Summary of Stylemamba : State Space Model For Efficient Text-driven Image Style Transfer, by Zijia Wang et al.
StyleMamba : State Space Model for Efficient Text-driven Image Style Transfer
by Zijia Wang, Zhi-Song Liu
First submitted to arxiv on: 8 May 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 paper presents StyleMamba, a novel framework for efficient image style transfer that can translate text prompts into corresponding visual styles while preserving the content integrity of original images. Unlike existing approaches that require hundreds of training iterations and significant computing resources, StyleMamba achieves this task in a fraction of the time using a conditional State Space Model. The model sequentially aligns image features to target text prompts, incorporating masked and second-order directional losses to enhance local and global style consistency. As a result, StyleMamba reduces training iterations by 5 times and inference time by 3 times compared to existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary StyleMamba is a new way to change the style of an image based on what it’s about. It can take a text prompt, like “beach scene”, and turn any picture into one that looks like it was taken at the beach. This is different from other methods because it does this quickly and uses less computer power. The researchers used a special kind of model to make this happen, which helps keep the important parts of the original image while changing how it looks. |
Keywords
» Artificial intelligence » Inference » Prompt » Style transfer
