Multistep Consistency Models
<|reference_start|>Multistep Consistency Models: Diffusion models are relatively easy to train but require many steps to generate samples. Consistency models are far more difficult to train, but generate samples in a single step.   In this paper we propose Multistep Consistency Models: A unification between Consistency Models (Song et al., 2023) and TRACT (Berthelot et al., 2023) that can interpolate between a consistency model and a diffusion model: a trade-off between sampling speed and sampling quality. Specifically, a 1-step consistency model is a conventional consistency model whereas a $\infty$-step consistency model is a diffusion model.   Multistep Consistency Models work really well in practice. By increasing the sample budget from a single step to 2-8 steps, we can train models more easily that generate higher quality samples, while retaining much of the sampling speed benefits. Notable results are 1.4 FID on Imagenet 64 in 8 step and 2.1 FID on Imagenet128 in 8 steps with consistency distillation, using simple losses without adversarial training. We also show that our method scales to a text-to-image diffusion model, generating samples that are close to the quality of the original model.<|reference_end|>
@article{heek2024multistep,
    title={Multistep Consistency Models},
    author={Jonathan Heek, Emiel Hoogeboom, Tim Salimans},
    journal={arXiv preprint arXiv:2403.06807},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06807},
    primaryClass={cs.LG cs.CV stat.ML}
}