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 bf726e65c94b..112884609901 100644
--- a/examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py
+++ b/examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py
@@ -2154,6 +2154,7 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
 
                 # encode batch prompts when custom prompts are provided for each image -
                 if train_dataset.custom_instance_prompts:
+                    elems_to_repeat = 1
                     if freeze_text_encoder:
                         prompt_embeds, pooled_prompt_embeds, text_ids = compute_text_embeddings(
                             prompts, text_encoders, tokenizers
@@ -2168,17 +2169,21 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                             max_sequence_length=args.max_sequence_length,
                             add_special_tokens=add_special_tokens_t5,
                         )
+                else:
+                    elems_to_repeat = len(prompts)
 
                 if not freeze_text_encoder:
                     prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt(
                         text_encoders=[text_encoder_one, text_encoder_two],
                         tokenizers=[None, None],
-                        text_input_ids_list=[tokens_one, tokens_two],
+                        text_input_ids_list=[
+                            tokens_one.repeat(elems_to_repeat, 1),
+                            tokens_two.repeat(elems_to_repeat, 1),
+                        ],
                         max_sequence_length=args.max_sequence_length,
                         device=accelerator.device,
                         prompt=prompts,
                     )
-
                 # Convert images to latent space
                 if args.cache_latents:
                     model_input = latents_cache[step].sample()
@@ -2371,6 +2376,9 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                     epoch=epoch,
                     torch_dtype=weight_dtype,
                 )
+                images = None
+                del pipeline
+
                 if freeze_text_encoder:
                     del text_encoder_one, text_encoder_two
                     free_memory()
@@ -2448,6 +2456,8 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                 commit_message="End of training",
                 ignore_patterns=["step_*", "epoch_*"],
             )
+        images = None
+        del pipeline
 
     accelerator.end_training()
 
diff --git a/examples/dreambooth/train_dreambooth_lora_flux.py b/examples/dreambooth/train_dreambooth_lora_flux.py
index 2c1126109a36..f73269a48967 100644
--- a/examples/dreambooth/train_dreambooth_lora_flux.py
+++ b/examples/dreambooth/train_dreambooth_lora_flux.py
@@ -1648,11 +1648,15 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                             prompt=prompts,
                         )
                 else:
+                    elems_to_repeat = len(prompts)
                     if args.train_text_encoder:
                         prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt(
                             text_encoders=[text_encoder_one, text_encoder_two],
                             tokenizers=[None, None],
-                            text_input_ids_list=[tokens_one, tokens_two],
+                            text_input_ids_list=[
+                                tokens_one.repeat(elems_to_repeat, 1),
+                                tokens_two.repeat(elems_to_repeat, 1),
+                            ],
                             max_sequence_length=args.max_sequence_length,
                             device=accelerator.device,
                             prompt=args.instance_prompt,