diff --git a/networks/resize_lora.py b/networks/resize_lora.py
index de405613..eb745333 100644
--- a/networks/resize_lora.py
+++ b/networks/resize_lora.py
@@ -59,14 +59,55 @@ def index_sv_fro(S, target):
   return index
 
 
+def rank_resize(S, rank, dynamic_method, dynamic_param, scale=1):
+    param_dict = {}
+
+    if dynamic_method=="sv_ratio":
+        # Calculate new dim and alpha based off ratio
+        max_sv = S[0]
+        min_sv = max_sv/dynamic_param
+        new_rank = max(torch.sum(S > min_sv).item(),1)
+        new_alpha = float(scale*new_rank)
+
+    elif dynamic_method=="sv_cumulative":
+        # Calculate new dim and alpha based off cumulative sum
+        new_rank = index_sv_cumulative(S, dynamic_param)
+        new_rank = max(new_rank, 1)
+        new_alpha = float(scale*new_rank)
+
+    elif dynamic_method=="sv_fro":
+        # Calculate new dim and alpha based off sqrt sum of squares
+        new_rank = index_sv_fro(S, dynamic_param)
+        new_rank = min(max(new_rank, 1), len(S)-1)
+        new_alpha = float(scale*new_rank)
+    else:
+        new_rank = rank
+        new_alpha = float(scale*new_rank)
+
+    # Calculate resize info
+    s_sum = torch.sum(torch.abs(S))
+    s_rank = torch.sum(torch.abs(S[:new_rank]))
+    
+    S_squared = S.pow(2)
+    s_fro = torch.sqrt(torch.sum(S_squared))
+    s_red_fro = torch.sqrt(torch.sum(S_squared[:new_rank]))
+    fro_percent = float(s_red_fro/s_fro)
+
+    param_dict["new_rank"] = new_rank
+    param_dict["new_alpha"] = new_alpha
+    param_dict["sum_retained"] = (s_rank)/s_sum
+    param_dict["fro_retained"] = fro_percent
+    param_dict["max_ratio"] = S[0]/S[new_rank]
+
+    return param_dict
+
+
 def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dynamic_param, verbose):
   network_alpha = None
   network_dim = None
   verbose_str = "\n"
   fro_list = []
 
-  CLAMP_QUANTILE = 0.99
-
   # Extract loaded lora dim and alpha
   for key, value in lora_sd.items():
     if network_alpha is None and 'alpha' in key:
@@ -82,9 +123,6 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dyn
 
   if dynamic_method:
     print(f"Dynamically determining new alphas and dims based off {dynamic_method}: {dynamic_param}")
-  else:
-    new_alpha = float(scale*new_rank)  # calculate new alpha from scale
-    print(f"old dimension: {network_dim}, old alpha: {network_alpha}, new dim: {new_rank}, new alpha: {new_alpha}")
 
   lora_down_weight = None
   lora_up_weight = None
@@ -93,7 +131,6 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dyn
   block_down_name = None
   block_up_name = None
 
-  print("resizing lora...")
   with torch.no_grad():
     for key, value in tqdm(lora_sd.items()):
       if 'lora_down' in key:
@@ -122,39 +159,21 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dyn
 
         U, S, Vh = torch.linalg.svd(full_weight_matrix)
 
-        if dynamic_method=="sv_ratio":
-          # Calculate new dim and alpha based off ratio
-          max_sv = S[0]
-          min_sv = max_sv/dynamic_param
-          new_rank = torch.sum(S > min_sv).item()
-          new_rank = max(new_rank, 1)
-          new_alpha = float(scale*new_rank)
 
-        elif dynamic_method=="sv_cumulative":
-          # Calculate new dim and alpha based off cumulative sum
-          new_rank = index_sv_cumulative(S, dynamic_param)
-          new_rank = max(new_rank, 1)
-          new_alpha = float(scale*new_rank)
+        param_dict = rank_resize(S, new_rank, dynamic_method, dynamic_param, scale)
+
+        new_rank = param_dict['new_rank']
+        new_alpha = param_dict['new_alpha']
 
-        elif dynamic_method=="sv_fro":
-          # Calculate new dim and alpha based off sqrt sum of squares
-          new_rank = index_sv_fro(S, dynamic_param)
-          new_rank = max(new_rank, 1)
-          new_alpha = float(scale*new_rank)
-          
         if verbose:
-          s_sum = torch.sum(torch.abs(S))
-          s_rank = torch.sum(torch.abs(S[:new_rank]))
-          
-          S_squared = S.pow(2)
-          s_fro = torch.sqrt(torch.sum(S_squared))
-          s_red_fro = torch.sqrt(torch.sum(S_squared[:new_rank]))
-          fro_percent = float(s_red_fro/s_fro)
-          if not np.isnan(fro_percent):
-            fro_list.append(float(fro_percent))
+          max_ratio = param_dict['max_ratio']
+          sum_retained = param_dict['sum_retained']
+          fro_retained = param_dict['fro_retained']
+          if not np.isnan(fro_retained):
+            fro_list.append(float(fro_retained))
 
           verbose_str+=f"{block_down_name:75} | "
-          verbose_str+=f"sum(S) retained: {(s_rank)/s_sum:.1%}, fro retained: {fro_percent:.1%}, max(S) ratio: {S[0]/S[new_rank]:0.1f}"
+          verbose_str+=f"sum(S) retained: {sum_retained:.1%}, fro retained: {fro_retained:.1%}, max(S) ratio: {max_ratio:0.1f}"
 
 
         if verbose and dynamic_method:
@@ -168,12 +187,11 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dyn
 
         Vh = Vh[:new_rank, :]
 
-        dist = torch.cat([U.flatten(), Vh.flatten()])
-        hi_val = torch.quantile(dist, CLAMP_QUANTILE)
-        low_val = -hi_val
-
-        U = U.clamp(low_val, hi_val)
-        Vh = Vh.clamp(low_val, hi_val)
+        # dist = torch.cat([U.flatten(), Vh.flatten()])
+        # hi_val = torch.quantile(dist, CLAMP_QUANTILE)
+        # low_val = -hi_val
+        # U = U.clamp(low_val, hi_val)
+        # Vh = Vh.clamp(low_val, hi_val)
 
         if conv2d:
           U = U.unsqueeze(2).unsqueeze(3)
@@ -223,7 +241,7 @@ def resize(args):
   print("loading Model...")
   lora_sd, metadata = load_state_dict(args.model, merge_dtype)
 
-  print("resizing rank...")
+  print("Resizing Lora...")
   state_dict, old_dim, new_alpha = resize_lora_model(lora_sd, args.new_rank, save_dtype, args.device, args.dynamic_method, args.dynamic_param, args.verbose)
 
   # update metadata