mm: Fix cast buffers with intel offloading (#12229)

Intel has offloading support but there were some nvidia calls in the
new cast buffer stuff.

Author: rattus128

comfy/model_management.py

@@ -1112,11 +1112,11 @@ def get_cast_buffer(offload_stream, device, size, ref):
             return None
         if cast_buffer is not None and cast_buffer.numel() > 50 * (1024 ** 2):
             #I want my wrongly sized 50MB+ of VRAM back from the caching allocator right now
-            torch.cuda.synchronize()
+            synchronize()
             del STREAM_CAST_BUFFERS[offload_stream]
             del cast_buffer
             #FIXME: This doesn't work in Aimdo because mempool cant clear cache
-            torch.cuda.empty_cache()
+            soft_empty_cache()
         with wf_context:
             cast_buffer = torch.empty((size), dtype=torch.int8, device=device)
             STREAM_CAST_BUFFERS[offload_stream] = cast_buffer
@@ -1132,9 +1132,7 @@ def reset_cast_buffers():
     for offload_stream in STREAM_CAST_BUFFERS:
         offload_stream.synchronize()
     STREAM_CAST_BUFFERS.clear()
-    if comfy.memory_management.aimdo_allocator is None:
-        #Pytorch 2.7 and earlier crashes if you try and empty_cache when mempools exist
-        torch.cuda.empty_cache()
+    soft_empty_cache()
 
 def get_offload_stream(device):
     stream_counter = stream_counters.get(device, 0)
@@ -1284,7 +1282,7 @@ def discard_cuda_async_error():
         a = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
         b = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
         _ = a + b
-        torch.cuda.synchronize()
+        synchronize()
     except torch.AcceleratorError:
         #Dump it! We already know about it from the synchronous return
         pass
@@ -1688,6 +1686,12 @@ def lora_compute_dtype(device):
     LORA_COMPUTE_DTYPES[device] = dtype
     return dtype
 
+def synchronize():
+    if is_intel_xpu():
+        torch.xpu.synchronize()
+    elif torch.cuda.is_available():
+        torch.cuda.synchronize()
+
 def soft_empty_cache(force=False):
     global cpu_state
     if cpu_state == CPUState.MPS: