mirror of
https://github.com/Azornes/Comfyui-LayerForge.git
synced 2026-03-21 20:52:12 -03:00
Revert "Refactor logging and formatting"
This reverts commit 83ce890ef4.
This commit is contained in:
Dariusz L
@@ -27,46 +27,33 @@ sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'python
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# Importuj logger
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try:
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from python.logger import logger, LogLevel, debug, info, warn, error, exception
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# Konfiguracja loggera dla modułu canvas_node
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logger.set_module_level('canvas_node', LogLevel.INFO) # Domyślnie INFO, można zmienić na DEBUG
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# Włącz logowanie do pliku
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logger.configure({
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'log_to_file': True,
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'log_dir': os.path.join(os.path.dirname(os.path.abspath(__file__)), 'logs')
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})
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# Funkcje pomocnicze dla modułu
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log_debug = lambda *args, **kwargs: debug('canvas_node', *args, **kwargs)
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log_info = lambda *args, **kwargs: info('canvas_node', *args, **kwargs)
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log_warn = lambda *args, **kwargs: warn('canvas_node', *args, **kwargs)
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log_error = lambda *args, **kwargs: error('canvas_node', *args, **kwargs)
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log_exception = lambda *args: exception('canvas_node', *args)
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log_info("Logger initialized for canvas_node")
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except ImportError as e:
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# Fallback jeśli logger nie jest dostępny
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print(f"Warning: Logger module not available: {e}")
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# Proste funkcje zastępcze
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def log_debug(*args):
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print("[DEBUG]", *args)
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def log_info(*args):
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print("[INFO]", *args)
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def log_warn(*args):
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print("[WARN]", *args)
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def log_error(*args):
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print("[ERROR]", *args)
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def log_debug(*args): print("[DEBUG]", *args)
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def log_info(*args): print("[INFO]", *args)
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def log_warn(*args): print("[WARN]", *args)
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def log_error(*args): print("[ERROR]", *args)
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def log_exception(*args):
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print("[ERROR]", *args)
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traceback.print_exc()
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@@ -108,7 +95,7 @@ class BiRefNet(torch.nn.Module):
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class CanvasNode:
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_canvas_data_storage = {}
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_storage_lock = threading.Lock()
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_canvas_cache = {
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'image': None,
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'mask': None,
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@@ -117,7 +104,7 @@ class CanvasNode:
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'persistent_cache': {},
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'last_execution_id': None
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}
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# Simple in-memory storage for canvas data, keyed by prompt_id
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# WebSocket-based storage for canvas data per node
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_websocket_data = {}
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@@ -260,13 +247,11 @@ class CanvasNode:
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# Zmienna blokująca równoczesne wykonania
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_processing_lock = threading.Lock()
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def process_canvas_image(self, trigger, output_switch, cache_enabled, node_id, prompt=None, unique_id=None,
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input_image=None,
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def process_canvas_image(self, trigger, output_switch, cache_enabled, node_id, prompt=None, unique_id=None, input_image=None,
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input_mask=None):
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log_info(
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f"[CanvasNode] 🔍 process_canvas_image wejście – node_id={node_id!r}, unique_id={unique_id!r}, trigger={trigger}, output_switch={output_switch}")
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log_info(f"[CanvasNode] 🔍 process_canvas_image wejście – node_id={node_id!r}, unique_id={unique_id!r}, trigger={trigger}, output_switch={output_switch}")
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try:
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# Sprawdź czy już trwa przetwarzanie
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if not self.__class__._processing_lock.acquire(blocking=False):
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@@ -274,12 +259,11 @@ class CanvasNode:
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# Return cached data if available to avoid breaking the flow
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return self.get_cached_data()
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log_info(
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f"Lock acquired. Starting process_canvas_image for node_id: {node_id} (fallback unique_id: {unique_id})")
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log_info(f"Lock acquired. Starting process_canvas_image for node_id: {node_id} (fallback unique_id: {unique_id})")
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# Use node_id as the primary key, as unique_id is proving unreliable
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storage_key = node_id
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processed_image = None
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processed_mask = None
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@@ -312,6 +296,7 @@ class CanvasNode:
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log_info("Using provided input_mask as fallback")
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processed_mask = input_mask
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# Fallback to default tensors if nothing is loaded
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if processed_image is None:
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log_warn(f"Processed image is still None, creating default blank image.")
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@@ -320,22 +305,22 @@ class CanvasNode:
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log_warn(f"Processed mask is still None, creating default blank mask.")
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processed_mask = torch.zeros((1, 512, 512), dtype=torch.float32)
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if not output_switch:
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log_debug(f"Output switch is OFF, returning empty tuple")
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return (None, None)
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log_debug(
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f"About to return output - Image shape: {processed_image.shape}, Mask shape: {processed_mask.shape}")
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log_debug(f"About to return output - Image shape: {processed_image.shape}, Mask shape: {processed_mask.shape}")
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self.update_persistent_cache()
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log_info(f"Successfully returning processed image and mask")
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return (processed_image, processed_mask)
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except Exception as e:
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log_exception(f"Error in process_canvas_image: {str(e)}")
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return (None, None)
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finally:
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# Zwolnij blokadę
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if self.__class__._processing_lock.locked():
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@@ -388,26 +373,26 @@ class CanvasNode:
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try:
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current_time = time.time()
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cleanup_threshold = 300 # 5 minutes
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nodes_to_remove = []
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for node_id, data in cls._websocket_data.items():
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# Remove invalid node IDs
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if node_id < 0:
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nodes_to_remove.append(node_id)
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continue
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# Remove old data
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if current_time - data.get('timestamp', 0) > cleanup_threshold:
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nodes_to_remove.append(node_id)
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continue
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for node_id in nodes_to_remove:
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del cls._websocket_data[node_id]
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log_debug(f"Cleaned up old WebSocket data for node {node_id}")
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if nodes_to_remove:
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log_info(f"Cleaned up {len(nodes_to_remove)} old WebSocket entries")
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except Exception as e:
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log_error(f"Error during WebSocket cleanup: {str(e)}")
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@@ -417,7 +402,7 @@ class CanvasNode:
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async def handle_canvas_websocket(request):
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ws = web.WebSocketResponse()
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await ws.prepare(request)
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async for msg in ws:
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if msg.type == web.WSMsgType.TEXT:
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try:
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@@ -426,17 +411,17 @@ class CanvasNode:
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if not node_id:
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await ws.send_json({'status': 'error', 'message': 'nodeId is required'})
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continue
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image_data = data.get('image')
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mask_data = data.get('mask')
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with cls._storage_lock:
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cls._canvas_data_storage[node_id] = {
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'image': image_data,
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'mask': mask_data,
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'timestamp': time.time()
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}
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log_info(f"Received canvas data for node {node_id} via WebSocket")
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# Send acknowledgment back to the client
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ack_payload = {
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@@ -446,7 +431,7 @@ class CanvasNode:
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}
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await ws.send_json(ack_payload)
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log_debug(f"Sent ACK for node {node_id}")
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except Exception as e:
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log_error(f"Error processing WebSocket message: {e}")
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await ws.send_json({'status': 'error', 'message': str(e)})
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@@ -694,11 +679,10 @@ class BiRefNetMatting:
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# Zmienna blokująca równoczesne wywołania matting
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_matting_lock = None
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@PromptServer.instance.routes.post("/matting")
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async def matting(request):
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global _matting_lock
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# Sprawdź czy już trwa przetwarzanie
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if _matting_lock is not None:
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log_warn("Matting already in progress, rejecting request")
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@@ -706,10 +690,10 @@ async def matting(request):
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"error": "Another matting operation is in progress",
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"details": "Please wait for the current operation to complete"
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}, status=429) # 429 Too Many Requests
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# Ustaw blokadę
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_matting_lock = True
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try:
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log_info("Received matting request")
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data = await request.json()
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