Summary of Optimizing Negative Prompts For Enhanced Aesthetics and Fidelity in Text-to-image Generation, by Michael Ogezi and Ning Shi
Optimizing Negative Prompts for Enhanced Aesthetics and Fidelity in Text-To-Image Generation
by Michael Ogezi, Ning Shi
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 presents NegOpt, a novel method for optimizing negative prompt generation in text-to-image generation. By using supervised fine-tuning and reinforcement learning, NegOpt significantly boosts image quality, achieving an Inception Score increase of 25% compared to other approaches and surpassing ground-truth negative prompts from the test set. The proposed method also allows for preferential optimization of desired metrics. Additionally, the paper introduces Negative Prompts DB, a publicly available dataset of negative prompts. The NegOpt approach has the potential to revolutionize text-to-image generation by providing high-quality images that meet specific requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create images from text descriptions without mistakes. This is the goal of a new method called NegOpt. It’s like having a superpower! NegOpt helps make better images by using special prompts that describe what not to include in an image. The researchers came up with a way to automatically generate these prompts, which makes it faster and easier to create high-quality images. With NegOpt, you can even choose what characteristics are most important for the images you want to create. This breakthrough has the potential to change how we use text-to-image generation. |
Keywords
* Artificial intelligence * Fine tuning * Image generation * Optimization * Prompt * Reinforcement learning * Supervised
