Summary of Glod: Composing Global Contexts and Local Details in Image Generation, by Moyuru Yamada
GLoD: Composing Global Contexts and Local Details in Image Generation
by Moyuru Yamada
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces Global-Local Diffusion (GLoD), a novel framework for text-to-image generation that enables simultaneous control over global contexts and local details. Current diffusion models struggle with understanding complex descriptions involving multiple objects, often failing to reflect specified visual attributes or ignoring them. GLoD addresses this limitation by assigning multiple global and local prompts to corresponding layers and composing their noises to guide a denoising process using pre-trained diffusion models. This allows for complex global-local compositions, conditioning objects in the global prompt with the local prompts while preserving other unspecified identities. The framework is evaluated through quantitative and qualitative assessments, demonstrating its effectiveness in generating images that adhere to both user-provided object interactions and object details. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to generate pictures from text using a method called Global-Local Diffusion (GLoD). Right now, these text-to-image models struggle with complex descriptions involving multiple objects. They often get it wrong or ignore certain details. GLoD is designed to fix this problem by letting you control both the big picture and small details at the same time. It does this by combining different prompts for the global context (like object layout) and local details (like colors). The results show that GLoD can generate complex images that match what you want, including interactions between objects and specific details. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Prompt
