diff --git a/docs/source/en/using-diffusers/write_own_pipeline.mdx b/docs/source/en/using-diffusers/write_own_pipeline.mdx
index fa47878e1b9b..3dca40dff714 100644
--- a/docs/source/en/using-diffusers/write_own_pipeline.mdx
+++ b/docs/source/en/using-diffusers/write_own_pipeline.mdx
@@ -82,8 +82,8 @@ To recreate the pipeline with the model and scheduler separately, let's write ou
     >>> for t in scheduler.timesteps:
     ...     with torch.no_grad():
     ...         noisy_residual = model(input, t).sample
-    >>> previous_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
-    >>> input = previous_noisy_sample
+    ...     previous_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
+    ...     input = previous_noisy_sample
     ```
 
     This is the entire denoising process, and you can use this same pattern to write any diffusion system.
@@ -287,4 +287,4 @@ This is really what 🧨 Diffusers is designed for: to make it intuitive and eas
 For your next steps, feel free to:
 
 * Learn how to [build and contribute a pipeline](using-diffusers/#contribute_pipeline) to 🧨 Diffusers. We can't wait and see what you'll come up with!
-* Explore [existing pipelines](./api/pipelines/overview) in the library, and see if you can deconstruct and build a pipeline from scratch using the models and schedulers separately.
\ No newline at end of file
+* Explore [existing pipelines](./api/pipelines/overview) in the library, and see if you can deconstruct and build a pipeline from scratch using the models and schedulers separately.