Summary of Human Aesthetic Preference-based Large Text-to-image Model Personalization: Kandinsky Generation As An Example, by Aven-le Zhou et al.
Human Aesthetic Preference-Based Large Text-to-Image Model Personalization: Kandinsky Generation as an Example
by Aven-Le Zhou, Yu-Ao Wang, Wei Wu, Kang Zhang
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Multimedia (cs.MM)
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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 a novel prompting-free generative approach to create personalized painterly content using GenAI. The method utilizes “semantic injection” to customize an artist model in a specific artistic style, then leverages a genetic algorithm for real-time iterative human feedback to optimize prompt generation. This enables users to generate aesthetically pleasing outcomes that incorporate their preferences and artistic style without tedious trial-and-error. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps artists use GenAI to create unique paintings without needing to write prompts. It lets people tell the AI what they like and dislike about an image, then uses this feedback to create a personalized painting style. The approach uses “semantic injection” to change how the AI generates images, making it easier for users to get the results they want. |
Keywords
» Artificial intelligence » Prompt » Prompting
