diff --git a/examples/flux-control/train_control_lora_flux.py b/examples/flux-control/train_control_lora_flux.py
index b176a685c963..99a05d54832f 100644
--- a/examples/flux-control/train_control_lora_flux.py
+++ b/examples/flux-control/train_control_lora_flux.py
@@ -923,11 +923,28 @@ def load_model_hook(models, input_dir):
                         transformer_ = model
                     else:
                         raise ValueError(f"unexpected save model: {model.__class__}")
-
             else:
                 transformer_ = FluxTransformer2DModel.from_pretrained(
                     args.pretrained_model_name_or_path, subfolder="transformer"
                 ).to(accelerator.device, weight_dtype)
+
+                # Handle input dimension doubling before adding adapter
+                with torch.no_grad():
+                    initial_input_channels = transformer_.config.in_channels
+                    new_linear = torch.nn.Linear(
+                        transformer_.x_embedder.in_features * 2,
+                        transformer_.x_embedder.out_features,
+                        bias=transformer_.x_embedder.bias is not None,
+                        dtype=transformer_.dtype,
+                        device=transformer_.device,
+                    )
+                    new_linear.weight.zero_()
+                    new_linear.weight[:, :initial_input_channels].copy_(transformer_.x_embedder.weight)
+                    if transformer_.x_embedder.bias is not None:
+                        new_linear.bias.copy_(transformer_.x_embedder.bias)
+                    transformer_.x_embedder = new_linear
+                    transformer_.register_to_config(in_channels=initial_input_channels * 2)
+
                 transformer_.add_adapter(transformer_lora_config)
 
             lora_state_dict = FluxControlPipeline.lora_state_dict(input_dir)