Small refactor.

comfy/model_management.py

@@ -129,7 +129,6 @@ def load_model_gpu(model):
     global current_loaded_model
     global vram_state
     global model_accelerated
-    global xpu_available
 
     if model is current_loaded_model:
         return
@@ -148,17 +147,14 @@ def load_model_gpu(model):
         pass
     elif vram_state == VRAMState.NORMAL_VRAM or vram_state == VRAMState.HIGH_VRAM:
         model_accelerated = False
-        if xpu_available:
-            real_model.to("xpu")
-        else:
-            real_model.cuda()
+        real_model.to(get_torch_device())
     else:
         if vram_state == VRAMState.NO_VRAM:
             device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "256MiB", "cpu": "16GiB"})
         elif vram_state == VRAMState.LOW_VRAM:
             device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "{}MiB".format(total_vram_available_mb), "cpu": "16GiB"})
 
-        accelerate.dispatch_model(real_model, device_map=device_map, main_device="xpu" if xpu_available else "cuda")
+        accelerate.dispatch_model(real_model, device_map=device_map, main_device=get_torch_device())
         model_accelerated = True
     return current_loaded_model
 
@@ -184,12 +180,8 @@ def load_controlnet_gpu(models):
 
 def load_if_low_vram(model):
     global vram_state
-    global xpu_available
     if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM:
-        if xpu_available:
-            return model.to("xpu")
-        else:
-            return model.cuda()
+        return model.to(get_torch_device())
     return model
 
 def unload_if_low_vram(model):