OmniBooth: Learning Latent Control for Image Synthesis with Multi-modal
  Instruction
Authors: name: Leheng Li, name: Weichao Qiu, name: Xu Yan, name: Jing He, name: Kaiqiang Zhou, name: Yingjie Cai, name: Qing Lian, name: Bingbing Liu, name: Ying-Cong Chen

We present OmniBooth, an image generation framework that enables spatial
control with instance-level multi-modal customization. For all instances, the
multimodal instruction can be described through text prompts or image
references. Given a set of user-defined masks and associated text or image
guidance, our objective is to generate an image, where multiple objects are
positioned at specified coordinates and their attributes are precisely aligned
with the corresponding guidance. This approach significantly expands the scope
of text-to-image generation, and elevates it to a more versatile and practical
dimension in controllability. In this paper, our core contribution lies in the
proposed latent control signals, a high-dimensional spatial feature that
provides a unified representation to integrate the spatial, textual, and image
conditions seamlessly. The text condition extends ControlNet to provide
instance-level open-vocabulary generation. The image condition further enables
fine-grained control with personalized identity. In practice, our method
empowers users with more flexibility in controllable generation, as users can
choose multi-modal conditions from text or images as needed. Furthermore,
thorough experiments demonstrate our enhanced performance in image synthesis
fidelity and alignment across different tasks and datasets. Project page:
https://len-li.github.io/omnibooth-web/