diff --git a/docs/source/using-diffusers/custom_pipelines.mdx b/docs/source/using-diffusers/custom_pipelines.mdx
index 9466f1dcbda6..b52d405581b1 100644
--- a/docs/source/using-diffusers/custom_pipelines.mdx
+++ b/docs/source/using-diffusers/custom_pipelines.mdx
@@ -91,24 +91,24 @@ class MyPipeline(DiffusionPipeline):
         # Sample gaussian noise to begin loop
         image = torch.randn((batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size))
 
-    image = image.to(self.device)
+        image = image.to(self.device)
 
-    # set step values
-    self.scheduler.set_timesteps(num_inference_steps)
+        # set step values
+        self.scheduler.set_timesteps(num_inference_steps)
 
-    for t in self.progress_bar(self.scheduler.timesteps):
-        # 1. predict noise model_output
-        model_output = self.unet(image, t).sample
+        for t in self.progress_bar(self.scheduler.timesteps):
+            # 1. predict noise model_output
+            model_output = self.unet(image, t).sample
 
-        # 2. predict previous mean of image x_t-1 and add variance depending on eta
-        # eta corresponds to η in paper and should be between [0, 1]
-        # do x_t -> x_t-1
-        image = self.scheduler.step(model_output, t, image, eta).prev_sample
+            # 2. predict previous mean of image x_t-1 and add variance depending on eta
+            # eta corresponds to η in paper and should be between [0, 1]
+            # do x_t -> x_t-1
+            image = self.scheduler.step(model_output, t, image, eta).prev_sample
 
-    image = (image / 2 + 0.5).clamp(0, 1)
-    image = image.cpu().permute(0, 2, 3, 1).numpy()
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
 
-    return image
+        return image
 ```
 
 Now you can upload this short file under the name `pipeline.py` in your preferred [model repository](https://huggingface.co/docs/hub/models-uploading). For Stable Diffusion pipelines, you may also [join the community organisation for shared pipelines](https://huggingface.co/organizations/sd-diffusers-pipelines-library/share/BUPyDUuHcciGTOKaExlqtfFcyCZsVFdrjr) to upload yours.