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1/ Introducing high precision masked editing. No bleeding. Highly targeted AI based image editing to give you more control. Here's an end-to-end workflow in 30 seconds:

358,786 görüntüleme • 3 yıl önce •via X (Twitter)

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Suhail3 yıl önce

2/ Make entirely new scenes by layering your edits on top of each other: "Make it snowy", "Change the door to brown", "Golden hour", "Add a bit of snow to the roof", "Add a cute bunny in the foreground"

Suhail profil fotoğrafı
Suhail3 yıl önce

3/ Modify memes: "Add a bathrobe"

Suhail profil fotoğrafı
Suhail3 yıl önce

4/ Bigger edits that only target the hair: "make her hair curly with natural color"

Suhail profil fotoğrafı
Suhail3 yıl önce

5/ You don't have to be exact in your masked edits: "make it red lava, black rocks"

Suhail profil fotoğrafı
Suhail3 yıl önce

6/ Use it at

Suhail profil fotoğrafı
Suhail3 yıl önce

7/ One shot AI avatar using masks:

GrepMed profil fotoğrafı
GrepMed3 yıl önce

"Create a badly receding hairline" 🤣

Suhail profil fotoğrafı
Suhail3 yıl önce

Oh my lord

Tim O'Shea profil fotoğrafı
Tim O'Shea3 yıl önce

I see why you burnt the boat Congrats, playground is fantastic

Adham profil fotoğrafı
Adham3 yıl önce

seems like instruct-pix-2-pix with masked attention control (from google's prompt-to-prompt) or inpainting task finetuning like runwayml with instruction training. Very cool @Suhail

Suhail profil fotoğrafı
Suhail3 yıl önce

def instructpix2pix :)

Benzer Videolar

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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71,201 görüntüleme • 1 yıl önce