CCM: Adding Conditional Controls to Text-to-Image Consistency Models
Jie Xiao Kai Zhu Han Zhang Zhiheng Liu Yujun Shen Yu Liu Xueyang Fu Zheng-Jun Zha
Consistency Models (CMs) have showed a promise in creating visual content
efficiently and with high quality. However, the way to add new conditional
controls to the pretrained CMs has not been explored. In this technical report,
we consider alternative strategies for adding ControlNet-like conditional
control to CMs and present three significant findings. 1) ControlNet trained
for diffusion models (DMs) can be directly applied to CMs for high-level
semantic controls but struggles with low-level detail and realism control. 2)
CMs serve as an independent class of generative models, based on which
ControlNet can be trained from scratch using Consistency Training proposed by
Song et al. 3) A lightweight adapter can be jointly optimized under multiple
conditions through Consistency Training, allowing for the swift transfer of
DMs-based ControlNet to CMs. We study these three solutions across various
conditional controls, including edge, depth, human pose, low-resolution image
and masked image with text-to-image latent consistency models.