OminiControl: Minimal and Universal Control for Diffusion Transformer
Authors: name: Zhenxiong Tan, name: Songhua Liu, name: Xingyi Yang, name: Qiaochu Xue, name: Xinchao Wang

In this paper, we introduce OminiControl, a highly versatile and
parameter-efficient framework that integrates image conditions into pre-trained
Diffusion Transformer (DiT) models. At its core, OminiControl leverages a
parameter reuse mechanism, enabling the DiT to encode image conditions using
itself as a powerful backbone and process them with its flexible multi-modal
attention processors. Unlike existing methods, which rely heavily on additional
encoder modules with complex architectures, OminiControl (1) effectively and
efficiently incorporates injected image conditions with only ~0.1% additional
parameters, and (2) addresses a wide range of image conditioning tasks in a
unified manner, including subject-driven generation and spatially-aligned
conditions such as edges, depth, and more. Remarkably, these capabilities are
achieved by training on images generated by the DiT itself, which is
particularly beneficial for subject-driven generation. Extensive evaluations
demonstrate that OminiControl outperforms existing UNet-based and DiT-adapted
models in both subject-driven and spatially-aligned conditional generation.
Additionally, we release our training dataset, Subjects200K, a diverse
collection of over 200,000 identity-consistent images, along with an efficient
data synthesis pipeline to advance research in subject-consistent generation.