Update script.py

script.py

@@ -4,7 +4,17 @@ from safetensors.torch import load_file
 from diffusers import AutoPipelineForText2Image
 import torch
 import re
+import argparse
 
+def parse_arguments():
+    parser = argparse.ArgumentParser(description="Process script arguments.")
+    parser.add_argument('--dataset_name', required=True, help='Name of the dataset.')
+    parser.add_argument('--output_dir', required=True, help='Output directory.')
+    parser.add_argument('--num_new_tokens_per_abstraction', type=int, default=0, help='Number of new tokens per abstraction.')
+    parser.add_argument('--train_text_encoder_ti', action='store_true', help='Flag to train text encoder TI.')
+
+    return parser.parse_args()
+    
 def do_train(script_args):
     # Pass all arguments to trainer.py
     subprocess.run(['python', 'trainer.py'] + script_args)
@@ -51,30 +61,31 @@ def do_inference(dataset_name, output_dir, num_tokens):
         repo_id=f"{username}/{output_dir}",
         repo_type="model",
     )
+
+import sys
+import argparse
+
 def main():
     # Capture all arguments except the script name
-    print(sys.argv)
     script_args = sys.argv[1:]
-    print(script_args)
-    # Extract dataset_name argument
-    dataset_name = None
-    output_dir = None
-    for arg in script_args:
-        if arg.startswith('--dataset_name='):
-            dataset_name = arg.split('=')[1]
-            continue
-        if arg.startswith('--output_dir='):
-            output_dir = arg.split('=')[1]
-            continue
-        if arg.startswith('--train_text_encoder_ti'):
-            num_tokens = 0
-        elif arg.startswith('--num_new_tokens_per_abstraction='):
-            num_tokens = arg.split('=')[1]
-    if dataset_name is None or output_dir is None:
-        raise ValueError("Dataset name not provided.")
 
+    # Create the argument parser
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataset_name', required=True)
+    parser.add_argument('--output_dir', required=True)
+    parser.add_argument('--num_new_tokens_per_abstraction', type=int, default=0)
+    parser.add_argument('--train_text_encoder_ti', action='store_true')
+
+    # Parse known arguments
+    args, _ = parser.parse_known_args(script_args)
+
+    # Set num_tokens to 0 if '--train_text_encoder_ti' is not present
+    if not args.train_text_encoder_ti:
+        args.num_new_tokens_per_abstraction = 0
+
+    # Proceed with training and inference
     do_train(script_args)
-    do_inference(dataset_name, output_dir, num_tokens)
+    do_inference(args.dataset_name, args.output_dir, args.num_new_tokens_per_abstraction)
 
 if __name__ == "__main__":
     main()