diff --git a/canvas_node.py b/canvas_node.py
index 8fb423b..b820c66 100644
--- a/canvas_node.py
+++ b/canvas_node.py
@@ -26,28 +26,38 @@ import io
 import sys
 import os
 
-try:
-    from python.log_system import create_module_logger
-
-    _module_logger = create_module_logger(__name__)
-    log_debug = _module_logger.debug
-    log_info = _module_logger.info
-    log_warn = _module_logger.warning
-    log_error = _module_logger.error
-    log_exception = _module_logger.exception
-    
-    log_info("Logger initialized for canvas_node")
-except ImportError as e:
+try:
+    from python.log_system import create_module_logger
+
+    log = create_module_logger(__name__)
+
+    log.info("Logger initialized for canvas_node")
+except ImportError as e:
 
     print(f"Warning: Logger module not available: {e}")
 
-    def log_debug(*args): print("[DEBUG]", *args)
-    def log_info(*args): print("[INFO]", *args)
-    def log_warn(*args): print("[WARN]", *args)
-    def log_error(*args): print("[ERROR]", *args)
-    def log_exception(*args):
-        print("[ERROR]", *args)
-        traceback.print_exc()
+    class _FallbackLogger:
+        @staticmethod
+        def debug(*args, **kwargs): print("[DEBUG]", *args)
+
+        @staticmethod
+        def info(*args, **kwargs): print("[INFO]", *args)
+
+        @staticmethod
+        def warning(*args, **kwargs): print("[WARN]", *args)
+
+        @staticmethod
+        def error(*args, **kwargs): print("[ERROR]", *args)
+
+        @staticmethod
+        def exception(*args, **kwargs):
+            print("[ERROR]", *args)
+            traceback.print_exc()
+
+        @staticmethod
+        def fatal(*args, **kwargs): print("[FATAL]", *args)
+
+    log = _FallbackLogger()
 
 torch.set_float32_matmul_precision('high')
 
@@ -85,7 +95,7 @@ class BiRefNet(torch.nn.Module):
         return [output]
 
 
-class LayerForgeNode:
+class LayerForgeNode:
     _canvas_data_storage = {}
     _storage_lock = threading.Lock()
     
@@ -115,7 +125,7 @@ class LayerForgeNode:
             current_execution = self.get_execution_id()
 
             if current_execution != self.__class__._canvas_cache['last_execution_id']:
-                log_info(f"New execution detected: {current_execution}")
+                log.info(f"New execution detected: {current_execution}")
                 self.__class__._canvas_cache['image'] = None
                 self.__class__._canvas_cache['mask'] = None
                 self.__class__._canvas_cache['last_execution_id'] = current_execution
@@ -123,12 +133,12 @@ class LayerForgeNode:
 
                 if persistent.get('image') is not None:
                     self.__class__._canvas_cache['image'] = persistent['image']
-                    log_info("Restored image from persistent cache")
+                    log.info("Restored image from persistent cache")
                 if persistent.get('mask') is not None:
                     self.__class__._canvas_cache['mask'] = persistent['mask']
-                    log_info("Restored mask from persistent cache")
+                    log.info("Restored mask from persistent cache")
         except Exception as e:
-            log_error(f"Error restoring cache: {str(e)}")
+            log.error(f"Error restoring cache: {str(e)}")
 
     def get_execution_id(self):
 
@@ -136,7 +146,7 @@ class LayerForgeNode:
 
             return str(int(time.time() * 1000))
         except Exception as e:
-            log_error(f"Error getting execution ID: {str(e)}")
+            log.error(f"Error getting execution ID: {str(e)}")
             return None
 
     def update_persistent_cache(self):
@@ -146,9 +156,9 @@ class LayerForgeNode:
                 'image': self.__class__._canvas_cache['image'],
                 'mask': self.__class__._canvas_cache['mask']
             }
-            log_debug("Updated persistent cache")
+            log.debug("Updated persistent cache")
         except Exception as e:
-            log_error(f"Error updating persistent cache: {str(e)}")
+            log.error(f"Error updating persistent cache: {str(e)}")
 
     def track_data_flow(self, stage, status, data_info=None):
 
@@ -158,9 +168,9 @@ class LayerForgeNode:
             'status': status,
             'data_info': data_info
         }
-        log_debug(f"Data Flow [{self.flow_id}] - Stage: {stage}, Status: {status}")
+        log.debug(f"Data Flow [{self.flow_id}] - Stage: {stage}, Status: {status}")
         if data_info:
-            log_debug(f"Data Info: {data_info}")
+            log.debug(f"Data Info: {data_info}")
 
         self.__class__._canvas_cache['data_flow_status'][self.flow_id] = flow_status
 
@@ -205,7 +215,7 @@ class LayerForgeNode:
             return input_image
 
         except Exception as e:
-            log_error(f"Error in add_image_to_canvas: {str(e)}")
+            log.error(f"Error in add_image_to_canvas: {str(e)}")
             return None
 
     def add_mask_to_canvas(self, input_mask, input_image):
@@ -233,7 +243,7 @@ class LayerForgeNode:
             return input_mask
 
         except Exception as e:
-            log_error(f"Error in add_mask_to_canvas: {str(e)}")
+            log.error(f"Error in add_mask_to_canvas: {str(e)}")
             return None
 
     _processing_lock = threading.Lock()
@@ -243,14 +253,14 @@ class LayerForgeNode:
         try:
 
             if not self.__class__._processing_lock.acquire(blocking=False):
-                log_warn(f"Process already in progress for node {node_id}, skipping...")
+                log.warning(f"Process already in progress for node {node_id}, skipping...")
 
                 return self.get_cached_data()
 
-            log_info(f"Lock acquired. Starting process_canvas_image for node_id: {node_id} (fallback unique_id: {unique_id})")
+            log.info(f"Lock acquired. Starting process_canvas_image for node_id: {node_id} (fallback unique_id: {unique_id})")
 
             # Always store fresh input data, even if None, to clear stale data
-            log_info(f"Storing input data for node {node_id} - Image: {input_image is not None}, Mask: {input_mask is not None}")
+            log.info(f"Storing input data for node {node_id} - Image: {input_image is not None}, Mask: {input_mask is not None}")
             
             with self.__class__._storage_lock:
                 input_data = {}
@@ -263,7 +273,7 @@ class LayerForgeNode:
                             input_image = input_image.unsqueeze(0)
                         
                         batch_size = input_image.shape[0]
-                        log_info(f"Processing batch of {batch_size} image(s)")
+                        log.info(f"Processing batch of {batch_size} image(s)")
                         
                         if batch_size == 1:
                             # Single image - keep backward compatibility
@@ -277,7 +287,7 @@ class LayerForgeNode:
                             input_data['input_image'] = f"data:image/png;base64,{img_str}"
                             input_data['input_image_width'] = pil_img.width
                             input_data['input_image_height'] = pil_img.height
-                            log_debug(f"Stored single input image: {pil_img.width}x{pil_img.height}")
+                            log.debug(f"Stored single input image: {pil_img.width}x{pil_img.height}")
                         else:
                             # Multiple images - store as array
                             images_array = []
@@ -294,10 +304,10 @@ class LayerForgeNode:
                                     'width': pil_img.width,
                                     'height': pil_img.height
                                 })
-                                log_debug(f"Stored batch image {i+1}/{batch_size}: {pil_img.width}x{pil_img.height}")
+                                log.debug(f"Stored batch image {i+1}/{batch_size}: {pil_img.width}x{pil_img.height}")
                             
                             input_data['input_images_batch'] = images_array
-                            log_info(f"Stored batch of {batch_size} images")
+                            log.info(f"Stored batch of {batch_size} images")
                 
                 if input_mask is not None:
                     # Convert mask tensor to base64
@@ -317,7 +327,7 @@ class LayerForgeNode:
                         pil_mask.save(mask_buffered, format="PNG")
                         mask_str = base64.b64encode(mask_buffered.getvalue()).decode()
                         input_data['input_mask'] = f"data:image/png;base64,{mask_str}"
-                        log_debug(f"Stored input mask: {pil_mask.width}x{pil_mask.height}")
+                        log.debug(f"Stored input mask: {pil_mask.width}x{pil_mask.height}")
                 
                 input_data['fit_on_add'] = fit_on_add
                 
@@ -333,14 +343,14 @@ class LayerForgeNode:
                 canvas_data = self.__class__._canvas_data_storage.pop(storage_key, None)
 
             if canvas_data:
-                log_info(f"Canvas data found for node {storage_key} from WebSocket")
+                log.info(f"Canvas data found for node {storage_key} from WebSocket")
                 if canvas_data.get('image'):
                     image_data = canvas_data['image'].split(',')[1]
                     image_bytes = base64.b64decode(image_data)
                     pil_image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
                     image_array = np.array(pil_image).astype(np.float32) / 255.0
                     processed_image = torch.from_numpy(image_array)[None,]
-                    log_debug(f"Image loaded from WebSocket, shape: {processed_image.shape}")
+                    log.debug(f"Image loaded from WebSocket, shape: {processed_image.shape}")
 
                 if canvas_data.get('mask'):
                     mask_data = canvas_data['mask'].split(',')[1]
@@ -348,33 +358,33 @@ class LayerForgeNode:
                     pil_mask = Image.open(io.BytesIO(mask_bytes)).convert('L')
                     mask_array = np.array(pil_mask).astype(np.float32) / 255.0
                     processed_mask = torch.from_numpy(mask_array)[None,]
-                    log_debug(f"Mask loaded from WebSocket, shape: {processed_mask.shape}")
+                    log.debug(f"Mask loaded from WebSocket, shape: {processed_mask.shape}")
             else:
-                log_warn(f"No canvas data found for node {storage_key} in WebSocket cache.")
+                log.warning(f"No canvas data found for node {storage_key} in WebSocket cache.")
 
             if processed_image is None:
-                log_warn(f"Processed image is still None, creating default blank image.")
+                log.warning(f"Processed image is still None, creating default blank image.")
                 processed_image = torch.zeros((1, 512, 512, 3), dtype=torch.float32)
             if processed_mask is None:
-                log_warn(f"Processed mask is still None, creating default blank mask.")
+                log.warning(f"Processed mask is still None, creating default blank mask.")
                 processed_mask = torch.zeros((1, 512, 512), dtype=torch.float32)
 
-            log_debug(f"About to return output - Image shape: {processed_image.shape}, Mask shape: {processed_mask.shape}")
+            log.debug(f"About to return output - Image shape: {processed_image.shape}, Mask shape: {processed_mask.shape}")
             
             self.update_persistent_cache()
             
-            log_info(f"Successfully returning processed image and mask")
+            log.info(f"Successfully returning processed image and mask")
             return (processed_image, processed_mask)
 
         except Exception as e:
-            log_exception(f"Error in process_canvas_image: {str(e)}")
+            log.exception(f"Error in process_canvas_image: {str(e)}")
             return (None, None)
             
         finally:
 
             if self.__class__._processing_lock.locked():
                 self.__class__._processing_lock.release()
-                log_debug(f"Process completed for node {node_id}, lock released")
+                log.debug(f"Process completed for node {node_id}, lock released")
 
     def get_cached_data(self):
         return {
@@ -454,13 +464,13 @@ class LayerForgeNode:
             
             for node_id in nodes_to_remove:
                 del cls._websocket_data[node_id]
-                log_debug(f"Cleaned up old WebSocket data for node {node_id}")
+                log.debug(f"Cleaned up old WebSocket data for node {node_id}")
             
             if nodes_to_remove:
-                log_info(f"Cleaned up {len(nodes_to_remove)} old WebSocket entries")
+                log.info(f"Cleaned up {len(nodes_to_remove)} old WebSocket entries")
                 
         except Exception as e:
-            log_error(f"Error during WebSocket cleanup: {str(e)}")
+            log.error(f"Error during WebSocket cleanup: {str(e)}")
 
     @classmethod
     def setup_routes(cls):
@@ -488,7 +498,7 @@ class LayerForgeNode:
                                 'timestamp': time.time()
                             }
                         
-                        log_info(f"Received canvas data for node {node_id} via WebSocket")
+                        log.info(f"Received canvas data for node {node_id} via WebSocket")
 
                         ack_payload = {
                             'type': 'ack',
@@ -496,43 +506,43 @@ class LayerForgeNode:
                             'status': 'success'
                         }
                         await ws.send_json(ack_payload)
-                        log_debug(f"Sent ACK for node {node_id}")
+                        log.debug(f"Sent ACK for node {node_id}")
                         
                     except Exception as e:
-                        log_error(f"Error processing WebSocket message: {e}")
+                        log.error(f"Error processing WebSocket message: {e}")
                         await ws.send_json({'status': 'error', 'message': str(e)})
                 elif msg.type == web.WSMsgType.ERROR:
-                    log_error(f"WebSocket connection closed with exception {ws.exception()}")
+                    log.error(f"WebSocket connection closed with exception {ws.exception()}")
 
-            log_info("WebSocket connection closed")
+            log.info("WebSocket connection closed")
             return ws
 
         @PromptServer.instance.routes.get("/layerforge/get_input_data/{node_id}")
         async def get_input_data(request):
             try:
                 node_id = request.match_info["node_id"]
-                log_debug(f"Checking for input data for node: {node_id}")
+                log.debug(f"Checking for input data for node: {node_id}")
                 
                 with cls._storage_lock:
                     input_key = f"{node_id}_input"
                     input_data = cls._canvas_data_storage.get(input_key, None)
                 
                 if input_data:
-                    log_info(f"Input data found for node {node_id}, sending to frontend")
+                    log.info(f"Input data found for node {node_id}, sending to frontend")
                     return web.json_response({
                         'success': True,
                         'has_input': True,
                         'data': input_data
                     })
                 else:
-                    log_debug(f"No input data found for node {node_id}")
+                    log.debug(f"No input data found for node {node_id}")
                     return web.json_response({
                         'success': True,
                         'has_input': False
                     })
                     
             except Exception as e:
-                log_error(f"Error in get_input_data: {str(e)}")
+                log.error(f"Error in get_input_data: {str(e)}")
                 return web.json_response({
                     'success': False,
                     'error': str(e)
@@ -542,15 +552,15 @@ class LayerForgeNode:
         async def clear_input_data(request):
             try:
                 node_id = request.match_info["node_id"]
-                log_info(f"Clearing input data for node: {node_id}")
+                log.info(f"Clearing input data for node: {node_id}")
                 
                 with cls._storage_lock:
                     input_key = f"{node_id}_input"
                     if input_key in cls._canvas_data_storage:
                         del cls._canvas_data_storage[input_key]
-                        log_info(f"Input data cleared for node {node_id}")
+                        log.info(f"Input data cleared for node {node_id}")
                     else:
-                        log_debug(f"No input data to clear for node {node_id}")
+                        log.debug(f"No input data to clear for node {node_id}")
                 
                 return web.json_response({
                     'success': True,
@@ -558,7 +568,7 @@ class LayerForgeNode:
                 })
                     
             except Exception as e:
-                log_error(f"Error in clear_input_data: {str(e)}")
+                log.error(f"Error in clear_input_data: {str(e)}")
                 return web.json_response({
                     'success': False,
                     'error': str(e)
@@ -568,11 +578,11 @@ class LayerForgeNode:
         async def get_canvas_data(request):
             try:
                 node_id = request.match_info["node_id"]
-                log_debug(f"Received request for node: {node_id}")
+                log.debug(f"Received request for node: {node_id}")
 
                 cache_data = cls._canvas_cache
-                log_debug(f"Cache content: {cache_data}")
-                log_debug(f"Image in cache: {cache_data['image'] is not None}")
+                log.debug(f"Cache content: {cache_data}")
+                log.debug(f"Image in cache: {cache_data['image'] is not None}")
 
                 response_data = {
                     'success': True,
@@ -599,7 +609,7 @@ class LayerForgeNode:
                 return web.json_response(response_data)
 
             except Exception as e:
-                log_error(f"Error in get_canvas_data: {str(e)}")
+                log.error(f"Error in get_canvas_data: {str(e)}")
                 return web.json_response({
                     'success': False,
                     'error': str(e)
@@ -623,7 +633,7 @@ class LayerForgeNode:
                     'images': images_data
                 })
             except Exception as e:
-                log_error(f"Error in get_latest_images_route: {str(e)}")
+                log.error(f"Error in get_latest_images_route: {str(e)}")
                 return web.json_response({
                     'success': False,
                     'error': str(e)
@@ -663,11 +673,11 @@ class LayerForgeNode:
                         'error': 'file_path is required'
                     }, status=400)
                 
-                log_info(f"Attempting to load image from path: {file_path}")
+                log.info(f"Attempting to load image from path: {file_path}")
                 
                 # Check if file exists and is accessible
                 if not os.path.exists(file_path):
-                    log_warn(f"File not found: {file_path}")
+                    log.warning(f"File not found: {file_path}")
                     return web.json_response({
                         'success': False,
                         'error': f'File not found: {file_path}'
@@ -693,7 +703,7 @@ class LayerForgeNode:
                         img.save(buffered, format="PNG")
                         img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
                         
-                        log_info(f"Successfully loaded image from path: {file_path}")
+                        log.info(f"Successfully loaded image from path: {file_path}")
                         return web.json_response({
                             'success': True,
                             'image_data': f"data:image/png;base64,{img_str}",
@@ -702,14 +712,14 @@ class LayerForgeNode:
                         })
                         
                 except Exception as img_error:
-                    log_error(f"Error processing image file {file_path}: {str(img_error)}")
+                    log.error(f"Error processing image file {file_path}: {str(img_error)}")
                     return web.json_response({
                         'success': False,
                         'error': f'Error processing image file: {str(img_error)}'
                     }, status=500)
                     
             except Exception as e:
-                log_error(f"Error in load_image_from_path_route: {str(e)}")
+                log.error(f"Error in load_image_from_path_route: {str(e)}")
                 return web.json_response({
                     'success': False,
                     'error': str(e)
@@ -724,148 +734,148 @@ class LayerForgeNode:
         else:
             self.cached_image = image_data
 
-    def get_cached_image(self):
-
-        if self.cached_image:
-            buffered = io.BytesIO()
-            self.cached_image.save(buffered, format="PNG")
-            img_str = base64.b64encode(buffered.getvalue()).decode()
-            return f"data:image/png;base64,{img_str}"
-        return None
-
-
-def _get_birefnet_base_paths():
-    paths = []
-
-    comfy_models_dir = getattr(folder_paths, "models_dir", None)
-    if comfy_models_dir:
-        paths.append(os.path.join(comfy_models_dir, "BiRefNet"))
-
-    legacy_models_dir = os.path.join(
-        os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))),
-        "models",
-        "BiRefNet"
-    )
-    paths.append(legacy_models_dir)
-
-    unique_paths = []
-    seen = set()
-    for path in paths:
-        normalized = os.path.normpath(path)
-        if normalized not in seen:
-            seen.add(normalized)
-            unique_paths.append(path)
-
-    return unique_paths
-
-
-def _is_valid_birefnet_model_dir(path):
-    if not os.path.isdir(path):
-        return False
-
-    try:
-        files = os.listdir(path)
-    except OSError:
-        return False
-
-    has_config = "config.json" in files
-    has_model = "model.safetensors" in files or "pytorch_model.bin" in files
-    has_backbone = "backbone_swin.pth" in files or "swin_base_patch4_window12_384_22kto1k.pth" in files
-    has_birefnet = "birefnet.pth" in files or any(f.endswith(".pth") for f in files)
-
-    return has_config and (has_model or has_backbone or has_birefnet)
-
-
-def _find_local_birefnet_model():
-    for base_path in _get_birefnet_base_paths():
-        if not os.path.isdir(base_path):
-            continue
-
-        if _is_valid_birefnet_model_dir(base_path):
-            return base_path
-
-        try:
-            existing_items = os.listdir(base_path)
-        except OSError:
-            continue
-
-        model_subdirs = [
-            d for d in existing_items
-            if os.path.isdir(os.path.join(base_path, d)) and
-            (d.startswith("models--") or d == "ZhengPeng7--BiRefNet")
-        ]
-
-        for subdir in model_subdirs:
-            snapshots_path = os.path.join(base_path, subdir, "snapshots")
-            if not os.path.isdir(snapshots_path):
-                continue
-
-            try:
-                snapshot_dirs = os.listdir(snapshots_path)
-            except OSError:
-                continue
-
-            for snapshot in snapshot_dirs:
-                snapshot_path = os.path.join(snapshots_path, snapshot)
-                if _is_valid_birefnet_model_dir(snapshot_path):
-                    return snapshot_path
-
-    return None
-
-
-class BiRefNetMatting:
-    def __init__(self):
-        self.model = None
-        self.model_path = None
-        self.model_cache = {}
-        self.base_paths = _get_birefnet_base_paths()
-
-    def load_model(self, model_path):
-        from json.decoder import JSONDecodeError
-        try:
-            if model_path not in self.model_cache:
-                local_model_path = _find_local_birefnet_model()
-                cache_dir = self.base_paths[0] if self.base_paths else None
-
-                if local_model_path:
-                    log_info(f"Loading BiRefNet model from local path {local_model_path}...")
-                    try:
-                        self.model = AutoModelForImageSegmentation.from_pretrained(
-                            local_model_path,
-                            trust_remote_code=True
-                        )
-                        self.model.eval()
-                        if torch.cuda.is_available():
-                            self.model = self.model.cuda()
-                        self.model_cache[model_path] = self.model
-                        log_info("Model loaded successfully from local disk")
-                        return
-                    except Exception as local_error:
-                        log_warn(f"Failed to load local BiRefNet model from {local_model_path}: {str(local_error)}")
-                        log_info("Falling back to Hugging Face model loading")
-
-                full_model_path = cache_dir or "BiRefNet"
-                log_info(f"Loading BiRefNet model from Hugging Face cache {full_model_path}...")
-                try:
-                    # Try loading with additional configuration to handle compatibility issues
-                    self.model = AutoModelForImageSegmentation.from_pretrained(
-                        "ZhengPeng7/BiRefNet",
-                        trust_remote_code=True,
-                        cache_dir=cache_dir,
-                        # Add force_download=False to use cached version if available
-                        force_download=False,
-                        # Add local_files_only=False to allow downloading if needed
-                        local_files_only=False
-                    )
+    def get_cached_image(self):
+
+        if self.cached_image:
+            buffered = io.BytesIO()
+            self.cached_image.save(buffered, format="PNG")
+            img_str = base64.b64encode(buffered.getvalue()).decode()
+            return f"data:image/png;base64,{img_str}"
+        return None
+
+
+def _get_birefnet_base_paths():
+    paths = []
+
+    comfy_models_dir = getattr(folder_paths, "models_dir", None)
+    if comfy_models_dir:
+        paths.append(os.path.join(comfy_models_dir, "BiRefNet"))
+
+    legacy_models_dir = os.path.join(
+        os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))),
+        "models",
+        "BiRefNet"
+    )
+    paths.append(legacy_models_dir)
+
+    unique_paths = []
+    seen = set()
+    for path in paths:
+        normalized = os.path.normpath(path)
+        if normalized not in seen:
+            seen.add(normalized)
+            unique_paths.append(path)
+
+    return unique_paths
+
+
+def _is_valid_birefnet_model_dir(path):
+    if not os.path.isdir(path):
+        return False
+
+    try:
+        files = os.listdir(path)
+    except OSError:
+        return False
+
+    has_config = "config.json" in files
+    has_model = "model.safetensors" in files or "pytorch_model.bin" in files
+    has_backbone = "backbone_swin.pth" in files or "swin_base_patch4_window12_384_22kto1k.pth" in files
+    has_birefnet = "birefnet.pth" in files or any(f.endswith(".pth") for f in files)
+
+    return has_config and (has_model or has_backbone or has_birefnet)
+
+
+def _find_local_birefnet_model():
+    for base_path in _get_birefnet_base_paths():
+        if not os.path.isdir(base_path):
+            continue
+
+        if _is_valid_birefnet_model_dir(base_path):
+            return base_path
+
+        try:
+            existing_items = os.listdir(base_path)
+        except OSError:
+            continue
+
+        model_subdirs = [
+            d for d in existing_items
+            if os.path.isdir(os.path.join(base_path, d)) and
+            (d.startswith("models--") or d == "ZhengPeng7--BiRefNet")
+        ]
+
+        for subdir in model_subdirs:
+            snapshots_path = os.path.join(base_path, subdir, "snapshots")
+            if not os.path.isdir(snapshots_path):
+                continue
+
+            try:
+                snapshot_dirs = os.listdir(snapshots_path)
+            except OSError:
+                continue
+
+            for snapshot in snapshot_dirs:
+                snapshot_path = os.path.join(snapshots_path, snapshot)
+                if _is_valid_birefnet_model_dir(snapshot_path):
+                    return snapshot_path
+
+    return None
+
+
+class BiRefNetMatting:
+    def __init__(self):
+        self.model = None
+        self.model_path = None
+        self.model_cache = {}
+        self.base_paths = _get_birefnet_base_paths()
+
+    def load_model(self, model_path):
+        from json.decoder import JSONDecodeError
+        try:
+            if model_path not in self.model_cache:
+                local_model_path = _find_local_birefnet_model()
+                cache_dir = self.base_paths[0] if self.base_paths else None
+
+                if local_model_path:
+                    log.info(f"Loading BiRefNet model from local path {local_model_path}...")
+                    try:
+                        self.model = AutoModelForImageSegmentation.from_pretrained(
+                            local_model_path,
+                            trust_remote_code=True
+                        )
+                        self.model.eval()
+                        if torch.cuda.is_available():
+                            self.model = self.model.cuda()
+                        self.model_cache[model_path] = self.model
+                        log.info("Model loaded successfully from local disk")
+                        return
+                    except Exception as local_error:
+                        log.warning(f"Failed to load local BiRefNet model from {local_model_path}: {str(local_error)}")
+                        log.info("Falling back to Hugging Face model loading")
+
+                full_model_path = cache_dir or "BiRefNet"
+                log.info(f"Loading BiRefNet model from Hugging Face cache {full_model_path}...")
+                try:
+                    # Try loading with additional configuration to handle compatibility issues
+                    self.model = AutoModelForImageSegmentation.from_pretrained(
+                        "ZhengPeng7/BiRefNet",
+                        trust_remote_code=True,
+                        cache_dir=cache_dir,
+                        # Add force_download=False to use cached version if available
+                        force_download=False,
+                        # Add local_files_only=False to allow downloading if needed
+                        local_files_only=False
+                    )
                     self.model.eval()
                     if torch.cuda.is_available():
                         self.model = self.model.cuda()
                     self.model_cache[model_path] = self.model
-                    log_info("Model loaded successfully from Hugging Face")
+                    log.info("Model loaded successfully from Hugging Face")
                 except AttributeError as e:
                     if "'Config' object has no attribute 'is_encoder_decoder'" in str(e):
-                        log_error("Compatibility issue detected with transformers library. This has been fixed in the code.")
-                        log_error("If you're still seeing this error, please clear the model cache and try again.")
+                        log.error("Compatibility issue detected with transformers library. This has been fixed in the code.")
+                        log.error("If you're still seeing this error, please clear the model cache and try again.")
                         raise RuntimeError(
                             "Model configuration compatibility issue detected. "
                             f"Please delete the model cache directory '{full_model_path}' and restart ComfyUI. "
@@ -874,14 +884,14 @@ class BiRefNetMatting:
                     else:
                         raise e
                 except JSONDecodeError as e:                    
-                    log_error(f"JSONDecodeError: Failed to load model from {full_model_path}. The model's config.json may be corrupted.")
+                    log.error(f"JSONDecodeError: Failed to load model from {full_model_path}. The model's config.json may be corrupted.")
                     raise RuntimeError(
                         "The matting model's configuration file (config.json) appears to be corrupted. "
                         f"Please manually delete the directory '{full_model_path}' and try again. "
                         "This will force a fresh download of the model."
                     ) from e
                 except Exception as e:
-                    log_error(f"Failed to load model from Hugging Face: {str(e)}")
+                    log.error(f"Failed to load model from Hugging Face: {str(e)}")
                     # Re-raise with a more informative message
                     raise RuntimeError(
                         "Failed to download or load the matting model. "
@@ -891,12 +901,12 @@ class BiRefNetMatting:
                     ) from e
             else:
                 self.model = self.model_cache[model_path]
-                log_debug("Using cached model")
+                log.debug("Using cached model")
 
         except Exception as e:
             # Catch the re-raised exception or any other error
-            log_error(f"Error loading model: {str(e)}")
-            log_exception("Model loading failed")
+            log.error(f"Error loading model: {str(e)}")
+            log.exception("Model loading failed")
             raise  # Re-raise the exception to be caught by the execute method
 
     def preprocess_image(self, image):
@@ -922,7 +932,7 @@ class BiRefNetMatting:
 
             return image_tensor
         except Exception as e:
-            log_error(f"Error preprocessing image: {str(e)}")
+            log.error(f"Error preprocessing image: {str(e)}")
             return None
 
     def execute(self, image, model_path, threshold=0.5, refinement=1):
@@ -936,25 +946,25 @@ class BiRefNetMatting:
             else:
                 original_size = image.size[::-1]
 
-            log_debug(f"Original size: {original_size}")
+            log.debug(f"Original size: {original_size}")
 
             processed_image = self.preprocess_image(image)
             if processed_image is None:
                 raise Exception("Failed to preprocess image")
 
-            log_debug(f"Processed image shape: {processed_image.shape}")
+            log.debug(f"Processed image shape: {processed_image.shape}")
 
             with torch.no_grad():
                 outputs = self.model(processed_image)
                 result = outputs[-1].sigmoid().cpu()
-                log_debug(f"Model output shape: {result.shape}")
+                log.debug(f"Model output shape: {result.shape}")
 
                 if result.dim() == 3:
                     result = result.unsqueeze(1)  # 添加通道维度
                 elif result.dim() == 2:
                     result = result.unsqueeze(0).unsqueeze(0)  # 添加batch和通道维度
 
-                log_debug(f"Reshaped result shape: {result.shape}")
+                log.debug(f"Reshaped result shape: {result.shape}")
 
                 result = F.interpolate(
                     result,
@@ -962,7 +972,7 @@ class BiRefNetMatting:
                     mode='bilinear',
                     align_corners=True
                 )
-                log_debug(f"Resized result shape: {result.shape}")
+                log.debug(f"Resized result shape: {result.shape}")
 
                 result = result.squeeze()  # 移除多余的维度
                 ma = torch.max(result)
@@ -1013,30 +1023,30 @@ async def check_matting_model(request):
                 "message": "The 'transformers' library is required for the matting feature. Please install it by running: pip install transformers"
             })
         
-        # Check if model exists in cache
-        local_model_path = _find_local_birefnet_model()
-
-        if local_model_path:
-            # Model files exist, assume it's ready
-            log_info(f"BiRefNet model files detected at {local_model_path}")
-            return web.json_response({
-                "available": True,
-                "reason": "ready",
-                "message": "Model is ready to use",
-                "model_path": local_model_path
-            })
-        else:
-            searched_paths = _get_birefnet_base_paths()
-            log_info(f"BiRefNet model not found in any of: {searched_paths}")
-            return web.json_response({
-                "available": False,
-                "reason": "not_downloaded",
-                "message": "The matting model needs to be downloaded. This will happen automatically when you first use the matting feature (requires internet connection).",
-                "model_path": searched_paths[0] if searched_paths else None
-            })
+        # Check if model exists in cache
+        local_model_path = _find_local_birefnet_model()
+
+        if local_model_path:
+            # Model files exist, assume it's ready
+            log.info(f"BiRefNet model files detected at {local_model_path}")
+            return web.json_response({
+                "available": True,
+                "reason": "ready",
+                "message": "Model is ready to use",
+                "model_path": local_model_path
+            })
+        else:
+            searched_paths = _get_birefnet_base_paths()
+            log.info(f"BiRefNet model not found in any of: {searched_paths}")
+            return web.json_response({
+                "available": False,
+                "reason": "not_downloaded",
+                "message": "The matting model needs to be downloaded. This will happen automatically when you first use the matting feature (requires internet connection).",
+                "model_path": searched_paths[0] if searched_paths else None
+            })
             
     except Exception as e:
-        log_error(f"Error checking matting model: {str(e)}")
+        log.error(f"Error checking matting model: {str(e)}")
         return web.json_response({
             "available": False,
             "reason": "error",
@@ -1048,14 +1058,14 @@ async def matting(request):
     global _matting_lock
 
     if not TRANSFORMERS_AVAILABLE:
-        log_error("Matting request failed: 'transformers' library is not installed.")
+        log.error("Matting request failed: 'transformers' library is not installed.")
         return web.json_response({
             "error": "Dependency Not Found",
             "details": "The 'transformers' library is required for the matting feature. Please install it by running: pip install transformers"
         }, status=400)
 
     if _matting_lock is not None:
-        log_warn("Matting already in progress, rejecting request")
+        log.warning("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"
@@ -1063,13 +1073,13 @@ async def matting(request):
 
     _matting_lock = True
     try:
-        log_info("Received matting request")
+        log.info("Received matting request")
         data = await request.json()
 
         matting_instance = BiRefNetMatting()
 
         image_tensor, original_alpha = convert_base64_to_tensor(data["image"])
-        log_debug(f"Input image shape: {image_tensor.shape}")
+        log.debug(f"Input image shape: {image_tensor.shape}")
 
         matted_image, alpha_mask = matting_instance.execute(
             image_tensor,
@@ -1087,19 +1097,19 @@ async def matting(request):
         })
 
     except RequestsConnectionError as e:
-        log_error(f"Connection error during matting model download: {e}")
+        log.error(f"Connection error during matting model download: {e}")
         return web.json_response({
             "error": "Network Connection Error",
             "details": "Failed to download the matting model from Hugging Face. Please check your internet connection."
         }, status=400)
     except RuntimeError as e:
-        log_error(f"Runtime error during matting: {e}")
+        log.error(f"Runtime error during matting: {e}")
         return web.json_response({
             "error": "Matting Model Error",
             "details": str(e)
         }, status=500)
     except Exception as e:
-        log_exception(f"Error in matting endpoint: {e}")
+        log.exception(f"Error in matting endpoint: {e}")
         # Check for offline error message from Hugging Face
         if "Offline mode is enabled" in str(e) or "Can't load 'ZhengPeng7/BiRefNet' offline" in str(e):
             return web.json_response({
@@ -1113,7 +1123,7 @@ async def matting(request):
         }, status=500)
     finally:
         _matting_lock = None
-        log_debug("Matting lock released")
+        log.debug("Matting lock released")
 
 
 def convert_base64_to_tensor(base64_str):
@@ -1146,7 +1156,7 @@ def convert_base64_to_tensor(base64_str):
         return img_tensor, None
 
     except Exception as e:
-        log_error(f"Error in convert_base64_to_tensor: {str(e)}")
+        log.error(f"Error in convert_base64_to_tensor: {str(e)}")
         raise
 
 
@@ -1193,6 +1203,6 @@ def convert_tensor_to_base64(tensor, alpha_mask=None, original_alpha=None):
         return f"data:image/png;base64,{img_str}"
 
     except Exception as e:
-        log_error(f"Error in convert_tensor_to_base64: {str(e)}")
-        log_debug(f"Tensor shape: {tensor.shape}, dtype: {tensor.dtype}")
+        log.error(f"Error in convert_tensor_to_base64: {str(e)}")
+        log.debug(f"Tensor shape: {tensor.shape}, dtype: {tensor.dtype}")
         raise
diff --git a/js/BatchPreviewManager.js b/js/BatchPreviewManager.js
index 6e355cc..233f684 100644
--- a/js/BatchPreviewManager.js
+++ b/js/BatchPreviewManager.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 const log = createModuleLogger('BatchPreviewManager');
 export class BatchPreviewManager {
     constructor(canvas, initialPosition = { x: 0, y: 0 }, generationArea = null) {
diff --git a/js/Canvas.js b/js/Canvas.js
index 66f8692..2838f58 100644
--- a/js/Canvas.js
+++ b/js/Canvas.js
@@ -11,7 +11,7 @@ import { CanvasRenderer } from "./CanvasRenderer.js";
 import { CanvasIO } from "./CanvasIO.js";
 import { ImageReferenceManager } from "./ImageReferenceManager.js";
 import { BatchPreviewManager } from "./BatchPreviewManager.js";
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { debounce, createCanvas } from "./utils/CommonUtils.js";
 import { MaskEditorIntegration } from "./MaskEditorIntegration.js";
 import { CanvasSelection } from "./CanvasSelection.js";
diff --git a/js/CanvasIO.js b/js/CanvasIO.js
index 75f58f7..b33dcf0 100644
--- a/js/CanvasIO.js
+++ b/js/CanvasIO.js
@@ -1,5 +1,5 @@
 import { createCanvas } from "./utils/CommonUtils.js";
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { showErrorNotification } from "./utils/NotificationUtils.js";
 import { webSocketManager } from "./utils/WebSocketManager.js";
 import { scaleImageToFit, createImageFromSource, tensorToImageData, createImageFromImageData } from "./utils/ImageUtils.js";
diff --git a/js/CanvasInteractions.js b/js/CanvasInteractions.js
index 50d019a..5bf60e5 100644
--- a/js/CanvasInteractions.js
+++ b/js/CanvasInteractions.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { snapToGrid, getSnapAdjustment } from "./utils/CommonUtils.js";
 const log = createModuleLogger('CanvasInteractions');
 export class CanvasInteractions {
diff --git a/js/CanvasLayers.js b/js/CanvasLayers.js
index ae816af..dd6373e 100644
--- a/js/CanvasLayers.js
+++ b/js/CanvasLayers.js
@@ -1,5 +1,5 @@
 import { saveImage } from "./db.js";
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { generateUUID, generateUniqueFileName, createCanvas } from "./utils/CommonUtils.js";
 import { withErrorHandling, createValidationError } from "./ErrorHandler.js";
 import { showErrorNotification } from "./utils/NotificationUtils.js";
diff --git a/js/CanvasLayersPanel.js b/js/CanvasLayersPanel.js
index 048852f..be8785d 100644
--- a/js/CanvasLayersPanel.js
+++ b/js/CanvasLayersPanel.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { iconLoader, LAYERFORGE_TOOLS } from "./utils/IconLoader.js";
 import { createCanvas } from "./utils/CommonUtils.js";
 import { addStylesheet, getUrl } from "./utils/ResourceManager.js";
diff --git a/js/CanvasRenderer.js b/js/CanvasRenderer.js
index 6223d6d..067832f 100644
--- a/js/CanvasRenderer.js
+++ b/js/CanvasRenderer.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 const log = createModuleLogger('CanvasRenderer');
 export class CanvasRenderer {
     constructor(canvas) {
diff --git a/js/CanvasSelection.js b/js/CanvasSelection.js
index f0d3fd2..28a6b0d 100644
--- a/js/CanvasSelection.js
+++ b/js/CanvasSelection.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { generateUUID } from "./utils/CommonUtils.js";
 const log = createModuleLogger('CanvasSelection');
 export class CanvasSelection {
diff --git a/js/CanvasState.js b/js/CanvasState.js
index 3fb28b9..4b725ab 100644
--- a/js/CanvasState.js
+++ b/js/CanvasState.js
@@ -1,5 +1,5 @@
 import { getCanvasState, setCanvasState, saveImage, getImage } from "./db.js";
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { showAlertNotification } from "./utils/NotificationUtils.js";
 import { generateUUID, cloneLayers, getStateSignature, debounce, createCanvas } from "./utils/CommonUtils.js";
 const log = createModuleLogger('CanvasState');
diff --git a/js/CanvasView.js b/js/CanvasView.js
index 5d052c6..bec5b6a 100644
--- a/js/CanvasView.js
+++ b/js/CanvasView.js
@@ -9,7 +9,7 @@ import { Canvas } from "./Canvas.js";
 import { clearAllCanvasStates, getCanvasState, setCanvasState } from "./db.js";
 import { ImageCache } from "./ImageCache.js";
 import { createCanvas } from "./utils/CommonUtils.js";
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { showErrorNotification, showSuccessNotification, showInfoNotification, showWarningNotification } from "./utils/NotificationUtils.js";
 import { iconLoader, LAYERFORGE_TOOLS } from "./utils/IconLoader.js";
 import { setupSAMDetectorHook } from "./SAMDetectorIntegration.js";
diff --git a/js/CustomShapeMenu.js b/js/CustomShapeMenu.js
index 4dd0353..ef28700 100644
--- a/js/CustomShapeMenu.js
+++ b/js/CustomShapeMenu.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { addStylesheet, getUrl } from "./utils/ResourceManager.js";
 const log = createModuleLogger('CustomShapeMenu');
 export class CustomShapeMenu {
diff --git a/js/ErrorHandler.js b/js/ErrorHandler.js
index 6741857..4fe1d4c 100644
--- a/js/ErrorHandler.js
+++ b/js/ErrorHandler.js
@@ -2,7 +2,7 @@
  * ErrorHandler - Centralna obsługa błędów
  * Eliminuje powtarzalne wzorce obsługi błędów w całym projekcie
  */
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 const log = createModuleLogger('ErrorHandler');
 /**
  * Typy błędów w aplikacji
diff --git a/js/ImageCache.js b/js/ImageCache.js
index e8eb996..7337011 100644
--- a/js/ImageCache.js
+++ b/js/ImageCache.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 const log = createModuleLogger('ImageCache');
 export class ImageCache {
     constructor() {
diff --git a/js/ImageReferenceManager.js b/js/ImageReferenceManager.js
index 34a36ce..991416c 100644
--- a/js/ImageReferenceManager.js
+++ b/js/ImageReferenceManager.js
@@ -1,5 +1,5 @@
 import { removeImage, getAllImageIds } from "./db.js";
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 const log = createModuleLogger('ImageReferenceManager');
 export class ImageReferenceManager {
     constructor(canvas) {
diff --git a/js/MaskEditorIntegration.js b/js/MaskEditorIntegration.js
index 3e52b14..573a987 100644
--- a/js/MaskEditorIntegration.js
+++ b/js/MaskEditorIntegration.js
@@ -2,7 +2,7 @@
 import { app } from "../../scripts/app.js";
 // @ts-ignore
 import { ComfyApp } from "../../scripts/app.js";
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { showErrorNotification } from "./utils/NotificationUtils.js";
 import { uploadImageBlob } from "./utils/ImageUploadUtils.js";
 import { processImageToMask, processMaskForViewport } from "./utils/MaskProcessingUtils.js";
diff --git a/js/MaskTool.js b/js/MaskTool.js
index 80f3f13..56d4bac 100644
--- a/js/MaskTool.js
+++ b/js/MaskTool.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { createCanvas } from "./utils/CommonUtils.js";
 const log = createModuleLogger('Mask_tool');
 export class MaskTool {
diff --git a/js/SAMDetectorIntegration.js b/js/SAMDetectorIntegration.js
index 4f07961..936e282 100644
--- a/js/SAMDetectorIntegration.js
+++ b/js/SAMDetectorIntegration.js
@@ -1,6 +1,6 @@
 // @ts-ignore
 import { ComfyApp } from "../../scripts/app.js";
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { showInfoNotification, showSuccessNotification, showErrorNotification } from "./utils/NotificationUtils.js";
 import { uploadCanvasAsImage, uploadImageBlob } from "./utils/ImageUploadUtils.js";
 import { processImageToMask } from "./utils/MaskProcessingUtils.js";
diff --git a/js/ShapeTool.js b/js/ShapeTool.js
index 05c7960..067d1b2 100644
--- a/js/ShapeTool.js
+++ b/js/ShapeTool.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 const log = createModuleLogger('ShapeTool');
 export class ShapeTool {
     constructor(canvas) {
diff --git a/js/db.js b/js/db.js
index dd889c6..8113d8a 100644
--- a/js/db.js
+++ b/js/db.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 const log = createModuleLogger('db');
 const DB_NAME = 'CanvasNodeDB';
 const STATE_STORE_NAME = 'CanvasState';
diff --git a/js/utils/ClipboardManager.js b/js/utils/ClipboardManager.js
index 32afbd5..4f85f75 100644
--- a/js/utils/ClipboardManager.js
+++ b/js/utils/ClipboardManager.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { showNotification, showInfoNotification, showErrorNotification, showWarningNotification } from "./NotificationUtils.js";
 import { withErrorHandling, createValidationError, createNetworkError, createFileError } from "../ErrorHandler.js";
 import { safeClipspacePaste } from "./ClipspaceUtils.js";
diff --git a/js/utils/ClipspaceUtils.js b/js/utils/ClipspaceUtils.js
index bf09afb..70e3e16 100644
--- a/js/utils/ClipspaceUtils.js
+++ b/js/utils/ClipspaceUtils.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 // @ts-ignore
 import { ComfyApp } from "../../../scripts/app.js";
 const log = createModuleLogger('ClipspaceUtils');
diff --git a/js/utils/IconLoader.js b/js/utils/IconLoader.js
index 3d51b23..b2b207a 100644
--- a/js/utils/IconLoader.js
+++ b/js/utils/IconLoader.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { createCanvas } from "./CommonUtils.js";
 import { withErrorHandling, createValidationError } from "../ErrorHandler.js";
 const log = createModuleLogger('IconLoader');
diff --git a/js/utils/ImageAnalysis.js b/js/utils/ImageAnalysis.js
index 35c05c7..66e43ba 100644
--- a/js/utils/ImageAnalysis.js
+++ b/js/utils/ImageAnalysis.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { createCanvas } from "./CommonUtils.js";
 import { withErrorHandling, createValidationError } from "../ErrorHandler.js";
 const log = createModuleLogger('ImageAnalysis');
diff --git a/js/utils/ImageUploadUtils.js b/js/utils/ImageUploadUtils.js
index 22cbdcb..5e11fb4 100644
--- a/js/utils/ImageUploadUtils.js
+++ b/js/utils/ImageUploadUtils.js
@@ -1,6 +1,6 @@
 // @ts-ignore
 import { api } from "../../../scripts/api.js";
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { withErrorHandling, createValidationError, createNetworkError } from "../ErrorHandler.js";
 const log = createModuleLogger('ImageUploadUtils');
 /**
diff --git a/js/utils/ImageUtils.js b/js/utils/ImageUtils.js
index 19c4538..31774fb 100644
--- a/js/utils/ImageUtils.js
+++ b/js/utils/ImageUtils.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { withErrorHandling, createValidationError } from "../ErrorHandler.js";
 import { createCanvas } from "./CommonUtils.js";
 const log = createModuleLogger('ImageUtils');
diff --git a/js/utils/MaskProcessingUtils.js b/js/utils/MaskProcessingUtils.js
index 091a399..1d0cc58 100644
--- a/js/utils/MaskProcessingUtils.js
+++ b/js/utils/MaskProcessingUtils.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { createCanvas } from "./CommonUtils.js";
 import { withErrorHandling, createValidationError } from "../ErrorHandler.js";
 const log = createModuleLogger('MaskProcessingUtils');
diff --git a/js/utils/NotificationUtils.js b/js/utils/NotificationUtils.js
index e72929e..f57ebcd 100644
--- a/js/utils/NotificationUtils.js
+++ b/js/utils/NotificationUtils.js
@@ -8,7 +8,7 @@ description: Notification Utilities (Toast System)
 // 1. IMPORTS & DEPENDENCIES
 // ==========================================
 // Comment out or adjust this import if copying to a project that does not use AzLogs
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 const CONFIG = {
     // Project display name (used in notifications tag and message prefix cleaning)
     PROJECT_NAME: "Layer Forge",
diff --git a/js/utils/PreviewUtils.js b/js/utils/PreviewUtils.js
index 94eda61..f89d219 100644
--- a/js/utils/PreviewUtils.js
+++ b/js/utils/PreviewUtils.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { withErrorHandling, createValidationError } from "../ErrorHandler.js";
 const log = createModuleLogger('PreviewUtils');
 /**
diff --git a/js/utils/ResourceManager.js b/js/utils/ResourceManager.js
index a652d36..8ed767d 100644
--- a/js/utils/ResourceManager.js
+++ b/js/utils/ResourceManager.js
@@ -1,6 +1,6 @@
 // @ts-ignore
 import { $el } from "../../../scripts/ui.js";
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { withErrorHandling, createValidationError, createNetworkError } from "../ErrorHandler.js";
 const log = createModuleLogger('ResourceManager');
 export const addStylesheet = withErrorHandling(function (url) {
diff --git a/js/utils/WebSocketManager.js b/js/utils/WebSocketManager.js
index dc26feb..4093962 100644
--- a/js/utils/WebSocketManager.js
+++ b/js/utils/WebSocketManager.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { withErrorHandling, createValidationError, createNetworkError } from "../ErrorHandler.js";
 const log = createModuleLogger('WebSocketManager');
 class WebSocketManager {
diff --git a/js/utils/mask_utils.js b/js/utils/mask_utils.js
index 0f92098..9d0c1b7 100644
--- a/js/utils/mask_utils.js
+++ b/js/utils/mask_utils.js
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { withErrorHandling, createValidationError } from "../ErrorHandler.js";
 const log = createModuleLogger('MaskUtils');
 export function new_editor(app) {
diff --git a/python/__init__.py b/python/__init__.py
index f3715c2..10c8969 100644
--- a/python/__init__.py
+++ b/python/__init__.py
@@ -1,4 +1,5 @@
 # This file makes the 'python' directory a package.
-from . import logger
+from . import log_system
+from .log_system import logger
 
-__all__ = ['logger']
+__all__ = ['log_system', 'logger']
diff --git a/src/BatchPreviewManager.ts b/src/BatchPreviewManager.ts
index 1819b55..6f8301d 100644
--- a/src/BatchPreviewManager.ts
+++ b/src/BatchPreviewManager.ts
@@ -1,4 +1,4 @@
-import {createModuleLogger} from "./utils/LoggerUtils.js";
+import {createModuleLogger} from "./log_system/log_funcs.js";
 import type { Canvas } from './Canvas';
 import type { Layer, Point } from './types';
 
diff --git a/src/Canvas.ts b/src/Canvas.ts
index 7e29b97..c49a01f 100644
--- a/src/Canvas.ts
+++ b/src/Canvas.ts
@@ -17,7 +17,7 @@ import {CanvasRenderer} from "./CanvasRenderer.js";
 import {CanvasIO} from "./CanvasIO.js";
 import {ImageReferenceManager} from "./ImageReferenceManager.js";
 import {BatchPreviewManager} from "./BatchPreviewManager.js";
-import {createModuleLogger} from "./utils/LoggerUtils.js";
+import {createModuleLogger} from "./log_system/log_funcs.js";
 import { debounce, createCanvas } from "./utils/CommonUtils.js";
 import {MaskEditorIntegration} from "./MaskEditorIntegration.js";
 import {CanvasSelection} from "./CanvasSelection.js";
diff --git a/src/CanvasIO.ts b/src/CanvasIO.ts
index 3212b0e..cb656e0 100644
--- a/src/CanvasIO.ts
+++ b/src/CanvasIO.ts
@@ -1,5 +1,5 @@
 import { createCanvas } from "./utils/CommonUtils.js";
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { showErrorNotification } from "./utils/NotificationUtils.js";
 import { webSocketManager } from "./utils/WebSocketManager.js";
 import { scaleImageToFit, createImageFromSource, tensorToImageData, createImageFromImageData } from "./utils/ImageUtils.js";
diff --git a/src/CanvasInteractions.ts b/src/CanvasInteractions.ts
index fc1f6e9..8c4a6dd 100644
--- a/src/CanvasInteractions.ts
+++ b/src/CanvasInteractions.ts
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { snapToGrid, getSnapAdjustment } from "./utils/CommonUtils.js";
 import type { Canvas } from './Canvas';
 import type { Layer, Point } from './types';
diff --git a/src/CanvasLayers.ts b/src/CanvasLayers.ts
index e66f5c8..8bf788e 100644
--- a/src/CanvasLayers.ts
+++ b/src/CanvasLayers.ts
@@ -1,5 +1,5 @@
 import {saveImage, removeImage} from "./db.js";
-import {createModuleLogger} from "./utils/LoggerUtils.js";
+import {createModuleLogger} from "./log_system/log_funcs.js";
 import {generateUUID, generateUniqueFileName, createCanvas} from "./utils/CommonUtils.js";
 import {withErrorHandling, createValidationError} from "./ErrorHandler.js";
 import {showErrorNotification, showSuccessNotification} from "./utils/NotificationUtils.js";
diff --git a/src/CanvasLayersPanel.ts b/src/CanvasLayersPanel.ts
index dfa8261..2ebd00c 100644
--- a/src/CanvasLayersPanel.ts
+++ b/src/CanvasLayersPanel.ts
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { iconLoader, LAYERFORGE_TOOLS } from "./utils/IconLoader.js";
 import { createCanvas } from "./utils/CommonUtils.js";
 import { addStylesheet, getUrl } from "./utils/ResourceManager.js";
diff --git a/src/CanvasRenderer.ts b/src/CanvasRenderer.ts
index 1ba81b7..d60206e 100644
--- a/src/CanvasRenderer.ts
+++ b/src/CanvasRenderer.ts
@@ -1,4 +1,4 @@
-import {createModuleLogger} from "./utils/LoggerUtils.js";
+import {createModuleLogger} from "./log_system/log_funcs.js";
 
 const log = createModuleLogger('CanvasRenderer');
 
diff --git a/src/CanvasSelection.ts b/src/CanvasSelection.ts
index f0de041..ab85d2d 100644
--- a/src/CanvasSelection.ts
+++ b/src/CanvasSelection.ts
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { generateUUID } from "./utils/CommonUtils.js";
 
 const log = createModuleLogger('CanvasSelection');
diff --git a/src/CanvasState.ts b/src/CanvasState.ts
index 5687662..a074bd9 100644
--- a/src/CanvasState.ts
+++ b/src/CanvasState.ts
@@ -1,5 +1,5 @@
 import {getCanvasState, setCanvasState, saveImage, getImage} from "./db.js";
-import {createModuleLogger} from "./utils/LoggerUtils.js";
+import {createModuleLogger} from "./log_system/log_funcs.js";
 import {showAlertNotification, showAllNotificationTypes} from "./utils/NotificationUtils.js";
 import {generateUUID, cloneLayers, getStateSignature, debounce, createCanvas} from "./utils/CommonUtils.js";
 import {withErrorHandling} from "./ErrorHandler.js";
diff --git a/src/CanvasView.ts b/src/CanvasView.ts
index d47c2c4..2b9185c 100644
--- a/src/CanvasView.ts
+++ b/src/CanvasView.ts
@@ -15,7 +15,7 @@ import {Canvas} from "./Canvas.js";
 import {clearAllCanvasStates, getCanvasState, setCanvasState} from "./db.js";
 import {ImageCache} from "./ImageCache.js";
 import {generateUniqueFileName, createCanvas} from "./utils/CommonUtils.js";
-import {createModuleLogger} from "./utils/LoggerUtils.js";
+import {createModuleLogger} from "./log_system/log_funcs.js";
 import {showErrorNotification, showSuccessNotification, showInfoNotification, showWarningNotification} from "./utils/NotificationUtils.js";
 import { iconLoader, LAYERFORGE_TOOLS } from "./utils/IconLoader.js";
 import { registerImageInClipspace, startSAMDetectorMonitoring, setupSAMDetectorHook } from "./SAMDetectorIntegration.js";
diff --git a/src/CustomShapeMenu.ts b/src/CustomShapeMenu.ts
index 15b44da..48747f3 100644
--- a/src/CustomShapeMenu.ts
+++ b/src/CustomShapeMenu.ts
@@ -1,4 +1,4 @@
-import {createModuleLogger} from "./utils/LoggerUtils.js";
+import {createModuleLogger} from "./log_system/log_funcs.js";
 import { addStylesheet, getUrl } from "./utils/ResourceManager.js";
 import type { Canvas } from './Canvas';
 
diff --git a/src/ErrorHandler.ts b/src/ErrorHandler.ts
index 69192c0..3b98782 100644
--- a/src/ErrorHandler.ts
+++ b/src/ErrorHandler.ts
@@ -3,7 +3,7 @@
  * Eliminuje powtarzalne wzorce obsługi błędów w całym projekcie
  */
 
-import {createModuleLogger} from "./utils/LoggerUtils.js";
+import {createModuleLogger} from "./log_system/log_funcs.js";
 
 const log = createModuleLogger('ErrorHandler');
 
diff --git a/src/ImageCache.ts b/src/ImageCache.ts
index 4e6337c..9d448d0 100644
--- a/src/ImageCache.ts
+++ b/src/ImageCache.ts
@@ -1,4 +1,4 @@
-import {createModuleLogger} from "./utils/LoggerUtils.js";
+import {createModuleLogger} from "./log_system/log_funcs.js";
 import type { ImageDataPixel } from './types';
 
 const log = createModuleLogger('ImageCache');
diff --git a/src/ImageReferenceManager.ts b/src/ImageReferenceManager.ts
index ca28180..e9aba05 100644
--- a/src/ImageReferenceManager.ts
+++ b/src/ImageReferenceManager.ts
@@ -1,5 +1,5 @@
 import {removeImage, getAllImageIds} from "./db.js";
-import {createModuleLogger} from "./utils/LoggerUtils.js";
+import {createModuleLogger} from "./log_system/log_funcs.js";
 import type { Canvas } from './Canvas';
 import type { Layer, CanvasState } from './types';
 
diff --git a/src/MaskEditorIntegration.ts b/src/MaskEditorIntegration.ts
index cbca603..53c1e5d 100644
--- a/src/MaskEditorIntegration.ts
+++ b/src/MaskEditorIntegration.ts
@@ -4,7 +4,7 @@ import {app} from "../../scripts/app.js";
 import {ComfyApp} from "../../scripts/app.js";
 // @ts-ignore
 import {api} from "../../scripts/api.js";
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { showErrorNotification } from "./utils/NotificationUtils.js";
 import { uploadCanvasAsImage, uploadCanvasWithMaskAsImage, uploadImageBlob } from "./utils/ImageUploadUtils.js";
 import { processImageToMask, processMaskForViewport } from "./utils/MaskProcessingUtils.js";
diff --git a/src/MaskTool.ts b/src/MaskTool.ts
index c8eb1a2..94590af 100644
--- a/src/MaskTool.ts
+++ b/src/MaskTool.ts
@@ -1,4 +1,4 @@
-import {createModuleLogger} from "./utils/LoggerUtils.js";
+import {createModuleLogger} from "./log_system/log_funcs.js";
 import { createCanvas } from "./utils/CommonUtils.js";
 import type { Canvas } from './Canvas';
 import type { Point, CanvasState } from './types';
diff --git a/src/SAMDetectorIntegration.ts b/src/SAMDetectorIntegration.ts
index af9c58f..f6eb32e 100644
--- a/src/SAMDetectorIntegration.ts
+++ b/src/SAMDetectorIntegration.ts
@@ -2,7 +2,7 @@
 import { api } from "../../scripts/api.js";
 // @ts-ignore
 import { ComfyApp } from "../../scripts/app.js";
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import { showInfoNotification, showSuccessNotification, showErrorNotification } from "./utils/NotificationUtils.js";
 import { uploadCanvasAsImage, uploadImageBlob } from "./utils/ImageUploadUtils.js";
 import { processImageToMask } from "./utils/MaskProcessingUtils.js";
diff --git a/src/ShapeTool.ts b/src/ShapeTool.ts
index aefe614..7366275 100644
--- a/src/ShapeTool.ts
+++ b/src/ShapeTool.ts
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./utils/LoggerUtils.js";
+import { createModuleLogger } from "./log_system/log_funcs.js";
 import type { Canvas } from './Canvas.js';
 import type { Point, Layer } from './types.js';
 
diff --git a/src/db.ts b/src/db.ts
index 2d73aaa..5eacea1 100644
--- a/src/db.ts
+++ b/src/db.ts
@@ -1,4 +1,4 @@
-import {createModuleLogger} from "./utils/LoggerUtils.js";
+import {createModuleLogger} from "./log_system/log_funcs.js";
 
 const log = createModuleLogger('db');
 
diff --git a/src/utils/ClipboardManager.ts b/src/utils/ClipboardManager.ts
index 2eef1e6..996d0e1 100644
--- a/src/utils/ClipboardManager.ts
+++ b/src/utils/ClipboardManager.ts
@@ -1,4 +1,4 @@
-import {createModuleLogger} from "./LoggerUtils.js";
+import {createModuleLogger} from "../log_system/log_funcs.js";
 import { showNotification, showInfoNotification, showErrorNotification, showWarningNotification } from "./NotificationUtils.js";
 import { withErrorHandling, createValidationError, createNetworkError, createFileError } from "../ErrorHandler.js";
 import { safeClipspacePaste } from "./ClipspaceUtils.js";
diff --git a/src/utils/ClipspaceUtils.ts b/src/utils/ClipspaceUtils.ts
index 7402da4..ad78a37 100644
--- a/src/utils/ClipspaceUtils.ts
+++ b/src/utils/ClipspaceUtils.ts
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 // @ts-ignore
 import { ComfyApp } from "../../../scripts/app.js";
 
diff --git a/src/utils/IconLoader.ts b/src/utils/IconLoader.ts
index 3eae143..1a43b67 100644
--- a/src/utils/IconLoader.ts
+++ b/src/utils/IconLoader.ts
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { createCanvas } from "./CommonUtils.js";
 import { withErrorHandling, createValidationError } from "../ErrorHandler.js";
 
diff --git a/src/utils/ImageAnalysis.ts b/src/utils/ImageAnalysis.ts
index ed350e7..955a98c 100644
--- a/src/utils/ImageAnalysis.ts
+++ b/src/utils/ImageAnalysis.ts
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { createCanvas } from "./CommonUtils.js";
 import { withErrorHandling, createValidationError } from "../ErrorHandler.js";
 
diff --git a/src/utils/ImageUploadUtils.ts b/src/utils/ImageUploadUtils.ts
index 713303c..d84d553 100644
--- a/src/utils/ImageUploadUtils.ts
+++ b/src/utils/ImageUploadUtils.ts
@@ -1,6 +1,6 @@
 // @ts-ignore
 import { api } from "../../../scripts/api.js";
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { withErrorHandling, createValidationError, createNetworkError } from "../ErrorHandler.js";
 
 const log = createModuleLogger('ImageUploadUtils');
diff --git a/src/utils/ImageUtils.ts b/src/utils/ImageUtils.ts
index d7d0cba..72f6e47 100644
--- a/src/utils/ImageUtils.ts
+++ b/src/utils/ImageUtils.ts
@@ -1,4 +1,4 @@
-import {createModuleLogger} from "./LoggerUtils.js";
+import {createModuleLogger} from "../log_system/log_funcs.js";
 import {withErrorHandling, createValidationError} from "../ErrorHandler.js";
 import { createCanvas } from "./CommonUtils.js";
 import type { Tensor, ImageDataPixel } from '../types';
diff --git a/src/utils/MaskProcessingUtils.ts b/src/utils/MaskProcessingUtils.ts
index 89df6dc..eb929da 100644
--- a/src/utils/MaskProcessingUtils.ts
+++ b/src/utils/MaskProcessingUtils.ts
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { createCanvas } from "./CommonUtils.js";
 import { convertToImage } from "./ImageUtils.js";
 import { withErrorHandling, createValidationError } from "../ErrorHandler.js";
diff --git a/src/utils/NotificationUtils.ts b/src/utils/NotificationUtils.ts
index 5fae050..e4a2395 100644
--- a/src/utils/NotificationUtils.ts
+++ b/src/utils/NotificationUtils.ts
@@ -9,7 +9,7 @@ description: Notification Utilities (Toast System)
 // 1. IMPORTS & DEPENDENCIES
 // ==========================================
 // Comment out or adjust this import if copying to a project that does not use AzLogs
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 
 // ==========================================
 // 2. CONFIGURATION
diff --git a/src/utils/PreviewUtils.ts b/src/utils/PreviewUtils.ts
index 4e9ab7e..6803fa8 100644
--- a/src/utils/PreviewUtils.ts
+++ b/src/utils/PreviewUtils.ts
@@ -1,4 +1,4 @@
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { withErrorHandling, createValidationError } from "../ErrorHandler.js";
 import type { ComfyNode } from '../types';
 
diff --git a/src/utils/ResourceManager.ts b/src/utils/ResourceManager.ts
index e33cbb5..d84013e 100644
--- a/src/utils/ResourceManager.ts
+++ b/src/utils/ResourceManager.ts
@@ -1,6 +1,6 @@
 // @ts-ignore
 import { $el } from "../../../scripts/ui.js";
-import { createModuleLogger } from "./LoggerUtils.js";
+import { createModuleLogger } from "../log_system/log_funcs.js";
 import { withErrorHandling, createValidationError, createNetworkError } from "../ErrorHandler.js";
 
 const log = createModuleLogger('ResourceManager');
diff --git a/src/utils/WebSocketManager.ts b/src/utils/WebSocketManager.ts
index c0bf6c3..206770f 100644
--- a/src/utils/WebSocketManager.ts
+++ b/src/utils/WebSocketManager.ts
@@ -1,4 +1,4 @@
-import {createModuleLogger} from "./LoggerUtils.js";
+import {createModuleLogger} from "../log_system/log_funcs.js";
 import { withErrorHandling, createValidationError, createNetworkError } from "../ErrorHandler.js";
 import type { WebSocketMessage, AckCallbacks } from "../types.js";
 
diff --git a/src/utils/mask_utils.ts b/src/utils/mask_utils.ts
index 4d82f4e..7fc1007 100644
--- a/src/utils/mask_utils.ts
+++ b/src/utils/mask_utils.ts
@@ -1,4 +1,4 @@
-import {createModuleLogger} from "./LoggerUtils.js";
+import {createModuleLogger} from "../log_system/log_funcs.js";
 import { withErrorHandling, createValidationError } from "../ErrorHandler.js";
 import type { Canvas } from '../Canvas.js';
 // @ts-ignore
diff --git a/tests/test_entrypoint_integration.py b/tests/test_entrypoint_integration.py
index fbc9a8f..87ccd95 100644
--- a/tests/test_entrypoint_integration.py
+++ b/tests/test_entrypoint_integration.py
@@ -78,7 +78,7 @@ def _install_runtime_stubs(monkeypatch, tmp_path):
     monkeypatch.setitem(sys.modules, "tqdm", tqdm)
 
     # Avoid creating project log files while the entry point is imported.
-    from python.logger import logger
+    from python.log_system import logger
 
     monkeypatch.setattr(logger, "configure", lambda config: logger)
 
diff --git a/tests/test_log_system.mjs b/tests/test_log_system.mjs
index 7169741..5f06485 100644
--- a/tests/test_log_system.mjs
+++ b/tests/test_log_system.mjs
@@ -1,9 +1,8 @@
 import assert from 'node:assert/strict';
 import test from 'node:test';
 
-import { Logger, LogLevel } from '../js/log_system/logger.js';
+import { Logger, LogLevel, logger as sharedLogger } from '../js/log_system/logger.js';
 import { createModuleLogger, withErrorLogging } from '../js/log_system/log_funcs.js';
-import * as legacyLogger from '../js/logger.js';
 
 
 function silenceConsole() {
@@ -20,7 +19,7 @@ function silenceConsole() {
 }
 
 
-test('canonical logger handles levels, retention, callsites, and compatibility exports', () => {
+test('canonical logger handles levels, retention, and callsites', () => {
   const restoreConsole = silenceConsole();
   try {
     const logger = new Logger();
@@ -52,7 +51,7 @@ test('canonical logger handles levels, retention, callsites, and compatibility e
     logger.setEnabled(false);
     assert.equal(logger.isLevelEnabled('LayerForge.test', LogLevel.ERROR), false);
 
-    assert.equal(legacyLogger.Logger, Logger);
+    assert.equal(sharedLogger.constructor, Logger);
     assert.equal(typeof createModuleLogger('LayerForge.test').error, 'function');
   } finally {
     restoreConsole();