Wuerstchen
Würstchen offers an innovative method for training text-conditional models, using a highly compressed latent space to achieve 42x compression. Detailed in the ICLR 2024 paper, this architecture employs multi-stage compression for fast and cost-effective text-to-image generation. Integrated with the diffusers library, Würstchen is easily accessible for implementation and testing through notebooks and scripts, providing a robust solution for researchers and developers involved in large-scale text-to-image diffusion models.
