diff --git a/patches/external_pr/hypernetwork.py b/patches/external_pr/hypernetwork.py
index 69cd736..5b22281 100644
--- a/patches/external_pr/hypernetwork.py
+++ b/patches/external_pr/hypernetwork.py
@@ -121,11 +121,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
 
     # Here we use optimizer from saved HN, or we can specify as UI option.
     if hypernetwork.optimizer_name in optimizer_dict:
-        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=0 if use_beta_scheduler else scheduler.learn_rate)
+        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
         optimizer_name = hypernetwork.optimizer_name
     else:
         print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
-        optimizer = torch.optim.AdamW(params=weights, lr=0 if use_beta_scheduler else scheduler.learn_rate)
+        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
         optimizer_name = 'AdamW'
 
     if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.
diff --git a/patches/hypernetwork.py b/patches/hypernetwork.py
index 28a0093..3447739 100644
--- a/patches/hypernetwork.py
+++ b/patches/hypernetwork.py
@@ -460,11 +460,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
     # Here we use optimizer from saved HN, or we can specify as UI option.
     if hypernetwork.optimizer_name in optimizer_dict:
-        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=0 if use_beta_scheduler else scheduler.learn_rate)
+        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
         optimizer_name = hypernetwork.optimizer_name
     else:
         print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
-        optimizer: torch.optim.Optimizer = torch.optim.AdamW(params=weights, lr=0 if use_beta_scheduler else scheduler.learn_rate)
+        optimizer: torch.optim.Optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
         optimizer_name = 'AdamW'
 
     if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.