diff --git a/networks/resize_lora.py b/networks/resize_lora.py
index 2f586a8a..2a44592b 100644
--- a/networks/resize_lora.py
+++ b/networks/resize_lora.py
@@ -85,13 +85,13 @@ def index_sv_ratio(S, target):
 
 
 # Modified from Kohaku-blueleaf's extract/merge functions
-def extract_conv(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
+def extract_conv(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1, svd_lowrank_niter=2):
     out_size, in_size, kernel_size, _ = weight.size()
     weight = weight.reshape(out_size, -1)
     _in_size = in_size * kernel_size * kernel_size
 
-    if out_size > 2048 and _in_size > 2048:
-        U, S, V = torch.svd_lowrank(weight.to(device), q=min(2 * lora_rank, out_size, _in_size))
+    if svd_lowrank_niter > 0 and out_size > 2048 and _in_size > 2048:
+        U, S, V = torch.svd_lowrank(weight.to(device), q=min(2 * lora_rank, out_size, _in_size), niter=svd_lowrank_niter)
         Vh = V.T
     else:
         U, S, Vh = torch.linalg.svd(weight.to(device))
@@ -110,11 +110,11 @@ def extract_conv(weight, lora_rank, dynamic_method, dynamic_param, device, scale
     return param_dict
 
 
-def extract_linear(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
+def extract_linear(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1, svd_lowrank_niter=2):
     out_size, in_size = weight.size()
 
-    if out_size > 2048 and in_size > 2048:
-        U, S, V = torch.svd_lowrank(weight.to(device), q=min(2 * lora_rank, out_size, in_size))
+    if svd_lowrank_niter > 0 and out_size > 2048 and in_size > 2048:
+        U, S, V = torch.svd_lowrank(weight.to(device), q=min(2 * lora_rank, out_size, in_size), niter=svd_lowrank_niter)
         Vh = V.T
     else:
         U, S, Vh = torch.linalg.svd(weight.to(device))
@@ -209,7 +209,7 @@ def rank_resize(S, rank, dynamic_method, dynamic_param, scale=1):
     return param_dict
 
 
-def resize_lora_model(lora_sd, new_rank, new_conv_rank, save_dtype, device, dynamic_method, dynamic_param, verbose):
+def resize_lora_model(lora_sd, new_rank, new_conv_rank, save_dtype, device, dynamic_method, dynamic_param, verbose, svd_lowrank_niter=2):
     max_old_rank = None
     new_alpha = None
     verbose_str = "\n"
@@ -273,10 +273,10 @@ def resize_lora_model(lora_sd, new_rank, new_conv_rank, save_dtype, device, dyna
 
                 if conv2d:
                     full_weight_matrix = merge_conv(lora_down_weight, lora_up_weight, device)
-                    param_dict = extract_conv(full_weight_matrix, new_conv_rank, dynamic_method, dynamic_param, device, scale)
+                    param_dict = extract_conv(full_weight_matrix, new_conv_rank, dynamic_method, dynamic_param, device, scale, svd_lowrank_niter)
                 else:
                     full_weight_matrix = merge_linear(lora_down_weight, lora_up_weight, device)
-                    param_dict = extract_linear(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
+                    param_dict = extract_linear(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale, svd_lowrank_niter)
 
                 if verbose:
                     max_ratio = param_dict["max_ratio"]
@@ -347,7 +347,7 @@ def resize(args):
 
     logger.info("Resizing Lora...")
     state_dict, old_dim, new_alpha = resize_lora_model(
-        lora_sd, args.new_rank, args.new_conv_rank, save_dtype, args.device, args.dynamic_method, args.dynamic_param, args.verbose
+        lora_sd, args.new_rank, args.new_conv_rank, save_dtype, args.device, args.dynamic_method, args.dynamic_param, args.verbose, args.svd_lowrank_niter
     )
 
     # update metadata
@@ -425,6 +425,13 @@ def setup_parser() -> argparse.ArgumentParser:
         help="Specify dynamic resizing method, --new_rank is used as a hard limit for max rank",
     )
     parser.add_argument("--dynamic_param", type=float, default=None, help="Specify target for dynamic reduction")
+    parser.add_argument(
+        "--svd_lowrank_niter",
+        type=int,
+        default=2,
+        help="Number of iterations for svd_lowrank on large matrices (>2048 dims). 0 to disable and use full SVD"
+        " / 大行列(2048次元超)に対するsvd_lowrankの反復回数。0で無効化し完全SVDを使用",
+    )
 
     return parser