title: Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse
    Controls to Any Diffusion Model
summary: ControlNets are widely used for adding spatial control in image generation
  with different conditions, such as depth maps, canny edges, and human poses.
  However, there are several challenges when leveraging the pretrained image
  ControlNets for controlled video generation. First, pretrained ControlNet
  cannot be directly plugged into new backbone models due to the mismatch of
  feature spaces, and the cost of training ControlNets for new backbones is a big
  burden. Second, ControlNet features for different frames might not effectively
  handle the temporal consistency. To address these challenges, we introduce
  Ctrl-Adapter, an efficient and versatile framework that adds diverse controls
  to any image/video diffusion models, by adapting pretrained ControlNets (and
  improving temporal alignment for videos). Ctrl-Adapter provides diverse
  capabilities including image control, video control, video control with sparse
  frames, multi-condition control, compatibility with different backbones,
  adaptation to unseen control conditions, and video editing. In Ctrl-Adapter, we
  train adapter layers that fuse pretrained ControlNet features to different
  image/video diffusion models, while keeping the parameters of the ControlNets
  and the diffusion models frozen. Ctrl-Adapter consists of temporal and spatial
  modules so that it can effectively handle the temporal consistency of videos.
  We also propose latent skipping and inverse timestep sampling for robust
  adaptation and sparse control. Moreover, Ctrl-Adapter enables control from
  multiple conditions by simply taking the (weighted) average of ControlNet
  outputs. With diverse image/video diffusion backbones (SDXL, Hotshot-XL,
  I2VGen-XL, and SVD), Ctrl-Adapter matches ControlNet for image control and
  outperforms all baselines for video control (achieving the SOTA accuracy on the
  DAVIS 2017 dataset) with significantly lower computational costs (less than 10
  GPU hours).
paper_authors._id: 661de89c69a65c0fb0852b2c
paper_authors.name: Han Lin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-18T19:35:21.773Z
paper_authors._id: 646e86350867c99c2d3f2ecf
paper_authors.avatarUrl: /avatars/b89798ff623abffb169eacda2ac32fde.svg
paper_authors.fullname: Han Lin
paper_authors.type: user
paper_authors.user: hanlincs
paper_authors._id: 661de89c69a65c0fb0852b2d
paper_authors.name: Jaemin Cho
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T09:42:56.382Z
paper_authors._id: 5ffe32d8942cf3533d364449
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1654821969191-5ffe32d8942cf3533d364449.jpeg
paper_authors.fullname: Jaemin Cho
paper_authors.type: user
paper_authors.user: j-min
paper_authors._id: 661de89c69a65c0fb0852b2e
paper_authors.name: Abhay Zala
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T09:43:02.641Z
paper_authors._id: 6361794819e7e4bf45741586
paper_authors.avatarUrl: /avatars/e4570b6ce752dc2b9b42969e21a50b57.svg
paper_authors.fullname: Abhay Zala
paper_authors.type: user
paper_authors.user: abhayzala
paper_authors._id: 661de89c69a65c0fb0852b2f
paper_authors.name: Mohit Bansal
paper_title: Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse
    Controls to Any Diffusion Model
paper_summary: ControlNets are widely used for adding spatial control in image generation
  with different conditions, such as depth maps, canny edges, and human poses.
  However, there are several challenges when leveraging the pretrained image
  ControlNets for controlled video generation. First, pretrained ControlNet
  cannot be directly plugged into new backbone models due to the mismatch of
  feature spaces, and the cost of training ControlNets for new backbones is a big
  burden. Second, ControlNet features for different frames might not effectively
  handle the temporal consistency. To address these challenges, we introduce
  Ctrl-Adapter, an efficient and versatile framework that adds diverse controls
  to any image/video diffusion models, by adapting pretrained ControlNets (and
  improving temporal alignment for videos). Ctrl-Adapter provides diverse
  capabilities including image control, video control, video control with sparse
  frames, multi-condition control, compatibility with different backbones,
  adaptation to unseen control conditions, and video editing. In Ctrl-Adapter, we
  train adapter layers that fuse pretrained ControlNet features to different
  image/video diffusion models, while keeping the parameters of the ControlNets
  and the diffusion models frozen. Ctrl-Adapter consists of temporal and spatial
  modules so that it can effectively handle the temporal consistency of videos.
  We also propose latent skipping and inverse timestep sampling for robust
  adaptation and sparse control. Moreover, Ctrl-Adapter enables control from
  multiple conditions by simply taking the (weighted) average of ControlNet
  outputs. With diverse image/video diffusion backbones (SDXL, Hotshot-XL,
  I2VGen-XL, and SVD), Ctrl-Adapter matches ControlNet for image control and
  outperforms all baselines for video control (achieving the SOTA accuracy on the
  DAVIS 2017 dataset) with significantly lower computational costs (less than 10
  GPU hours).
paper_ai_summary: Ctrl-Adapter efficiently integrates pretrained ControlNets into various diffusion models to enable versatile image and video control with superior performance and reduced computational costs.
paper_ai_keywords: ControlNets diffusion models spatial control image generation video generation feature spaces temporal consistency adapter layers latent skipping inverse timestep sampling SDXL Hotshot-XL I2VGen-XL SVD DAVIS 2017