CCM: Adding Conditional Controls to Text-to-Image Consistency Models

• URL: http://arxiv.org/abs/2312.06971v1
• Date: Tue, 12 Dec 2023 04:16:03 GMT
• Title: CCM: Adding Conditional Controls to Text-to-Image Consistency Models
• Authors: Jie Xiao, Kai Zhu, Han Zhang, Zhiheng Liu, Yujun Shen, Yu Liu, Xueyang Fu, Zheng-Jun Zha
• Abstract summary: We consider alternative strategies for adding ControlNet-like conditional control to Consistency Models. A lightweight adapter can be jointly optimized under multiple conditions through Consistency Training. We study these three solutions across various conditional controls, including edge, depth, human pose, low-resolution image and masked image.
• Score: 89.75377958996305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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