Summary of Creativesynth: Creative Blending and Synthesis Of Visual Arts Based on Multimodal Diffusion, by Nisha Huang et al.
CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion
by Nisha Huang, Weiming Dong, Yuxin Zhang, Fan Tang, Ronghui Li, Chongyang Ma, Xiu Li, Changsheng Xu
First submitted to arxiv on: 25 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 Large-scale text-to-image generative models have achieved significant advancements, producing high-quality images with remarkable accuracy. However, adapting these models for artistic image editing poses two primary challenges: crafting precise textual prompts and maintaining the overall artistic style while making specific modifications. To overcome these hurdles, we introduce CreativeSynth, a unified framework based on a diffusion model that integrates multimodal inputs and multitasks in artistic image generation. By incorporating multimodal features with customized attention mechanisms, CreativeSynth enables the seamless importation of real-world semantic content into art through inversion and real-time style transfer. This allows for precise control over image style and content while preserving original model parameters. Rigorous evaluations demonstrate that CreativeSynth excels in enhancing artistic images’ fidelity and preserves their inherent aesthetic essence. By bridging the gap between generative models and artistic finesse, CreativeSynth becomes a custom digital palette. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A big challenge in making computers create art is getting them to understand what we want them to do. We want computers to take pictures and edit them like an artist would. But it’s hard because the computers don’t always get what we mean. And even when they do, they can make the picture look weird if they change too much of it. To solve this problem, we created a new way for computers to create art that lets us tell them exactly how we want the picture to look. We did this by teaching the computer to understand both words and pictures, and then using that understanding to make changes to the picture. This lets us edit the picture in a way that makes it look like an artist would have done it. Our new method works really well and can even make old pictures look better than they do now. |
Keywords
» Artificial intelligence » Attention » Diffusion model » Image generation » Style transfer
