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The rapid development and application of foundation models haverevolutionized the field of artificial intelligence. Large diffusion modelshave gained significant attention for their ability to generate photorealisticimages and support various tasks. On-device deployment of these models providesbenefits such as lower server costs, offline functionality, and improved userprivacy. However, common large diffusion models have over 1 billion parametersand pose challenges due to restricted computational and memory resources ondevices. We present a series of implementation optimizations for largediffusion models that achieve the fastest reported inference latency to-date(under 12 seconds for Stable Diffusion 1.4 without int8 quantization on SamsungS23 Ultra for a 512x512 image with 20 iterations) on GPU-equipped mobiledevices. These enhancements broaden the applicability of generative AI andimprove the overall user experience across a wide range of devices.

Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations