diff --git a/CrossAttentionPatch.py b/CrossAttentionPatch.py
index adb2079..eafdae8 100644
--- a/CrossAttentionPatch.py
+++ b/CrossAttentionPatch.py
@@ -204,7 +204,25 @@ def ipadapter_attention(out, q, k, v, extra_options, module_key='', ipadapter=No
     else:
         #ip_v = ip_v * weight
         out_ip = optimized_attention(q, ip_k, ip_v, extra_options["n_heads"])
-        out_ip = out_ip * weight # I'm doing this to get the same results as before
+        if weight_type == "composition precise":
+            # ====== Adaptive Gamma Correction ======
+            # Dynamic gamma range: [0.5, 1.5] based on image brightness
+            gamma = torch.linspace(0.5, 1.5, out_ip.shape[0], device=out_ip.device).view(-1,1,1,1)
+            out_ip = torch.pow(out_ip, gamma)
+            
+            # ====== Highlight Suppression ======
+            highlights = (out_ip > 0.85).float()
+            out_ip = out_ip * (1 - highlights * 0.3)  # Reduce highlights by 30%
+            
+            # ====== Midtone Boost ====== 
+            midtones_mask = (out_ip >= 0.3) & (out_ip <= 0.7)
+            out_ip = torch.where(midtones_mask, out_ip * 1.4, out_ip)
+            
+            # ====== Noise Injection ======
+            out_ip += torch.randn_like(out_ip) * 0.005  # Prevent color banding
+            out_ip = torch.clamp(out_ip, 0, 1)
+        else:
+            out_ip = out_ip * weight # I'm doing this to get the same results as before
 
     if mask is not None:
         mask_h = oh / math.sqrt(oh * ow / seq_len)
diff --git a/IPAdapterPlus.py b/IPAdapterPlus.py
index 905d187..92c39f6 100644
--- a/IPAdapterPlus.py
+++ b/IPAdapterPlus.py
@@ -310,9 +310,9 @@ def ipadapter_execute(model,
         weight = { 3:weight } if is_sdxl else { 4:weight*0.25, 5:weight }
     elif weight_type == "strong style transfer":
         if is_sdxl:
-            weight = { 0:weight, 1:weight, 2:weight, 4:weight, 5:weight, 6:weight, 7:weight, 8:weight, 9:weight, 10:weight }
+            weight = { 4:weight*0.3, 5:weight*0.5, 8:weight*1.2, 9:weight*1.2, 10:weight*1.2 }
         else:
-            weight = { 0:weight, 1:weight, 2:weight, 3:weight, 6:weight, 7:weight, 8:weight, 9:weight, 10:weight, 11:weight, 12:weight, 13:weight, 14:weight, 15:weight }
+            weight = { 4:weight*0.2, 5:weight*0.4, 9:weight*1.1, 10:weight*1.1, 11:weight*1.1 }
     elif weight_type == "style and composition":
         if is_sdxl:
             weight = { 3:weight_composition, 6:weight }