Fix matting model check and frontend flow

Added proper backend validation for both config.json and model.safetensors to confirm model availability. Updated frontend logic to use /matting/check-model response, preventing unnecessary download notifications.

canvas_node.py

@@ -64,6 +64,8 @@ class BiRefNetConfig(PretrainedConfig):
 
     def __init__(self, bb_pretrained=False, **kwargs):
         self.bb_pretrained = bb_pretrained
+        # Add the missing is_encoder_decoder attribute for compatibility with newer transformers
+        self.is_encoder_decoder = False
         super().__init__(**kwargs)
 
 
@@ -755,16 +757,32 @@ class BiRefNetMatting:
                 full_model_path = os.path.join(self.base_path, "BiRefNet")
                 log_info(f"Loading BiRefNet model from {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=full_model_path
+                        cache_dir=full_model_path,
+                        # 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")
+                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.")
+                        raise RuntimeError(
+                            "Model configuration compatibility issue detected. "
+                            f"Please delete the model cache directory '{full_model_path}' and restart ComfyUI. "
+                            "This will download a fresh copy of the model with the updated configuration."
+                        ) from e
+                    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.")
                     raise RuntimeError(
@@ -894,6 +912,95 @@ class BiRefNetMatting:
 
 _matting_lock = None
 
+@PromptServer.instance.routes.get("/matting/check-model")
+async def check_matting_model(request):
+    """Check if the matting model is available and ready to use"""
+    try:
+        if not TRANSFORMERS_AVAILABLE:
+            return web.json_response({
+                "available": False,
+                "reason": "missing_dependency",
+                "message": "The 'transformers' library is required for the matting feature. Please install it by running: pip install transformers"
+            })
+        
+        # Check if model exists in cache
+        base_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "models")
+        model_path = os.path.join(base_path, "BiRefNet")
+        
+        # Look for the actual BiRefNet model structure
+        model_files_exist = False
+        if os.path.exists(model_path):
+            # BiRefNet model from Hugging Face has a specific structure
+            # Check for subdirectories that indicate the model is downloaded
+            existing_items = os.listdir(model_path) if os.path.isdir(model_path) else []
+            
+            # Look for the model subdirectory (usually named with the model ID)
+            model_subdirs = [d for d in existing_items if os.path.isdir(os.path.join(model_path, d)) and 
+                           (d.startswith("models--") or d == "ZhengPeng7--BiRefNet")]
+            
+            if model_subdirs:
+                # Found model subdirectory, check inside for actual model files
+                for subdir in model_subdirs:
+                    subdir_path = os.path.join(model_path, subdir)
+                    # Navigate through the cache structure
+                    if os.path.exists(os.path.join(subdir_path, "snapshots")):
+                        snapshots_path = os.path.join(subdir_path, "snapshots")
+                        snapshot_dirs = os.listdir(snapshots_path) if os.path.isdir(snapshots_path) else []
+                        
+                        for snapshot in snapshot_dirs:
+                            snapshot_path = os.path.join(snapshots_path, snapshot)
+                            snapshot_files = os.listdir(snapshot_path) if os.path.isdir(snapshot_path) else []
+                            
+                            # Check for essential files - BiRefNet uses model.safetensors
+                            has_config = "config.json" in snapshot_files
+                            has_model = "model.safetensors" in snapshot_files or "pytorch_model.bin" in snapshot_files
+                            has_backbone = "backbone_swin.pth" in snapshot_files or "swin_base_patch4_window12_384_22kto1k.pth" in snapshot_files
+                            has_birefnet = "birefnet.pth" in snapshot_files or any(f.endswith(".pth") for f in snapshot_files)
+                            
+                            # Model is valid if it has config and either model.safetensors or other model files
+                            if has_config and (has_model or has_backbone or has_birefnet):
+                                model_files_exist = True
+                                log_info(f"Found model files in: {snapshot_path} (config: {has_config}, model: {has_model})")
+                                break
+                    
+                    if model_files_exist:
+                        break
+            
+            # Also check if there are .pth files directly in the model_path
+            if not model_files_exist:
+                direct_files = existing_items
+                has_config = "config.json" in direct_files
+                has_model_files = any(f.endswith((".pth", ".bin", ".safetensors")) for f in direct_files)
+                model_files_exist = has_config and has_model_files
+                
+                if model_files_exist:
+                    log_info(f"Found model files directly in: {model_path}")
+        
+        if model_files_exist:
+            # Model files exist, assume it's ready
+            log_info("BiRefNet model files detected")
+            return web.json_response({
+                "available": True,
+                "reason": "ready",
+                "message": "Model is ready to use"
+            })
+        else:
+            log_info(f"BiRefNet model not found in {model_path}")
+            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": model_path
+            })
+            
+    except Exception as e:
+        log_error(f"Error checking matting model: {str(e)}")
+        return web.json_response({
+            "available": False,
+            "reason": "error",
+            "message": f"Error checking model status: {str(e)}"
+        }, status=500)
+
 @PromptServer.instance.routes.post("/matting")
 async def matting(request):
     global _matting_lock