diff --git a/examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py b/examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py
index 318db82661cc..5d3e75603a81 100644
--- a/examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py
+++ b/examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py
@@ -1916,7 +1916,6 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
             if args.with_prior_preservation:
                 prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)
                 pooled_prompt_embeds = torch.cat([pooled_prompt_embeds, class_pooled_prompt_embeds], dim=0)
-                text_ids = torch.cat([text_ids, class_text_ids], dim=0)
         # if we're optimizing the text encoder (both if instance prompt is used for all images or custom prompts)
         # we need to tokenize and encode the batch prompts on all training steps
         else:
diff --git a/examples/dreambooth/train_dreambooth_lora_flux.py b/examples/dreambooth/train_dreambooth_lora_flux.py
index 3375a583e2f2..1fd95efe8c8b 100644
--- a/examples/dreambooth/train_dreambooth_lora_flux.py
+++ b/examples/dreambooth/train_dreambooth_lora_flux.py
@@ -1533,7 +1533,6 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
             if args.with_prior_preservation:
                 prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)
                 pooled_prompt_embeds = torch.cat([pooled_prompt_embeds, class_pooled_prompt_embeds], dim=0)
-                text_ids = torch.cat([text_ids, class_text_ids], dim=0)
         # if we're optimizing the text encoder (both if instance prompt is used for all images or custom prompts)
         # we need to tokenize and encode the batch prompts on all training steps
         else: