TY - INPR A1 - Pernias, Pablo A1 - Rampas, Dominic A1 - Aubreville, Marc T1 - Würstchen: Efficient Pretraining of Text-to-Image Models N2 - We introduce Wuerstchen, a novel technique for text-to-image synthesis that unites competitive performance with unprecedented cost-effectiveness and ease of training on constrained hardware. Building on recent advancements in machine learning, our approach, which utilizes latent diffusion strategies at strong latent image compression rates, significantly reduces the computational burden, typically associated with state-of-the-art models, while preserving, if not enhancing, the quality of generated images. Wuerstchen achieves notable speed improvements at inference time, thereby rendering real-time applications more viable. One of the key advantages of our method lies in its modest training requirements of only 9,200 GPU hours, slashing the usual costs significantly without compromising the end performance. In a comparison against the state-of-the-art, we found the approach to yield strong competitiveness. This paper opens the door to a new line of research that prioritizes both performance and computational accessibility, hence democratizing the use of sophisticated AI technologies. Through Wuerstchen, we demonstrate a compelling stride forward in the realm of text-to-image synthesis, offering an innovative path to explore in future research. UR - https://doi.org/10.48550/arXiv.2306.00637 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2306.00637 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-39160 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Pernias, Pablo A1 - Rampas, Dominic A1 - Richter, Mats Leon A1 - Pal, Christopher A1 - Aubreville, Marc T1 - Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models T2 - The Twelfth International Conference on Learning Representations (ICLR 2024) Y1 - 2024 UR - https://openreview.net/forum?id=gU58d5QeGv PB - OpenReview ER -