An AutoComplete For Your Imagination

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Generate high-quality images, on your mobile device, as you type. It’s that old feeling of magic once more, oh Google, how we have missed ye.

MobileDiffusion is the deployment of an array of optimization methods to address what the Google team states are the two major issues in image diffusion models:

• their inherent design is to reduce noise in images iteratively, which requires multiple evaluations of the model

• the network architecture consists of a large number of parameters making them computationally expensive

MobileDiffusion achieves a remarkable sub-second inference speed for generating a 512 × 512 image on mobile devices, establishing a new state of the art.

The whole paper is a great read on how individual small optimizations each yielding 10-30% reduction in parameter count or computation can total to something significant. Their big breakthrough though is in getting the diffusion down to a single step using a GAN (a Generative Adversarial Network), reaching a new level of sampling efficiency. In effect, the model takes a single wild guess rather than multiple iterative how-am-I-doing check-ins to form an image from white noise and a text prompt.

It is not often you see an order of magnitude increase in performance (2 billion parameters for 50 steps vs 400 million parameters for 1 step), but there you have it.

Now just think what happens when the input prompt is a brain EEG.

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