Squeezing Large-Scale Diffusion Models for Mobile
Jiwoong Choi Minkyu Kim Daehyun Ahn Taesu Kim Yulhwa Kim Dongwon Jo Hyesung Jeon Jae-Joon Kim Hyungjun Kim
The emergence of diffusion models has greatly broadened the scope of
high-fidelity image synthesis, resulting in notable advancements in both
practical implementation and academic research. With the active adoption of the
model in various real-world applications, the need for on-device deployment has
grown considerably. However, deploying large diffusion models such as Stable
Diffusion with more than one billion parameters to mobile devices poses
distinctive challenges due to the limited computational and memory resources,
which may vary according to the device. In this paper, we present the
challenges and solutions for deploying Stable Diffusion on mobile devices with
TensorFlow Lite framework, which supports both iOS and Android devices. The
resulting Mobile Stable Diffusion achieves the inference latency of smaller
than 7 seconds for a 512x512 image generation on Android devices with mobile
GPUs.