For the sake of usability, Würstchen can be used with a single pipeline. This pipeline can be used as follows:  
```python
import torch
from diffusers import AutoPipelineForText2Image
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS

pipe = AutoPipelineForText2Image.from_pretrained("warp-ai/wuerstchen", torch_dtype=torch.float16).to("cuda")

caption = "Anthropomorphic cat dressed as a fire fighter"
images = pipe(
caption,
width=1024,
height=1536,
prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,
prior_guidance_scale=4.0,
num_images_per_prompt=2,
).images
```  
For explanation purposes, we can also initialize the two main pipelines of Würstchen individually. Würstchen consists of 3 stages: Stage C, Stage B, Stage A. They all have different jobs and work only together. When generating text-conditional images, Stage C will first generate the latents in a very compressed latent space. This is what happens in the `prior_pipeline`. Afterwards, the generated latents will be passed to Stage B, which decompresses the latents into a bigger latent space of a VQGAN. These latents can then be decoded by Stage A, which is a VQGAN, into the pixel-space. Stage B & Stage A are both encapsulated in the `decoder_pipeline`. For more details, take a look at the [paper](https://huggingface.co/papers/2306.00637).  
```python
import torch
from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS

device = "cuda"
dtype = torch.float16
num_images_per_prompt = 2

prior_pipeline = WuerstchenPriorPipeline.from_pretrained(
"warp-ai/wuerstchen-prior", torch_dtype=dtype
).to(device)
decoder_pipeline = WuerstchenDecoderPipeline.from_pretrained(
"warp-ai/wuerstchen", torch_dtype=dtype
).to(device)

caption = "Anthropomorphic cat dressed as a fire fighter"
negative_prompt = ""

prior_output = prior_pipeline(
prompt=caption,
height=1024,
width=1536,
timesteps=DEFAULT_STAGE_C_TIMESTEPS,
negative_prompt=negative_prompt,
guidance_scale=4.0,
num_images_per_prompt=num_images_per_prompt,
)
decoder_output = decoder_pipeline(
image_embeddings=prior_output.image_embeddings,
prompt=caption,
negative_prompt=negative_prompt,
guidance_scale=0.0,
output_type="pil",
).images[0]
decoder_output
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/wuerstchen.md