to the device
+            for key in input_prc:
+                input_prc[key] = input_prc[key].to(device)
+
+            outputs = model(**input_prc)
+
+            tensor = torch.sigmoid(outputs.logits)
+            tensor_thresholded = torch.where(tensor > threshold, tensor, torch.tensor(0, dtype=torch.float))
+            tensor_normalized = (tensor_thresholded - tensor_thresholded.min()) / (tensor_thresholded.max() - tensor_thresholded.min())
+            tensor = tensor_normalized
+
+            # Resize the mask
+            resized_tensor = F.interpolate(tensor.unsqueeze(1), size=(height, width), mode='nearest')
+
+            # Remove the extra dimensions
+            resized_tensor = resized_tensor.squeeze(1)
+
+        results = resized_tensor.cpu().float()
 
-                # Remove the extra dimensions
-                resized_tensor = resized_tensor[0, 0, :, :]
-                pbar.update(1)
-                out.append(resized_tensor)
-          
-        results = torch.stack(out).cpu().float()
-        
         if combine_mask:
             combined_results = torch.max(results, dim=0)[0]
             results = combined_results.unsqueeze(0).repeat(len(images),1,1)
 
         if binary_mask:
             results = results.round()
-        
+
         return results,
 
 class CreateTextMask:
@@ -1163,4 +1157,4 @@ Sets new min and max values for the mask.
         # Clamp the values to ensure they are within [0.0, 1.0]
         scaled_mask = torch.clamp(scaled_mask, min=0.0, max=1.0)
         
-        return (scaled_mask, )
\ No newline at end of file
+        return (scaled_mask, )