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 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.
67cf98fd59dbba733d8c531e
Yuxuan Zhang
claimed_verified
2025-03-11T08:22:57.021Z
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yuxuan
user
zzyx
67cf98fd59dbba733d8c531f
Yirui Yuan
67cf98fd59dbba733d8c5320
Yiren Song
admin_assigned
2025-03-11T11:12:12.348Z
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Yiren Song
user
yiren98
67cf98fd59dbba733d8c5321
Haofan Wang
claimed_verified
2025-03-11T08:22:54.821Z
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Haofan Wang
user
wanghaofan
67cf98fd59dbba733d8c5322
Jiaming Liu
claimed_verified
2025-06-02T13:40:37.485Z
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LIU JIAMING
user
jamesliu1217
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 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.
EasyControl enhances diffusion transformers with efficient and flexible condition handling, position awareness, and causal attention for high-performance image synthesis.
Unet-based diffusion models ControlNet IP-Adapter DiT diffusion transformers Condition Injection LoRA Module Position-Aware Training Paradigm Causal Attention Mechanism KV Cache zero-shot multi-condition generalization arbitrary aspect ratios flexible resolutions