diff --git a/ldm/invoke/generator/img2img.py b/ldm/invoke/generator/img2img.py
index 6fa0d0c6dd..cfe3ff99bc 100644
--- a/ldm/invoke/generator/img2img.py
+++ b/ldm/invoke/generator/img2img.py
@@ -34,7 +34,7 @@ class Img2Img(Generator):
 
         t_enc = int(strength * steps)
         uc, c, ec, edit_opcodes   = conditioning
-        extra_conditioning_info = InvokeAIDiffuserComponent.StructuredConditioning(edited_conditioning=ec, edit_opcodes=edit_opcodes)
+        extra_conditioning_info = InvokeAIDiffuserComponent.ExtraConditioningInfo(edited_conditioning=ec, edit_opcodes=edit_opcodes)
 
         def make_image(x_T):
             # encode (scaled latent)
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index 657cccc592..7e739860c3 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -22,7 +22,7 @@ class Txt2Img(Generator):
         """
         self.perlin = perlin
         uc, c, ec, edit_opcodes   = conditioning
-        extra_conditioning_info = InvokeAIDiffuserComponent.StructuredConditioning(edited_conditioning=ec, edit_opcodes=edit_opcodes)
+        extra_conditioning_info = InvokeAIDiffuserComponent.ExtraConditioningInfo(edited_conditioning=ec, edit_opcodes=edit_opcodes)
 
         @torch.no_grad()
         def make_image(x_T):
diff --git a/ldm/invoke/generator/txt2img2img.py b/ldm/invoke/generator/txt2img2img.py
index 64d0468418..2d67a44346 100644
--- a/ldm/invoke/generator/txt2img2img.py
+++ b/ldm/invoke/generator/txt2img2img.py
@@ -24,7 +24,7 @@ class Txt2Img2Img(Generator):
         kwargs are 'width' and 'height'
         """
         uc, c, ec, edit_opcodes = conditioning
-        extra_conditioning_info = InvokeAIDiffuserComponent.StructuredConditioning(edited_conditioning=ec, edit_opcodes=edit_opcodes)
+        extra_conditioning_info = InvokeAIDiffuserComponent.ExtraConditioningInfo(edited_conditioning=ec, edit_opcodes=edit_opcodes)
 
         @torch.no_grad()
         def make_image(x_T):           
diff --git a/ldm/models/diffusion/shared_invokeai_diffusion.py b/ldm/models/diffusion/shared_invokeai_diffusion.py
index 0a613091d5..290925fc8c 100644
--- a/ldm/models/diffusion/shared_invokeai_diffusion.py
+++ b/ldm/models/diffusion/shared_invokeai_diffusion.py
@@ -6,23 +6,6 @@ import torch
 
 
 class InvokeAIDiffuserComponent:
-
-    class StructuredConditioning:
-        def __init__(self, edited_conditioning: torch.Tensor = None, edit_opcodes: list[tuple] = None):
-            """
-            :param edited_conditioning: if doing cross-attention control, the edited conditioning (1 x 77 x 768)
-            :param edit_opcodes: if doing cross-attention control, opcodes from a SequenceMatcher describing how to map original conditioning tokens to edited conditioning tokens
-            """
-            # TODO migrate conditioning and unconditioning here, too
-            #self.conditioning = conditioning
-            #self.unconditioning = unconditioning
-            self.edited_conditioning = edited_conditioning
-            self.edit_opcodes = edit_opcodes
-
-        @property
-        def wants_cross_attention_control(self):
-            return self.edited_conditioning is not None
-
     '''
     The aim of this component is to provide a single place for code that can be applied identically to
     all InvokeAI diffusion procedures.
@@ -31,6 +14,20 @@ class InvokeAIDiffuserComponent:
     * Cross Attention Control ("prompt2prompt")
     '''
 
+
+    class ExtraConditioningInfo:
+        def __init__(self, edited_conditioning: torch.Tensor = None, edit_opcodes: list[tuple] = None):
+            """
+            :param edited_conditioning: if doing cross-attention control, the edited conditioning (1 x 77 x 768)
+            :param edit_opcodes: if doing cross-attention control, opcodes from a SequenceMatcher describing how to map original conditioning tokens to edited conditioning tokens
+            """
+            self.edited_conditioning = edited_conditioning
+            self.edit_opcodes = edit_opcodes
+
+        @property
+        def wants_cross_attention_control(self):
+            return self.edited_conditioning is not None
+
     def __init__(self, model, model_forward_callback: Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]):
         """
         :param model: the unet model to pass through to cross attention control
@@ -40,7 +37,7 @@ class InvokeAIDiffuserComponent:
         self.model_forward_callback = model_forward_callback
 
 
-    def setup_cross_attention_control(self, conditioning: StructuredConditioning):
+    def setup_cross_attention_control(self, conditioning: ExtraConditioningInfo):
         self.conditioning = conditioning
         CrossAttentionControl.setup_cross_attention_control(self.model, conditioning.edited_conditioning, conditioning.edit_opcodes)