Summary of Hicast: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models, by Hanzhang Wang et al.
HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models
by Hanzhang Wang, Haoran Wang, Jinze Yang, Zhongrui Yu, Zeke Xie, Lei Tian, Xinyan Xiao, Junjun Jiang, Xianming Liu, Mingming Sun
First submitted to arxiv on: 11 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 This novel Arbitrary Style Transfer (AST) approach, called HiCAST, enables flexible and customized stylization results by explicitly incorporating various semantic clues. By leveraging Latent Diffusion Model (LDM) and introducing a Style Adapter, users can manipulate the output to align with multi-level style information and intrinsic knowledge in LDM. This approach outperforms existing SoTA methods in generating visually plausible stylization results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HiCAST is a new way to make images look like art by adding styles from other pictures or videos. Normally, this process tries to balance the artistic style with what’s already in the image. But HiCAST lets you control how much of the original picture shows through and how much artistic style comes in. This makes it more useful for real-life applications. |
Keywords
» Artificial intelligence » Diffusion model » Style transfer
