diff --git a/docs/source/en/using-diffusers/write_own_pipeline.md b/docs/source/en/using-diffusers/write_own_pipeline.md
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--- a/docs/source/en/using-diffusers/write_own_pipeline.md
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@@ -106,7 +106,7 @@ Let's try it out!
 
 ## Deconstruct the Stable Diffusion pipeline
 
-Stable Diffusion is a text-to-image *latent diffusion* model. It is called a latent diffusion model because it works with a lower-dimensional representation of the image instead of the actual pixel space, which makes it more memory efficient. The encoder compresses the image into a smaller representation, and a decoder to convert the compressed representation back into an image. For text-to-image models, you'll need a tokenizer and an encoder to generate text embeddings. From the previous example, you already know you need a UNet model and a scheduler.
+Stable Diffusion is a text-to-image *latent diffusion* model. It is called a latent diffusion model because it works with a lower-dimensional representation of the image instead of the actual pixel space, which makes it more memory efficient. The encoder compresses the image into a smaller representation, and a decoder converts the compressed representation back into an image. For text-to-image models, you'll need a tokenizer and an encoder to generate text embeddings. From the previous example, you already know you need a UNet model and a scheduler.
 
 As you can see, this is already more complex than the DDPM pipeline which only contains a UNet model. The Stable Diffusion model has three separate pretrained models.