UniControl: A Unified Diffusion Model for Controllable Visual Generation
  In the Wild
Authors: name: Can Qin, name: Shu Zhang, name: Ning Yu, name: Yihao Feng, name: Xinyi Yang, name: Yingbo Zhou, name: Huan Wang, name: Juan Carlos Niebles, name: Caiming Xiong, name: Silvio Savarese, name: Stefano Ermon, name: Yun Fu, name: Ran Xu

Achieving machine autonomy and human control often represent divergent
objectives in the design of interactive AI systems. Visual generative
foundation models such as Stable Diffusion show promise in navigating these
goals, especially when prompted with arbitrary languages. However, they often
fall short in generating images with spatial, structural, or geometric
controls. The integration of such controls, which can accommodate various
visual conditions in a single unified model, remains an unaddressed challenge.
In response, we introduce UniControl, a new generative foundation model that
consolidates a wide array of controllable condition-to-image (C2I) tasks within
a singular framework, while still allowing for arbitrary language prompts.
UniControl enables pixel-level-precise image generation, where visual
conditions primarily influence the generated structures and language prompts
guide the style and context. To equip UniControl with the capacity to handle
diverse visual conditions, we augment pretrained text-to-image diffusion models
and introduce a task-aware HyperNet to modulate the diffusion models, enabling
the adaptation to different C2I tasks simultaneously. Trained on nine unique
C2I tasks, UniControl demonstrates impressive zero-shot generation abilities
with unseen visual conditions. Experimental results show that UniControl often
surpasses the performance of single-task-controlled methods of comparable model
sizes. This control versatility positions UniControl as a significant
advancement in the realm of controllable visual generation.