EasyControl: Adding Efficient and Flexible Control for Diffusion
  Transformer
Authors: name: Yuxuan Zhang, name: Yirui Yuan, name: Yiren Song, name: Haofan Wang, name: Jiaming Liu

Recent advancements in Unet-based diffusion models, such as ControlNet and
IP-Adapter, have introduced effective spatial and subject control mechanisms.
However, the DiT (Diffusion Transformer) architecture still struggles with
efficient and flexible control. To tackle this issue, we propose EasyControl, a
novel framework designed to unify condition-guided diffusion transformers with
high efficiency and flexibility. Our framework is built on three key
innovations. First, we introduce a lightweight Condition Injection LoRA Module.
This module processes conditional signals in isolation, acting as a
plug-and-play solution. It avoids modifying the base model weights, ensuring
compatibility with customized models and enabling the flexible injection of
diverse conditions. Notably, this module also supports harmonious and robust
zero-shot multi-condition generalization, even when trained only on
single-condition data. Second, we propose a Position-Aware Training Paradigm.
This approach standardizes input conditions to fixed resolutions, allowing the
generation of images with arbitrary aspect ratios and flexible resolutions. At
the same time, it optimizes computational efficiency, making the framework more
practical for real-world applications. Third, we develop a Causal Attention
Mechanism combined with the KV Cache technique, adapted for conditional
generation tasks. This innovation significantly reduces the latency of image
synthesis, improving the overall efficiency of the framework. Through extensive
experiments, we demonstrate that EasyControl achieves exceptional performance
across various application scenarios. These innovations collectively make our
framework highly efficient, flexible, and suitable for a wide range of tasks.