From fe6f5bfd5db5a721a35cfd92c7dd0aa6594a3d4f Mon Sep 17 00:00:00 2001 From: Ikpreet S Babra Date: Mon, 3 Feb 2025 14:48:46 +0530 Subject: [PATCH] Fixed grammar in "write_own_pipeline" readme --- docs/source/en/using-diffusers/write_own_pipeline.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/en/using-diffusers/write_own_pipeline.md b/docs/source/en/using-diffusers/write_own_pipeline.md index bdcd4e5d1307..283397ff3e9d 100644 --- a/docs/source/en/using-diffusers/write_own_pipeline.md +++ b/docs/source/en/using-diffusers/write_own_pipeline.md @@ -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.