Summary of High-resolution Image Synthesis Via Next-token Prediction, by Dengsheng Chen et al.
High-Resolution Image Synthesis via Next-Token Prediction
by Dengsheng Chen, Jie Hu, Tiezhu Yue, Xiaoming Wei, Enhua Wu
First submitted to arxiv on: 22 Nov 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 The proposed autoregressive model, D-JEPA•T2I, leverages innovations in architecture and training strategy to generate photorealistic images at arbitrary resolutions, up to 4K. Building on denoising joint embedding predictive architecture (D-JEPA) and a multimodal visual transformer, the model integrates textual and visual features. Additionally, flow matching loss and Visual Rotary Positional Embedding (VoPE) enable continuous resolution learning. The training strategy involves a data feedback mechanism that adjusts sampling procedures based on statistical analysis and an online learning critic model. This encourages the model to address more challenging cases with suboptimal generation quality, achieving state-of-the-art high-resolution image synthesis via next-token prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new autoregressive model that can generate high-quality images at any size up to 4K. The model uses special architecture and training methods to make the pictures look very realistic. It combines text and visual information together, which helps it understand what kind of image to create. The model also has some special tools that help it learn how to make better pictures by trying new things. |
Keywords
* Artificial intelligence * Autoregressive * Embedding * Image synthesis * Online learning * Token * Transformer
