EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer

2025-03-10

AI Summary

DiT architectures face limitations in efficient and flexible conditional control. This paper proposes a lightweight, plug-and-play conditional injection framework to address three key challenges: poor zero-shot generalization across multiple conditions, fixed-resolution generation, and high inference latency. Our method introduces (1) a novel Condition Injection LoRA module that enables conditional decoupling and parameter-efficient injection; (2) a Position-Aware normalization training paradigm to enhance aspect-ratio robustness; and (3) a causally masked attention mechanism with KV caching, optimized for conditional generation to reduce GPU memory consumption and accelerate inference. The framework requires training on only a single condition yet supports zero-shot multi-condition control and arbitrary-resolution/aspect-ratio image synthesis. It preserves full compatibility with existing DiT backbones while significantly improving control flexibility and inference efficiency.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Efficient and flexible control for Diffusion Transformer (DiT)
• Lightweight Condition Injection LoRA Module for diverse conditions
• Position-Aware Training Paradigm for flexible image resolutions

Innovation

• Lightweight Condition Injection LoRA Module
• Position-Aware Training Paradigm
• Causal Attention Mechanism with KV Cache