diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
index c8f02b5896d6..01f79b818bc7 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
@@ -281,12 +281,9 @@ def __call__(
         latents = init_latents
 
         t_start = max(num_inference_steps - init_timestep + offset, 0)
+        timesteps = self.scheduler.timesteps[t_start:]
 
-        # Some schedulers like PNDM have timesteps as arrays
-        # It's more optimzed to move all timesteps to correct device beforehand
-        timesteps_tensor = torch.tensor(self.scheduler.timesteps[t_start:], device=self.device)
-
-        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+        for i, t in enumerate(self.progress_bar(timesteps)):
             t_index = t_start + i
 
             # expand the latents if we are doing classifier free guidance
diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
index 21490d975730..197a6ae3f0b8 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
@@ -315,12 +315,9 @@ def __call__(
         latents = init_latents
 
         t_start = max(num_inference_steps - init_timestep + offset, 0)
+        timesteps = self.scheduler.timesteps[t_start:]
 
-        # Some schedulers like PNDM have timesteps as arrays
-        # It's more optimzed to move all timesteps to correct device beforehand
-        timesteps_tensor = torch.tensor(self.scheduler.timesteps[t_start:], device=self.device)
-
-        for i, t in tqdm(enumerate(timesteps_tensor)):
+        for i, t in tqdm(enumerate(timesteps)):
             t_index = t_start + i
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents