diff --git a/examples/t2i_adapter/test_t2i_adapter.py b/examples/t2i_adapter/test_t2i_adapter.py
index ff07b8cd02d6..ba6987b14a03 100644
--- a/examples/t2i_adapter/test_t2i_adapter.py
+++ b/examples/t2i_adapter/test_t2i_adapter.py
@@ -49,3 +49,22 @@ def test_t2i_adapter_sdxl(self):
             run_command(self._launch_args + test_args)
 
             assert os.path.isfile(os.path.join(tmpdir, "diffusion_pytorch_model.safetensors"))
+
+    def test_t2i_adapter_sdxl_num_train_epochs(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+            examples/t2i_adapter/train_t2i_adapter_sdxl.py
+            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-xl-pipe
+            --adapter_model_name_or_path=hf-internal-testing/tiny-adapter
+            --dataset_name=hf-internal-testing/fill10
+            --output_dir={tmpdir}
+            --resolution=64
+            --train_batch_size=1
+            --gradient_accumulation_steps=1
+            --num_train_epochs=1
+            --checkpointing_steps=2
+            """.split()
+
+            run_command(self._launch_args + test_args)
+
+            assert os.path.isfile(os.path.join(tmpdir, "diffusion_pytorch_model.safetensors"))
diff --git a/examples/t2i_adapter/train_t2i_adapter_sdxl.py b/examples/t2i_adapter/train_t2i_adapter_sdxl.py
index 32863d469a6c..5564ff2e6b34 100644
--- a/examples/t2i_adapter/train_t2i_adapter_sdxl.py
+++ b/examples/t2i_adapter/train_t2i_adapter_sdxl.py
@@ -1080,17 +1080,22 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
     )
 
     # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    # Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.
+    num_warmup_steps_for_scheduler = args.lr_warmup_steps * accelerator.num_processes
     if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
+        len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes)
+        num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps)
+        num_training_steps_for_scheduler = (
+            args.num_train_epochs * num_update_steps_per_epoch * accelerator.num_processes
+        )
+    else:
+        num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes
 
     lr_scheduler = get_scheduler(
         args.lr_scheduler,
         optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps,
-        num_training_steps=args.max_train_steps,
+        num_warmup_steps=num_warmup_steps_for_scheduler,
+        num_training_steps=num_training_steps_for_scheduler,
         num_cycles=args.lr_num_cycles,
         power=args.lr_power,
     )
@@ -1102,8 +1107,14 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
+    if args.max_train_steps is None:
         args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        if num_training_steps_for_scheduler != args.max_train_steps * accelerator.num_processes:
+            logger.warning(
+                f"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match "
+                f"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. "
+                f"This inconsistency may result in the learning rate scheduler not functioning properly."
+            )
     # Afterwards we recalculate our number of training epochs
     args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)