Summary of Improving Long-text Alignment For Text-to-image Diffusion Models, by Luping Liu et al.
Improving Long-Text Alignment for Text-to-Image Diffusion Models
by Luping Liu, Chao Du, Tianyu Pang, Zehan Wang, Chongxuan Li, Dong Xu
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM)
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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 LongAlign method tackles the limitations of existing encoding methods for text-to-image (T2I) diffusion models when processing long texts. By segmenting long texts and using a decomposed preference optimization approach, LongAlign overcomes maximum input length limits and fine-tunes diffusion models for effective alignment training. The method involves a reweighting strategy to reduce overfitting and enhance T2I alignment. After fine-tuning Stable Diffusion v1.5, the results outperform stronger foundation models in T2I alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to make text-to-image models better at generating images that match long texts. The problem is that current methods can’t handle very long texts and get stuck with shorter inputs. LongAlign solves this by breaking down long texts into smaller chunks, which can be processed separately. This approach also helps the model learn how to align generated images with long texts more effectively. |
Keywords
» Artificial intelligence » Alignment » Diffusion » Fine tuning » Optimization » Overfitting
