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.

介绍了一种新颖的文本到图像合成技术 Wuerstchen，它在有限的硬件资源下实现了竞争性能和无前例的成本效益和易训练性。该方法利用最近机器学习的进展，在强潜在图像压缩率上采用潜在扩散策略，可显著降低计算负担，同时保留或提高所生成图像的质量，为实时应用提供了可行性。与最先进的模型相比，Wuerstchen 具有显著的推理速度改进，并且仅需 9200 GPU 小时的有限训练成本，能够大幅降低一般成本且不影响最终性能。通过 Wuerstchen，我们在文本到图像合成领域向前迈出了一大步，并为未来研究提供了一种创新的探索路径，从而将 AI 技术的应用民主化