Update model_handler.py

model_handler.py

@@ -2,6 +2,39 @@
 Model handler for WAN-VACE video generation
 """
 import torch
+
+# -----------------------------------------------------------------------------
+# XPU shim for CPU‑only environments
+#
+# Newer versions of `diffusers` attempt to call `torch.xpu.empty_cache()` for
+# Intel GPU support. If the installed PyTorch build does not include XPU
+# support (as is the case on CPU‑only environments), accessing `torch.xpu`
+# results in an AttributeError. To avoid this, we define a dummy `xpu`
+# namespace on the `torch` module when it is missing. This namespace
+# implements the minimal methods used by `diffusers` (`empty_cache`,
+# `is_available`, and `device_count`).
+#
+# Intel’s `intel-extension-for-pytorch` provides XPU support, but even when
+# installed, some CPU builds of PyTorch may not expose `torch.xpu`. This
+# shim ensures that the application runs regardless of whether XPU support is
+# present.
+# -----------------------------------------------------------------------------
+if not hasattr(torch, "xpu"):
+    class _DummyXPU:
+        @staticmethod
+        def empty_cache() -> None:
+            # No‑op: nothing to clear on CPU‑only builds.
+            return None
+        @staticmethod
+        def is_available() -> bool:
+            # Always report unavailable on CPU‑only builds.
+            return False
+        @staticmethod
+        def device_count() -> int:
+            # Zero XPU devices available.
+            return 0
+    # Attach dummy XPU to torch
+    torch.xpu = _DummyXPU()  # type: ignore[attr-defined]
 import time
 from typing import Optional, Tuple, Any
 from transformers import UMT5EncoderModel