T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model
  with Mixed Reward Feedback
Authors: name: Jiachen Li, name: Weixi Feng, name: Tsu-Jui Fu, name: Xinyi Wang, name: Sugato Basu, name: Wenhu Chen, name: William Yang Wang

Diffusion-based text-to-video (T2V) models have achieved significant success
but continue to be hampered by the slow sampling speed of their iterative
sampling processes. To address the challenge, consistency models have been
proposed to facilitate fast inference, albeit at the cost of sample quality. In
this work, we aim to break the quality bottleneck of a video consistency model
(VCM) to achieve both fast and high-quality video generation. We
introduce T2V-Turbo, which integrates feedback from a mixture of differentiable
reward models into the consistency distillation (CD) process of a pre-trained
T2V model. Notably, we directly optimize rewards associated with single-step
generations that arise naturally from computing the CD loss, effectively
bypassing the memory constraints imposed by backpropagating gradients through
an iterative sampling process. Remarkably, the 4-step generations from our
T2V-Turbo achieve the highest total score on VBench, even surpassing Gen-2 and
Pika. We further conduct human evaluations to corroborate the results,
validating that the 4-step generations from our T2V-Turbo are preferred over
the 50-step DDIM samples from their teacher models, representing more than a
tenfold acceleration while improving video generation quality.