diff --git a/invokeai/backend/stable_diffusion/diffusion/custom_attention.py b/invokeai/backend/stable_diffusion/diffusion/custom_attention.py
index c0ac6d63e1..8dc402928f 100644
--- a/invokeai/backend/stable_diffusion/diffusion/custom_attention.py
+++ b/invokeai/backend/stable_diffusion/diffusion/custom_attention.py
@@ -1,3 +1,4 @@
+import math
 from typing import Optional
 
 import torch
@@ -105,24 +106,13 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
                     dtype=hidden_states.dtype, device=hidden_states.device
                 )
 
+                attn_mask_weight = 1.0
             else:  # self-attention
                 prompt_region_attention_mask = regional_prompt_data.get_self_attn_mask(
                     query_seq_len=query_seq_len,
                     percent_through=percent_through,
                 )
-
-            if attention_mask is None:
-                attention_mask = prompt_region_attention_mask
-            else:
-                attention_mask = prompt_region_attention_mask + attention_mask
-
-        # Start unmodified block from AttnProcessor2_0.
-        # vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+                attn_mask_weight = 0.3
 
         if attn.group_norm is not None:
             hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
@@ -146,6 +136,36 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
         key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
         value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
 
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if regional_prompt_data is not None:
+            prompt_region_attention_mask = attn.prepare_attention_mask(
+                prompt_region_attention_mask, sequence_length, batch_size
+            )
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            prompt_region_attention_mask = prompt_region_attention_mask.view(
+                batch_size, attn.heads, -1, prompt_region_attention_mask.shape[-1]
+            )
+
+            scale_factor = 1 / math.sqrt(query.size(-1))
+            attn_weight = query @ key.transpose(-2, -1) * scale_factor
+            m_pos = attn_weight.max() - attn_weight
+            m_neg = attn_weight - attn_weight.min()
+
+            prompt_region_attention_mask = attn_mask_weight * (
+                m_pos * prompt_region_attention_mask - m_neg * (1.0 - prompt_region_attention_mask)
+            )
+
+        if attention_mask is None:
+            attention_mask = prompt_region_attention_mask
+        else:
+            attention_mask = prompt_region_attention_mask + attention_mask
+
         # the output of sdp = (batch, num_heads, seq_len, head_dim)
         # TODO: add support for attn.scale when we move to Torch 2.1
         hidden_states = F.scaled_dot_product_attention(
diff --git a/invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py b/invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py
index b7b7e61768..244b8a3276 100644
--- a/invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py
+++ b/invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py
@@ -145,9 +145,11 @@ class RegionalPromptData:
                     * batch_sample_regions.positive_self_attn_mask_scores[prompt_idx]
                 )
 
-            attn_mask_min = attn_mask[batch_idx].min()
+        attn_mask[attn_mask > 0.5] = 1.0
+        attn_mask[attn_mask <= 0.5] = 0.0
+        # attn_mask_min = attn_mask[batch_idx].min()
 
-            # Adjust so that the minimum value is 0.0 regardless of whether all pixels are covered or not.
-            if abs(attn_mask_min) > 0.0001:
-                attn_mask[batch_idx] = attn_mask[batch_idx] - attn_mask_min
+        # # Adjust so that the minimum value is 0.0 regardless of whether all pixels are covered or not.
+        # if abs(attn_mask_min) > 0.0001:
+        #     attn_mask[batch_idx] = attn_mask[batch_idx] - attn_mask_min
         return attn_mask