Fix network args, update example folder path to support template loader

networks/lora_flux.py

@@ -504,6 +504,9 @@ class LoRANetwork(torch.nn.Module):
             logger.info(f"train T5XXL as well")
 
         #self.only_if_contains = ["lora_unet_single_blocks_20_linear2"]
+        print(self.only_if_contains)
+        print(self.only_if_contains)
+        print(self.only_if_contains)
 
         # create module instances
         def create_modules(

nodes.py

@@ -499,6 +499,7 @@ class InitFluxLoRATraining:
 
         parser = train_network_setup_parser()
         flux_train_utils.add_flux_train_arguments(parser)
+
         if additional_args is not None:
             print(f"additional_args: {additional_args}")
             args, _ = parser.parse_known_args(args=shlex.split(additional_args))
@@ -576,21 +577,6 @@ class InitFluxLoRATraining:
             if T5_lr != "NaN":
                 config_dict["text_encoder_lr"] = [clip_l_lr, T5_lr]
 
-        #network args
-        additional_network_args = []
-        
-        if "T5" in train_text_encoder:
-            additional_network_args.append("train_t5xxl=True")
-       
-        if block_args:
-            additional_network_args.append(block_args["include"])
-        
-        # Handle network_args in args Namespace
-        if hasattr(args, 'network_args') and isinstance(args.network_args, list):
-            args.network_args.extend(additional_network_args)
-        else:
-            setattr(args, 'network_args', additional_network_args)
-
         if gradient_checkpointing == "disabled":
             config_dict["gradient_checkpointing"] = False
         elif gradient_checkpointing == "enabled_with_cpu_offloading":
@@ -613,6 +599,21 @@ class InitFluxLoRATraining:
 
         for key, value in config_dict.items():
             setattr(args, key, value)
+
+        #network args
+        additional_network_args = []
+        
+        if "T5" in train_text_encoder:
+            additional_network_args.append("train_t5xxl=True")
+       
+        if block_args:
+            additional_network_args.append(block_args["include"])
+        
+        # Handle network_args in args Namespace
+        if hasattr(args, 'network_args') and isinstance(args.network_args, list):
+            args.network_args.extend(additional_network_args)
+        else:
+            setattr(args, 'network_args', additional_network_args)
         
         saved_args_file_path = os.path.join(output_dir, f"{output_name}_args.json")
         with open(saved_args_file_path, 'w') as f:
@@ -702,6 +703,8 @@ class InitFluxTraining:
         dataset_toml = toml.dumps(json.loads(dataset_config))
         
         parser = train_setup_parser()
+        flux_train_utils.add_flux_train_arguments(parser)
+        
         if additional_args is not None:
             print(f"additional_args: {additional_args}")
             args, _ = parser.parse_known_args(args=shlex.split(additional_args))