Summary of Pixelman: Consistent Object Editing with Diffusion Models Via Pixel Manipulation and Generation, by Liyao Jiang et al.
PixelMan: Consistent Object Editing with Diffusion Models via Pixel Manipulation and Generation
by Liyao Jiang, Negar Hassanpour, Mohammad Salameh, Mohammadreza Samadi, Jiao He, Fengyu Sun, Di Niu
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent research has explored the potential of Diffusion Models (DMs) for consistent object editing, which aims to modify object position, size, and composition while preserving image consistency. Current inference-time methods often rely on DDIM inversion, compromising efficiency and achieving consistency. Other approaches utilize energy guidance, updating predicted noise and driving latents away from the original image, resulting in distortions. This paper proposes PixelMan, an inversion-free and training-free method for consistent object editing via Pixel Manipulation and generation. PixelMan directly creates a duplicate copy of the source object at target location in pixel space, iteratively harmonizing the manipulated object into the target location while ensuring image consistency. The approach also introduces various optimization techniques during inference. Experimental evaluations show that PixelMan outperforms state-of-the-art methods on multiple consistent object editing tasks, requiring as few as 16 inference steps compared to typically 50 steps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to edit objects in images. Right now, we can only make small changes and it’s not very efficient. The new method is called PixelMan and it allows us to make bigger changes while still keeping the image looking good. Instead of using complicated formulas, PixelMan creates a copy of the object and moves it to where you want it. It then makes sure that the rest of the image looks right too. This approach works really well and can make changes in just 16 steps, which is much faster than other methods. |
Keywords
» Artificial intelligence » Diffusion » Inference » Optimization
