diff --git a/docs/source/en/api/pipelines/wuerstchen.md b/docs/source/en/api/pipelines/wuerstchen.md
index 3cd59f996b..0342631488 100644
--- a/docs/source/en/api/pipelines/wuerstchen.md
+++ b/docs/source/en/api/pipelines/wuerstchen.md
@@ -9,7 +9,7 @@ The abstract from the paper is:
 *We introduce Würstchen, a novel technique for text-to-image synthesis that unites competitive performance with unprecedented cost-effectiveness and ease of training on constrained hardware. Building on recent advancements in machine learning, our approach, which utilizes latent diffusion strategies at strong latent image compression rates, significantly reduces the computational burden, typically associated with state-of-the-art models, while preserving, if not enhancing, the quality of generated images. Wuerstchen achieves notable speed improvements at inference time, thereby rendering real-time applications more viable. One of the key advantages of our method lies in its modest training requirements of only 9,200 GPU hours, slashing the usual costs significantly without compromising the end performance. In a comparison against the state-of-the-art, we found the approach to yield strong competitiveness. This paper opens the door to a new line of research that prioritizes both performance and computational accessibility, hence democratizing the use of sophisticated AI technologies. Through Wuerstchen, we demonstrate a compelling stride forward in the realm of text-to-image synthesis, offering an innovative path to explore in future research.*
 
 ## Würstchen Overview
-Würstchen is a diffusion model, whose text-conditional model works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by magnitudes. Training on 1024x1024 images is way more expensive than training on 32x32. Usually, other works make use of a relatively small compression, in the range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, we achieve a 42x spatial compression. This was unseen before because common methods fail to faithfully reconstruct detailed images after 16x spatial compression. Würstchen employs a two-stage compression, what we call Stage A and Stage B. Stage A is a VQGAN, and Stage B is a Diffusion Autoencoder (more details can be found in the [paper](https://huggingface.co/papers/2306.00637)). A third model, Stage C, is learned in that highly compressed latent space. This training requires fractions of the compute used for current top-performing models, while also allowing cheaper and faster inference.
+Würstchen is a diffusion model, whose text-conditional model works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by magnitudes. Training on 1024x1024 images is way more expensive than training on 32x32. Usually, other works make use of a relatively small compression, in the range of 4x - 8x spatial compression. Würstchen takes this to an extreme. Through its novel design, we achieve a 42x spatial compression. This was unseen before because common methods fail to faithfully reconstruct detailed images after 16x spatial compression. Würstchen employs a two-stage compression, what we call Stage A and Stage B. Stage A is a VQGAN, and Stage B is a Diffusion Autoencoder (more details can be found in the [paper](https://huggingface.co/papers/2306.00637) ). A third model, Stage C, is learned in that highly compressed latent space. This training requires fractions of the compute used for current top-performing models, while also allowing cheaper and faster inference.
 
 ## Würstchen v2 comes to Diffusers
 
@@ -113,7 +113,7 @@ after 1024x1024 is 1152x1152
 
 The original codebase, as well as experimental ideas, can be found at [dome272/Wuerstchen](https://github.com/dome272/Wuerstchen).
 
-## WuerschenPipeline
+## WuerstchenCombinedPipeline
 
 [[autodoc]] WuerstchenCombinedPipeline
 	- all
@@ -121,8 +121,7 @@ The original codebase, as well as experimental ideas, can be found at [dome272/W
 
 ## WuerstchenPriorPipeline
 
-[[autodoc]] WuerstchenDecoderPipeline
-
+[[autodoc]] WuerstchenPriorPipeline
 	- all
 	- __call__
 
diff --git a/src/diffusers/pipelines/wuerstchen/__init__.py b/src/diffusers/pipelines/wuerstchen/__init__.py
index 3a6a464aef..dcdafdfd30 100644
--- a/src/diffusers/pipelines/wuerstchen/__init__.py
+++ b/src/diffusers/pipelines/wuerstchen/__init__.py
@@ -40,7 +40,7 @@ if TYPE_CHECKING:
         from .modeling_wuerstchen_prior import WuerstchenPrior
         from .pipeline_wuerstchen import WuerstchenDecoderPipeline
         from .pipeline_wuerstchen_combined import WuerstchenCombinedPipeline
-        from .pipeline_wuerstchen_prior import WuerstchenPriorPipeline
+        from .pipeline_wuerstchen_prior import DEFAULT_STAGE_C_TIMESTEPS, WuerstchenPriorPipeline
 else:
     import sys