EasyControl：为扩散Transformer增添高效灵活的控制机制

EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer

March 10, 2025

作者: Yuxuan Zhang, Yirui Yuan, Yiren Song, Haofan Wang, Jiaming Liu

cs.AI

摘要

近期，基于Unet的扩散模型如ControlNet和IP-Adapter在空间与主体控制机制上取得了显著进展。然而，DiT（扩散变换器）架构在实现高效灵活控制方面仍面临挑战。为解决这一问题，我们提出了EasyControl，一个旨在统一条件引导扩散变换器的新框架，兼具高效性与灵活性。该框架建立在三大创新之上。首先，我们引入了轻量级的条件注入LoRA模块。该模块独立处理条件信号，作为即插即用的解决方案，无需修改基础模型权重，确保了与定制模型的兼容性，并支持灵活注入多种条件。值得注意的是，即便仅使用单条件数据进行训练，该模块也能实现和谐且鲁棒的零样本多条件泛化。其次，我们提出了位置感知训练范式。该方法将输入条件标准化至固定分辨率，从而支持生成任意宽高比和灵活分辨率的图像，同时优化了计算效率，使框架更适用于实际应用。第三，我们开发了结合KV缓存技术的因果注意力机制，专为条件生成任务设计。这一创新显著降低了图像合成的延迟，提升了框架的整体效率。通过大量实验，我们证明EasyControl在多种应用场景中均表现出色。这些创新共同使我们的框架高效、灵活，适用于广泛的任务领域。

English

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.