---
title: "EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer"
canonical_url: "https://www.modelscope.cn/papers/124672"
md_url: "https://www.modelscope.cn/papers/124672.md"
arxiv_id: 2503.07027
published: 2025-03-10
last_updated: 2025-03-10
authors:
  - "Yuxuan Zhang"
  - "Yirui Yuan"
  - "Yiren Song"
  - "Haofan Wang"
  - "Jiaming Liu"
model_name: EasyControl
model_developer: "Tiamat AI、ShanghaiTech University、National University of Singapore、Liblib AI"
domain:
  - "计算机视觉"
  - "图像生成"
  - "扩散模型"
  - "可控生成"
  - "Diffusion Transformer"
type:
  - "计算机视觉"
  - "图像生成"
  - "扩散模型"
  - "可控生成"
  - "Diffusion Transformer"
  - "Computer Vision and Pattern Recognition"
arxiv_url: "https://arxiv.org/abs/2503.07027"
pdf_url: "https://arxiv.org/pdf/2503.07027"
code_link: "https://github.com/Xiaojiu-z/EasyControl"
---

# EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer

> 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…

「EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer」是 ModelScope 魔搭社区收录的论文，arXiv 2503.07027，作者为 Yuxuan Zhang, Yirui Yuan, Yiren Song et al.，发表于 2025-03-10，属于 计算机视觉、图像生成、扩散模型 领域。

- **ArXiv**: 2503.07027
- **Published**: 2025-03-10
- **Authors**: Yuxuan Zhang, Yirui Yuan, Yiren Song, Haofan Wang, Jiaming Liu
- **Model**: EasyControl
- **Developer**: Tiamat AI、ShanghaiTech University、National University of Singapore、Liblib AI
- **Domain**: 计算机视觉, 图像生成, 扩散模型, 可控生成, Diffusion Transformer
- **ArXiv URL**: https://arxiv.org/abs/2503.07027
- **PDF**: https://arxiv.org/pdf/2503.07027
- **Code**: https://github.com/Xiaojiu-z/EasyControl

Source: https://www.modelscope.cn/papers/124672

---

> EasyControl：为扩散变换器注入高效灵活的条件控制

## 摘要

本文提出了一种名为EasyControl的新型框架，旨在为扩散变换器（Diffusion Transformer）提供高效且灵活的条件控制机制。现有的基于U-Net的扩散模型如ControlNet和IP-Adapter已经引入了有效的空间和主体控制机制，但扩散变换器架构在这一方面仍面临挑战。为了克服这些挑战，EasyControl通过三个关键创新来提升效率和灵活性：首先，引入了一个轻量级的条件注入LoRA模块，该模块可以在不修改基础模型权重的情况下，实现条件信号的独立注入，确保与定制模型的兼容性，并支持零样本多条件泛化。其次，提出了一种位置感知训练范式，通过将输入条件标准化为固定分辨率，使生成图像具有任意宽高比和灵活分辨率，同时优化计算效率。最后，开发了一种因果注意力机制并结合KV缓存技术，显著减少了图像合成的延迟。通过广泛的实验验证，EasyControl展示了其在各种应用场景中的卓越性能，特别是在多条件协同控制和高效推理方面。

## Abstract

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.