diff --git a/examples/dreambooth/train_dreambooth_lora_sdxl.py b/examples/dreambooth/train_dreambooth_lora_sdxl.py
index 81250db78412..247d111c06e2 100644
--- a/examples/dreambooth/train_dreambooth_lora_sdxl.py
+++ b/examples/dreambooth/train_dreambooth_lora_sdxl.py
@@ -843,11 +843,15 @@ def load_model_hook(models, input_dir):
 
         lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)
         LoraLoaderMixin.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=unet_)
+
+        text_encoder_state_dict = {k: v for k, v in lora_state_dict.items() if "text_encoder." in k}
         LoraLoaderMixin.load_lora_into_text_encoder(
-            lora_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_one_
+            text_encoder_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_one_
         )
+
+        text_encoder_2_state_dict = {k: v for k, v in lora_state_dict.items() if "text_encoder_2." in k}
         LoraLoaderMixin.load_lora_into_text_encoder(
-            lora_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_two_
+            text_encoder_2_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_two_
         )
 
     accelerator.register_save_state_pre_hook(save_model_hook)
diff --git a/examples/test_examples.py b/examples/test_examples.py
index c06c9417d594..2b4f49dd6bdd 100644
--- a/examples/test_examples.py
+++ b/examples/test_examples.py
@@ -421,6 +421,77 @@ def test_dreambooth_lora_sdxl_with_text_encoder(self):
             )
             self.assertTrue(starts_with_unet)
 
+    def test_dreambooth_lora_sdxl_checkpointing_checkpoints_total_limit(self):
+        pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                examples/dreambooth/train_dreambooth_lora_sdxl.py
+                --pretrained_model_name_or_path {pipeline_path}
+                --instance_data_dir docs/source/en/imgs
+                --instance_prompt photo
+                --resolution 64
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 7
+                --checkpointing_steps=2
+                --checkpoints_total_limit=2
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                """.split()
+
+            run_command(self._launch_args + test_args)
+
+            pipe = DiffusionPipeline.from_pretrained(pipeline_path)
+            pipe.load_lora_weights(tmpdir)
+            pipe("a prompt", num_inference_steps=2)
+
+            # check checkpoint directories exist
+            self.assertEqual(
+                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
+                # checkpoint-2 should have been deleted
+                {"checkpoint-4", "checkpoint-6"},
+            )
+
+    def test_dreambooth_lora_sdxl_text_encoder_checkpointing_checkpoints_total_limit(self):
+        pipeline_path = "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                examples/dreambooth/train_dreambooth_lora_sdxl.py
+                --pretrained_model_name_or_path {pipeline_path}
+                --instance_data_dir docs/source/en/imgs
+                --instance_prompt photo
+                --resolution 64
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 7
+                --checkpointing_steps=2
+                --checkpoints_total_limit=2
+                --train_text_encoder
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                """.split()
+
+            run_command(self._launch_args + test_args)
+
+            pipe = DiffusionPipeline.from_pretrained(pipeline_path)
+            pipe.load_lora_weights(tmpdir)
+            pipe("a prompt", num_inference_steps=2)
+
+            # check checkpoint directories exist
+            self.assertEqual(
+                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
+                # checkpoint-2 should have been deleted
+                {"checkpoint-4", "checkpoint-6"},
+            )
+
     def test_custom_diffusion(self):
         with tempfile.TemporaryDirectory() as tmpdir:
             test_args = f"""