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MagicQuill An Intelligent Interactive Image Editing System

77,440 görüntüleme • 1 yıl önce •via X (Twitter)

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AK profil fotoğrafı
AK1 yıl önce

discuss:

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app:

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github:

Bruce Yue Yu profil fotoğrafı
Bruce Yue Yu1 yıl önce

Our Github repo - all open sourced🤗

Fred profil fotoğrafı
Fred1 yıl önce

It doesn't accept prompt?

Narendra Rathore profil fotoğrafı
Narendra Rathore1 yıl önce

🔥can i make it work with videos as well?

xuelaos profil fotoğrafı
xuelaos1 yıl önce

太吊了 这个项目

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InstantDrag Improving Interactivity in Drag-based Image Editing discuss: Drag-based image editing has recently gained popularity for its interactivity and precision. However, despite the ability of text-to-image models to generate samples within a second, drag editing still lags behind due to the challenge of accurately reflecting user interaction while maintaining image content. Some existing approaches rely on computationally intensive per-image optimization or intricate guidance-based methods, requiring additional inputs such as masks for movable regions and text prompts, thereby compromising the interactivity of the editing process. We introduce InstantDrag, an optimization-free pipeline that enhances interactivity and speed, requiring only an image and a drag instruction as input. InstantDrag consists of two carefully designed networks: a drag-conditioned optical flow generator (FlowGen) and an optical flow-conditioned diffusion model (FlowDiffusion). InstantDrag learns motion dynamics for drag-based image editing in real-world video datasets by decomposing the task into motion generation and motion-conditioned image generation. We demonstrate InstantDrag's capability to perform fast, photo-realistic edits without masks or text prompts through experiments on facial video datasets and general scenes. These results highlight the efficiency of our approach in handling drag-based image editing, making it a promising solution for interactive, real-time applications.

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