Auto Windows Installer For Würstchen: Fast Diffusion for Image Generation

What is Würstchen? Würstchen is a diffusion model, whose text-conditional component 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 orders of magnitude. Training on 1024×1024 images is way more expensive than training on 32×32. 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, it achieves a 42x spatial compression! This had never been seen 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). Together Stage A and B are called the Decoder, because they decode the compressed images back into pixel space. 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. We refer to Stage C as the Prior. Why another text-to-image model? Well, this one is pretty fast and efficient. Würstchen’s biggest benefits come from the fact that it can generate images much faster than models like Stable Diffusion XL, while using a lot less memory! So for all of us who don’t have A100s lying around, this will come in handy. Here is a comparison with SDXL over different batch sizes: <a href="https://medium.com/@furkangozukara/auto-windows-installer-for-w%C3%BCrstchen-fast-diffusion-for-image-generation-aee59f085749">Click Here</a>