warp-ai/

wuerstchen-prior-model-finetuned

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). 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, allowing also cheaper and faster inference.

The Prior is what we refer to as "Stage C". It is the text-conditional model, operating in the small latent space that Stage A and Stage B encode images into. During inference, its job is to generate the image latents given text. These image latents are then sent to Stages A & B to decode the latents into pixel space.

This is the fully finetuned checkpoint. We recommend using the interpolated model, as this checkpoint is overfitted to being very artistic. However, if you are specifically looking for a very artistic checkpoint, go for this one. In the documentation We also give a short overview of the different Prior (Stage C) checkpoints.

Note: This model is only able to generate 1024x1024 images and shows repetitive patterns when sampling at different resolutions as the finetuning was only done on 1024x1024. The interpolated model does not have this problem.

Würstchen was trained on image resolutions between 1024x1024 & 1536x1536. We sometimes also observe good outputs at resolutions like 1024x2048. Feel free to try it out. We also observed that the Prior (Stage C) adapts extremely fast to new resolutions. So finetuning it at 2048x2048 should be computationally cheap.

This pipeline should be run together with https://huggingface.co/warp-ai/wuerstchen:

Würstchen v2 Estimated Emissions Based on that information, we estimate the following CO2 emissions using the Machine Learning Impact calculator presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.