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On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations
- djoldman 4y agoAbstract 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 for a 512 × 512 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.
- ArtWomb 4y ago"explore the potential benefits of employing Winograd with varying tile sizes on the 3 × 3 kernel convolutions. Our findings led us to select a 4 × 4 tile size" A very common GPU optimization ;) 12s Text-to-Image running native on mobile gpus? I'm on Verizon.com right now ordering a Samsung Ultra 23 as my new AI Research Cray-1 Supercomputer!