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MagicQuill An Intelligent Interactive Image Editing System

77,440 次观看 • 1 年前 •via X (Twitter)

7 条评论

AK 的头像
AK1 年前

discuss:

AK 的头像
AK1 年前

app:

AK 的头像
AK1 年前

github:

Bruce Yue Yu 的头像
Bruce Yue Yu1 年前

Our Github repo - all open sourced🤗

Fred 的头像
Fred1 年前

It doesn't accept prompt?

Narendra Rathore 的头像
Narendra Rathore1 年前

🔥can i make it work with videos as well?

xuelaos 的头像
xuelaos1 年前

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.

AK

71,201 次观看 • 1 年前