BrushNet/docs/source/en/api/pipelines/stable_cascade.md

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# Stable Cascade

This model is built upon the [Würstchen](https://openreview.net/forum?id=gU58d5QeGv) architecture and its main 
difference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this 
important? The smaller the latent space, the **faster** you can run inference and the **cheaper** the training becomes. 
How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being 
encoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a 
1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the 
highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable 
Diffusion 1.5.

Therefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions
like finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.

The original codebase can be found at [Stability-AI/StableCascade](https://github.com/Stability-AI/StableCascade).

## Model Overview
Stable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images,
hence the name "Stable Cascade".

Stage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion. 
However, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a 
spatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves 
a compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the 
image. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible 
for generating the small 24 x 24 latents given a text prompt.

## Uses

### Direct Use

The model is intended for research purposes for now. Possible research areas and tasks include

- Research on generative models.
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.

Excluded uses are described below.

### Out-of-Scope Use

The model was not trained to be factual or true representations of people or events, 
and therefore using the model to generate such content is out-of-scope for the abilities of this model.
The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy).

## Limitations and Bias

### Limitations
- Faces and people in general may not be generated properly.
- The autoencoding part of the model is lossy.


## StableCascadeCombinedPipeline

[[autodoc]] StableCascadeCombinedPipeline
	- all
	- __call__

## StableCascadePriorPipeline

[[autodoc]] StableCascadePriorPipeline
	- all
	- __call__

## StableCascadePriorPipelineOutput

[[autodoc]] pipelines.stable_cascade.pipeline_stable_cascade_prior.StableCascadePriorPipelineOutput

## StableCascadeDecoderPipeline

[[autodoc]] StableCascadeDecoderPipeline
	- all
	- __call__