Summary of Zero-shot Text-guided Infinite Image Synthesis with Llm Guidance, by Soyeong Kwon et al.
Zero-shot Text-guided Infinite Image Synthesis with LLM guidance
by Soyeong Kwon, Taegyeong Lee, Taehwan Kim
First submitted to arxiv on: 17 Jul 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 A novel approach is proposed to address the challenges of text-guided infinite image synthesis, utilizing Large Language Models (LLMs) for global coherence and local context understanding. The method trains a diffusion model to expand an image conditioned on global and local captions generated from the LLM and visual feature. At inference, the LLM generates a next local caption to expand the input image, considering global consistency and spatial local context. The approach outperforms baselines both quantitatively and qualitatively, demonstrating the capability of text-guided arbitrary-sized image generation in zero-shot manner with LLM guidance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in making pictures from words. Right now, we can only make simple images like landscapes, but we want to be able to make more complex images too. To do this, we need lots of examples of what the finished picture should look like, but these are hard to come by. The researchers came up with a new way to use big language models to help us understand both the whole picture and small details at the same time. This allows us to make more realistic and detailed images from words. |
Keywords
» Artificial intelligence » Diffusion model » Image generation » Image synthesis » Inference » Zero shot
