diff --git a/train.json b/train.json
index 8791b81..ebd1998 100644
--- a/train.json
+++ b/train.json
@@ -38,5 +38,6 @@
   "wandb": false,
   "write_schedule": false,
   "rated_dataset": false,
-  "rated_dataset_target_dropout_percent": 50
+  "rated_dataset_target_dropout_percent": 50,
+  "zero_frequency_noise_ratio": 0.0
 }
diff --git a/train.py b/train.py
index 8ca8b65..56f02ec 100644
--- a/train.py
+++ b/train.py
@@ -27,6 +27,7 @@ import gc
 import random
 import traceback
 import shutil
+import importlib
 
 import torch.nn.functional as F
 from torch.cuda.amp import autocast, GradScaler
@@ -765,7 +766,7 @@ def main(args):
     assert len(train_batch) > 0, "train_batch is empty, check that your data_root is correct"
 
     # actual prediction function - shared between train and validate
-    def get_model_prediction_and_target(image, tokens):
+    def get_model_prediction_and_target(image, tokens, zero_frequency_noise_ratio=0.0):
         with torch.no_grad():
             with autocast(enabled=args.amp):
                 pixel_values = image.to(memory_format=torch.contiguous_format).to(unet.device)
@@ -773,7 +774,12 @@ def main(args):
             del pixel_values
             latents = latents[0].sample() * 0.18215
 
-            noise = torch.randn_like(latents)
+            if zero_frequency_noise_ratio > 0.0:
+                zero_frequency_noise = zero_frequency_noise_ratio * torch.randn(latents.shape[0], latents.shape[1], 1, 1, device=latents.device)
+                noise = torch.randn_like(latents) + zero_frequency_noise
+            else:
+                noise = torch.randn_like(latents)
+            
             bsz = latents.shape[0]
 
             timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
@@ -839,7 +845,7 @@ def main(args):
             for step, batch in enumerate(train_dataloader):
                 step_start_time = time.time()
 
-                model_pred, target = get_model_prediction_and_target(batch["image"], batch["tokens"])
+                model_pred, target = get_model_prediction_and_target(batch["image"], batch["tokens"], args.zero_frequency_noise_ratio)
 
                 #del timesteps, encoder_hidden_states, noisy_latents
                 #with autocast(enabled=args.amp):
@@ -944,7 +950,7 @@ def main(args):
 
             if validator:
                 validator.do_validation_if_appropriate(epoch, global_step, get_model_prediction_and_target)
-
+            
             gc.collect()
             # end of epoch
 
@@ -1029,6 +1035,7 @@ if __name__ == "__main__":
     argparser.add_argument("--write_schedule", action="store_true", default=False, help="write schedule of images and their batches to file (def: False)")
     argparser.add_argument("--rated_dataset", action="store_true", default=False, help="enable rated image set training, to less often train on lower rated images through the epochs")
     argparser.add_argument("--rated_dataset_target_dropout_percent", type=int, default=50, help="how many images (in percent) should be included in the last epoch (Default 50)")
+    argparser.add_argument("--zero_frequency_noise_ratio", type=float, default=0.0, help="adds zero frequency noise, for improving contrast (def: 0.0) use 0.0 to 0.15")
 
     # load CLI args to overwrite existing config args
     args = argparser.parse_args(args=argv, namespace=args)