Summary of Ace: All-round Creator and Editor Following Instructions Via Diffusion Transformer, by Zhen Han et al.
ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer
by Zhen Han, Zeyinzi Jiang, Yulin Pan, Jingfeng Zhang, Chaojie Mao, Chenwei Xie, Yu Liu, Jingren Zhou
First submitted to arxiv on: 30 Sep 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 proposed ACE model is an all-round creator and editor that achieves comparable performance to expert models in various visual generation tasks. It uses a unified condition format called Long-context Condition Unit (LCU) as input, allowing for joint training across different generation and editing tasks. The model also involves an efficient data collection approach, which acquires pairwise images with synthesis-based or clustering-based pipelines and supplies accurate textual instructions using a fine-tuned multi-modal large language model. To evaluate the performance of the ACE model, a benchmark of manually annotated pairs data is established across various visual generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The ACE model is an AI that can create and edit images in many different ways. It uses a special format called LCU to understand what it should do, and it’s trained on lots of pictures and text instructions. The model is good at generating new images and editing old ones, and it can even build a chat system that lets people ask for specific images. |
Keywords
» Artificial intelligence » Clustering » Large language model » Multi modal
