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Description: Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model
video (51234) image (3240) control (2617) adapter (407) diffusion (242) genai (29) image generation (26) video generation (8) controlnet (7)
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 for many users. Second, ControlNet features for d
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 strong and diverse capabilities including image control, video control, video control with sparse frames, multi-condition control, compatibility with different backbone models, adaptation to unseen control conditions, and video editing. In the Ctrl-A
From our experiments with diverse image and video diffusion backbones (SDXL, Hotshot-XL, I2VGen-XL, and SVD), Ctrl-Adapter matches ControlNet on the COCO dataset for image control, and even outperforms all baselines for video control (achieving the state-of-the-art accuracy on the DAVIS 2017 dataset) with significantly lower computational costs (Ctrl-Adapter outperforms baselines in less than 10 GPU hours). Lastly, we provide comprehensive ablations for our design choices and qualitative examples.
About me - Welcome to Han Lin’s Website!