diff --git a/examples/text_to_image/train_text_to_image_sdxl.py b/examples/text_to_image/train_text_to_image_sdxl.py
index 7f4917b5464c..2ca511c857ae 100644
--- a/examples/text_to_image/train_text_to_image_sdxl.py
+++ b/examples/text_to_image/train_text_to_image_sdxl.py
@@ -1084,7 +1084,7 @@ def unwrap_model(model):
 
                 # Add noise to the model input according to the noise magnitude at each timestep
                 # (this is the forward diffusion process)
-                noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)
+                noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps).to(dtype=weight_dtype)
 
                 # time ids
                 def compute_time_ids(original_size, crops_coords_top_left):
@@ -1101,7 +1101,7 @@ def compute_time_ids(original_size, crops_coords_top_left):
 
                 # Predict the noise residual
                 unet_added_conditions = {"time_ids": add_time_ids}
-                prompt_embeds = batch["prompt_embeds"].to(accelerator.device)
+                prompt_embeds = batch["prompt_embeds"].to(accelerator.device, dtype=weight_dtype)
                 pooled_prompt_embeds = batch["pooled_prompt_embeds"].to(accelerator.device)
                 unet_added_conditions.update({"text_embeds": pooled_prompt_embeds})
                 model_pred = unet(