Summary of Diffusiongpt: Llm-driven Text-to-image Generation System, by Jie Qin et al.
DiffusionGPT: LLM-Driven Text-to-Image Generation System
by Jie Qin, Jie Wu, Weifeng Chen, Yuxi Ren, Huixia Li, Hefeng Wu, Xuefeng Xiao, Rui Wang, Shilei Wen
First submitted to arxiv on: 18 Jan 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 proposed system, DiffusionGPT, combines the strengths of large language models (LLMs) and domain-expert models to create a unified text-to-image generation system that can handle various types of prompts. This system leverages LLMs to parse input prompts and employ Trees-of-Thought to guide the selection of an appropriate model. The approach relaxes input constraints, ensuring exceptional performance across diverse domains. Additionally, Advantage Databases are introduced, enriching the Tree-of-Thought with human feedback to align the model selection process with human preferences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a system that can create images based on text prompts! This is what researchers have achieved by combining two types of AI models: ones that understand language (LLMs) and ones that are experts in creating specific types of images. The new system, called DiffusionGPT, is like a librarian that helps you find the right book (or image) based on your request. It can handle different types of prompts and even learn from human feedback to make it better. |
Keywords
» Artificial intelligence » Image generation
