  The rapid development and application of foundation models have
revolutionized the field of artificial intelligence. Large diffusion models
have gained significant attention for their ability to generate photorealistic
images and support various tasks. On-device deployment of these models provides
benefits such as lower server costs, offline functionality, and improved user
privacy. However, common large diffusion models have over 1 billion parameters
and pose challenges due to restricted computational and memory resources on
devices. We present a series of implementation optimizations for large
diffusion models that achieve the fastest reported inference latency to-date
(under 12 seconds for Stable Diffusion 1.4 without int8 quantization on Samsung
S23 Ultra for a 512x512 image with 20 iterations) on GPU-equipped mobile
devices. These enhancements broaden the applicability of generative AI and
improve the overall user experience across a wide range of devices.
