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