fix norm not training in train_control_lora_flux.py (#11832)

examples/flux-control/train_control_lora_flux.py

@@ -837,11 +837,6 @@ def main(args):
     assert torch.all(flux_transformer.x_embedder.weight[:, initial_input_channels:].data == 0)
     flux_transformer.register_to_config(in_channels=initial_input_channels * 2, out_channels=initial_input_channels)
 
-    if args.train_norm_layers:
-        for name, param in flux_transformer.named_parameters():
-            if any(k in name for k in NORM_LAYER_PREFIXES):
-                param.requires_grad = True
-
     if args.lora_layers is not None:
         if args.lora_layers != "all-linear":
             target_modules = [layer.strip() for layer in args.lora_layers.split(",")]
@@ -879,6 +874,11 @@ def main(args):
     )
     flux_transformer.add_adapter(transformer_lora_config)
 
+    if args.train_norm_layers:
+        for name, param in flux_transformer.named_parameters():
+            if any(k in name for k in NORM_LAYER_PREFIXES):
+                param.requires_grad = True
+
     def unwrap_model(model):
         model = accelerator.unwrap_model(model)
         model = model._orig_mod if is_compiled_module(model) else model