IPEX fixes

modules/apg/__init__.py

@@ -27,12 +27,15 @@ def project(
     v0: torch.Tensor, # [B, C, H, W]
     v1: torch.Tensor, # [B, C, H, W]
     ):
+    device = v0.device
     dtype = v0.dtype
+    if device.type == "xpu":
+        v0, v1 = v0.to("cpu"), v1.to("cpu")
     v0, v1 = v0.double(), v1.double()
     v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3])
     v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1
     v0_orthogonal = v0 - v0_parallel
-    return v0_parallel.to(dtype), v0_orthogonal.to(dtype)
+    return v0_parallel.to(device, dtype=dtype), v0_orthogonal.to(device, dtype=dtype)
 
 
 def normalized_guidance(

modules/intel/ipex/__init__.py

@@ -199,7 +199,7 @@ def ipex_init(): # pylint: disable=too-many-statements
             torch.cuda.ipc_collect = lambda *args, **kwargs: None
             torch.cuda.utilization = lambda *args, **kwargs: 0
 
-            ipex_hijacks()
+            ipex_hijacks(legacy=legacy)
             try:
                 from .diffusers import ipex_diffusers
                 ipex_diffusers()

modules/intel/ipex/hijacks.py

@@ -293,7 +293,9 @@ def torch_load(f, map_location=None, *args, **kwargs):
 
 
 # Hijack Functions:
-def ipex_hijacks():
+def ipex_hijacks(legacy=True):
+    if legacy:
+        torch.nn.functional.interpolate = interpolate
     torch.tensor = torch_tensor
     torch.Tensor.to = Tensor_to
     torch.Tensor.cuda = Tensor_cuda
@@ -319,7 +321,6 @@ def ipex_hijacks():
     torch.nn.functional.layer_norm = functional_layer_norm
     torch.nn.functional.linear = functional_linear
     torch.nn.functional.conv2d = functional_conv2d
-    torch.nn.functional.interpolate = interpolate
     torch.nn.functional.pad = functional_pad
 
     torch.bmm = torch_bmm