From fd79579fb73a236c1b08e9f3d389e3c6064fa68d Mon Sep 17 00:00:00 2001
From: Yousef Rafat <81116377+yousef-rafat@users.noreply.github.com>
Date: Wed, 4 Mar 2026 00:58:07 +0200
Subject: [PATCH] spacing

---
 comfy/ops.py | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index 9a658f269..7bc0b40f1 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -438,24 +438,24 @@ class disable_weight_init:
             else:
                 return super().forward(*args, **kwargs)
 
-        class BatchNorm2d(torch.nn.BatchNorm2d, CastWeightBiasOp):
-            def reset_parameters(self):
-                return None
+    class BatchNorm2d(torch.nn.BatchNorm2d, CastWeightBiasOp):
+        def reset_parameters(self):
+            return None
 
-            def forward_comfy_cast_weights(self, input):
-                weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
-                running_mean = self.running_mean.to(weight.dtype) if self.running_mean is not None else None
-                running_var = self.running_var.to(weight.dtype) if self.running_var is not None else None
-                x = torch.nn.functional.batch_norm(input, running_mean, running_var, weight, bias, False, self.momentum, self.eps)
-                uncast_bias_weight(self, weight, bias, offload_stream)
-                return x
+        def forward_comfy_cast_weights(self, input):
+            weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
+            running_mean = self.running_mean.to(weight.dtype) if self.running_mean is not None else None
+            running_var = self.running_var.to(weight.dtype) if self.running_var is not None else None
+            x = torch.nn.functional.batch_norm(input, running_mean, running_var, weight, bias, False, self.momentum, self.eps)
+            uncast_bias_weight(self, weight, bias, offload_stream)
+            return x
 
-            def forward(self, *args, **kwargs):
-                run_every_op()
-                if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
-                    return self.forward_comfy_cast_weights(*args, **kwargs)
-                else:
-                    return super().forward(*args, **kwargs)
+        def forward(self, *args, **kwargs):
+            run_every_op()
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+                return self.forward_comfy_cast_weights(*args, **kwargs)
+            else:
+                return super().forward(*args, **kwargs)
 
     class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
         def reset_parameters(self):