title: EasyControl: Adding Efficient and Flexible Control for Diffusion
    Transformer
summary: 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.
paper_authors._id: 67cf98fd59dbba733d8c531e
paper_authors.name: Yuxuan Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-11T08:22:57.021Z
paper_authors._id: 636b3f9ce3ad78bc68b67541
paper_authors.avatarUrl: /avatars/2b7e745953ae39e01222e99fb63b279e.svg
paper_authors.fullname: yuxuan
paper_authors.type: user
paper_authors.user: zzyx
paper_authors._id: 67cf98fd59dbba733d8c531f
paper_authors.name: Yirui Yuan
paper_authors._id: 67cf98fd59dbba733d8c5320
paper_authors.name: Yiren Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T11:12:12.348Z
paper_authors._id: 64311a95034ecbefddd141ef
paper_authors.avatarUrl: /avatars/b6dc5ca373bedbaa368208517954c375.svg
paper_authors.fullname: Yiren Song
paper_authors.type: user
paper_authors.user: yiren98
paper_authors._id: 67cf98fd59dbba733d8c5321
paper_authors.name: Haofan Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-11T08:22:54.821Z
paper_authors._id: 637745113a63a2983ffbde13
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
paper_authors.fullname: Haofan Wang
paper_authors.type: user
paper_authors.user: wanghaofan
paper_authors._id: 67cf98fd59dbba733d8c5322
paper_authors.name: Jiaming Liu
paper_title: EasyControl: Adding Efficient and Flexible Control for Diffusion
    Transformer
paper_summary: 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.
paper_ai_keywords: Unet-based diffusion models ControlNet IP-Adapter DiT (Diffusion Transformer) Condition Injection LoRA Module Condition Injection zero-shot multi-condition generalization Position-Aware Training Paradigm Position-Aware Causal Attention Mechanism KV Cache conditional generation tasks image synthesis