Research map / Video / Video generation
Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Ctrl-Adapter adapts pretrained image ControlNets (SDv1.5) to diverse image/video diffusion backbones via lightweight adapters with temporal layers, sparse-frame control, inverse timestep sampling, and patch-level MoE multi-condition composition.
Problem
Pretrained image ControlNets cannot be plugged into new diffusion backbones (different feature spaces and noise/timestep schedules), training new ControlNets is very expensive, and per-frame ControlNet application lacks temporal consistency for video control.
Approach
Train small adapter modules (spatial/temporal convolution and attention) that map features from a frozen pretrained image ControlNet into a frozen target U-Net/DiT backbone, skip the noisy latent as ControlNet input for robustness to noise scales and sparse frames, use inverse timestep sampling to align discrete ControlNet and continuous backbone timesteps, and fuse multiple conditions with a patc
Key results
- On DAVIS 2017 with SVD backbone, Ctrl-Adapter achieves FID 3.82 and optical flow error 2.96 for depth maps, the best among compared video control methods.
- On COCO val2017 with SDXL backbone, Ctrl-Adapter matches SDXL ControlNet in depth FID/MSE and outperforms it in SSIM (0.8534 vs 0.8363) for depth maps.
- Adapters are trained in under 10 GPU hours, versus roughly 700 A100 GPU hours for SDXL ControlNet training.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Adapt SDv1.5 ControlNet to SVD with Ctrl-Adapter | SVD · DAVIS 2017, depth map | FID: 4.91 -> 3.82 | Helped |
| Adapt SDv1.5 ControlNet to SVD with Ctrl-Adapter | SVD · DAVIS 2017, depth map | Optical Flow Error: 4.84 -> 2.96 | Helped |
| Adapt SDv1.5 ControlNet to SDXL with Ctrl-Adapter | SDXL · COCO val2017, depth map | SSIM: 0.8363 -> 0.8534 | Helped |
| Adapt SDv1.5 ControlNet to PixArt-α with Ctrl-Adapter | PixArt-α · COCO val2017, soft edge / HED | FID: 20.41 -> 17.52 | Helped |
| Use patch-level MLP weights instead of equal weights (ablation) | Ctrl-Adapter multi-condition video control · DAVIS 2017, D+C | Flow Error: 2.84 -> 2.34 | Helped |
| Use patch-level Q-Former weights instead of equal weights (ablation) | Ctrl-Adapter multi-condition video control · DAVIS 2017, D+C | FID: 8.50 -> 7.54 | Helped |
| Use patch-level MLP weights instead of equal weights (ablation) | Ctrl-Adapter multi-condition video control · DAVIS 2017, D+P | FID: 11.32 -> 9.37 | Helped |
| Use patch-level Q-Former weights instead of equal weights (ablation) | Ctrl-Adapter multi-condition video control · DAVIS 2017, D+C+N+S | Flow Error: 2.40 -> 2.31 | Helped |
| Use unconditional global weights instead of equal weights (ablation) | Ctrl-Adapter multi-condition video control · DAVIS 2017, D+C | FID: 8.50 -> 9.14 | Hurt |
| Use SC+SA twice instead of one SC+SA layer (ablation) | Ctrl-Adapter image control · COCO val2017, image control | FID | Helped |
| Use SC+SA twice instead of one SC+SA layer (ablation) | Ctrl-Adapter image control · COCO val2017, image control | MSE | Hurt |
| Use SC+SA three times (ablation) | Ctrl-Adapter image control · COCO val2017, image control | FID | Hurt |
Limitations
- Adapters must be trained separately for each backbone and condition set.
- Multi-condition and video results rely on specific backbones and DAVIS 2017 evaluation only.
- Downstream tasks such as video editing and style transfer are evaluated only qualitatively.
Experimental setup
Datasets: Panda-70M LAION POP COCO val2017 DAVIS 2017
Benchmarks: DAVIS 2017 COCO val2017
Metrics: FID optical flow error MSE SSIM
Baselines: SDXL ControlNet X-Adapter T2I-Adapter GLIGEN Uni-ControlNet PixArt-delta ControlNet ControlVideo VideoComposer Control-A-Video Text2Video-Zero SVD Temporal ControlNet SparseCtrl
Builds on: ControlNet X-Adapter Multi-ControlNet SparseCtrl Q-Former
Closest papers by meaning
- Universal Few-shot Spatial Control for Diffusion Models (NeurIPS 2025 · similarity 0.90)
- Training-free Camera Control for Video Generation (ICLR 2025 · similarity 0.89)
- CtrLoRA: An Extensible and Efficient Framework for Controllable Image Generation (ICLR 2025 · similarity 0.89)
- VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control (ICLR 2025 · similarity 0.89)
- Video Diffusion Models are Training-free Motion Interpreter and Controller (NeurIPS 2024 · similarity 0.88)
- CCEdit: Creative and Controllable Video Editing via Diffusion Models (CVPR 2024 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.