MobileDiffusion: Rapid text-to-image generation on-device

Text-to-image diffusion models have shown exceptional capabilities in generating high-quality images from text prompts. However, leading models feature billions of parameters and are consequently expensive to run, requiring powerful desktops or servers (e.g., Stable Diffusion, DALL·E, and Imagen). While recent advancements in inference solutions on Android via MediaPipe and iOS via Core ML have been made in the past year, rapid (sub-second) text-to-image generation on mobile devices has remained out of reach.

To that end, in “MobileDiffusion: Subsecond Text-to-Image Generation on Mobile Devices”, we introduce a novel approach with the potential for rapid text-to-image generation on-device. MobileDiffusion is an efficient latent diffusion model specifically designed for mobile devices. We also adopt DiffusionGAN to achieve one-step sampling during inference, which fine-tunes a pre-trained diffusion model while leveraging a GAN to model the denoising step. We have tested MobileDiffusion on iOS and Android premium devices, and it can run in half a second to generate a 512×512 high-quality image. Its comparably small model size of just 520M parameters makes it uniquely suited for mobile deployment.

Background

The relative inefficiency of text-to-image diffusion models arises from two primary challenges. First, the inherent design of diffusion models requires iterative denoising to generate images, necessitating multiple evaluations of the model. Second, the complexity of the network architecture in text-to-image diffusion models involves a substantial number of parameters, regularly reaching into the billions and resulting in computationally expensive evaluations. As a result, despite the potential benefits of deploying generative models…