diff --git a/modules/intel/ipex/diffusers.py b/modules/intel/ipex/diffusers.py
index 6e56866c1..e03592197 100644
--- a/modules/intel/ipex/diffusers.py
+++ b/modules/intel/ipex/diffusers.py
@@ -1,7 +1,7 @@
 import torch
 import intel_extension_for_pytorch as ipex
 import torch.nn.functional as F
-import diffusers #1.19.3
+import diffusers #0.20.2
 
 Attention = diffusers.models.attention_processor.Attention
 
diff --git a/modules/intel/ipex/hijacks.py b/modules/intel/ipex/hijacks.py
index cb008e099..9e1d9713e 100644
--- a/modules/intel/ipex/hijacks.py
+++ b/modules/intel/ipex/hijacks.py
@@ -90,6 +90,10 @@ def ipex_hijacks():
     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)
+    #Hypernetwork training:
+    CondFunc('torch.nn.modules.linear.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),