fix: MPS memory reporting and cache synchronization

- Split MPS from CPU in get_total_memory() and get_free_memory() to use
  torch.mps APIs (recommended_max_memory, driver_allocated_memory,
  current_allocated_memory) instead of relying solely on psutil
- Add torch.mps.synchronize() to synchronize() and soft_empty_cache()
- Add inter-node MPS cache flush when torch free memory is low
- Move MPS SHARED vram assignment before --lowvram/--novram checks so
  users can override it for large models

comfy/model_management.py

@@ -211,9 +211,15 @@ def get_total_memory(dev=None, torch_total_too=False):
     if dev is None:
         dev = get_torch_device()
 
-    if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
+    if hasattr(dev, 'type') and dev.type == 'cpu':
         mem_total = psutil.virtual_memory().total
         mem_total_torch = mem_total
+    elif hasattr(dev, 'type') and dev.type == 'mps':
+        mem_total = psutil.virtual_memory().total
+        try:
+            mem_total_torch = torch.mps.recommended_max_memory()
+        except Exception:
+            mem_total_torch = mem_total
     else:
         if directml_enabled:
             mem_total = 1024 * 1024 * 1024 #TODO
@@ -460,7 +466,10 @@ if cpu_state != CPUState.GPU:
     vram_state = VRAMState.DISABLED
 
 if cpu_state == CPUState.MPS:
-    vram_state = VRAMState.SHARED
+    if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
+        vram_state = set_vram_to
+    else:
+        vram_state = VRAMState.SHARED
 
 logging.info(f"Set vram state to: {vram_state.name}")
 
@@ -1485,9 +1494,18 @@ def get_free_memory(dev=None, torch_free_too=False):
     if dev is None:
         dev = get_torch_device()
 
-    if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
+    if hasattr(dev, 'type') and dev.type == 'cpu':
         mem_free_total = psutil.virtual_memory().available
         mem_free_torch = mem_free_total
+    elif hasattr(dev, 'type') and dev.type == 'mps':
+        try:
+            driver_mem = torch.mps.driver_allocated_memory()
+            current_mem = torch.mps.current_allocated_memory()
+            mem_free_torch = max(driver_mem - current_mem, 0)
+            mem_free_total = psutil.virtual_memory().available + mem_free_torch
+        except Exception:
+            mem_free_total = psutil.virtual_memory().available
+            mem_free_torch = 0
     else:
         if directml_enabled:
             mem_free_total = 1024 * 1024 * 1024 #TODO
@@ -1756,7 +1774,9 @@ def lora_compute_dtype(device):
 def synchronize():
     if cpu_mode():
         return
-    if is_intel_xpu():
+    if mps_mode():
+        torch.mps.synchronize()
+    elif is_intel_xpu():
         torch.xpu.synchronize()
     elif torch.cuda.is_available():
         torch.cuda.synchronize()
@@ -1766,6 +1786,7 @@ def soft_empty_cache(force=False):
         return
     global cpu_state
     if cpu_state == CPUState.MPS:
+        torch.mps.synchronize()
         torch.mps.empty_cache()
     elif is_intel_xpu():
         torch.xpu.empty_cache()

execution.py

@@ -780,6 +780,11 @@ class PromptExecutor:
                     if self.cache_type == CacheType.RAM_PRESSURE:
                         comfy.model_management.free_memory(0, None, pins_required=ram_headroom, ram_required=ram_headroom)
                         comfy.memory_management.extra_ram_release(ram_headroom)
+                    elif comfy.model_management.mps_mode():
+                        mem_free_total, mem_free_torch = comfy.model_management.get_free_memory(
+                            comfy.model_management.get_torch_device(), torch_free_too=True)
+                        if mem_free_torch < mem_free_total * 0.25:
+                            comfy.model_management.soft_empty_cache()
                 else:
                     # Only execute when the while-loop ends without break
                     # Send cached UI for intermediate output nodes that weren't executed