diff --git a/06_gpu_and_ml/flan_t5/flan_t5_finetune.py b/06_gpu_and_ml/flan_t5/flan_t5_finetune.py
index 6a7c576d5..e277ffea8 100644
--- a/06_gpu_and_ml/flan_t5/flan_t5_finetune.py
+++ b/06_gpu_and_ml/flan_t5/flan_t5_finetune.py
@@ -75,7 +75,6 @@ def track_restarts(restart_tracker: modal.Dict) -> int:
     gpu="A10g",
     timeout=7200,
     volumes={VOL_MOUNT_PATH: output_vol},
-    _allow_background_volume_commits=True,
 )
 def finetune(num_train_epochs: int = 1, size_percentage: int = 10):
     from datasets import load_dataset
diff --git a/06_gpu_and_ml/stable_diffusion/stable_diffusion_xl.py b/06_gpu_and_ml/stable_diffusion/stable_diffusion_xl.py
index 6f0c37df8..26244ae09 100644
--- a/06_gpu_and_ml/stable_diffusion/stable_diffusion_xl.py
+++ b/06_gpu_and_ml/stable_diffusion/stable_diffusion_xl.py
@@ -9,7 +9,7 @@
 # example, but it generates images from the larger SDXL 1.0 model. Specifically, it runs the
 # first set of steps with the base model, followed by the refiner model.
 #
-# [Try out the live demo here!](https://modal-labs--stable-diffusion-xl-app.modal.run/) The first
+# [Try out the live demo here!](https://modal-labs--stable-diffusion-xl-ui.modal.run/) The first
 # generation may include a cold-start, which takes around 20 seconds. The inference speed depends on the GPU
 # and step count (for reference, an A100 runs 40 steps in 8 seconds).