diff --git a/__init__.py b/__init__.py
index 0401603..df11bdd 100644
--- a/__init__.py
+++ b/__init__.py
@@ -1,16 +1,19 @@
 from .sharp_node import SharpnessAnalyzer, SharpFrameSelector
 from .parallel_loader import ParallelSharpnessLoader
+from .fast_saver import FastAbsoluteSaver  # <--- Added this missing import
 
 NODE_CLASS_MAPPINGS = {
     "SharpnessAnalyzer": SharpnessAnalyzer,
     "SharpFrameSelector": SharpFrameSelector,
-    "ParallelSharpnessLoader": ParallelSharpnessLoader
+    "ParallelSharpnessLoader": ParallelSharpnessLoader,
+    "FastAbsoluteSaver": FastAbsoluteSaver
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "SharpnessAnalyzer": "1. Sharpness Analyzer",
     "SharpFrameSelector": "2. Sharp Frame Selector",
-    "ParallelSharpnessLoader": "3. Parallel Video Loader (Sharpness)"
+    "ParallelSharpnessLoader": "3. Parallel Video Loader (Sharpness)",
+    "FastAbsoluteSaver": "Fast Absolute Saver (Metadata)"
 }
 
 __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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diff --git a/fast_saver.py b/fast_saver.py
new file mode 100644
index 0000000..d5b05a9
--- /dev/null
+++ b/fast_saver.py
@@ -0,0 +1,110 @@
+import os
+import torch
+import numpy as np
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+import concurrent.futures
+import re
+
+class FastAbsoluteSaver:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE", ),
+                "output_path": ("STRING", {"default": "D:\\Datasets\\Sharp_Output"}),
+                "filename_prefix": ("STRING", {"default": "frame"}),
+                # NEW: User can define the metadata key name
+                "metadata_key": ("STRING", {"default": "sharpness_score", "label": "Metadata Key Name"}),
+            },
+            "optional": {
+                "scores_info": ("STRING", {"forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images_fast"
+    OUTPUT_NODE = True
+    CATEGORY = "BetaHelper/IO"
+
+    def parse_scores(self, scores_str, batch_size):
+        """
+        Parses the string "F:10 (Score:500)..." into a list of floats.
+        Robust to spaces: handles "Score:500" and "Score: 500"
+        """
+        if not scores_str:
+            return [0.0] * batch_size
+
+        # Regex explanation:
+        # Score:\s* -> Matches "Score:" followed by optional spaces
+        # (\d+(\.\d+)?) -> Matches integer or float (Capture Group 1)
+        patterns = re.findall(r"Score:\s*(\d+(\.\d+)?)", scores_str)
+        
+        scores = []
+        for match in patterns:
+            try:
+                scores.append(float(match[0]))
+            except ValueError:
+                scores.append(0.0)
+        
+        # Fill missing scores with 0.0 if batch size mismatches
+        if len(scores) < batch_size:
+            scores.extend([0.0] * (batch_size - len(scores)))
+        return scores[:batch_size]
+
+    def save_single_image(self, tensor_img, full_path, score, key_name):
+        """Worker function to save one image with metadata"""
+        try:
+            array = 255. * tensor_img.cpu().numpy()
+            img = Image.fromarray(np.clip(array, 0, 255).astype(np.uint8))
+
+            metadata = PngInfo()
+            
+            # Use the user-defined key. 
+            # If you want to force no spaces, uncomment the line below:
+            # key_name = key_name.replace(" ", "_") 
+            
+            metadata.add_text(key_name, str(score))
+            metadata.add_text("software", "ComfyUI_Parallel_Node")
+
+            # compress_level=1 is fast.
+            img.save(full_path, pnginfo=metadata, compress_level=1) 
+            return True
+        except Exception as e:
+            print(f"xx- Error saving {full_path}: {e}")
+            return False
+
+    def save_images_fast(self, images, output_path, filename_prefix, metadata_key, scores_info=None):
+        
+        # 1. Clean Path
+        output_path = output_path.strip('"')
+        if not os.path.exists(output_path):
+            try:
+                os.makedirs(output_path, exist_ok=True)
+            except OSError:
+                raise ValueError(f"Could not create directory: {output_path}")
+
+        # 2. Parse Scores
+        batch_size = len(images)
+        scores_list = self.parse_scores(scores_info, batch_size)
+
+        print(f"xx- FastSaver: Saving {batch_size} images to {output_path}...")
+        
+        # 3. Parallel Saving
+        with concurrent.futures.ThreadPoolExecutor(max_workers=16) as executor:
+            futures = []
+            
+            for i, img_tensor in enumerate(images):
+                import time
+                timestamp = int(time.time())
+                # Added index `i` to filename to ensure uniqueness in same batch
+                fname = f"{filename_prefix}_{timestamp}_{i:03d}.png"
+                full_path = os.path.join(output_path, fname)
+                
+                # Pass the metadata_key to the worker
+                futures.append(executor.submit(self.save_single_image, img_tensor, full_path, scores_list[i], metadata_key))
+
+            concurrent.futures.wait(futures)
+
+        print("xx- FastSaver: Save Complete.")
+        return {"ui": {"images": []}}
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