Summary of Primecomposer: Faster Progressively Combined Diffusion For Image Composition with Attention Steering, by Yibin Wang and Weizhong Zhang and Jianwei Zheng and Cheng Jin
PrimeComposer: Faster Progressively Combined Diffusion for Image Composition with Attention Steering
by Yibin Wang, Weizhong Zhang, Jianwei Zheng, Cheng Jin
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Image composition is a critical task that involves integrating given objects into a specific visual context without any training data. Current methods rely on composing attention weights from multiple samplers to guide the generator, but this approach can lead to coherence confusion and loss of appearance information. Our solution formulates image composition as a subject-based local editing task, focusing solely on foreground generation while maintaining scene consistency. We propose PrimeComposer, a faster training-free diffuser that composites images by well-designed attention steering across different noise levels. This is achieved through our Correlation Diffuser’s self-attention layers, which capture intricate details and coherent relationships between the synthesized subject, referenced object, and background. Additionally, we introduce a Region-constrained Cross-Attention to confine the impact of specific subject-related tokens to desired regions, addressing unwanted artifacts in the transition area. Our method exhibits fast inference efficiency and outperforms others both qualitatively and quantitatively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a picture with friends in the background. Image composition is about putting them together seamlessly. Current methods try to combine different parts of the image to guide the generator, but this can cause problems like losing important details or making the background messy. Our solution is different: we focus on the main subject and edit it locally while keeping the rest of the image consistent. We propose a new method called PrimeComposer that works quickly and accurately, capturing small details and relationships between objects in the image. It also helps to remove unwanted effects from the transition area where the background meets the foreground. Our method is fast and outperforms others in terms of quality. |
Keywords
» Artificial intelligence » Attention » Cross attention » Inference » Self attention
