diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index c0b68fb8c..2f71703b6 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -130,9 +130,22 @@ class LowVramPatch:
         self.set_func = set_func
 
     def __call__(self, weight):
+        # Detect SageAttention and skip conversion for compatibility
+        sage_attention_active = False
+        try:
+            import comfy.cli_args
+            sage_attention_active = hasattr(comfy.cli_args.args, 'use_sage_attention') and \
+                                    comfy.cli_args.args.use_sage_attention
+        except:
+            pass
+
         intermediate_dtype = weight.dtype
-        if self.convert_func is not None:
+
+        # Skip convert_func when SageAttention is active (compatibility mode)
+        if self.convert_func is not None and not sage_attention_active:
             weight = self.convert_func(weight.to(dtype=torch.float32, copy=True), inplace=True)
+        elif sage_attention_active and self.convert_func is not None:
+            logging.debug(f"Skipping convert_func for {self.key} (SageAttention compatibility)")
 
         if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops
             intermediate_dtype = torch.float32
@@ -140,10 +153,16 @@ class LowVramPatch:
             if self.set_func is None:
                 return comfy.float.stochastic_rounding(out, weight.dtype, seed=string_to_seed(self.key))
             else:
-                return self.set_func(out, seed=string_to_seed(self.key), return_weight=True)
+                # Skip set_func when SageAttention is active (compatibility mode)
+                if not sage_attention_active:
+                    return self.set_func(out, seed=string_to_seed(self.key), return_weight=True)
+                else:
+                    return comfy.float.stochastic_rounding(out, weight.dtype, seed=string_to_seed(self.key))
 
         out = comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
-        if self.set_func is not None:
+
+        # Skip set_func when SageAttention is active (compatibility mode)
+        if self.set_func is not None and not sage_attention_active:
             return self.set_func(out, seed=string_to_seed(self.key), return_weight=True).to(dtype=intermediate_dtype)
         else:
             return out