diff --git a/invokeai/backend/stable_diffusion/diffusion/regional_prompt_attention.py b/invokeai/backend/stable_diffusion/diffusion/regional_prompt_attention.py
index fe9bdbc951..15da2c3151 100644
--- a/invokeai/backend/stable_diffusion/diffusion/regional_prompt_attention.py
+++ b/invokeai/backend/stable_diffusion/diffusion/regional_prompt_attention.py
@@ -32,15 +32,16 @@ class RegionalPromptData:
 
         Args:
             masks (list[torch.Tensor]): masks[i] contains the regions masks for the i'th sample in the batch.
-                The shape of masks[i] is (num_prompts, height, width), and dtype=bool. The mask is set to True in
-                regions where the prompt should be applied, and 0.0 elsewhere.
+                The shape of masks[i] is (num_prompts, height, width). The mask is set to 1.0 in regions where the
+                prompt should be applied, and 0.0 elsewhere.
 
             embedding_ranges (list[list[Range]]): embedding_ranges[i][j] contains the embedding range for the j'th
                 prompt in the i'th batch sample. masks[i][j, ...] is applied to the embeddings in:
                 encoder_hidden_states[i, embedding_ranges[j].start:embedding_ranges[j].end, :].
 
             key_seq_len (int): The sequence length of the expected prompt embeddings (which act as the key in the
-                cross-attention layers).
+                cross-attention layers). This is most likely equal to the max embedding range end, but we pass it
+                explicitly to be sure.
         """
         attn_masks_by_seq_len = {}
 
diff --git a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
index f22cf1375e..327161b736 100644
--- a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
+++ b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
@@ -121,13 +121,14 @@ class RegionalTextConditioningInfo:
             )
 
             if is_sdxl:
-                # We just use the the first SDXLConditioningInfo's pooled_embeds and add_time_ids.
-                # TODO(ryand): Think about this some more. If we can't use the pooled_embeds and add_time_ids from all
-                # the conditioning info, then we shouldn't allow it to be passed in.
-                # How does Compel handle this? Options that come to mind:
-                # - Blend the pooled_embeds and add_time_ids from all of the text embeddings.
-                # - Use the pooled_embeds and add_time_ids from the text embedding with the largest mask area, since
-                #   this is likely the global prompt.
+                # HACK(ryand): We just use the the first SDXLConditioningInfo's pooled_embeds and add_time_ids. This is
+                # fundamentally an interface issue, as the SDXL Compel nodes are not designed to be used in the way that
+                # we use them for regional prompting. Ideally, the DenoiseLatents invocation should accept a single
+                # pooled_embeds tensor and a list of standard text embeds with region masks. This change would be a
+                # pretty major breaking change to a popular node, so for now we use this hack.
+                #
+                # An improvement could be to use the pooled embeds from the prompt with the largest region, as this is
+                # most likely to be a global prompt.
                 if pooled_embedding is None:
                     pooled_embedding = text_embedding_info.pooled_embeds
                 if add_time_ids is None:
@@ -433,28 +434,6 @@ class InvokeAIDiffuserComponent:
 
         return torch.cat([unconditioning, conditioning]), encoder_attention_mask
 
-    def _preprocess_regional_prompt_mask(
-        self, mask: Optional[torch.Tensor], target_height: int, target_width: int
-    ) -> torch.Tensor:
-        """Preprocess a regional prompt mask to match the target height and width.
-
-        If mask is None, returns a mask of all ones with the target height and width.
-        If mask is not None, resizes the mask to the target height and width using nearest neighbor interpolation.
-
-        Returns:
-            torch.Tensor: The processed mask. dtype: torch.bool, shape: (1, 1, target_height, target_width).
-        """
-        if mask is None:
-            return torch.ones((1, 1, target_height, target_width), dtype=torch.bool)
-
-        tf = torchvision.transforms.Resize(
-            (target_height, target_width), interpolation=torchvision.transforms.InterpolationMode.NEAREST
-        )
-        mask = mask.unsqueeze(0)  # Shape: (1, h, w) -> (1, 1, h, w)
-        mask = tf(mask)
-
-        return mask
-
     def _apply_standard_conditioning(
         self,
         x,
@@ -470,8 +449,12 @@ class InvokeAIDiffuserComponent:
         x_twice = torch.cat([x] * 2)
         sigma_twice = torch.cat([sigma] * 2)
 
-        # HACK(ryand): We should only have to call _prepare_text_embeddings once, but we currently re-run it on every
-        # denoising step.
+        # TODO(ryand): We currently call from_text_conditioning_and_masks(...) and from_masks_and_ranges(...) for every
+        # denoising step. The text conditionings and masks are not changing from step-to-step, so this really only needs
+        # to be done once. While this seems painfully inefficient, the time spent is typically negligible compared to
+        # the forward inference pass of the UNet. The main reason that this hasn't been moved up to eliminate redundancy
+        # is that it is slightly awkward to handle both standard conditioning and sequential conditioning further up the
+        # stack.
         cross_attention_kwargs = None
         _, _, h, w = x.shape
         cond_text = RegionalTextConditioningInfo.from_text_conditioning_and_masks(