diff --git a/modules/ipex_specific/__init__.py b/modules/ipex_specific/__init__.py
index f7af296c1..ecb72641f 100644
--- a/modules/ipex_specific/__init__.py
+++ b/modules/ipex_specific/__init__.py
@@ -3,8 +3,8 @@ import contextlib
 import torch
 import intel_extension_for_pytorch as ipex
 from modules import shared
-from modules.sd_hijack_utils import CondFunc
 from .diffusers import ipex_diffusers
+from .hijacks import ipex_hijacks
 
 #ControlNet depth_leres++
 class DummyDataParallel(torch.nn.Module):
@@ -13,11 +13,6 @@ class DummyDataParallel(torch.nn.Module):
             shared.log.warning("IPEX backend doesn't support DataParallel on multiple XPU devices")
         return module.to(shared.device)
 
-def ipex_no_cuda(orig_func, *args, **kwargs): # pylint: disable=redefined-outer-name
-    torch.cuda.is_available = lambda: False
-    orig_func(*args, **kwargs)
-    torch.cuda.is_available = torch.xpu.is_available
-
 def return_null_context(*args, **kwargs):
     return contextlib.nullcontext()
 
@@ -89,81 +84,5 @@ def ipex_init():
     torch.cuda.ipc_collect = lambda: None
     torch.cuda.utilization = lambda: 0
 
-    #Libraries that blindly uses cuda:
-    #Adetailer:
-    CondFunc('torch.Tensor.to',
-        lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, shared.device, *args, **kwargs),
-        lambda orig_func, self, device=None, *args, **kwargs: (type(device) is torch.device and device.type == "cuda") or (type(device) is str and "cuda" in device))
-    CondFunc('torch.empty',
-        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=shared.device, **kwargs),
-        lambda orig_func, *args, device=None, **kwargs: (type(device) is torch.device and device.type == "cuda") or (type(device) is str and "cuda" in device))
-    #ControlNet depth_leres
-    CondFunc('torch.load',
-        lambda orig_func, f, map_location=None, *args, **kwargs: orig_func(f, shared.device, *args, **kwargs),
-        lambda orig_func, f, map_location=None, *args, **kwargs: (map_location is None) or (type(map_location) is torch.device and map_location.type == "cuda") or (type(map_location) is str and "cuda" in map_location))
-
-    #Broken functions when torch.cuda.is_available is True:
-    #Pin Memory:
-    CondFunc('torch.utils.data.dataloader._BaseDataLoaderIter.__init__',
-        lambda orig_func, *args, **kwargs: ipex_no_cuda(orig_func, *args, **kwargs),
-        lambda orig_func, *args, **kwargs: True)
-
-    #Functions with dtype errors:
-    CondFunc('torch.nn.modules.GroupNorm.forward',
-        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
-        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
-    #FP32:
-    CondFunc('torch.nn.modules.Linear.forward',
-        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
-        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
-    #Embedding FP32:
-    CondFunc('torch.bmm',
-        lambda orig_func, input, mat2, *args, **kwargs: orig_func(input, mat2.to(input.dtype), *args, **kwargs),
-        lambda orig_func, input, mat2, *args, **kwargs: input.dtype != mat2.dtype)
-    #BF16:
-    CondFunc('torch.nn.functional.layer_norm',
-        lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
-        orig_func(input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs),
-        lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
-        input.dtype != weight.data.dtype and weight is not None)
-    #Embedding BF16
-    CondFunc('torch.cat',
-        lambda orig_func, input, *args, **kwargs: orig_func([input[0].to(input[1].dtype), input[1], input[2].to(input[1].dtype)], *args, **kwargs),
-        lambda orig_func, input, *args, **kwargs: len(input) == 3 and (input[0].dtype != input[1].dtype or input[2].dtype != input[1].dtype))
-    #Diffusers BF16:
-    CondFunc('torch.nn.functional.conv2d',
-        lambda orig_func, input, weight, *args, **kwargs: orig_func(input.to(weight.data.dtype), weight, *args, **kwargs),
-        lambda orig_func, input, weight, *args, **kwargs: input.dtype != weight.data.dtype)
-
-    #Functions that does not work with the XPU:
-    #UniPC:
-    CondFunc('torch.linalg.solve',
-        lambda orig_func, A, B, *args, **kwargs: orig_func(A.to("cpu"), B.to("cpu"), *args, **kwargs).to(shared.device),
-        lambda orig_func, A, B, *args, **kwargs: A.device != torch.device("cpu") or B.device != torch.device("cpu"))
-    #SDE Samplers:
-    CondFunc('torch.Generator',
-        lambda orig_func, device: torch.xpu.Generator(device),
-        lambda orig_func, device: device != torch.device("cpu") and device != "cpu")
-    #Latent antialias:
-    CondFunc('torch.nn.functional.interpolate',
-        lambda orig_func, input, *args, **kwargs: orig_func(input.to("cpu"), *args, **kwargs).to(shared.device),
-        lambda orig_func, input, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False: antialias)
-    #Diffusers Float64 (ARC GPUs doesn't support double or Float64):
-    if not torch.xpu.has_fp64_dtype():
-        CondFunc('torch.from_numpy',
-        lambda orig_func, ndarray: orig_func(ndarray.astype('float32')),
-        lambda orig_func, ndarray: ndarray.dtype == float)
-    #ControlNet and TiledVAE:
-    CondFunc('torch.batch_norm',
-        lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
-        weight if weight is not None else torch.ones(input.size()[1], device=shared.device),
-        bias if bias is not None else torch.zeros(input.size()[1], device=shared.device), *args, **kwargs),
-        lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
-    #ControlNet
-    CondFunc('torch.instance_norm',
-        lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
-        weight if weight is not None else torch.ones(input.size()[1], device=shared.device),
-        bias if bias is not None else torch.zeros(input.size()[1], device=shared.device), *args, **kwargs),
-        lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
-
+    ipex_hijacks()
     ipex_diffusers()
diff --git a/modules/ipex_specific/hijacks.py b/modules/ipex_specific/hijacks.py
new file mode 100644
index 000000000..32e075204
--- /dev/null
+++ b/modules/ipex_specific/hijacks.py
@@ -0,0 +1,91 @@
+import torch
+import intel_extension_for_pytorch as ipex
+from modules import shared
+from modules.sd_hijack_utils import CondFunc
+
+def ipex_no_cuda(orig_func, *args, **kwargs): # pylint: disable=redefined-outer-name
+    torch.cuda.is_available = lambda: False
+    orig_func(*args, **kwargs)
+    torch.cuda.is_available = torch.xpu.is_available
+
+def ipex_hijacks():
+    #Libraries that blindly uses cuda:
+    #Adetailer:
+    CondFunc('torch.Tensor.to',
+        lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, shared.device, *args, **kwargs),
+        lambda orig_func, self, device=None, *args, **kwargs: (type(device) is torch.device and device.type == "cuda") or (type(device) is str and "cuda" in device))
+    CondFunc('torch.empty',
+        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=shared.device, **kwargs),
+        lambda orig_func, *args, device=None, **kwargs: (type(device) is torch.device and device.type == "cuda") or (type(device) is str and "cuda" in device))
+    #ControlNet depth_leres
+    CondFunc('torch.load',
+        lambda orig_func, *args, map_location=None, **kwargs: orig_func(*args, shared.device, **kwargs),
+        lambda orig_func, *args, map_location=None, **kwargs: (map_location is None) or (type(map_location) is torch.device and map_location.type == "cuda") or (type(map_location) is str and "cuda" in map_location))
+    #Diffusers Model CPU Offload:
+    CondFunc('torch.randn',
+        lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=shared.device, **kwargs),
+        lambda orig_func, *args, device=None, **kwargs: (type(device) is torch.device and device.type == "cuda") or (type(device) is str and "cuda" in device))
+
+    #Broken functions when torch.cuda.is_available is True:
+    #Pin Memory:
+    CondFunc('torch.utils.data.dataloader._BaseDataLoaderIter.__init__',
+        lambda orig_func, *args, **kwargs: ipex_no_cuda(orig_func, *args, **kwargs),
+        lambda orig_func, *args, **kwargs: True)
+
+    #Functions with dtype errors:
+    CondFunc('torch.nn.modules.GroupNorm.forward',
+        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
+        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
+    #FP32:
+    CondFunc('torch.nn.modules.Linear.forward',
+        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
+        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
+    #Embedding FP32:
+    CondFunc('torch.bmm',
+        lambda orig_func, input, mat2, *args, **kwargs: orig_func(input, mat2.to(input.dtype), *args, **kwargs),
+        lambda orig_func, input, mat2, *args, **kwargs: input.dtype != mat2.dtype)
+    #BF16:
+    CondFunc('torch.nn.functional.layer_norm',
+        lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
+        orig_func(input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs),
+        lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
+        input.dtype != weight.data.dtype and weight is not None)
+    #Embedding BF16
+    CondFunc('torch.cat',
+        lambda orig_func, input, *args, **kwargs: orig_func([input[0].to(input[1].dtype), input[1], input[2].to(input[1].dtype)], *args, **kwargs),
+        lambda orig_func, input, *args, **kwargs: len(input) == 3 and (input[0].dtype != input[1].dtype or input[2].dtype != input[1].dtype))
+    #Diffusers BF16:
+    CondFunc('torch.nn.functional.conv2d',
+        lambda orig_func, input, weight, *args, **kwargs: orig_func(input.to(weight.data.dtype), weight, *args, **kwargs),
+        lambda orig_func, input, weight, *args, **kwargs: input.dtype != weight.data.dtype)
+
+    #Functions that does not work with the XPU:
+    #UniPC:
+    CondFunc('torch.linalg.solve',
+        lambda orig_func, A, B, *args, **kwargs: orig_func(A.to("cpu"), B.to("cpu"), *args, **kwargs).to(shared.device),
+        lambda orig_func, A, B, *args, **kwargs: A.device != torch.device("cpu") or B.device != torch.device("cpu"))
+    #SDE Samplers:
+    CondFunc('torch.Generator',
+        lambda orig_func, device: torch.xpu.Generator(device),
+        lambda orig_func, device: device != torch.device("cpu") and device != "cpu")
+    #Latent antialias:
+    CondFunc('torch.nn.functional.interpolate',
+        lambda orig_func, input, *args, **kwargs: orig_func(input.to("cpu"), *args, **kwargs).to(shared.device),
+        lambda orig_func, input, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False: antialias)
+    #Diffusers Float64 (ARC GPUs doesn't support double or Float64):
+    if not torch.xpu.has_fp64_dtype():
+        CondFunc('torch.from_numpy',
+        lambda orig_func, ndarray: orig_func(ndarray.astype('float32')),
+        lambda orig_func, ndarray: ndarray.dtype == float)
+    #ControlNet and TiledVAE:
+    CondFunc('torch.batch_norm',
+        lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
+        weight if weight is not None else torch.ones(input.size()[1], device=shared.device),
+        bias if bias is not None else torch.zeros(input.size()[1], device=shared.device), *args, **kwargs),
+        lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
+    #ControlNet
+    CondFunc('torch.instance_norm',
+        lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
+        weight if weight is not None else torch.ones(input.size()[1], device=shared.device),
+        bias if bias is not None else torch.zeros(input.size()[1], device=shared.device), *args, **kwargs),
+        lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))