Summary of E-car: Efficient Continuous Autoregressive Image Generation Via Multistage Modeling, by Zhihang Yuan et al.
E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling
by Zhihang Yuan, Yuzhang Shang, Hanling Zhang, Tongcheng Fang, Rui Xie, Bingxin Xu, Yan Yan, Shengen Yan, Guohao Dai, Yu Wang
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 ECAR, an efficient approach for continuous auto-regressive image generation. By leveraging stage-wise token generation and multistage flow-based distribution modeling, ECAR reduces computational complexity while maintaining high-quality results. The model operates in continuous token space, generating tokens at increasing resolutions while denoising the image at each stage. This design enables parallel processing and reduces token-to-image transformation costs by a factor of the stage number. Experimental results show that ECAR achieves comparable image quality to DiT Peebles & Xie [2023] while requiring 10x fewer FLOPs and a 5x speedup to generate a 256×256 image. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ECAR is a new way to make pictures using computers. Right now, some computer models can make pictures that look pretty good, but they take a long time and use a lot of computer power. The ECAR model helps solve this problem by breaking down the process into smaller steps and doing some parts in parallel. This makes it faster and more efficient. It’s like building a picture piece by piece, instead of trying to draw the whole thing at once. |
Keywords
* Artificial intelligence * Image generation * Token
