Refactor canvas and mask handling for clarity and maintainability

Removed redundant comments and streamlined logic across canvas-related modules, including mask positioning, garbage collection, and WebSocket communication. Improved code readability and maintainability by eliminating unnecessary explanations and clarifying intent in both Python and JavaScript files. No functional changes were made; this is a cleanup and refactor for better developer experience.

canvas_node.py

@@ -21,23 +21,18 @@ import io
 import sys
 import os
 
-# Dodaj ścieżkę do katalogu python/ do sys.path
 sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'python'))
 
-# Importuj logger
 try:
     from python.logger import logger, LogLevel, debug, info, warn, error, exception
-    
-    # Konfiguracja loggera dla modułu canvas_node
+
     logger.set_module_level('canvas_node', LogLevel.INFO)  # Domyślnie INFO, można zmienić na DEBUG
-    
-    # Włącz logowanie do pliku
+
     logger.configure({
         'log_to_file': True,
         'log_dir': os.path.join(os.path.dirname(os.path.abspath(__file__)), 'logs')
     })
-    
-    # Funkcje pomocnicze dla modułu
+
     log_debug = lambda *args, **kwargs: debug('canvas_node', *args, **kwargs)
     log_info = lambda *args, **kwargs: info('canvas_node', *args, **kwargs)
     log_warn = lambda *args, **kwargs: warn('canvas_node', *args, **kwargs)
@@ -46,10 +41,9 @@ try:
     
     log_info("Logger initialized for canvas_node")
 except ImportError as e:
-    # Fallback jeśli logger nie jest dostępny
+
     print(f"Warning: Logger module not available: {e}")
-    
-    # Proste funkcje zastępcze
+
     def log_debug(*args): print("[DEBUG]", *args)
     def log_info(*args): print("[INFO]", *args)
     def log_warn(*args): print("[WARN]", *args)
@@ -104,9 +98,8 @@ class CanvasNode:
         'persistent_cache': {},
         'last_execution_id': None
     }
-    
-    # Simple in-memory storage for canvas data, keyed by prompt_id
-    # WebSocket-based storage for canvas data per node
+
+
     _websocket_data = {}
     _websocket_listeners = {}
 
@@ -244,7 +237,6 @@ class CanvasNode:
             log_error(f"Error in add_mask_to_canvas: {str(e)}")
             return None
 
-    # Zmienna blokująca równoczesne wykonania
     _processing_lock = threading.Lock()
 
     def process_canvas_image(self, trigger, output_switch, cache_enabled, node_id, prompt=None, unique_id=None, input_image=None,
@@ -253,15 +245,14 @@ class CanvasNode:
         log_info(f"[CanvasNode] 🔍 process_canvas_image wejście – node_id={node_id!r}, unique_id={unique_id!r}, trigger={trigger}, output_switch={output_switch}")
         
         try:
-            # Sprawdź czy już trwa przetwarzanie
+
             if not self.__class__._processing_lock.acquire(blocking=False):
                 log_warn(f"Process already in progress for node {node_id}, skipping...")
-                # Return cached data if available to avoid breaking the flow
+
                 return self.get_cached_data()
 
             log_info(f"Lock acquired. Starting process_canvas_image for node_id: {node_id} (fallback unique_id: {unique_id})")
-            
-            # Use node_id as the primary key, as unique_id is proving unreliable
+
             storage_key = node_id
             
             processed_image = None
@@ -296,8 +287,6 @@ class CanvasNode:
                     log_info("Using provided input_mask as fallback")
                     processed_mask = input_mask
 
-
-            # Fallback to default tensors if nothing is loaded
             if processed_image is None:
                 log_warn(f"Processed image is still None, creating default blank image.")
                 processed_image = torch.zeros((1, 512, 512, 3), dtype=torch.float32)
@@ -322,7 +311,7 @@ class CanvasNode:
             return (None, None)
             
         finally:
-            # Zwolnij blokadę
+
             if self.__class__._processing_lock.locked():
                 self.__class__._processing_lock.release()
                 log_debug(f"Process completed for node {node_id}, lock released")
@@ -376,12 +365,11 @@ class CanvasNode:
             
             nodes_to_remove = []
             for node_id, data in cls._websocket_data.items():
-                # Remove invalid node IDs
+
                 if node_id < 0:
                     nodes_to_remove.append(node_id)
                     continue
-                
-                # Remove old data
+
                 if current_time - data.get('timestamp', 0) > cleanup_threshold:
                     nodes_to_remove.append(node_id)
                     continue
@@ -423,7 +411,7 @@ class CanvasNode:
                             }
                         
                         log_info(f"Received canvas data for node {node_id} via WebSocket")
-                        # Send acknowledgment back to the client
+
                         ack_payload = {
                             'type': 'ack',
                             'nodeId': node_id,
@@ -675,23 +663,19 @@ class BiRefNetMatting:
         m.update(str(refinement).encode())
         return m.hexdigest()
 
-
-# Zmienna blokująca równoczesne wywołania matting
 _matting_lock = None
 
 @PromptServer.instance.routes.post("/matting")
 async def matting(request):
     global _matting_lock
-    
-    # Sprawdź czy już trwa przetwarzanie
+
     if _matting_lock is not None:
         log_warn("Matting already in progress, rejecting request")
         return web.json_response({
             "error": "Another matting operation is in progress",
             "details": "Please wait for the current operation to complete"
         }, status=429)  # 429 Too Many Requests
-        
-    # Ustaw blokadę
+
     _matting_lock = True
     
     try:
@@ -725,7 +709,7 @@ async def matting(request):
             "details": traceback.format_exc()
         }, status=500)
     finally:
-        # Zwolnij blokadę
+
         _matting_lock = None
         log_debug("Matting lock released")
 
@@ -811,8 +795,6 @@ def convert_tensor_to_base64(tensor, alpha_mask=None, original_alpha=None):
         log_debug(f"Tensor shape: {tensor.shape}, dtype: {tensor.dtype}")
         raise
 
-
-# Setup original API routes when module is loaded
 CanvasNode.setup_routes()
 
 NODE_CLASS_MAPPINGS = {