Summary of Lazy Diffusion Transformer For Interactive Image Editing, by Yotam Nitzan et al.
Lazy Diffusion Transformer for Interactive Image Editing
by Yotam Nitzan, Zongze Wu, Richard Zhang, Eli Shechtman, Daniel Cohen-Or, Taesung Park, Michaël Gharbi
First submitted to arxiv on: 18 Apr 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 This paper presents LazyDiffusion, a novel diffusion transformer that efficiently generates partial image updates. The model targets interactive image editing applications where users specify localized modifications using binary masks and text prompts. The generator operates in two phases: a context encoder processes the canvas and user mask to produce a compact global context, and then a diffusion-based transformer decoder synthesizes masked pixels in a “lazy” fashion, only generating the specified region. This approach contrasts with previous works that either regenerate the full canvas or confine processing to a tight rectangular crop around the mask. The decoder’s runtime scales with the mask size, which is typically small, while the encoder introduces negligible overhead. The paper demonstrates that LazyDiffusion is competitive with state-of-the-art inpainting methods in terms of quality and fidelity, providing a 10x speedup for typical user interactions where the editing mask represents 10% of the image. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LazyDiffusion is a new way to edit images. Imagine you have a picture and want to add some details or remove things. Usually, computers would need to process the whole picture to make changes, but LazyDiffusion only looks at the part that needs changing. This makes it faster and more efficient. The model uses two parts: one that looks at what’s already in the picture, and another that adds new details based on what you want to change. This is useful for interactive image editing, where users want to make changes to a picture without having to wait too long for the computer to process everything. |
Keywords
» Artificial intelligence » Decoder » Diffusion » Encoder » Mask » Transformer
