fast_saver.py

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+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"}),
+            },
+            "optional": {
+                # We take the string output from your Parallel Loader here
+                "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), F:12 (Score:800)..." into a list of floats.
+        If inputs don't match, returns a list of 0.0.
+        """
+        if not scores_str:
+            return [0.0] * batch_size
+
+        # Regex to find 'Score:NUMBER' or just numbers
+        # Matches your specific format: (Score: 123)
+        patterns = re.findall(r"Score:(\d+(\.\d+)?)", scores_str)
+        
+        scores = []
+        for match in patterns:
+            # match is a tuple due to the group inside regex, index 0 is the full number
+            try:
+                scores.append(float(match[0]))
+            except ValueError:
+                scores.append(0.0)
+        
+        # Validation: If we found more or fewer scores than images, pad or truncate
+        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):
+        """Worker function to save one image with metadata"""
+        try:
+            # 1. Convert Tensor to Pillow
+            array = 255. * tensor_img.cpu().numpy()
+            img = Image.fromarray(np.clip(array, 0, 255).astype(np.uint8))
+
+            # 2. Add Metadata
+            metadata = PngInfo()
+            metadata.add_text("sharpness_score", str(score))
+            # You can add more keys here if needed
+            metadata.add_text("software", "ComfyUI_Parallel_Node")
+
+            # 3. Save (Optimized)
+            img.save(full_path, pnginfo=metadata, compress_level=1) 
+            # compress_level=1 is FAST. Default is 6 (slow). 0 is uncompressed (huge files).
+            
+            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, 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)
+
+        # 3. Parallel Saving
+        # We use a ThreadPool to save files concurrently.
+        # This saturates the SSD write speed, mimicking VHS performance.
+        print(f"xx- FastSaver: Saving {batch_size} images to {output_path}...")
+        
+        with concurrent.futures.ThreadPoolExecutor(max_workers=16) as executor:
+            futures = []
+            
+            for i, img_tensor in enumerate(images):
+                # Construct filename: prefix_00001.png
+                # We use a unique counter or just the batch index
+                # Ideally, we should use the Frame Index if we can extract it, 
+                # but for now we use simple batch increment to avoid overwriting.
+                
+                # If we want unique filenames based on existing files, it slows things down.
+                # We will assume the user manages folders or prefixes well.
+                import time
+                timestamp = int(time.time())
+                fname = f"{filename_prefix}_{timestamp}_{i:05d}.png"
+                full_path = os.path.join(output_path, fname)
+                
+                # Submit to thread
+                futures.append(executor.submit(self.save_single_image, img_tensor, full_path, scores_list[i]))
+
+            # Wait for all to finish
+            concurrent.futures.wait(futures)
+
+        print("xx- FastSaver: Save Complete.")
+        
+        # Return nothing to UI to prevent Lag
+        return {"ui": {"images": []}}