  The iterative sampling procedure employed by diffusion models (DMs) often
leads to significant inference latency. To address this, we propose Stochastic
Consistency Distillation (SCott) to enable accelerated text-to-image
generation, where high-quality generations can be achieved with just 1-2
sampling steps, and further improvements can be obtained by adding additional
steps. In contrast to vanilla consistency distillation (CD) which distills the
ordinary differential equation solvers-based sampling process of a pretrained
teacher model into a student, SCott explores the possibility and validates the
efficacy of integrating stochastic differential equation (SDE) solvers into CD
to fully unleash the potential of the teacher. SCott is augmented with
elaborate strategies to control the noise strength and sampling process of the
SDE solver. An adversarial loss is further incorporated to strengthen the
sample quality with rare sampling steps. Empirically, on the MSCOCO-2017 5K
dataset with a Stable Diffusion-V1.5 teacher, SCott achieves an FID (Frechet
Inceptio Distance) of 22.1, surpassing that (23.4) of the 1-step InstaFlow (Liu
et al., 2023) and matching that of 4-step UFOGen (Xue et al., 2023b). Moreover,
SCott can yield more diverse samples than other consistency models for
high-resolution image generation (Luo et al., 2023a), with up to 16%
improvement in a qualified metric. The code and checkpoints are coming soon.
