Summary of Fit: Flexible Vision Transformer For Diffusion Model, by Zeyu Lu et al.
FiT: Flexible Vision Transformer for Diffusion Model
by Zeyu Lu, Zidong Wang, Di Huang, Chengyue Wu, Xihui Liu, Wanli Ouyang, Lei Bai
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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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 introduces the Flexible Vision Transformer (FiT), a transformer architecture designed to generate images with unrestricted resolutions and aspect ratios. Unlike traditional methods, FiT perceives images as sequences of dynamically-sized tokens, allowing it to adapt to diverse aspect ratios during both training and inference phases. The FiT model is enhanced by a meticulously adjusted network structure and the integration of training-free extrapolation techniques. Comprehensive experiments demonstrate the exceptional performance of FiT across a broad range of resolutions, showcasing its effectiveness in generating images with varying sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create images of any size or shape without having to train your computer specifically for that size. That’s what this new model, called Flexible Vision Transformer (FiT), can do. Instead of looking at an image as a fixed-size grid, FiT sees it as a series of tokens that can change size and shape. This allows it to generate images with different aspect ratios without any special training. The model is very good at creating images of all sizes and has many potential uses in areas like art, design, and computer vision. |
Keywords
* Artificial intelligence * Inference * Transformer * Vision transformer
