diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py
index 5c0373b74..2433caf8e 100644
--- a/comfy/ldm/modules/diffusionmodules/model.py
+++ b/comfy/ldm/modules/diffusionmodules/model.py
@@ -9,6 +9,11 @@ from comfy import model_management
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
+import inspect
+import os
+if 'vae_attention_counter' not in globals():
+    vae_attention_counter = {}
+
 if model_management.xformers_enabled_vae():
     import xformers
     import xformers.ops
@@ -298,7 +303,49 @@ def vae_attention():
         logging.info("Using xformers attention in VAE")
         return xformers_attention
     elif model_management.pytorch_attention_enabled_vae():
-        logging.info("Using pytorch attention in VAE")
+        #Common causes for duplicate VAE loading:
+        #1.Different precision/dtype loading - ComfyUI might load the same VAE twice with different precisions (fp16/fp32) or for different devices (CPU/GPU)
+        #2.Encoder and Decoder initialization - VAEs have separate encoder and decoder components that might initialize attention separately
+        #3.Model switching or reinitialization - If you have workflows or settings that switch between models during startup
+        #4.Checkpoint with embedded VAE + separate VAE - You might have a checkpoint that includes a VAE, plus a separate VAE file
+        # Global counter for VAE attention initialization
+        def get_detailed_vae_info():
+            frame = inspect.currentframe()
+            try:
+                # Get model info
+                model_name = "Unknown model"
+                for f in inspect.getouterframes(frame):
+                    local_vars = f.frame.f_locals
+                    for var_name in ['model_path', 'vae_path', 'checkpoint_path', 'config_path']:
+                        if var_name in local_vars and local_vars[var_name]:
+                            model_name = os.path.basename(str(local_vars[var_name]))
+                            break
+                    
+                    if 'self' in local_vars:
+                        obj = local_vars['self']
+                        if hasattr(obj, 'model_path') and obj.model_path:
+                            model_name = os.path.basename(obj.model_path)
+                            break
+                    
+                    if model_name != "Unknown model":
+                        break
+                
+                # Use the global counter
+                if model_name not in vae_attention_counter:
+                    vae_attention_counter[model_name] = 0
+                
+                vae_attention_counter[model_name] += 1
+                count = vae_attention_counter[model_name]
+                
+                # Determine component based on call count
+                components = ["encoder", "decoder"]
+                component = components[min(count - 1, len(components) - 1)]
+                
+                return model_name, component, count
+            finally:
+                del frame
+        model_name, component, count = get_detailed_vae_info()
+        logging.info(f"Using pytorch attention in VAE {component} ({count}/2) - Model: {model_name}")
         return pytorch_attention
     else:
         logging.info("Using split attention in VAE")