[00:00] Introduction
[00:23] Computational cost of traditional diffusion models
[00:59] Reducing iterations in image generation
[01:06] Shortcut models
[01:39] Training process and self-consistency property
[02:22] Advantages over other methods
[03:05] Results on image generation benchmarks
[03:45] Application to robotic control
[04:15] Limitations and future work
[04:54] Best practices
Authors: Kevin Frans, Danijar Hafner, Sergey Levine, Pieter Abbeel
Affiliations: UC Berkeley
Abstract: Diffusion models and flow-matching models have enabled generating diverse and realistic images by learning to transfer noise to data. However, sampling from these models involves iterative denoising over many neural network passes, making generation slow and expensive. Previous approaches for speeding up sampling require complex training regimes, such as multiple training phases, multiple networks, or fragile scheduling. We introduce shortcut models, a family of generative models that use a single network and training phase to produce high-quality samples in a single or multiple sampling steps. Shortcut models condition the network not only on the current noise level but also on the desired step size, allowing the model to skip ahead in the generation process. Across a wide range of sampling step budgets, shortcut models consistently produce higher quality samples than previous approaches, such as consistency models and reflow. Compared to distillation, shortcut models reduce complexity to a single network and training phase and additionally allow varying step budgets at inference time.
