diff --git a/models/turbine_models/custom_models/sdxl_inference/sdxl_pipeline.py b/models/turbine_models/custom_models/sdxl_inference/sdxl_pipeline.py
index a3ae609cf..884c979ee 100644
--- a/models/turbine_models/custom_models/sdxl_inference/sdxl_pipeline.py
+++ b/models/turbine_models/custom_models/sdxl_inference/sdxl_pipeline.py
@@ -270,6 +270,7 @@ def generate_images(args, vmfbs: dict, weights: dict):
             dtype=iree_dtype,
         ),
     ]
+    print(unet_inputs)
     latents = pipe_runner.ctx.modules.sdxl_compiled_pipeline["produce_image_latents"](
         *unet_inputs,
     )
@@ -289,7 +290,7 @@ def generate_images(args, vmfbs: dict, weights: dict):
     image[0].save(img_path)
     print(img_path, "saved")
     print("Pipeline arguments: ", args)
-    print("Total time: ", pipe_end - pipe_start, "sec")
+    print("Total time: ", pipe_end - clip_start, "sec")
     print("Loading time: ", clip_start - pipe_start, "sec")
     print("Clip time: ", unet_start - clip_start, "sec")
     print("UNet time(", args.num_inference_steps, "): ", vae_start - unet_start, "sec,")
diff --git a/models/turbine_models/custom_models/sdxl_inference/sdxl_scheduled_unet.py b/models/turbine_models/custom_models/sdxl_inference/sdxl_scheduled_unet.py
index 45a9c7a6f..819e57a83 100644
--- a/models/turbine_models/custom_models/sdxl_inference/sdxl_scheduled_unet.py
+++ b/models/turbine_models/custom_models/sdxl_inference/sdxl_scheduled_unet.py
@@ -76,7 +76,8 @@ def initialize(self, sample):
         add_time_ids = add_time_ids.repeat(sample.shape[0], 1).type(self.dtype)
         timesteps = self.scheduler.timesteps
         step_indexes = torch.tensor(len(timesteps))
-        return sample * self.scheduler.init_noise_sigma, add_time_ids, step_indexes
+        sample = sample * self.scheduler.init_noise_sigma
+        return sample.type(self.dtype), add_time_ids, step_indexes
 
     def forward(
         self, sample, prompt_embeds, text_embeds, time_ids, guidance_scale, step_index
@@ -103,7 +104,7 @@ def forward(
                 noise_pred_text - noise_pred_uncond
             )
             sample = self.scheduler.step(noise_pred, t, sample, return_dict=False)[0]
-        return sample
+        return sample.type(self.dtype)
 
 
 def export_scheduled_unet_model(