diff --git a/nodes/lora_nodes.py b/nodes/lora_nodes.py
index fe9959c..0ec86d6 100644
--- a/nodes/lora_nodes.py
+++ b/nodes/lora_nodes.py
@@ -10,7 +10,7 @@ device = comfy.model_management.get_torch_device()
 
 CLAMP_QUANTILE = 0.99
 
-def extract_lora(diff, key, rank, algorithm, lora_type, lowrank_iters=7, adaptive_param=1.0):
+def extract_lora(diff, key, rank, algorithm, lora_type, lowrank_iters=7, adaptive_param=1.0, clamp_quantile=True):
     """
     Extracts LoRA weights from a weight difference tensor using SVD.
     """
@@ -59,25 +59,26 @@ def extract_lora(diff, key, rank, algorithm, lora_type, lowrank_iters=7, adaptiv
         U = U @ torch.diag(S)
         Vh = Vh[:lora_rank, :]
 
-    dist = torch.cat([U.flatten(), Vh.flatten()])
-    if dist.numel() > 100_000:
-        # Sample 100,000 elements for quantile estimation
-        idx = torch.randperm(dist.numel(), device=dist.device)[:100_000]
-        dist_sample = dist[idx]
-        hi_val = torch.quantile(dist_sample, CLAMP_QUANTILE)
-    else:
-        hi_val = torch.quantile(dist, CLAMP_QUANTILE)
-    low_val = -hi_val
+    if clamp_quantile:
+        dist = torch.cat([U.flatten(), Vh.flatten()])
+        if dist.numel() > 100_000:
+            # Sample 100,000 elements for quantile estimation
+            idx = torch.randperm(dist.numel(), device=dist.device)[:100_000]
+            dist_sample = dist[idx]
+            hi_val = torch.quantile(dist_sample, CLAMP_QUANTILE)
+        else:
+            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)
+        U = U.clamp(low_val, hi_val)
+        Vh = Vh.clamp(low_val, hi_val)
     if conv2d:
         U = U.reshape(out_dim, lora_rank, 1, 1)
         Vh = Vh.reshape(lora_rank, in_dim, kernel_size[0], kernel_size[1])
     return (U, Vh)
 
 
-def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, algorithm, lowrank_iters, out_dtype, bias_diff=False, adaptive_param=1.0):
+def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, algorithm, lowrank_iters, out_dtype, bias_diff=False, adaptive_param=1.0, clamp_quantile=True):
     comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True)
     model_diff.model.diffusion_model.cpu()
     sd = model_diff.model_state_dict(filter_prefix=prefix_model)
@@ -110,7 +111,7 @@ def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora
                     comfy_pbar.update(1)
                     continue
                 try:
-                    out = extract_lora(weight_diff.to(device), k, rank, algorithm, lora_type, lowrank_iters=lowrank_iters, adaptive_param=adaptive_param)
+                    out = extract_lora(weight_diff.to(device), k, rank, algorithm, lora_type, lowrank_iters=lowrank_iters, adaptive_param=adaptive_param, clamp_quantile=clamp_quantile)
                     output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().to(out_dtype).cpu()
                     output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().to(out_dtype).cpu()
                 except Exception as e:
@@ -146,6 +147,7 @@ class LoraExtractKJ:
                     "output_dtype": (["fp16", "bf16", "fp32"], {"default": "fp16"}),
                     "bias_diff": ("BOOLEAN", {"default": True}),
                     "adaptive_param": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "For ratio mode, this is the ratio of the maximum singular value. For quantile mode, this is the quantile of the singular values."}),
+                    "clamp_quantile": ("BOOLEAN", {"default": True}),
                 },
 
     }
@@ -155,7 +157,7 @@ class LoraExtractKJ:
 
     CATEGORY = "KJNodes/lora"
 
-    def save(self, finetuned_model, original_model, filename_prefix, rank, lora_type, algorithm, lowrank_iters, output_dtype, bias_diff, adaptive_param):
+    def save(self, finetuned_model, original_model, filename_prefix, rank, lora_type, algorithm, lowrank_iters, output_dtype, bias_diff, adaptive_param, clamp_quantile):
         if algorithm == "svd_lowrank" and lora_type != "standard":
             raise ValueError("svd_lowrank algorithm is only supported for standard LoRA extraction.")
 
@@ -170,7 +172,7 @@ class LoraExtractKJ:
 
         output_sd = {}
         if model_diff is not None:
-            output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, algorithm, lowrank_iters, dtype, bias_diff=bias_diff, adaptive_param=adaptive_param)
+            output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, algorithm, lowrank_iters, dtype, bias_diff=bias_diff, adaptive_param=adaptive_param, clamp_quantile=clamp_quantile)
         if "adaptive" in lora_type:
             rank_str = f"{lora_type}_{adaptive_param:.2f}"
         else: