diff --git a/invokeai/backend/stable_diffusion/diffusion/custom_attention.py b/invokeai/backend/stable_diffusion/diffusion/custom_attention.py
index 017ee700d1..625fa41259 100644
--- a/invokeai/backend/stable_diffusion/diffusion/custom_attention.py
+++ b/invokeai/backend/stable_diffusion/diffusion/custom_attention.py
@@ -106,13 +106,13 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
                     dtype=hidden_states.dtype, device=hidden_states.device
                 )
 
-                attn_mask_weight = 0.8
+                attn_mask_weight = 1.0 * ((1 - percent_through) ** 5)
             else:  # self-attention
                 prompt_region_attention_mask = regional_prompt_data.get_self_attn_mask(
                     query_seq_len=query_seq_len,
                     percent_through=percent_through,
                 )
-                attn_mask_weight = 0.5
+                attn_mask_weight = 0.3 * ((1 - percent_through) ** 5)
 
         if attn.group_norm is not None:
             hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
@@ -142,7 +142,9 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
             # (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 and percent_through < 0.5:
+        if regional_prompt_data is not None and percent_through < 0.3:
+            # Don't apply to uncond????
+
             prompt_region_attention_mask = attn.prepare_attention_mask(
                 prompt_region_attention_mask, sequence_length, batch_size
             )
@@ -154,8 +156,8 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
 
             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()
+            m_pos = attn_weight.max(dim=-1, keepdim=True)[0] - attn_weight
+            m_neg = attn_weight - attn_weight.min(dim=-1, keepdim=True)[0]
 
             prompt_region_attention_mask = attn_mask_weight * (
                 m_pos * prompt_region_attention_mask - m_neg * (1.0 - prompt_region_attention_mask)
diff --git a/invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py b/invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py
index 244b8a3276..76b5e83b53 100644
--- a/invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py
+++ b/invokeai/backend/stable_diffusion/diffusion/regional_prompt_data.py
@@ -97,11 +97,11 @@ class RegionalPromptData:
 
             for prompt_idx, embedding_range in enumerate(batch_sample_regions.ranges):
                 batch_sample_query_scores = batch_sample_query_masks[0, prompt_idx, :, :].clone()
+                size = batch_sample_query_scores.sum() / batch_sample_query_scores.numel()
+                size = size.to(dtype=batch_sample_query_scores.dtype)
                 batch_sample_query_mask = batch_sample_query_scores > 0.5
-                batch_sample_query_scores[
-                    batch_sample_query_mask
-                ] = batch_sample_regions.positive_cross_attn_mask_scores[prompt_idx]
-                batch_sample_query_scores[~batch_sample_query_mask] = self._negative_cross_attn_mask_score
+                batch_sample_query_scores[batch_sample_query_mask] = 1.0 * (1.0 - size)
+                batch_sample_query_scores[~batch_sample_query_mask] = 0.0
                 attn_mask[batch_idx, :, embedding_range.start : embedding_range.end] = batch_sample_query_scores
 
         return attn_mask
@@ -133,20 +133,21 @@ class RegionalPromptData:
             batch_sample_query_masks = batch_sample_spatial_masks.view((1, num_prompts, query_seq_len, 1))
 
             for prompt_idx in range(num_prompts):
-                if percent_through > batch_sample_regions.self_attn_adjustment_end_step_percents[prompt_idx]:
-                    continue
                 prompt_query_mask = batch_sample_query_masks[0, prompt_idx, :, 0]  # Shape: (query_seq_len,)
+                size = prompt_query_mask.sum() / prompt_query_mask.numel()
+                size = size.to(dtype=prompt_query_mask.dtype)
                 # Multiply a (1, query_seq_len) mask by a (query_seq_len, 1) mask to get a (query_seq_len,
                 # query_seq_len) mask.
                 # TODO(ryand): Is += really the best option here?
                 attn_mask[batch_idx, :, :] += (
-                    prompt_query_mask.unsqueeze(0)
-                    * prompt_query_mask.unsqueeze(1)
-                    * batch_sample_regions.positive_self_attn_mask_scores[prompt_idx]
+                    prompt_query_mask.unsqueeze(0) * prompt_query_mask.unsqueeze(1) * (1 - size)
                 )
 
-        attn_mask[attn_mask > 0.5] = 1.0
-        attn_mask[attn_mask <= 0.5] = 0.0
+            # if attn_mask[batch_idx].max() < 0.01:
+            #     attn_mask[batch_idx, ...] = 1.0
+
+        # 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.