diff --git a/examples/dreambooth/train_dreambooth_lora_flux.py b/examples/dreambooth/train_dreambooth_lora_flux.py
index 2ee8fee80644..5fb666a4d42c 100644
--- a/examples/dreambooth/train_dreambooth_lora_flux.py
+++ b/examples/dreambooth/train_dreambooth_lora_flux.py
@@ -1823,10 +1823,11 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                     # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
                     model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
                     target, target_prior = torch.chunk(target, 2, dim=0)
+                    weighting, weighting_prior = torch.chunk(weighting, 2, dim=0)
 
                     # Compute prior loss
                     prior_loss = torch.mean(
-                        (weighting.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(
+                        (weighting_prior.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(
                             target_prior.shape[0], -1
                         ),
                         1,
diff --git a/examples/dreambooth/train_dreambooth_lora_sd3.py b/examples/dreambooth/train_dreambooth_lora_sd3.py
index 81f4681dcc3d..396f18113bf5 100644
--- a/examples/dreambooth/train_dreambooth_lora_sd3.py
+++ b/examples/dreambooth/train_dreambooth_lora_sd3.py
@@ -1824,10 +1824,11 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                     # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
                     model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
                     target, target_prior = torch.chunk(target, 2, dim=0)
+                    weighting, weighting_prior = torch.chunk(weighting, 2, dim=0)
 
                     # Compute prior loss
                     prior_loss = torch.mean(
-                        (weighting.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(
+                        (weighting_prior.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(
                             target_prior.shape[0], -1
                         ),
                         1,