title: RelaCtrl: Relevance-Guided Efficient Control for Diffusion Transformers
summary: The Diffusion Transformer plays a pivotal role in advancing text-to-image and
  text-to-video generation, owing primarily to its inherent scalability. However,
  existing controlled diffusion transformer methods incur significant parameter
  and computational overheads and suffer from inefficient resource allocation due
  to their failure to account for the varying relevance of control information
  across different transformer layers. To address this, we propose the
  Relevance-Guided Efficient Controllable Generation framework, RelaCtrl,
  enabling efficient and resource-optimized integration of control signals into
  the Diffusion Transformer. First, we evaluate the relevance of each layer in
  the Diffusion Transformer to the control information by assessing the
  "ControlNet Relevance Score"-i.e., the impact of skipping each control layer on
  both the quality of generation and the control effectiveness during inference.
  Based on the strength of the relevance, we then tailor the positioning,
  parameter scale, and modeling capacity of the control layers to reduce
  unnecessary parameters and redundant computations. Additionally, to further
  improve efficiency, we replace the self-attention and FFN in the commonly used
  copy block with the carefully designed Two-Dimensional Shuffle Mixer (TDSM),
  enabling efficient implementation of both the token mixer and channel mixer.
  Both qualitative and quantitative experimental results demonstrate that our
  approach achieves superior performance with only 15% of the parameters and
  computational complexity compared to PixArt-delta. More examples are available
  at https://relactrl.github.io/RelaCtrl/.
paper_authors._id: 67b7f350357c2729ac216494
paper_authors.name: Ke Cao
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/0dad41e3e2f38f89b7b21c12d673f432.svg
paper_authors.fullname: Ke Cao
paper_authors.type: user
paper_authors.user: kecao
paper_authors._id: 67b7f350357c2729ac216495
paper_authors.name: Jing Wang
paper_authors._id: 67b7f350357c2729ac216496
paper_authors.name: Ao Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-21T15:07:53.347Z
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paper_authors.avatarUrl: /avatars/7f212daaa20ab0d7405e9d6351ec308c.svg
paper_authors.fullname: Ao Ma
paper_authors.type: user
paper_authors.user: AoMa
paper_authors._id: 67b7f350357c2729ac216497
paper_authors.name: Jiasong Feng
paper_authors._id: 67b7f350357c2729ac216498
paper_authors.name: Zhanjie Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-21T15:07:37.282Z
paper_authors._id: 640496240ab5e22719f31719
paper_authors.avatarUrl: /avatars/be957a1b0e1c037f03f1439d6142e9ce.svg
paper_authors.fullname: Zhanjie Zhang
paper_authors.type: user
paper_authors.user: zhangzhanjay
paper_authors._id: 67b7f350357c2729ac216499
paper_authors.name: Xuanhua He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-21T15:07:32.005Z
paper_authors._id: 64375035946fb080c6fc4551
paper_authors.avatarUrl: /avatars/8dba2c8911726656d4088862a2b8fe7c.svg
paper_authors.fullname: Xuanhua He
paper_authors.type: user
paper_authors.user: Alexhe101
paper_authors._id: 67b7f350357c2729ac21649a
paper_authors.name: Shanyuan Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-21T15:07:26.104Z
paper_authors._id: 666197e302a5e5f4377a545c
paper_authors.avatarUrl: /avatars/6d6ef51cc403ffccc81265ad8adf43bc.svg
paper_authors.fullname: liu
paper_authors.type: user
paper_authors.user: shanyuanLiu
paper_authors._id: 67b7f350357c2729ac21649b
paper_authors.name: Bo Cheng
paper_authors._id: 67b7f350357c2729ac21649c
paper_authors.name: Dawei Leng
paper_authors._id: 67b7f350357c2729ac21649d
paper_authors.name: Yuhui Yin
paper_authors._id: 67b7f350357c2729ac21649e
paper_authors.name: Jie Zhang
paper_title: RelaCtrl: Relevance-Guided Efficient Control for Diffusion Transformers
paper_summary: The Diffusion Transformer plays a pivotal role in advancing text-to-image and
  text-to-video generation, owing primarily to its inherent scalability. However,
  existing controlled diffusion transformer methods incur significant parameter
  and computational overheads and suffer from inefficient resource allocation due
  to their failure to account for the varying relevance of control information
  across different transformer layers. To address this, we propose the
  Relevance-Guided Efficient Controllable Generation framework, RelaCtrl,
  enabling efficient and resource-optimized integration of control signals into
  the Diffusion Transformer. First, we evaluate the relevance of each layer in
  the Diffusion Transformer to the control information by assessing the
  "ControlNet Relevance Score"-i.e., the impact of skipping each control layer on
  both the quality of generation and the control effectiveness during inference.
  Based on the strength of the relevance, we then tailor the positioning,
  parameter scale, and modeling capacity of the control layers to reduce
  unnecessary parameters and redundant computations. Additionally, to further
  improve efficiency, we replace the self-attention and FFN in the commonly used
  copy block with the carefully designed Two-Dimensional Shuffle Mixer (TDSM),
  enabling efficient implementation of both the token mixer and channel mixer.
  Both qualitative and quantitative experimental results demonstrate that our
  approach achieves superior performance with only 15% of the parameters and
  computational complexity compared to PixArt-delta. More examples are available
  at https://relactrl.github.io/RelaCtrl/.
paper_ai_keywords: Diffusion Transformer text-to-image text-to-video generation parameter overheads computational overheads controlled diffusion Relevance-Guided Efficient Controllable Generation RelaCtrl ControlNet Relevance Score control signals token mixer channel mixer Two-Dimensional Shuffle Mixer TDSM qualitative experimental results quantitative experimental results PixArt-delta