Update README, add template args

comfyui_to_python.py

@@ -306,12 +306,12 @@ class CodeGenerator:
         for func in [get_value_at_index, find_path, add_comfyui_directory_to_sys_path, add_extra_model_paths, import_custom_nodes, parse_arg]:
             func_strings.append(f'\n{inspect.getsource(func)}')
         
-        argparse_code = ""
-        if arg_inputs:
-            argparse_code = f'\nparser = argparse.ArgumentParser(description="A converted ComfyUI workflow. Required inputs listed below. Values passed should be in JSON")\n'
-            for i, (input_name, arg_desc) in enumerate(arg_inputs):
-                argparse_code += f'parser.add_argument("{input_name}", help="{arg_desc} (autogenerated)")\n'
-            argparse_code += 'args = parser.parse_args()\nsys.argv = [sys.argv[0]]\n'
+        argparse_code = f'\nparser = argparse.ArgumentParser(description="A converted ComfyUI workflow. Required inputs listed below. Values passed should be valid JSON (assumes string if not valid JSON).")\n'
+        for i, (input_name, arg_desc) in enumerate(arg_inputs):
+            argparse_code += f'parser.add_argument("{input_name}", help="{arg_desc} (autogenerated)")\n'
+        argparse_code += f'parser.add_argument("--queue-size", "-q", type=int, default={queue_size}, help="How many times the workflow will be executed (default: {queue_size})")\n'
+        argparse_code += f'parser.add_argument("--comfyui-directory", "-c", default=None, help="Where to look for ComfyUI (default: current directory)")\n'
+        argparse_code += 'args = parser.parse_args()\nsys.argv = [sys.argv[0]]\n'
         
         # Define static import statements required for the script
         static_imports = ['import os', 'import random', 'import sys', 'import json', 'import argparse', 'from typing import Sequence, Mapping, Any, Union', 
@@ -327,7 +327,7 @@ class CodeGenerator:
         imports_code = [f"from nodes import {', '.join([class_name for class_name in import_statements])}", '']
         # Assemble the main function code, including custom nodes if applicable
         main_function_code = "def main():\n\t" + f'{custom_nodes}with torch.inference_mode():\n\t\t' + '\n\t\t'.join(speical_functions_code) \
-            + f'\n\n\t\tfor q in range({queue_size}):\n\t\t' + '\n\t\t'.join(code)
+            + f'\n\n\t\tfor q in range(args.queue_size):\n\t\t' + '\n\t\t'.join(code)
         # Concatenate all parts to form the final code
         final_code = '\n'.join(static_imports + imports_code + [main_function_code, '', 'if __name__ == "__main__":', '\tmain()'])
         # Format the final code according to PEP 8 using the Black library