Summary of Visual Autoregressive Modeling: Scalable Image Generation Via Next-scale Prediction, by Keyu Tian et al.
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
by Keyu Tian, Yi Jiang, Zehuan Yuan, Bingyue Peng, Liwei Wang
First submitted to arxiv on: 3 Apr 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 The paper presents Visual AutoRegressive (VAR) modeling, a new approach that reframes autoregressive learning on images as “next-scale prediction” or “next-resolution prediction”. This methodology enables autoregressive transformers to learn visual distributions quickly and generalize well. VAR outperforms diffusion transformers in image generation, achieving significant improvements in Frechet inception distance (FID), inception score (IS), and inference speed. The paper also explores the scaling laws of VAR models, which exhibit a power-law relationship similar to large language models (LLMs). Additionally, VAR demonstrates zero-shot generalization ability in downstream tasks such as image editing and in-painting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces Visual AutoRegressive modeling, which changes how we learn from images. Instead of looking at small parts of an image, VAR looks at bigger sections or even the whole image. This makes it better at generating new images that look like real ones. The results are impressive, with the generated images being much more realistic and taking less time to create than before. The paper also shows that these models can be used for tasks like editing and filling in missing parts of an image. Overall, VAR is a powerful tool for creating and manipulating images. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion » Generalization » Image generation » Inference » Scaling laws » Zero shot
