diff --git a/nodes.py b/nodes.py
index 897338c..88ad401 100644
--- a/nodes.py
+++ b/nodes.py
@@ -14,6 +14,9 @@ import hashlib
 import warnings
 import comfy.model_management as mm
 import folder_paths
+from comfy_execution.graph_utils import GraphBuilder
+from comfy.utils import ProgressBar
+
 
 script_directory = os.path.dirname(os.path.abspath(__file__))
 
@@ -201,12 +204,100 @@ class LLaVA_OneVision_Run:
 
        
         return (text_outputs[0],)
-     
+
+
+class OneVisionCaptionFolder:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "llava_model": ("LLAVAMODEL", ),
+                "folder_path": ("STRING", ),
+                "prompt": ("STRING", {"default": "", "multiline": True} ),
+                "max_tokens": ("INT", {"default": 4096, "min": 1, "max": 8192}),
+                "keep_model_loaded": ("BOOLEAN", {"default": True}),
+                "temperature": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "seed": ("INT", {"default": 1, "min": 1, "max": 0xffffffffffffffff}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "caption"
+    OUTPUT_IS_LIST = (True,)
+    CATEGORY = "LLaVA-OneVision"
+
+    def caption(self, folder_path, llava_model, prompt, max_tokens, keep_model_loaded, temperature, seed):
+        from PIL import Image
+        image_files = []
+        for filename in os.listdir(folder_path):
+            if filename.endswith(('.png', '.jpg', '.jpeg')):
+                image_files.append(filename)
+        pbar = ProgressBar(len(image_files))
+
+        transform = transforms.ToTensor()
+        vision_node = LLaVA_OneVision_Run()
+        results_list = []
+        for filename in image_files:
+            img_path = os.path.join(folder_path, filename)
+            try:
+                img = Image.open(img_path).convert('RGB')
+                img_tensor = transform(img)
+                img_tensor = img_tensor.unsqueeze(0).permute(0, 2, 3, 1)
+            except IOError:
+                print(f"Cannot open image: {img_path}")
+
+            result, = vision_node.run(
+                llava_model=llava_model,
+                image=img_tensor, prompt=prompt, 
+                max_tokens=max_tokens, 
+                keep_model_loaded=keep_model_loaded, 
+                temperature=temperature, 
+                seed=seed)
+
+            results_list.append(result)
+            
+            base_filename = os.path.splitext(img_path)[0]
+            with open(f'{base_filename}.txt', 'w') as file:
+                file.write(result)
+            pbar.update(1)
+
+        return (results_list,)
+    
+class SaveCaptionToTextFile:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": ("STRING", ),
+                "filename": ("STRING", ),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "caption"
+
+    CATEGORY = "LLaVA-OneVision"
+
+    def caption(self, txt, filename):
+        print("SaveCaptionToTextFile: ", txt)
+       
+        return txt,
+
 NODE_CLASS_MAPPINGS = {
     "DownloadAndLoadLLaVAOneVisionModel": DownloadAndLoadLLaVAOneVisionModel,
     "LLaVA_OneVision_Run": LLaVA_OneVision_Run,
+    "OneVisionCaptionFolder": OneVisionCaptionFolder,
+    "SaveCaptionToTextFile": SaveCaptionToTextFile,
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "DownloadAndLoadLLaVAOneVisionModel": "(Down)Load LLaVA-OneVision Model",
     "LLaVA_OneVision_Run": "LLaVA-OneVision Run",
+    "OneVisionCaptionFolder": "OneVision Caption Folder",
+    "SaveCaptionToTextFile": "SaveCaptionToTextFile",
 }