Summary of Dart: Denoising Autoregressive Transformer For Scalable Text-to-image Generation, by Jiatao Gu et al.
DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation
by Jiatao Gu, Yuyang Wang, Yizhe Zhang, Qihang Zhang, Dinghuai Zhang, Navdeep Jaitly, Josh Susskind, Shuangfei Zhai
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The proposed paper introduces DART, a transformer-based model that combines autoregressive (AR) and diffusion techniques in a non-Markovian framework, addressing the limitations of traditional diffusion models. By iteratively denoising image patches spatially and spectrally using an AR model, DART achieves more effective image modeling without relying on image quantization. This unified approach enables seamless training with both text and image data, resulting in competitive performance on class-conditioned and text-to-image generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DART is a new way to make images using computers. It’s like combining two old methods into one new method that works better. The old methods were called diffusion models and autoregressive models. DART uses these together in a special way to make images without messing them up too much. This means it can make good pictures quickly, which is important for things like making movies or creating art. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion » Image generation » Quantization » Transformer
