Add Intel GPU (xpu) support via PyTorch's native xpu backend

kokoro/pipeline.py

@@ -76,9 +76,9 @@ def __init__(
             lang_code: Language code for G2P processing
             model: KModel instance, True to create new model, False for no model
             trf: Whether to use transformer-based G2P
-            device: Override default device selection ('cuda' or 'cpu', or None for auto)
-                   If None, will auto-select cuda if available
-                   If 'cuda' and not available, will explicitly raise an error
+            device: Override default device selection ('cuda', 'xpu', 'mps' or 'cpu', or None for auto)
+                   If None, will auto-select cuda, then xpu, then mps if available
+                   If an accelerator is explicitly requested and not available, will explicitly raise an error
         """
         if repo_id is None:
             repo_id = 'hexgrad/Kokoro-82M'
@@ -92,15 +92,23 @@ def __init__(
         if isinstance(model, KModel):
             self.model = model
         elif model:
+            xpu_available = hasattr(torch, 'xpu') and torch.xpu.is_available()
             if device == 'cuda' and not torch.cuda.is_available():
                 raise RuntimeError("CUDA requested but not available")
+            if device == 'xpu' and not xpu_available:
+                raise RuntimeError("XPU requested but not available")
             if device == 'mps' and not torch.backends.mps.is_available():
                 raise RuntimeError("MPS requested but not available")
             if device == 'mps' and os.environ.get('PYTORCH_ENABLE_MPS_FALLBACK') != '1':
                 raise RuntimeError("MPS requested but fallback not enabled")
             if device is None:
                 if torch.cuda.is_available():
                     device = 'cuda'
+                elif xpu_available:
+                    # Intel GPU (Arc / integrated Xe/Arc, e.g. Meteor Lake+) via
+                    # PyTorch's native xpu backend (built in since torch 2.5,
+                    # no intel-extension-for-pytorch required).
+                    device = 'xpu'
                 elif os.environ.get('PYTORCH_ENABLE_MPS_FALLBACK') == '1' and torch.backends.mps.is_available():
                     device = 'mps'
                 else:
@@ -109,8 +117,11 @@ def __init__(
                 self.model = KModel(repo_id=repo_id).to(device).eval()
             except RuntimeError as e:
                 if device == 'cuda':
-                    raise RuntimeError(f"""Failed to initialize model on CUDA: {e}. 
+                    raise RuntimeError(f"""Failed to initialize model on CUDA: {e}.
                                        Try setting device='cpu' or check CUDA installation.""")
+                if device == 'xpu':
+                    raise RuntimeError(f"""Failed to initialize model on XPU: {e}.
+                                       Try setting device='cpu' or check Intel GPU driver installation.""")
                 raise
         self.voices = {}
         if lang_code in 'ab':

pyproject.toml

@@ -26,6 +26,25 @@ dependencies = [
     "transformers"
 ]
 
+[project.optional-dependencies]
+# See NOTES.md / draft-pr.md for why this is `cpu`+`xpu` extras rather than
+# a single opt-in `xpu` extra layered on the bare `torch` above: uv's
+# resolver treats an unconstrained `torch` requirement as satisfiable by
+# *any* source that matches, including the xpu-indexed wheel once one is
+# introduced anywhere in the graph -- it collapses to one universal answer
+# instead of forking per-extra, so a plain `uv sync` silently installed
+# `torch==2.13.0+xpu` even with no extra requested (reproduced empirically,
+# see draft-pr.md). Declaring `cpu` as an explicit, mutually-exclusive
+# alternative (uv's own documented pattern for cuda/rocm/xpu) is what
+# actually keeps the two forks separate.
+cpu = [
+    "torch>=2.5"
+]
+xpu = [
+    "torch>=2.5",
+    "triton-xpu; sys_platform == 'linux'"
+]
+
 [project.scripts]
 kokoro = "kokoro.__main__:main"
 
@@ -36,3 +55,30 @@ only-packages = true
 [project.urls]
 Homepage = "https://github.com/hexgrad/kokoro"
 Repository = "https://github.com/hexgrad/kokoro"
+
+[tool.uv]
+conflicts = [
+    [
+        { extra = "cpu" },
+        { extra = "xpu" },
+    ],
+]
+
+[tool.uv.sources]
+torch = [
+    { index = "pytorch-cpu", extra = "cpu" },
+    { index = "pytorch-xpu", extra = "xpu" },
+]
+triton-xpu = [
+    { index = "pytorch-xpu", extra = "xpu" }
+]
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
+[[tool.uv.index]]
+name = "pytorch-xpu"
+url = "https://download.pytorch.org/whl/xpu"
+explicit = true