passing filtered hyperparameters to accelerate

library/train_util.py

@@ -3378,6 +3378,20 @@ def add_masked_loss_arguments(parser: argparse.ArgumentParser):
         help="apply mask for calculating loss. conditioning_data_dir is required for dataset. / 損失計算時にマスクを適用する。datasetにはconditioning_data_dirが必要",
     )
 
+def filter_sensitive_args(args: argparse.Namespace):
+    sensitive_args = ["wandb_api_key", "huggingface_token"]
+    sensitive_path_args = [
+        "pretrained_model_name_or_path",
+        "vae",
+        "tokenizer_cache_dir",
+        "train_data_dir",
+        "conditioning_data_dir",
+        "reg_data_dir",
+        "output_dir",
+        "logging_dir",
+    ]
+    filtered_args = {k: v for k, v in vars(args).items() if k not in sensitive_args + sensitive_path_args}
+    return filtered_args
 
 # verify command line args for training
 def verify_command_line_training_args(args: argparse.Namespace):