# Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model

arXiv: https://arxiv.org/abs/2404.09967 · HTML: https://mlsanity.com/paper/2404.09967/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 67 of its 91 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 30 of 67)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022)
- [PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis](https://mlsanity.com/paper/2310.00426/) (2023)
- [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://mlsanity.com/paper/2301.07093/) (2023)

## Built on by (most cited first, 30 of 66)

- [Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control](https://mlsanity.com/paper/2503.14492/) (2025)
- [Controllable Video Generation: A Survey](https://mlsanity.com/paper/2507.16869/) (2025)
- [CineMaster: A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generation](https://mlsanity.com/paper/2502.08639/) (2025)
- [Evaluation of Text-to-Video Generation Models: A Dynamics Perspective](https://mlsanity.com/paper/2407.01094/) (2024)
- [FullDiT: Multi-Task Video Generative Foundation Model with Full Attention](https://mlsanity.com/paper/2503.19907/) (2025)
- [Parameter-Efficient Fine-Tuning for Foundation Models](https://mlsanity.com/paper/2501.13787/) (2025)
- [Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices](https://mlsanity.com/paper/2410.11795/) (2024)
- [CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design](https://mlsanity.com/paper/2505.19114/) (2025)
- [Efficient Diffusion Models: A Survey](https://mlsanity.com/paper/2502.06805/) (2025)
- [DreamRenderer: Taming Multi-Instance Attribute Control in Large-Scale Text-to-Image Models](https://mlsanity.com/paper/2503.12885/) (2025)
- [StableV2V: Stablizing Shape Consistency in Video-to-Video Editing](https://mlsanity.com/paper/2411.11045/) (2024)
- [CtrLoRA: An Extensible and Efficient Framework for Controllable Image Generation](https://mlsanity.com/paper/2410.09400/) (2024)
- [EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance](https://mlsanity.com/paper/2505.21876/) (2025)
- [Video-As-Prompt: Unified Semantic Control for Video Generation](https://mlsanity.com/paper/2510.20888/) (2025)
- [PreciseCam: Precise Camera Control for Text-to-Image Generation](https://mlsanity.com/paper/2501.12910/) (2025)
- [CoGen: 3D Consistent Video Generation via Adaptive Conditioning for Autonomous Driving](https://mlsanity.com/paper/2503.22231/) (2025)
- [ManipDreamer: Boosting Robotic Manipulation World Model with Action Tree and Visual Guidance](https://mlsanity.com/paper/2504.16464/) (2025)
- [Any2Caption:Interpreting Any Condition to Caption for Controllable Video Generation](https://mlsanity.com/paper/2503.24379/) (2025)
- [BideDPO: Conditional Image Generation with Simultaneous Text and Condition Alignment](https://mlsanity.com/paper/2511.19268/) (2025)
- [Edicho: Consistent Image Editing in the Wild](https://mlsanity.com/paper/2412.21079/) (2024)
- [InterLCM: Low-Quality Images as Intermediate States of Latent Consistency Models for Effective Blind Face Restoration](https://mlsanity.com/paper/2502.02215/) (2025)
- [TimeStep Master: Asymmetrical Mixture of Timestep LoRA Experts for Versatile and Efficient Diffusion Models in Vision](https://mlsanity.com/paper/2503.07416/) (2025)
- [I2V3D: Controllable image-to-video generation with 3D guidance](https://mlsanity.com/paper/2503.09733/) (2025)
- [MVOC: a training-free multiple video object composition method with diffusion models](https://mlsanity.com/paper/2406.15829/) (2024)
- [DreamRunner: Fine-Grained Compositional Story-to-Video Generation with Retrieval-Augmented Motion Adaptation](https://mlsanity.com/paper/2411.16657/) (2024)
- [FlexTraj: Image-to-Video Generation with Flexible Point Trajectory Control](https://mlsanity.com/paper/2510.08527/) (2025)
- [On Equivariance and Fast Sampling in Video Diffusion Models Trained with Warped Noise](https://mlsanity.com/paper/2504.09789/) (2025)
- [AVControl: Efficient Framework for Training Audio-Visual Controls](https://mlsanity.com/paper/2603.24793/) (2026)
- [Inverse Virtual Try-On: Generating Multi-Category Product-Style Images from Clothed Individuals](https://mlsanity.com/paper/2505.21062/) (2025)
- [EF-VI: Enhancing End-Frame Injection for Video Inbetweening](https://mlsanity.com/paper/2505.21205/) (2025)

## Abstract (arXiv)

ControlNets are widely used for adding spatial control to text-to-image diffusion models with different conditions, such as depth maps, scribbles/sketches, and human poses. However, when it comes to controllable video generation, ControlNets cannot be directly integrated into new backbones due to feature space mismatches, and training ControlNets for new backbones can be a significant burden for many users. Furthermore, applying ControlNets independently to different frames cannot effectively maintain object temporal consistency. To address these challenges, we introduce Ctrl-Adapter, an efficient and versatile framework that adds diverse controls to any image/video diffusion model through the adaptation of pretrained ControlNets. Ctrl-Adapter offers strong and diverse capabilities, including image and video control, sparse-frame video control, fine-grained patch-level multi-condition control (via an MoE router), zero-shot adaptation to unseen conditions, and supports a variety of downstream tasks beyond spatial control, including video editing, video style transfer, and text-guided motion control. With six diverse U-Net/DiT-based image/video diffusion models (SDXL, PixArt-α, I2VGen-XL, SVD, Latte, Hotshot-XL), Ctrl-Adapter matches the performance of pretrained ControlNets on COCO and achieves the state-of-the-art on DAVIS 2017 with significantly lower computation (< 10 GPU hours).