  Text-to-image diffusion models can create stunning images from natural
language descriptions that rival the work of professional artists and
photographers. However, these models are large, with complex network
architectures and tens of denoising iterations, making them computationally
expensive and slow to run. As a result, high-end GPUs and cloud-based inference
are required to run diffusion models at scale. This is costly and has privacy
implications, especially when user data is sent to a third party. To overcome
these challenges, we present a generic approach that, for the first time,
unlocks running text-to-image diffusion models on mobile devices in less than
$2$ seconds. We achieve so by introducing efficient network architecture and
improving step distillation. Specifically, we propose an efficient UNet by
identifying the redundancy of the original model and reducing the computation
of the image decoder via data distillation. Further, we enhance the step
distillation by exploring training strategies and introducing regularization
from classifier-free guidance. Our extensive experiments on MS-COCO show that
our model with $8$ denoising steps achieves better FID and CLIP scores than
Stable Diffusion v$1.5$ with $50$ steps. Our work democratizes content creation
by bringing powerful text-to-image diffusion models to the hands of users.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning