diff --git a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
index 21fb8d5780..80b671df65 100644
--- a/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
+++ b/invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
@@ -172,40 +172,46 @@ class TextConditioningData:
                 regional_prompt_data=RegionalPromptData(regions=tmp_regions, device=device, dtype=dtype),
             )
 
-    def _concat_conditionings_for_batch(self, conditionings):
-        def _pad_zeros(t: torch.Tensor, pad_shape: tuple, dim: int):
-            return torch.cat([t, torch.zeros(pad_shape, device=t.device, dtype=t.dtype)], dim=dim)
+    @staticmethod
+    def _pad_zeros(t: torch.Tensor, pad_shape: tuple, dim: int):
+        return torch.cat([t, torch.zeros(pad_shape, device=t.device, dtype=t.dtype)], dim=dim)
 
-        def _pad_conditioning(cond, target_len, encoder_attention_mask):
-            conditioning_attention_mask = torch.ones(
-                (cond.shape[0], cond.shape[1]), device=cond.device, dtype=cond.dtype
+    @classmethod
+    def _pad_conditioning(
+        cls,
+        cond: torch.Tensor,
+        target_len: int,
+        encoder_attention_mask: Optional[torch.Tensor],
+    ):
+        conditioning_attention_mask = torch.ones((cond.shape[0], cond.shape[1]), device=cond.device, dtype=cond.dtype)
+
+        if cond.shape[1] < target_len:
+            conditioning_attention_mask = cls._pad_zeros(
+                conditioning_attention_mask,
+                pad_shape=(cond.shape[0], target_len - cond.shape[1]),
+                dim=1,
             )
 
-            if cond.shape[1] < max_len:
-                conditioning_attention_mask = _pad_zeros(
-                    conditioning_attention_mask,
-                    pad_shape=(cond.shape[0], max_len - cond.shape[1]),
-                    dim=1,
-                )
+            cond = cls._pad_zeros(
+                cond,
+                pad_shape=(cond.shape[0], target_len - cond.shape[1], cond.shape[2]),
+                dim=1,
+            )
 
-                cond = _pad_zeros(
-                    cond,
-                    pad_shape=(cond.shape[0], max_len - cond.shape[1], cond.shape[2]),
-                    dim=1,
-                )
+        if encoder_attention_mask is None:
+            encoder_attention_mask = conditioning_attention_mask
+        else:
+            encoder_attention_mask = torch.cat([encoder_attention_mask, conditioning_attention_mask])
 
-            if encoder_attention_mask is None:
-                encoder_attention_mask = conditioning_attention_mask
-            else:
-                encoder_attention_mask = torch.cat([encoder_attention_mask, conditioning_attention_mask])
-
-            return cond, encoder_attention_mask
+        return cond, encoder_attention_mask
 
+    @classmethod
+    def _concat_conditionings_for_batch(cls, conditionings: List[torch.Tensor]):
         encoder_attention_mask = None
         max_len = max([c.shape[1] for c in conditionings])
         if any(c.shape[1] != max_len for c in conditionings):
             for i in range(len(conditionings)):
-                conditionings[i], encoder_attention_mask = _pad_conditioning(
+                conditionings[i], encoder_attention_mask = cls._pad_conditioning(
                     conditionings[i], max_len, encoder_attention_mask
                 )