diff --git a/train_network.py b/train_network.py
index b3c7ff52..083e5993 100644
--- a/train_network.py
+++ b/train_network.py
@@ -1278,7 +1278,7 @@ class NetworkTrainer:
         original_args_min_timestep = args.min_timestep
         original_args_max_timestep = args.max_timestep
 
-        def get_rng_state() -> tuple[torch.ByteTensor, Optional[torch.ByteTensor], tuple]:
+        def switch_rng_state(seed:int) -> tuple[torch.ByteTensor, Optional[torch.ByteTensor], tuple]:
             cpu_rng_state = torch.get_rng_state()
             if accelerator.device.type == "cuda":
                 gpu_rng_state = torch.cuda.get_rng_state()
@@ -1289,9 +1289,13 @@ class NetworkTrainer:
             else:
                 gpu_rng_state = None
             python_rng_state = random.getstate()
+
+            torch.manual_seed(seed)
+            random.seed(seed)
+
             return (cpu_rng_state, gpu_rng_state, python_rng_state)
 
-        def set_rng_state(rng_states: tuple[torch.ByteTensor, Optional[torch.ByteTensor], tuple]):
+        def restore_rng_state(rng_states: tuple[torch.ByteTensor, Optional[torch.ByteTensor], tuple]):
             cpu_rng_state, gpu_rng_state, python_rng_state = rng_states
             torch.set_rng_state(cpu_rng_state)
             if gpu_rng_state is not None:
@@ -1416,8 +1420,7 @@ class NetworkTrainer:
                 if accelerator.sync_gradients and validation_steps > 0 and should_validate_step:
                     optimizer_eval_fn()
                     accelerator.unwrap_model(network).eval()
-                    rng_states = get_rng_state()
-                    torch.manual_seed(args.validation_seed if args.validation_seed is not None else args.seed)
+                    rng_states = switch_rng_state(args.validation_seed if args.validation_seed is not None else args.seed)
 
                     val_progress_bar = tqdm(
                         range(validation_total_steps),
@@ -1478,7 +1481,7 @@ class NetworkTrainer:
                         }
                         accelerator.log(logs, step=global_step)
 
-                    set_rng_state(rng_states)
+                    restore_rng_state(rng_states)
                     args.min_timestep = original_args_min_timestep
                     args.max_timestep = original_args_max_timestep
                     optimizer_train_fn()
@@ -1495,8 +1498,7 @@ class NetworkTrainer:
             if should_validate_epoch and len(val_dataloader) > 0:
                 optimizer_eval_fn()
                 accelerator.unwrap_model(network).eval()
-                rng_states = get_rng_state()
-                torch.manual_seed(args.validation_seed if args.validation_seed is not None else args.seed)
+                rng_states = switch_rng_state(args.validation_seed if args.validation_seed is not None else args.seed)
 
                 val_progress_bar = tqdm(
                     range(validation_total_steps),
@@ -1561,7 +1563,7 @@ class NetworkTrainer:
                     }
                     accelerator.log(logs, step=global_step)
 
-                set_rng_state(rng_states)
+                restore_rng_state(rng_states)
                 args.min_timestep = original_args_min_timestep
                 args.max_timestep = original_args_max_timestep
                 optimizer_train_fn()