OmniBooth: Learning Latent Control for Image Synthesis with Multi-modal Instruction

Leheng Li, Weichao Qiu, Xu Yan, Jing He, Kaiqiang Zhou, Yingjie CAI, Qing LIAN, Bingbing Liu, Ying-Cong Chen

Keywords: Image Synthesis, Diffusion Model, Controllable Generation, Spatial Control, Multi-modal Instruction

TL;DR: We propose an image generation framework that enables spatial control with instance-level multi-model customization.

Abstract: We present OmniBooth, an image generation framework that enables spatial control with instance-level multi-modal customization. For all instances, the multi-modal 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.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 336