diff --git a/invokeai/backend/flux/extensions/regional_prompting_extension.py b/invokeai/backend/flux/extensions/regional_prompting_extension.py
index 21b2279b14..43a3cc4395 100644
--- a/invokeai/backend/flux/extensions/regional_prompting_extension.py
+++ b/invokeai/backend/flux/extensions/regional_prompting_extension.py
@@ -10,7 +10,10 @@ from invokeai.backend.util.mask import to_standard_float_mask
 
 
 class RegionalPromptingExtension:
-    """A class for managing regional prompting with FLUX."""
+    """A class for managing regional prompting with FLUX.
+
+    Implementation inspired by: https://arxiv.org/pdf/2411.02395
+    """
 
     def __init__(self, regional_text_conditioning: FluxRegionalTextConditioning):
         self.regional_text_conditioning = regional_text_conditioning
@@ -19,6 +22,51 @@ class RegionalPromptingExtension:
     def from_text_conditioning(cls, text_conditioning: list[FluxTextConditioning]):
         return cls(regional_text_conditioning=cls._concat_regional_text_conditioning(text_conditioning))
 
+    def _prepare_attn_mask(self) -> torch.Tensor:
+        device = self.regional_text_conditioning.image_masks[0].device
+        # img_seq_len = latent_height * latent_width
+        img_seq_len = (
+            self.regional_text_conditioning.image_masks.shape[-1]
+            * self.regional_text_conditioning.image_masks.shape[-2]
+        )
+        txt_seq_len = self.regional_text_conditioning.t5_embeddings.shape[1]
+
+        # In the double stream attention blocks, the txt seq and img seq are concatenated and then attention is applied.
+        # Concatenation happens in the following order: [txt_seq, img_seq].
+        # There are 4 portions of the attention mask to consider as we prepare it:
+        # 1. txt attends to itself
+        # 2. txt attends to corresponding regional img
+        # 3. regional img attends to corresponding txt
+        # 4. regional img attends to itself
+
+        # Initialize empty attention mask.
+        regional_attention_mask = torch.zeros(
+            (txt_seq_len + img_seq_len, txt_seq_len + img_seq_len), device=device, dtype=torch.bool
+        )
+
+        for i in range(len(self.regional_text_conditioning.t5_embeddings)):
+            image_mask = self.regional_text_conditioning.image_masks[i].flatten()
+            t5_embedding_range = self.regional_text_conditioning.t5_embedding_ranges[i]
+
+            # 1. txt attends to itself
+            regional_attention_mask[
+                t5_embedding_range.start : t5_embedding_range.end, t5_embedding_range.start : t5_embedding_range.end
+            ] = True
+
+            # 2. txt attends to corresponding regional img
+            # TODO(ryand): Make sure that broadcasting works as expected.
+            regional_attention_mask[t5_embedding_range.start : t5_embedding_range.end, txt_seq_len:] = image_mask
+
+            # 3. regional img attends to corresponding txt
+            # TODO(ryand): Make sure that broadcasting works as expected.
+            regional_attention_mask[txt_seq_len:, t5_embedding_range.start : t5_embedding_range.end] = image_mask
+
+            # 4. regional img attends to itself
+            # TODO(ryand): Make sure that broadcasting works as expected.
+            regional_attention_mask[txt_seq_len:, txt_seq_len:] = image_mask @ image_mask.T
+
+        return regional_attention_mask
+
     @classmethod
     def _concat_regional_text_conditioning(
         cls,